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Li W, Mao S, Mahoney AS, Coyle JL, Sejdić E. Automatic Tracking of Hyoid Bone Displacement and Rotation Relative to Cervical Vertebrae in Videofluoroscopic Swallow Studies Using Deep Learning. J Imaging Inform Med 2024:10.1007/s10278-024-01039-4. [PMID: 38383805 DOI: 10.1007/s10278-024-01039-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/04/2023] [Revised: 01/17/2024] [Accepted: 02/01/2024] [Indexed: 02/23/2024]
Abstract
The hyoid bone displacement and rotation are critical kinematic events of the swallowing process in the assessment of videofluoroscopic swallow studies (VFSS). However, the quantitative analysis of such events requires frame-by-frame manual annotation, which is labor-intensive and time-consuming. Our work aims to develop a method of automatically tracking hyoid bone displacement and rotation in VFSS. We proposed a full high-resolution network, a deep learning architecture, to detect the anterior and posterior of the hyoid bone to identify its location and rotation. Meanwhile, the anterior-inferior corners of the C2 and C4 vertebrae were detected simultaneously to automatically establish a new coordinate system and eliminate the effect of posture change. The proposed model was developed by 59,468 VFSS frames collected from 1488 swallowing samples, and it achieved an average landmark localization error of 2.38 pixels (around 0.5% of the image with 448 × 448 pixels) and an average angle prediction error of 0.065 radians in predicting C2-C4 and hyoid bone angles. In addition, the displacement of the hyoid bone center was automatically tracked on a frame-by-frame analysis, achieving an average mean absolute error of 2.22 pixels and 2.78 pixels in the x-axis and y-axis, respectively. The results of this study support the effectiveness and accuracy of the proposed method in detecting hyoid bone displacement and rotation. Our study provided an automatic method of analyzing hyoid bone kinematics during VFSS, which could contribute to early diagnosis and effective disease management.
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Affiliation(s)
- Wuqi Li
- Edward S. Rogers Department of Electrical and Computer Engineering, University of Toronto, Toronto, ON, Canada
| | - Shitong Mao
- Department of Head and Neck Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Amanda S Mahoney
- Department of Communication Science and Disorders, School of Health and Rehabilitation Sciences, University of Pittsburgh, Pittsburgh, PA, USA
| | - James L Coyle
- Department of Communication Science and Disorders, School of Health and Rehabilitation Sciences, University of Pittsburgh, Pittsburgh, PA, USA
| | - Ervin Sejdić
- Edward S. Rogers Department of Electrical and Computer Engineering, University of Toronto, Toronto, ON, Canada.
- North York General Hospital, Toronto, ON, Canada.
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Li W, Lin HM, Lin A, Napoleone M, Moreland R, Murari A, Stepanov M, Ivanov E, Prasad AS, Shih G, Hu Z, Zulbayar S, Sejdić E, Colak E. Machine Learning Classification of Body Part, Imaging Axis, and Intravenous Contrast Enhancement on CT Imaging. Can Assoc Radiol J 2024; 75:82-91. [PMID: 37439250 DOI: 10.1177/08465371231180844] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/14/2023] Open
Abstract
Purpose: The development and evaluation of machine learning models that automatically identify the body part(s) imaged, axis of imaging, and the presence of intravenous contrast material of a CT series of images. Methods: This retrospective study included 6955 series from 1198 studies (501 female, 697 males, mean age 56.5 years) obtained between January 2010 and September 2021. Each series was annotated by a trained board-certified radiologist with labels consisting of 16 body parts, 3 imaging axes, and whether an intravenous contrast agent was used. The studies were randomly assigned to the training, validation and testing sets with a proportion of 70%, 20% and 10%, respectively, to develop a 3D deep neural network for each classification task. External validation was conducted with a total of 35,272 series from 7 publicly available datasets. The classification accuracy for each series was independently assessed for each task to evaluate model performance. Results: The accuracies for identifying the body parts, imaging axes, and the presence of intravenous contrast were 96.0% (95% CI: 94.6%, 97.2%), 99.2% (95% CI: 98.5%, 99.7%), and 97.5% (95% CI: 96.4%, 98.5%) respectively. The generalizability of the models was demonstrated through external validation with accuracies of 89.7 - 97.8%, 98.6 - 100%, and 87.8 - 98.6% for the same tasks. Conclusions: The developed models demonstrated high performance on both internal and external testing in identifying key aspects of a CT series.
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Affiliation(s)
- Wuqi Li
- The Edward S. Rogers Department of Electrical and Computer Engineering, University of Toronto, Toronto, ON, Canada
| | - Hui Ming Lin
- Department of Medical Imaging, Unity Health Toronto, Toronto, ON, Canada
| | - Amy Lin
- Department of Medical Imaging, Unity Health Toronto, Toronto, ON, Canada
- Department of Medical Imaging, Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Marc Napoleone
- Department of Medical Imaging, Unity Health Toronto, Toronto, ON, Canada
- Department of Medical Imaging, Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Robert Moreland
- Department of Medical Imaging, Unity Health Toronto, Toronto, ON, Canada
- Department of Medical Imaging, Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Alexis Murari
- The Edward S. Rogers Department of Electrical and Computer Engineering, University of Toronto, Toronto, ON, Canada
| | - Maxim Stepanov
- The Edward S. Rogers Department of Electrical and Computer Engineering, University of Toronto, Toronto, ON, Canada
| | - Eric Ivanov
- The Edward S. Rogers Department of Electrical and Computer Engineering, University of Toronto, Toronto, ON, Canada
| | - Abhinav Sanjeeva Prasad
- The Edward S. Rogers Department of Electrical and Computer Engineering, University of Toronto, Toronto, ON, Canada
| | - George Shih
- Department of Radiology, Weill Cornell Medicine, New York, NY, USA
| | - Zixuan Hu
- The Edward S. Rogers Department of Electrical and Computer Engineering, University of Toronto, Toronto, ON, Canada
| | - Suvd Zulbayar
- Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
| | - Ervin Sejdić
- The Edward S. Rogers Department of Electrical and Computer Engineering, University of Toronto, Toronto, ON, Canada
- North York General Hospital, Toronto, ON, Canada
| | - Errol Colak
- Department of Medical Imaging, Unity Health Toronto, Toronto, ON, Canada
- Department of Medical Imaging, Faculty of Medicine, University of Toronto, Toronto, ON, Canada
- Li Ka Shing Knowledge Institute, St Michael's Hospital, Unity Health Toronto, Toronto, ON, Canada
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Suri A, VanSwearingen J, Rosano C, Brach JS, Redfern MS, Sejdić E, Rosso AL. Uneven surface and cognitive dual-task independently affect gait quality in older adults. Gait Posture 2023; 106:34-41. [PMID: 37647710 PMCID: PMC10591986 DOI: 10.1016/j.gaitpost.2023.08.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Revised: 07/07/2023] [Accepted: 08/21/2023] [Indexed: 09/01/2023]
Abstract
BACKGROUND Real-world mobility involves walking in challenging conditions. Assessing gait during simultaneous physical and cognitive challenges provides insights on cognitive health. RESEARCH QUESTION How does uneven surface, cognitive task, and their combination affect gait quality and does this gait performance relate to cognitive functioning? METHODS Community-dwelling older adults (N = 104, age=75 ± 6 years, 60 % females) performed dual-task walking paradigms (even and uneven surface; with and without alphabeting cognitive task (ABC)) to mimic real-world demands. Gait quality measures [speed(m/s), rhythmicity(steps/minute), stride time variability (%), adaptability (m/s2), similarity, smoothness, power (Hz) and regularity] were calculated from an accelerometer worn on the lower back. Linear-mixed modelling and Tukey analysis were used to analyze independent effects of surface and cognitive task and their interaction on gait quality. Partial Spearman correlations compared gait quality with global cognition and executive function. RESULTS No interaction effects between surface and cognitive task were found. Uneven surface reduced gait speed(m/s) (β = -0.07). Adjusted for speed, uneven surface reduced gait smoothness (β = -0.27) and increased regularity (β = 0.09), Tukey p < .05, for even vs uneven and even-ABC vs uneven-ABC. Cognitive task reduced gait speed(m/s) (β = -0.12). Adjusted for speed, cognitive task increased variability (β = 7.60), reduced rhythmicity (β = -6.68) and increased regularity (β = 0.05), Tukey p < .05, for even vs even-ABC and uneven vs uneven-ABC. With demographics as covariates, gait speed was not associated with cognition. Gait quality [lower variability during even-ABC (ρp =-.31) and uneven-ABC (ρp =-.28); greater rhythmicity (ρp between.22 and.29) and greater signal-adaptability AP (ρp between.22 and.26) during all walking tasks] was associated with better global cognition. Gait adaptability during even (ρp =-0.21, p = 0.03) and uneven(ρp =-0.19, p = 0.04) walking was associated with executive function. SIGNIFICANCE Surface and cognitive walking tasks independently affected gait quality. Our study with high-functioning older adults suggests that task-related changes in gait quality are related to subtle changes in cognitive functioning.
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Affiliation(s)
- Anisha Suri
- Department of Electrical and Computer Engineering, Swanson School of Engineering, University of Pittsburgh, PA, USA
| | - Jessie VanSwearingen
- Department of Physical Therapy, School of Rehabilitation Sciences, University of Pittsburgh, PA, USA
| | - Caterina Rosano
- Department of Epidemiology, School of Public Health, University of Pittsburgh, PA, USA
| | - Jennifer S Brach
- Department of Physical Therapy, School of Rehabilitation Sciences, University of Pittsburgh, PA, USA
| | - Mark S Redfern
- Department of Bioengineering, Swanson School of Engineering, University of Pittsburgh, PA, USA
| | - Ervin Sejdić
- Department of Electrical and Computer Engineering, Swanson School of Engineering, University of Pittsburgh, PA, USA; The Edward S. Rogers Department of Electrical and Computer Engineering, University of Toronto, Toronto, ON, Canada; North York General Hospital, Toronto, ON, Canada
| | - Andrea L Rosso
- Department of Epidemiology, School of Public Health, University of Pittsburgh, PA, USA.
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Shu K, Perera S, Mahoney AS, Mao S, Coyle JL, Sejdić E. Temporal Sequence of Laryngeal Vestibule Closure and Reopening is Associated With Airway Protection. Laryngoscope 2023; 133:521-527. [PMID: 35657100 PMCID: PMC9718890 DOI: 10.1002/lary.30222] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Revised: 05/05/2022] [Accepted: 05/11/2022] [Indexed: 11/06/2022]
Abstract
BACKGROUND Upper esophageal sphincter opening (UESO), and laryngeal vestibule closure (LVC) are two essential kinematic events whose timings are crucial for adequate bolus clearance and airway protection during swallowing. Their temporal characteristics can be quantified through time-consuming analysis of videofluoroscopic swallow studies (VFSS). OBJECTIVES We sought to establish a model to predict the odds of penetration or aspiration during swallowing based on 15 temporal factors of UES and laryngeal vestibule kinematics. METHODS Manual temporal measurements and ratings of penetration and aspiration were conducted on a videofluoroscopic dataset of 408 swallows from 99 patients. A generalized estimating equation model was deployed to analyze association between individual factors and the risk of penetration or aspiration. RESULTS The results indicated that the latencies of laryngeal vestibular events and the time lapse between UESO onset and LVC were highly related to penetration or aspiration. The predictive model incorporating patient demographics and bolus presentation showed that delayed LVC by 0.1 s or delayed LVO by 1% of the swallow duration (average 0.018 s) was associated with a 17.19% and 2.68% increase in odds of airway invasion, respectively. CONCLUSION This predictive model provides insight into kinematic factors that underscore the interaction between the intricate timing of laryngeal kinematics and airway protection. Recent investigation in automatic noninvasive or videofluoroscopic detection of laryngeal kinematics would provide clinicians access to objective measurements not commonly quantified in VFSS. Consequently, the temporal and sequential understanding of these kinematics may interpret such measurements to an estimation of the risk of aspiration or penetration which would give rise to rapid computer-assisted dysphagia diagnosis. LEVEL OF EVIDENCE 2 Laryngoscope, 133:521-527, 2023.
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Affiliation(s)
- Kechen Shu
- Department of Electrical and Computer Engineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Subashan Perera
- Division of Geriatrics, Department of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
- Department of Biostatistics, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Amanda S. Mahoney
- Department of Communication Science and Disorders, School of Health and Rehabilitation Sciences, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Shitong Mao
- Department of Electrical and Computer Engineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - James L. Coyle
- Department of Electrical and Computer Engineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
- Department of Communication Science and Disorders, School of Health and Rehabilitation Sciences, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
- Department of Otolaryngology, School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Ervin Sejdić
- Edward S. Rogers Department of Electrical and Computer Engineering, Faculty of Applied Science and Engineering, University of Toronto, Toronto, Ontario, Canada
- North York General Hospital, Toronto, Ontario, Canada
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Khalifa Y, Mahoney AS, Lucatorto E, Coyle JL, Sejdić E. Non-Invasive Sensor-Based Estimation of Anterior-Posterior Upper Esophageal Sphincter Opening Maximal Distension. IEEE J Transl Eng Health Med 2023; 11:182-190. [PMID: 36873304 PMCID: PMC9976940 DOI: 10.1109/jtehm.2023.3246919] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/25/2022] [Revised: 01/25/2023] [Accepted: 02/15/2023] [Indexed: 02/22/2023]
Abstract
OBJECTIVE Dysphagia management relies on the evaluation of the temporospatial kinematic events of swallowing performed in videofluoroscopy (VF) by trained clinicians. The upper esophageal sphincter (UES) opening distension represents one of the important kinematic events that contribute to healthy swallowing. Insufficient distension of UES opening can lead to an accumulation of pharyngeal residue and subsequent aspiration which in turn can lead to adverse outcomes such as pneumonia. VF is usually used for the temporal and spatial evaluation of the UES opening; however, VF is not available in all clinical settings and may be inappropriate or undesirable for some patients. High resolution cervical auscultation (HRCA) is a noninvasive technology that uses neck-attached sensors and machine learning to characterize swallowing physiology by analyzing the swallow-induced vibrations/sounds in the anterior neck region. We investigated the ability of HRCA to noninvasively estimate the maximal distension of anterior-posterior (A-P) UES opening as accurately as the measurements performed by human judges from VF images. METHODS AND PROCEDURES Trained judges performed the kinematic measurement of UES opening duration and A-P UES opening maximal distension on 434 swallows collected from 133 patients. We used a hybrid convolutional recurrent neural network supported by attention mechanisms which takes HRCA raw signals as input and estimates the value of the A-P UES opening maximal distension as output. RESULTS The proposed network estimated the A-P UES opening maximal distension with an absolute percentage error of 30% or less for more than 64.14% of the swallows in the dataset. CONCLUSION This study provides substantial evidence for the feasibility of using HRCA to estimate one of the key spatial kinematic measurements used for dysphagia characterization and management. Clinical and Translational Impact Statement: The findings in this study have a direct impact on dysphagia diagnosis and management through providing a non-invasive and cheap way to estimate one of the most important swallowing kinematics, the UES opening distension, that contributes to safe swallowing. This study, along with other studies that utilize HRCA for swallowing kinematic analysis, paves the way for developing a widely available and easy-to-use tool for dysphagia diagnosis and management.
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Affiliation(s)
- Yassin Khalifa
- Department of Biomedical EngineeringCairo UniversityGiza12613Egypt
- Department of Electrical and Computer EngineeringSwanson School of EngineeringUniversity of PittsburghPittsburghPA15260USA
- Case Western Reserve University School of MedicineClevelandOH44106USA
- University Hospitals Harrington Heart and Vascular InstituteClevelandOH44106USA
| | - Amanda S. Mahoney
- Department of Communication Science and DisordersUniversity of PittsburghPittsburghPA15260USA
| | - Erin Lucatorto
- Department of Communication Science and DisordersUniversity of PittsburghPittsburghPA15260USA
| | - James L. Coyle
- Department of Communication Science and DisordersUniversity of PittsburghPittsburghPA15260USA
- Department of OtolaryngologyUniversity of PittsburghPittsburghPA15260USA
| | - Ervin Sejdić
- The Edward S. Rogers Sr. Department of Electrical and Computer EngineeringFaculty of Applied Science and EngineeringUniversity of TorontoTorontoONM5S 1A1Canada
- North York General HospitalTorontoONM2K 1E1Canada
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Mahoney AS, Khalifa Y, Lucatorto E, Sejdić E, Coyle JL. Cervical Vertebral Height Approximates Hyoid Displacement in Videofluoroscopic Images of Healthy Adults. Dysphagia 2022; 37:1689-1696. [PMID: 35230537 DOI: 10.1007/s00455-022-10414-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2021] [Accepted: 12/28/2021] [Indexed: 12/16/2022]
Abstract
Clinicians and researchers commonly judge the completeness of hyoid displacement from videofluoroscopic swallow study (VFSS) videos. Judgments made during the clinical exam are often subjective, and post-examination analysis reduces the measure's immediate value. This study aimed to determine the validity and feasibility of a visual, anatomically scaled benchmark for judging complete hyoid displacement during a VFSS. The third and fourth cervical vertebral bodies (C3 and C4) lie at roughly the same vertical position as the hyoid body and are strongly correlated with patient height. We hypothesized that anterior and superior displacement of the hyoid bone would approximate the height of one C3 or C4 body during safe swallows. Trained raters marked points of interest on C3, C4, and the hyoid body on 1414 swallows of adult patients with suspected dysphagia (n = 195) and 50 swallows of age-matched healthy participants (n = 17), and rated Penetration Aspiration Scale scores. Results indicated that the mean displacements of the hyoid bone were greater than one C3 unit in the superior direction for all swallows from patient and healthy participants, though significantly and clinically greater in healthy participant swallows (p < .001, d > .8). The mean anterior and superior displacements from patient and healthy participant swallows were greater than one C4 unit. Results show preliminary evidence that use of the C3 and/or C4 anatomic scalars can add interpretive value to the immediate judgment of hyoid displacement during the conduct of a clinical VFSS examination.
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Affiliation(s)
- Amanda S Mahoney
- Department of Communication Science and Disorders, School of Health and Rehabilitation Sciences, University of Pittsburgh, 6035 Forbes Tower, Pittsburgh, PA, 15260, USA
| | - Yassin Khalifa
- Department of Electrical and Computer Engineering, School of Computing and Information, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA, 15260, USA.,Department of Bioengineering, Swanson School of Engineering, School of Computing and Information, University of Pittsburgh, Pittsburgh, PA, 15260, USA.,Department of Biomedical Informatics, School of Medicine Intelligent Systems Program, School of Computing and Information, University of Pittsburgh, Pittsburgh, PA, 15260, USA
| | - Erin Lucatorto
- Department of Communication Science and Disorders, School of Health and Rehabilitation Sciences, University of Pittsburgh, 6035 Forbes Tower, Pittsburgh, PA, 15260, USA
| | - Ervin Sejdić
- The Edward S. Rogers Department of Electrical and Computer Engineering, Faculty of Applied Science and Engineering, University of Toronto, Toronto, ON, Canada.,North York General Hospital, Toronto, ON, Canada
| | - James L Coyle
- Department of Communication Science and Disorders, School of Health and Rehabilitation Sciences, University of Pittsburgh, 6035 Forbes Tower, Pittsburgh, PA, 15260, USA.
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Suri A, VanSwearingen J, Dunlap P, Redfern MS, Rosso AL, Sejdić E. Facilitators and barriers to real-life mobility in community-dwelling older adults: a narrative review of accelerometry- and global positioning system-based studies. Aging Clin Exp Res 2022; 34:1733-1746. [PMID: 35275373 PMCID: PMC8913857 DOI: 10.1007/s40520-022-02096-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2021] [Accepted: 02/14/2022] [Indexed: 11/01/2022]
Abstract
Real-life mobility, also called "enacted" mobility, characterizes an individual's activity and participation in the community. Real-life mobility may be facilitated or hindered by a variety of factors, such as physical abilities, cognitive function, psychosocial aspects, and external environment characteristics. Advances in technology have allowed for objective quantification of real-life mobility using wearable sensors, specifically, accelerometry and global positioning systems (GPSs). In this review article, first, we summarize the common mobility measures extracted from accelerometry and GPS. Second, we summarize studies assessing the associations of facilitators and barriers influencing mobility of community-dwelling older adults with mobility measures from sensor technology. We found the most used accelerometry measures focus on the duration and intensity of activity in daily life. Gait quality measures, e.g., cadence, variability, and symmetry, are not usually included. GPS has been used to investigate mobility behavior, such as spatial and temporal measures of path traveled, location nodes traversed, and mode of transportation. Factors of note that facilitate/hinder community mobility were cognition and psychosocial influences. Fewer studies have included the influence of external environments, such as sidewalk quality, and socio-economic status in defining enacted mobility. Increasing our understanding of the facilitators and barriers to enacted mobility can inform wearable technology-enabled interventions targeted at delaying mobility-related disability and improving participation of older adults in the community.
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Affiliation(s)
- Anisha Suri
- Department of Electrical and Computer Engineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA, USA
| | - Jessie VanSwearingen
- Department of Physical Therapy, School of Rehabilitation Sciences, University of Pittsburgh, Pittsburgh, PA, USA
| | - Pamela Dunlap
- Department of Physical Therapy, School of Rehabilitation Sciences, University of Pittsburgh, Pittsburgh, PA, USA
| | - Mark S Redfern
- Department of Bioengineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA, USA
| | - Andrea L Rosso
- Department of Epidemiology, School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA
| | - Ervin Sejdić
- Department of Electrical and Computer Engineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA, USA.
- Department of Bioengineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA, USA.
- The Edward S. Rogers Department of Electrical and Computer Engineering, University of Toronto, Toronto, ON, Canada.
- North York General Hospital, Toronto, ON, Canada.
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Donohue C, Khalifa Y, Mao S, Perera S, Sejdić E, Coyle JL. Characterizing Swallows From People With Neurodegenerative Diseases Using High-Resolution Cervical Auscultation Signals and Temporal and Spatial Swallow Kinematic Measurements. J Speech Lang Hear Res 2021; 64:3416-3431. [PMID: 34428093 PMCID: PMC8642099 DOI: 10.1044/2021_jslhr-21-00134] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Revised: 04/21/2021] [Accepted: 05/21/2021] [Indexed: 06/13/2023]
Abstract
Purpose The prevalence of dysphagia in patients with neurodegenerative diseases (ND) is alarmingly high and frequently results in morbidity and accelerated mortality due to subsequent adverse events (e.g., aspiration pneumonia). Swallowing in patients with ND should be continuously monitored due to the progressive disease nature. Access to instrumental swallow evaluations can be challenging, and limited studies have quantified changes in temporal/spatial swallow kinematic measures in patients with ND. High-resolution cervical auscultation (HRCA), a dysphagia screening method, has accurately differentiated between safe and unsafe swallows, identified swallow kinematic events (e.g., laryngeal vestibule closure [LVC]), and classified swallows between healthy adults and patients with ND. This study aimed to (a) compare temporal/spatial swallow kinematic measures between patients with ND and healthy adults and (b) investigate HRCA's ability to annotate swallow kinematic events in patients with ND. We hypothesized there would be significant differences in temporal/spatial swallow measurements between groups and that HRCA would accurately annotate swallow kinematic events in patients with ND. Method Participants underwent videofluoroscopic swallowing studies with concurrent HRCA. We used linear mixed models to compare temporal/spatial swallow measurements (n = 170 ND patient swallows, n = 171 healthy adult swallows) and deep learning machine-learning algorithms to annotate specific temporal and spatial kinematic events in swallows from patients with ND. Results Differences (p < .05) were found between groups for several temporal and spatial swallow kinematic measures. HRCA signal features were used as input to machine-learning algorithms and annotated upper esophageal sphincter (UES) opening, UES closure, LVC, laryngeal vestibule reopening, and hyoid bone displacement with 66.25%, 85%, 68.18%, 70.45%, and 44.6% accuracy, respectively, compared to human judges' measurements. Conclusion This study demonstrates HRCA's potential in characterizing swallow function in patients with ND and other patient populations.
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Affiliation(s)
- Cara Donohue
- Department of Communication Science and Disorders, School of Health and Rehabilitation Sciences, University of Pittsburgh, PA
| | - Yassin Khalifa
- Department of Electrical and Computer Engineering, Swanson School of Engineering, University of Pittsburgh, PA
| | - Shitong Mao
- Department of Electrical and Computer Engineering, Swanson School of Engineering, University of Pittsburgh, PA
| | - Subashan Perera
- Division of Geriatric Medicine, Department of Medicine, University of Pittsburgh, PA
| | - Ervin Sejdić
- Department of Electrical and Computer Engineering, Swanson School of Engineering, University of Pittsburgh, PA
- Department of Bioengineering, Swanson School of Engineering, University of Pittsburgh, PA
- Department of Biomedical Informatics, University of Pittsburgh School of Medicine, PA
- Intelligent Systems Program, School of Computing and Information, University of Pittsburgh, PA
| | - James L. Coyle
- Department of Communication Science and Disorders, School of Health and Rehabilitation Sciences, University of Pittsburgh, PA
- Department of Otolaryngology, School of Medicine, University of Pittsburgh Medical Center, PA
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Zhang Z, Mao S, Coyle J, Sejdić E. Automatic annotation of cervical vertebrae in videofluoroscopy images via deep learning. Med Image Anal 2021; 74:102218. [PMID: 34487983 DOI: 10.1016/j.media.2021.102218] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2020] [Revised: 08/19/2021] [Accepted: 08/24/2021] [Indexed: 01/07/2023]
Abstract
Judging swallowing kinematic impairments via videofluoroscopy represents the gold standard for the detection and evaluation of swallowing disorders. However, the efficiency and accuracy of such a biomechanical kinematic analysis vary significantly among human judges affected mainly by their training and experience. Here, we showed that a novel machine learning algorithm can with high accuracy automatically detect key anatomical points needed for a routine swallowing assessment in real-time. We trained a novel two-stage convolutional neural network to localize and measure the vertebral bodies using 1518 swallowing videofluoroscopies from 265 patients. Our network model yielded high accuracy as the mean distance between predicted points and annotations was 4.20 ± 5.54 pixels. In comparison, human inter-rater error was 4.35 ± 3.12 pixels. Furthermore, 93% of predicted points were less than five pixels from annotated pixels when tested on an independent dataset from 70 subjects. Our model offers more choices for speech language pathologists in their routine clinical swallowing assessments as it provides an efficient and accurate method for anatomic landmark localization in real-time, a task previously accomplished using an off-line time-sinking procedure.
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Affiliation(s)
- Zhenwei Zhang
- Department of Electrical and Computer Engineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA, 15261, USA
| | - Shitong Mao
- Department of Electrical and Computer Engineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA, 15261, USA
| | - James Coyle
- Department of Communication Science and Disorders, School of Health and Rehabilitation Science, University of Pittsburgh, Pittsburgh, PA, 15261, USA
| | - Ervin Sejdić
- The Edward S. Rogers Department of Electrical and Computer Engineering, Faculty of Applied Science and Engineering, University of Toronto, Toronto, Ontario, Canada; North York General Hospital, Toronto, Ontario, Canada.
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Bouzid Z, Faramand Z, Gregg RE, Helman S, Martin-Gill C, Saba S, Callaway C, Sejdić E, Al-Zaiti S. Novel ECG features and machine learning to optimize culprit lesion detection in patients with suspected acute coronary syndrome. J Electrocardiol 2021; 69S:31-37. [PMID: 34332752 PMCID: PMC8665032 DOI: 10.1016/j.jelectrocard.2021.07.012] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2021] [Revised: 06/24/2021] [Accepted: 07/15/2021] [Indexed: 10/20/2022]
Abstract
BACKGROUND Novel temporal-spatial features of the 12‑lead ECG can conceptually optimize culprit lesions' detection beyond that of classical ST amplitude measurements. We sought to develop a data-driven approach for ECG feature selection to build a clinically relevant algorithm for real-time detection of culprit lesion. METHODS This was a prospective observational cohort study of chest pain patients transported by emergency medical services to three tertiary care hospitals in the US. We obtained raw 10-s, 12‑lead ECGs (500 s/s, HeartStart MRx, Philips Healthcare) during prehospital transport and followed patients 30 days after the encounter to adjudicate clinical outcomes. A total of 557 global and lead-specific features of P-QRS-T waveform were harvested from the representative average beats. We used Recursive Feature Elimination and LASSO to identify 35/557, 29/557, and 51/557 most recurrent and important features for LAD, LCX, and RCA culprits, respectively. Using the union of these features, we built a random forest classifier with 10-fold cross-validation to predict the presence or absence of culprit lesions. We compared this model to the performance of a rule-based commercial proprietary software (Philips DXL ECG Algorithm). RESULTS Our sample included 2400 patients (age 59 ± 16, 47% female, 41% Black, 10.7% culprit lesions). The area under the ROC curves of our random forest classifier was 0.85 ± 0.03 with sensitivity, specificity, and negative predictive value of 71.1%, 84.7%, and 96.1%. This outperformed the accuracy of the automated interpretation software of 37.2%, 95.6%, and 92.7%, respectively, and corresponded to a net reclassification improvement index of 23.6%. Metrics of ST80; Tpeak-Tend; spatial angle between QRS and T vectors; PCA ratio of STT waveform; T axis; and QRS waveform characteristics played a significant role in this incremental gain in performance. CONCLUSIONS Novel computational features of the 12‑lead ECG can be used to build clinically relevant machine learning-based classifiers to detect culprit lesions, which has important clinical implications.
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Affiliation(s)
- Zeineb Bouzid
- Department of Electrical & Computer Engineering, PA, USA
| | - Ziad Faramand
- Department of Acute & Tertiary Care Nursing, PA, USA; University of Pittsburgh Medical Center (UPMC), Pittsburgh, PA, USA
| | - Richard E Gregg
- Advanced Algorithm Research Center, Philips Healthcare, Andover, MA, USA
| | | | - Christian Martin-Gill
- Department of Emergency Medicine, PA, USA; University of Pittsburgh Medical Center (UPMC), Pittsburgh, PA, USA
| | - Samir Saba
- Division of Cardiology at University of Pittsburgh, PA, USA; University of Pittsburgh Medical Center (UPMC), Pittsburgh, PA, USA
| | - Clifton Callaway
- Department of Emergency Medicine, PA, USA; University of Pittsburgh Medical Center (UPMC), Pittsburgh, PA, USA
| | - Ervin Sejdić
- Department of Electrical & Computer Engineering, PA, USA; Department of Bioengineering at Swanson School of Engineering, PA, USA; Department of Biomedical Informatics at School of Medicine, PA, USA; Intelligent Systems Program at School of Computing and Information, PA, USA
| | - Salah Al-Zaiti
- Department of Acute & Tertiary Care Nursing, PA, USA; Department of Emergency Medicine, PA, USA; Division of Cardiology at University of Pittsburgh, PA, USA.
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11
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Dasgupta P, Hughes JA, Daley M, Sejdić E. Is Human Walking a Network Medicine Problem? An Analysis Using Symbolic Regression Models with Genetic Programming. Comput Methods Programs Biomed 2021; 206:106104. [PMID: 33951562 PMCID: PMC8205964 DOI: 10.1016/j.cmpb.2021.106104] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/28/2020] [Accepted: 04/05/2021] [Indexed: 06/12/2023]
Abstract
BACKGROUND AND OBJECTIVE Human walking is typically assessed using a sensor placed on the lower back or the hip. Such analyses often ignore that the arms, legs, and body trunk movements all have significant roles during walking; in other words, these body nodes with accelerometers form a body sensor network (BSN). BSN refers to a network of wearable sensors or devices on the human body that collects physiological signals. Our study proposes that human locomotion could be considered as a network of well-connected nodes. METHODS While hypothesizing that accelerometer data can model this BSN, we collected accelerometer signals from six body areas from ten healthy participants performing a cognitive task. Machine learning based on genetic programming was used to produce a collection of non-linear symbolic models of human locomotion. RESULTS With implications in precision medicine, our primary finding was that our BSN models fit the data from the lower back's accelerometer and describe subject-specific data the best compared to all other models. Across subjects, models were less effective due to the diversity of human sizes. CONCLUSIONS A BSN relationship between all six body nodes has been shown to describe the subject-specific data, which indicates that the network-medicine relationship between these nodes is essential in adequately describing human walking. Our gait analyses can be used for several clinical applications such as medical diagnostics as well as creating a baseline for healthy walking with and without a cognitive load.
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Affiliation(s)
- Pritika Dasgupta
- Department of Biomedical Informatics, School of Medicine, University of Pittsburgh, Pittsburgh, PA, 15261, USA.
| | - James Alexander Hughes
- Department of Computer Science, St. Francis Xavier University, Antigonish, Nova Scotia, B2G 2W5, Canada
| | - Mark Daley
- Department of Computer Science, Middlesex College, University of Western Ontario, London, Ontario, N6A 3K7, Canada
| | - Ervin Sejdić
- Department of Biomedical Informatics, School of Medicine, University of Pittsburgh, Pittsburgh, PA, 15261, USA; Department of Electrical and Computer Engineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA, 15261, USA; Department of Bioengineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA, 15261, USA
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12
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Zhang Z, Kurosu A, Coyle JL, Perera S, Sejdić E. A generalized equation approach for hyoid bone displacement and penetration–aspiration scale analysis. SN Appl Sci 2021. [DOI: 10.1007/s42452-021-04632-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022] Open
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13
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Suri A, Rosso AL, VanSwearingen J, Coffman LM, Redfern MS, Brach JS, Sejdić E. Mobility of Older Adults: Gait Quality Measures are associated with Life-Space Assessment Scores. J Gerontol A Biol Sci Med Sci 2021; 76:e299-e306. [PMID: 34038537 DOI: 10.1093/gerona/glab151] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2021] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND The relation of gait quality to real-life mobility among older adults is poorly understood. This study examined the association between gait quality, consisting of step variability, smoothness, regularity, symmetry and gait speed with the Life-Space Assessment (LSA). METHODS In community-dwelling older adults (N=232, age 77.5±6.6, 65% females), gait quality was derived from: a) an instrumented walkway: gait speed, variability and walk-ratio; and b) accelerometer: signal variability, smoothness, regularity, symmetry, and time-frequency spatiotemporal variables during 6-minute walk. In addition to collecting LSA scores, cognitive functioning, walking-confidence, and falls were recorded. Spearman correlations (speed as covariate) and Random Forest Regression were used to assess associations between gait quality and LSA, and Gaussian-mixture modeling (GMM) was used to cluster participants. RESULTS Spearman correlations of ρp=0.11 (signal amplitude variability ML), ρp=0.15, ρp=-0.13 (symmetry AP-V, ML-AP), ρp=0.16 (power V) and ρ=0.26 (speed), all p<0.05 and marginally related, ρp=-0.12 (regularity V), ρp=0.11 (smoothness AP) and ρp=-0.11 (step-time variability), p<0.1 were obtained. The cross-validated Random Forest model indicated good fit LSA prediction error of 17.77; gait and cognition were greater contributors than age and gender. GMM indicated two clusters. Group-1(N=189) had better gait quality than Group-2(N=43): greater smoothness AP (2.94±0.75 vs 2.30±0.71); greater similarity AP-V (0.58±0.13 vs 0.40±0.19); lower regularity V (0.83±0.08 vs 0.87±0.10); greater power V (1.86±0.18 vs 0.97±1.84); greater speed (1.09±0.16 vs 1.00±0.16 m/s); lower step time CoV (3.70±1.09 vs 5.09±2.37) and better LSA (76±18 vs 67±18), padjusted<0.004. CONCLUSIONS Gait quality measures taken in the clinic are associated with real-life mobility in the community.
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Affiliation(s)
- Anisha Suri
- Department of Electrical and Computer Engineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh
| | - Andrea L Rosso
- Department of Epidemiology, School of Public Health, University of Pittsburgh, Pittsburgh
| | - Jessie VanSwearingen
- Department of Physical Therapy, School of Rehabilitation Sciences, University of Pittsburgh, Pittsburgh
| | - Leslie M Coffman
- Department of Physical Therapy, School of Rehabilitation Sciences, University of Pittsburgh, Pittsburgh
| | - Mark S Redfern
- Department of Bioengineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh
| | - Jennifer S Brach
- Department of Physical Therapy, School of Rehabilitation Sciences, University of Pittsburgh, Pittsburgh
| | - Ervin Sejdić
- Department of Electrical and Computer Engineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh.,Department of Bioengineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh
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14
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Donohue C, Khalifa Y, Mao S, Perera S, Sejdić E, Coyle JL. Establishing Reference Values for Temporal Kinematic Swallow Events Across the Lifespan in Healthy Community Dwelling Adults Using High-Resolution Cervical Auscultation. Dysphagia 2021; 37:664-675. [PMID: 34018024 DOI: 10.1007/s00455-021-10317-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2020] [Accepted: 05/13/2021] [Indexed: 11/29/2022]
Abstract
Few research studies have investigated temporal kinematic swallow events in healthy adults to establish normative reference values. Determining cutoffs for normal and disordered swallowing is vital for differentially diagnosing presbyphagia, variants of normal swallowing, and dysphagia; and for ensuring that different swallowing research laboratories produce consistent results in common measurements from different samples within the same population. High-resolution cervical auscultation (HRCA), a sensor-based dysphagia screening method, has accurately annotated temporal kinematic swallow events in patients with dysphagia, but hasn't been used to annotate temporal kinematic swallow events in healthy adults to establish dysphagia screening cutoffs. This study aimed to determine: (1) Reference values for temporal kinematic swallow events, (2) Whether HRCA can annotate temporal kinematic swallow events in healthy adults. We hypothesized (1) Our reference values would align with a prior study; (2) HRCA would detect temporal kinematic swallow events as accurately as human judges. Trained judges completed temporal kinematic measurements on 659 swallows (N = 70 adults). Swallow reaction time and LVC duration weren't different (p > 0.05) from a previously published historical cohort (114 swallows, N = 38 adults), while other temporal kinematic measurements were different (p < 0.05), suggesting a need for further standardization to feasibly pool data analyses across laboratories. HRCA signal features were used as input to machine learning algorithms and annotated UES opening (69.96% accuracy), UES closure (64.52% accuracy), LVC (52.56% accuracy), and LV re-opening (69.97% accuracy); providing preliminary evidence that HRCA can noninvasively and accurately annotate temporal kinematic measurements in healthy adults to determine dysphagia screening cutoffs.
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Affiliation(s)
- Cara Donohue
- Department of Communication Science and Disorders, School of Health and Rehabilitation Sciences, University of Pittsburgh, 6035 Forbes Tower, Pittsburgh, PA, 15260, USA
| | - Yassin Khalifa
- Department of Electrical and Computer Engineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA, 15260, USA
| | - Shitong Mao
- Department of Electrical and Computer Engineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA, 15260, USA
| | - Subashan Perera
- Division of Geriatrics, Department of Medicine, University of Pittsburgh, Pittsburgh, PA, 15261, USA
| | - Ervin Sejdić
- Department of Electrical and Computer Engineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA, 15260, USA.,Department of Bioengineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA, 15260, USA.,Department of Biomedical Informatics, School of Medicine Intelligent Systems Program, School of Computing and Information, University of Pittsburgh, Pittsburgh, PA, 15260, USA
| | - James L Coyle
- Department of Communication Science and Disorders, School of Health and Rehabilitation Sciences, University of Pittsburgh, 6035 Forbes Tower, Pittsburgh, PA, 15260, USA. .,Department of Otolaryngology, School of Medicine, University of Pittsburgh Medical Center, Pittsburgh, PA, 15260, USA.
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15
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Shu K, Coyle JL, Perera S, Khalifa Y, Sabry A, Sejdić E. Anterior-posterior distension of maximal upper esophageal sphincter opening is correlated with high-resolution cervical auscultation signal features. Physiol Meas 2021; 42. [PMID: 33601360 DOI: 10.1088/1361-6579/abe7cb] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2020] [Accepted: 02/18/2021] [Indexed: 12/22/2022]
Abstract
Objective. Adequate upper esophageal sphincter (UES) opening is essential during swallowing to enable clearance of material into the digestive system, and videofluoroscopy (VF) is the most commonly deployed instrumental examination for assessment of UES opening. High-resolution cervical auscultation (HRCA) has been shown to be an effective, portable and cost-efficient screening tool for dysphagia with strong capabilities in non-invasively and accurately approximating manual measurements of VF images. In this study, we aimed to examine whether the HRCA signals are correlated to the manually measured anterior-posterior (AP) distension of maximal UES opening from VF recordings, under the hypothesis that they would be strongly associated.Approach. We developed a standardized method to spatially measure the AP distension of maximal UES opening in 203 swallows VF recording from 27 patients referred for VF due to suspected dysphagia. Statistical analysis was conducted to compare the manually measured AP distension of maximal UES opening from lateral plane VF images and features extracted from two sets of HRCA signal segments: whole swallow segments and segments excluding all events other than the duration of UES is opening.Main results. HRCA signal features were significantly associated with the normalized AP distension of the maximal UES opening in the longer whole swallowing segments and the association became much stronger when analysis was performed solely during the duration of UES opening.Significance. This preliminary feasibility study demonstrated the potential value of HRCA signals features in approximating the objective measurements of maximal UES AP distension and paves the way of developing HRCA to non-invasively and accurately predict human spatial measurement of VF kinematic events.
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Affiliation(s)
- Kechen Shu
- Department of Electrical and Computer Engineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA, 15261, United States of America
| | - James L Coyle
- Department of Communication Science and Disorders, School of Health and Rehabilitation Sciences, Department of Otolaryngology, School of Medicine, University of Pittsburgh, PA, 15260, United States of America
| | - Subashan Perera
- Division of Geriatrics, Department of Medecine, University of Pittsburgh, Pittsburgh, PA, 15261, United States of America
| | - Yassin Khalifa
- Department of Electrical and Computer Engineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA, 15261, United States of America
| | - Aliaa Sabry
- Department of Communication Science and Disorders, School of Health and Rehabilitation Sciences, University of Pittsburgh, PA, 15260, United States of America
| | - Ervin Sejdić
- Department of Electrical and Computer Engineering, Swanson School of Engineering, Department of Bioengineering, Swanson School of Engineering, Department of Biomedical informatics, School of Medecine, Intelligent Systems Program, School of Computing and Information, University of Pittsburgh, PA, 15260, United States of America
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16
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Bouzid Z, Faramand Z, Gregg RE, Frisch SO, Martin-Gill C, Saba S, Callaway C, Sejdić E, Al-Zaiti S. In Search of an Optimal Subset of ECG Features to Augment the Diagnosis of Acute Coronary Syndrome at the Emergency Department. J Am Heart Assoc 2021; 10:e017871. [PMID: 33459029 PMCID: PMC7955430 DOI: 10.1161/jaha.120.017871] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
Background Classical ST-T waveform changes on standard 12-lead ECG have limited sensitivity in detecting acute coronary syndrome (ACS) in the emergency department. Numerous novel ECG features have been previously proposed to augment clinicians' decision during patient evaluation, yet their clinical utility remains unclear. Methods and Results This was an observational study of consecutive patients evaluated for suspected ACS (Cohort 1 n=745, age 59±17, 42% female, 15% ACS; Cohort 2 n=499, age 59±16, 49% female, 18% ACS). Out of 554 temporal-spatial ECG waveform features, we used domain knowledge to select a subset of 65 physiology-driven features that are mechanistically linked to myocardial ischemia and compared their performance to a subset of 229 data-driven features selected by multiple machine learning algorithms. We then used random forest to select a final subset of 73 most important ECG features that had both data- and physiology-driven basis to ACS prediction and compared their performance to clinical experts. On testing set, a regularized logistic regression classifier based on the 73 hybrid features yielded a stable model that outperformed clinical experts in predicting ACS, with 10% to 29% of cases reclassified correctly. Metrics of nondipolar electrical dispersion (ie, circumferential ischemia), ventricular activation time (ie, transmural conduction delays), QRS and T axes and angles (ie, global remodeling), and principal component analysis ratio of ECG waveforms (ie, regional heterogeneity) played an important role in the improved reclassification performance. Conclusions We identified a subset of novel ECG features predictive of ACS with a fully interpretable model highly adaptable to clinical decision support applications. Registration URL: https://www.clinicaltrials.gov; Unique Identifier: NCT04237688.
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Affiliation(s)
- Zeineb Bouzid
- Department of Electrical & Computer Engineering Swanson School of EngineeringUniversity of Pittsburgh PA
| | - Ziad Faramand
- Department of Acute & Tertiary Care Nursing University of Pittsburgh PA.,University of Pittsburgh Medical Center Pittsburgh PA
| | - Richard E Gregg
- Advanced Algorithm Research Center Philips Healthcare Andover MA
| | - Stephanie O Frisch
- Department of Biomedical Informatics at School of Medicine University of Pittsburgh PA.,Department of Acute & Tertiary Care Nursing University of Pittsburgh PA
| | - Christian Martin-Gill
- Department of Emergency Medicine University of Pittsburgh PA.,University of Pittsburgh Medical Center Pittsburgh PA
| | - Samir Saba
- Division of Cardiology University of Pittsburgh PA.,University of Pittsburgh Medical Center Pittsburgh PA
| | - Clifton Callaway
- Department of Emergency Medicine University of Pittsburgh PA.,University of Pittsburgh Medical Center Pittsburgh PA
| | - Ervin Sejdić
- Department of Electrical & Computer Engineering Swanson School of EngineeringUniversity of Pittsburgh PA.,Department of Bioengineering Swanson School of EngineeringUniversity of Pittsburgh PA.,Department of Biomedical Informatics at School of Medicine University of Pittsburgh PA.,Intelligent Systems Program at School of Computing and Information University of Pittsburgh PA
| | - Salah Al-Zaiti
- Department of Acute & Tertiary Care Nursing University of Pittsburgh PA.,Department of Emergency Medicine University of Pittsburgh PA.,Division of Cardiology University of Pittsburgh PA
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17
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Sabry A, Mahoney AS, Mao S, Khalifa Y, Sejdić E, Coyle JL. Automatic Estimation of Laryngeal Vestibule Closure Duration Using High- Resolution Cervical Auscultation Signals. Perspect ASHA Spec Interest Groups 2020; 5:1647-1656. [PMID: 35937555 PMCID: PMC9355454 DOI: 10.1044/2020_persp-20-00073] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Purpose Safe swallowing requires adequate protection of the airway to prevent swallowed materials from entering the trachea or lungs (i.e., aspiration). Laryngeal vestibule closure (LVC) is the first line of defense against swallowed materials entering the airway. Absent LVC or mistimed/ shortened closure duration can lead to aspiration, adverse medical consequences, and even death. LVC mechanisms can be judged commonly through the videofluoroscopic swallowing study; however, this type of instrumentation exposes patients to radiation and is not available or acceptable to all patients. There is growing interest in noninvasive methods to assess/monitor swallow physiology. In this study, we hypothesized that our noninvasive sensor- based system, which has been shown to accurately track hyoid displacement and upper esophageal sphincter opening duration during swallowing, could predict laryngeal vestibule status, including the onset of LVC and the onset of laryngeal vestibule reopening, in real time and estimate the closure duration with a comparable degree of accuracy as trained human raters. Method The sensor-based system used in this study is high-resolution cervical auscultation (HRCA). Advanced machine learning techniques enable HRCA signal analysis through feature extraction and complex algorithms. A deep learning model was developed with a data set of 588 swallows from 120 patients with suspected dysphagia and further tested on 45 swallows from 16 healthy participants. Results The new technique achieved an overall mean accuracy of 74.90% and 75.48% for the two data sets, respectively, in distinguishing LVC status. Closure duration ratios between automated and gold-standard human judgment of LVC duration were 1.13 for the patient data set and 0.93 for the healthy participant data set. Conclusions This study found that HRCA signal analysis using advanced machine learning techniques can effectively predict laryngeal vestibule status (closure or opening) and further estimate LVC duration. HRCA is potentially a noninvasive tool to estimate LVC duration for diagnostic and biofeedback purposes without X-ray imaging.
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Affiliation(s)
- Aliaa Sabry
- Department of Communication Science and Disorders, School of Health and Rehabilitation Sciences, University of Pittsburgh, PA
- Krembil Research Institute, University Health Network, Toronto, Ontario, Canada
| | - Amanda S. Mahoney
- Department of Communication Science and Disorders, School of Health and Rehabilitation Sciences, University of Pittsburgh, PA
| | - Shitong Mao
- Department of Electrical and Computer Engineering, Swanson School of Engineering, University of Pittsburgh, PA
| | - Yassin Khalifa
- Department of Electrical and Computer Engineering, Swanson School of Engineering, University of Pittsburgh, PA
| | - Ervin Sejdić
- Department of Electrical and Computer Engineering, Swanson School of Engineering, University of Pittsburgh, PA
- Department of Bioengineering, Swanson School of Engineering, University of Pittsburgh, PA
- Department of Biomedical Informatics, School of Medicine Intelligent Systems Program, School of Computing and Information, University of Pittsburgh, PA
| | - James L. Coyle
- Department of Communication Science and Disorders, School of Health and Rehabilitation Sciences, University of Pittsburgh, PA
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18
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Donohue C, Khalifa Y, Perera S, Sejdić E, Coyle JL. How Closely do Machine Ratings of Duration of UES Opening During Videofluoroscopy Approximate Clinician Ratings Using Temporal Kinematic Analyses and the MBSImP? Dysphagia 2020; 36:707-718. [PMID: 32955619 DOI: 10.1007/s00455-020-10191-2] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2020] [Accepted: 09/14/2020] [Indexed: 10/23/2022]
Abstract
Clinicians evaluate swallow kinematic events by analyzing videofluoroscopy (VF) images for dysphagia management. The duration of upper esophageal sphincter opening (DUESO) is one important temporal swallow event, because reduced DUESO can result in pharyngeal residue and penetration/aspiration. VF is frequently used for evaluating swallowing but exposes patients to radiation and is not always feasible/readily available. High resolution cervical auscultation (HRCA) is a non-invasive, sensor-based dysphagia screening method that uses signal processing and machine learning to characterize swallowing. We investigated HRCA's ability to annotate DUESO and predict Modified Barium Swallow Impairment Profile (MBSImP) scores (component #14). We hypothesized that HRCA and machine learning techniques would detect DUESO with similar accuracy as human judges. Trained judges completed temporal kinematic measurements of DUESO on 719 swallows (116 patients) and 50 swallows (15 age-matched healthy adults). An MBSImP certified clinician completed MBSImP ratings on 100 swallows. A multi-layer convolutional recurrent neural network (CRNN) using HRCA signal features for input was used to detect DUESO. Generalized estimating equations models were used to determine statistically significant HRCA signal features for predicting DUESO MBSImP scores. A support vector machine (SVM) classifier and a leave-one-out procedure was used to predict DUESO MBSImP scores. The CRNN detected UES opening within a 3-frame tolerance for 82.6% of patient and 86% of healthy swallows and UES closure for 72.3% of patient and 64% of healthy swallows. The SVM classifier predicted DUESO MBSImP scores with 85.7% accuracy. This study provides evidence of HRCA's feasibility in detecting DUESO without VF images.
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Affiliation(s)
- Cara Donohue
- Department of Communication Science and Disorders, School of Health and Rehabilitation Sciences, University of Pittsburgh, 6035 Forbes Tower, Pittsburgh, PA, 15260, USA
| | - Yassin Khalifa
- Department of Electrical and Computer Engineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA, 15260, USA
| | - Subashan Perera
- Division of Geriatrics, Department of Medicine, University of Pittsburgh, Pittsburgh, PA, 15261, USA
| | - Ervin Sejdić
- Department of Electrical and Computer Engineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA, 15260, USA.,Department of Bioengineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA, 15260, USA.,Department of Biomedical Informatics, School of Medicine, University of Pittsburgh, Pittsburgh, PA, 15260, USA.,Intelligent Systems Program, School of Computing and Information, University of Pittsburgh, Pittsburgh, PA, 15260, USA
| | - James L Coyle
- Department of Communication Science and Disorders, School of Health and Rehabilitation Sciences, University of Pittsburgh, 6035 Forbes Tower, Pittsburgh, PA, 15260, USA.
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19
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Donohue C, Khalifa Y, Perera S, Sejdić E, Coyle JL. A Preliminary Investigation of Whether HRCA Signals Can Differentiate Between Swallows from Healthy People and Swallows from People with Neurodegenerative Diseases. Dysphagia 2020; 36:635-643. [PMID: 32889627 DOI: 10.1007/s00455-020-10177-0] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2019] [Accepted: 08/24/2020] [Indexed: 12/13/2022]
Abstract
High-resolution cervical auscultation (HRCA) is an emerging method for non-invasively assessing swallowing by using acoustic signals from a contact microphone, vibratory signals from an accelerometer, and advanced signal processing and machine learning techniques. HRCA has differentiated between safe and unsafe swallows, predicted components of the Modified Barium Swallow Impairment Profile, and predicted kinematic events of swallowing such as hyoid bone displacement, laryngeal vestibular closure, and upper esophageal sphincter opening with a high degree of accuracy. However, HRCA has not been used to characterize swallow function in specific patient populations. This study investigated the ability of HRCA to differentiate between swallows from healthy people and people with neurodegenerative diseases. We hypothesized that HRCA would differentiate between swallows from healthy people and people with neurodegenerative diseases with a high degree of accuracy. We analyzed 170 swallows from 20 patients with neurodegenerative diseases and 170 swallows from 51 healthy age-matched adults who underwent concurrent video fluoroscopy with non-invasive neck sensors. We used a linear mixed model and several supervised machine learning classifiers that use HRCA signal features and a leave-one-out procedure to differentiate between swallows. Twenty-two HRCA signal features were statistically significant (p < 0.05) for predicting whether swallows were from healthy people or from patients with neurodegenerative diseases. Using the HRCA signal features alone, logistic regression and decision trees classified swallows between the two groups with 99% accuracy, 100% sensitivity, and 99% specificity. This provides preliminary research evidence that HRCA can differentiate swallow function between healthy and patient populations.
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Affiliation(s)
- Cara Donohue
- Department of Communication Science and Disorders, School of Health and Rehabilitation Sciences, University of Pittsburgh, 6035 Forbes Tower, Pittsburgh, PA, 15260, USA.
| | - Yassin Khalifa
- Department of Electrical and Computer Engineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA, 15260, USA
| | - Subashan Perera
- Division of Geriatrics, Department of Medicine, University of Pittsburgh, Pittsburgh, PA, 15261, USA
| | - Ervin Sejdić
- Department of Electrical and Computer Engineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA, 15260, USA.,Department of Bioengineering, Swanson School of Engineering, Department of Biomedical Informatics, School of Medicine Intelligent Systems Program, School of Computing and Information, University of Pittsburgh, Pittsburgh, PA, 15260, USA
| | - James L Coyle
- Department of Communication Science and Disorders, School of Health and Rehabilitation Sciences, University of Pittsburgh, 6035 Forbes Tower, Pittsburgh, PA, 15260, USA
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Al-Zaiti S, Besomi L, Bouzid Z, Faramand Z, Frisch S, Martin-Gill C, Gregg R, Saba S, Callaway C, Sejdić E. Machine learning-based prediction of acute coronary syndrome using only the pre-hospital 12-lead electrocardiogram. Nat Commun 2020; 11:3966. [PMID: 32769990 PMCID: PMC7414145 DOI: 10.1038/s41467-020-17804-2] [Citation(s) in RCA: 65] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2019] [Accepted: 07/16/2020] [Indexed: 11/30/2022] Open
Abstract
Prompt identification of acute coronary syndrome is a challenge in clinical practice. The 12-lead electrocardiogram (ECG) is readily available during initial patient evaluation, but current rule-based interpretation approaches lack sufficient accuracy. Here we report machine learning-based methods for the prediction of underlying acute myocardial ischemia in patients with chest pain. Using 554 temporal-spatial features of the 12-lead ECG, we train and test multiple classifiers on two independent prospective patient cohorts (n = 1244). While maintaining higher negative predictive value, our final fusion model achieves 52% gain in sensitivity compared to commercial interpretation software and 37% gain in sensitivity compared to experienced clinicians. Such an ultra-early, ECG-based clinical decision support tool, when combined with the judgment of trained emergency personnel, would help to improve clinical outcomes and reduce unnecessary costs in patients with chest pain.
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Affiliation(s)
- Salah Al-Zaiti
- Department of Acute & Tertiary Care Nursing, University of Pittsburgh, Pittsburgh, PA, USA.
- Department of Emergency Medicine, University of Pittsburgh, Pittsburgh, PA, USA.
- Division of Cardiology, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA.
| | - Lucas Besomi
- Department of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA, USA
| | - Zeineb Bouzid
- Department of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA, USA
| | - Ziad Faramand
- Department of Acute & Tertiary Care Nursing, University of Pittsburgh, Pittsburgh, PA, USA
| | - Stephanie Frisch
- Department of Acute & Tertiary Care Nursing, University of Pittsburgh, Pittsburgh, PA, USA
| | - Christian Martin-Gill
- Department of Emergency Medicine, University of Pittsburgh, Pittsburgh, PA, USA
- University of Pittsburgh Medical Center (UPMC), Pittsburgh, PA, USA
| | - Richard Gregg
- Advanced Algorithms Development Research Center, Philips Healthcare, Andover, MA, USA
| | - Samir Saba
- Division of Cardiology, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
- University of Pittsburgh Medical Center (UPMC), Pittsburgh, PA, USA
| | - Clifton Callaway
- Department of Emergency Medicine, University of Pittsburgh, Pittsburgh, PA, USA
- University of Pittsburgh Medical Center (UPMC), Pittsburgh, PA, USA
| | - Ervin Sejdić
- Department of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Intelligent Systems, University of Pittsburgh, Pittsburgh, PA, USA
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21
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Coyle JL, Sejdić E. High-Resolution Cervical Auscultation and Data Science: New Tools to Address an Old Problem. Am J Speech Lang Pathol 2020; 29:992-1000. [PMID: 32650655 PMCID: PMC7844341 DOI: 10.1044/2020_ajslp-19-00155] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/17/2019] [Revised: 01/15/2020] [Accepted: 02/16/2020] [Indexed: 06/11/2023]
Abstract
High-resolution cervical auscultation (HRCA) is an evolving clinical method for noninvasive screening of dysphagia that relies on data science, machine learning, and wearable sensors to investigate the characteristics of disordered swallowing function in people with dysphagia. HRCA has shown promising results in categorizing normal and disordered swallowing (i.e., screening) independent of human input, identifying a variety of swallowing physiological events as accurately as trained human judges. The system has been developed through a collaboration of data scientists, computer-electrical engineers, and speech-language pathologists. Its potential to automate dysphagia screening and contribute to evaluation lies in its noninvasive nature (wearable electronic sensors) and its growing ability to accurately replicate human judgments of swallowing data typically formed on the basis of videofluoroscopic imaging data. Potential contributions of HRCA when videofluoroscopic swallowing study may be unavailable, undesired, or not feasible for many patients in various settings are discussed, along with the development and capabilities of HRCA. The use of technological advances and wearable devices can extend the dysphagia clinician's reach and reinforce top-of-license practice for patients with swallowing disorders.
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Affiliation(s)
- James L. Coyle
- Department of Communication Science and Disorders, School of Health and Rehabilitation Sciences, University of Pittsburgh, PA
- Department of Otolaryngology, School of Medicine, University of Pittsburgh, PA
| | - Ervin Sejdić
- Department of Electrical and Computer Engineering, Swanson School of Engineering, University of Pittsburgh, PA
- Department of Bioengineering, Swanson School of Engineering, University of Pittsburgh, PA
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22
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Frisch SO, Brown J, Faramand Z, Stemler J, Sejdić E, Martin-Gill C, Callaway C, Sereika SM, Al-Zaiti SS. Exploring the complex interactions of baseline patient factors to improve nursing triage of acute coronary syndrome. Res Nurs Health 2020; 43:356-364. [PMID: 32491206 DOI: 10.1002/nur.22045] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2019] [Revised: 04/25/2020] [Accepted: 05/21/2020] [Indexed: 01/14/2023]
Abstract
Emergency department (ED) nurses need to identify patients with potential acute coronary syndrome (ACS) rapidly because treatment delay could impact patient outcomes. Aims of this secondary analysis were to identify key patient factors that could be available at initial ED nurse triage that predict ACS. Consecutive patients with chest pain who called 9-1-1, received a 12-lead electrocardiogram in the prehospital setting, and were transported via emergency medical service were included in the study. A total of 750 patients were recruited. The sample had an average age of 59 years old, was 57% male, and 40% Black. One hundred and fifteen patients were diagnosed with ACS. Older age, non-Caucasian race, and faster respiratory rate were independent predictors of ACS. There was an interaction between heart rate by Type II diabetes receiving insulin in the context of ACS. Type II diabetics requiring insulin for better glycemic control manifested a faster heart rate. By identifying patient factors at ED nurse triage that could be predictive of ACS, accuracy rates of triage may improve, thus impacting patient outcomes.
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Affiliation(s)
- Stephanie O Frisch
- Department of Acute and Tertiary Care, University of Pittsburgh School of Nursing, Pittsburgh, Pennsylvania.,University of Pittsburgh Medical Center (UPMC), Pittsburgh, Pennsylvania
| | - Julissa Brown
- Department of Acute and Tertiary Care, University of Pittsburgh School of Nursing, Pittsburgh, Pennsylvania
| | - Ziad Faramand
- Department of Acute and Tertiary Care, University of Pittsburgh School of Nursing, Pittsburgh, Pennsylvania
| | - Jennifer Stemler
- Department of Acute and Tertiary Care, University of Pittsburgh School of Nursing, Pittsburgh, Pennsylvania
| | - Ervin Sejdić
- Department of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Christian Martin-Gill
- University of Pittsburgh Medical Center (UPMC), Pittsburgh, Pennsylvania.,Department of Emergency Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Clifton Callaway
- University of Pittsburgh Medical Center (UPMC), Pittsburgh, Pennsylvania.,Department of Emergency Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Susan M Sereika
- Center for Research and Evaluation, University of Pittsburgh School of Nursing, Pittsburgh, Pennsylvania
| | - Salah S Al-Zaiti
- Department of Acute and Tertiary Care, University of Pittsburgh School of Nursing, Pittsburgh, Pennsylvania.,Department of Emergency Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania
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23
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Khalifa Y, Coyle JL, Sejdić E. Non-invasive identification of swallows via deep learning in high resolution cervical auscultation recordings. Sci Rep 2020; 10:8704. [PMID: 32457331 PMCID: PMC7251089 DOI: 10.1038/s41598-020-65492-1] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2019] [Accepted: 05/05/2020] [Indexed: 11/22/2022] Open
Abstract
High resolution cervical auscultation is a very promising noninvasive method for dysphagia screening and aspiration detection, as it does not involve the use of harmful ionizing radiation approaches. Automatic extraction of swallowing events in cervical auscultation is a key step for swallowing analysis to be clinically effective. Using time-varying spectral estimation of swallowing signals and deep feed forward neural networks, we propose an automatic segmentation algorithm for swallowing accelerometry and sounds that works directly on the raw swallowing signals in an online fashion. The algorithm was validated qualitatively and quantitatively using the swallowing data collected from 248 patients, yielding over 3000 swallows manually labeled by experienced speech language pathologists. With a detection accuracy that exceeded 95%, the algorithm has shown superior performance in comparison to the existing algorithms and demonstrated its generalizability when tested over 76 completely unseen swallows from a different population. The proposed method is not only of great importance to any subsequent swallowing signal analysis steps, but also provides an evidence that such signals can capture the physiological signature of the swallowing process.
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Affiliation(s)
- Yassin Khalifa
- Department of Electrical and Computer Engineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA, USA
| | - James L Coyle
- Department of Communication Science and Disorders, School of Health and Rehabilitation Sciences, University of Pittsburgh, Pittsburgh, PA, USA
| | - Ervin Sejdić
- Department of Electrical and Computer Engineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA, USA.
- Department of Bioengineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA, USA.
- Department of Biomedical Informatics, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA.
- Intelligent Systems Program, School of Computing and Information, University of Pittsburgh, Pittsburgh, PA, USA.
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24
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Donohue C, Mao S, Sejdić E, Coyle JL. Tracking Hyoid Bone Displacement During Swallowing Without Videofluoroscopy Using Machine Learning of Vibratory Signals. Dysphagia 2020; 36:259-269. [PMID: 32419103 DOI: 10.1007/s00455-020-10124-z] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2019] [Accepted: 04/29/2020] [Indexed: 10/24/2022]
Abstract
Identifying physiological impairments of swallowing is essential for determining accurate diagnosis and appropriate treatment for patients with dysphagia. The hyoid bone is an anatomical landmark commonly monitored during analysis of videofluoroscopic swallow studies (VFSSs). Its displacement is predictive of penetration/aspiration and is associated with other swallow kinematic events. However, VFSSs are not always readily available/feasible and expose patients to radiation. High-resolution cervical auscultation (HRCA), which uses acoustic and vibratory signals from a microphone and tri-axial accelerometer, is under investigation as a non-invasive dysphagia screening method and potential adjunct to VFSS when it is unavailable or not feasible. We investigated the ability of HRCA to independently track hyoid bone displacement during swallowing with similar accuracy to VFSS, by analyzing vibratory signals from a tri-axial accelerometer using machine learning techniques. We hypothesized HRCA would track hyoid bone displacement with a high degree of accuracy compared to humans. Trained judges completed frame-by-frame analysis of hyoid bone displacement on 400 swallows from 114 patients and 48 swallows from 16 age-matched healthy adults. Extracted features from vibratory signals were used to train the predictive algorithm to generate a bounding box surrounding the hyoid body on each frame. A metric of relative overlapped percentage (ROP) compared human and machine ratings. The mean ROP for all swallows analyzed was 50.75%, indicating > 50% of the bounding box containing the hyoid bone was accurately predicted in every frame. This provides evidence of the feasibility of accurate, automated hyoid bone displacement tracking using HRCA signals without use of VFSS images.
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Affiliation(s)
- Cara Donohue
- Department of Communication Science and Disorders, School of Health and Rehabilitation Sciences, University of Pittsburgh, 6035 Forbes Tower, Pittsburgh, PA, 15260, USA
| | - Shitong Mao
- Department of Electrical and Computer Engineering, Swanson School of Engineering, Department of Bioengineering, Swanson School of Engineering, Department of Biomedical Informatics, School of Medicine Intelligent Systems Program, School of Computing and Information, University of Pittsburgh, Pittsburgh, PA, 15260, USA
| | - Ervin Sejdić
- Department of Electrical and Computer Engineering, Swanson School of Engineering, Department of Bioengineering, Swanson School of Engineering, Department of Biomedical Informatics, School of Medicine Intelligent Systems Program, School of Computing and Information, University of Pittsburgh, Pittsburgh, PA, 15260, USA
| | - James L Coyle
- Department of Communication Science and Disorders, School of Health and Rehabilitation Sciences, University of Pittsburgh, 6035 Forbes Tower, Pittsburgh, PA, 15260, USA.
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25
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Sejdić E, Su FC. What the future holds for Biomedical Engineering Online? Biomed Eng Online 2019; 18:81. [PMID: 31340815 PMCID: PMC6657155 DOI: 10.1186/s12938-019-0702-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2019] [Accepted: 07/18/2019] [Indexed: 11/10/2022] Open
Abstract
The future of biomedical engineering is exciting, and prospects for biomedical engineering journals are rapidly growing. In this editorial, a brief history of Biomedical Engineering Online is outlined, along with our plans for future directions of the journal.
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Affiliation(s)
- Ervin Sejdić
- Department of Electrical and Computer Engineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA, 15261, USA. .,Department of Bioengineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA, 15261, USA. .,Department of Biomedical Informatics, School of Medicine, University of Pittsburgh, Pittsburgh, PA, 15261, USA. .,Intelligent Systems Program, School of Computing and Information, University of Pittsburgh, Pittsburgh, PA, 15261, USA.
| | - Fong-Chin Su
- Department of Biomedical Engineering, Medical Device Innovation Center, National Cheng Kung University, Tainan, 701, Taiwan, ROC
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26
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Phalen H, Coffman BA, Ghuman A, Sejdić E, Salisbury DF. Non-negative Matrix Factorization Reveals Resting-State Cortical Alpha Network Abnormalities in the First-Episode Schizophrenia Spectrum. Biol Psychiatry Cogn Neurosci Neuroimaging 2019; 5:961-970. [PMID: 31451387 DOI: 10.1016/j.bpsc.2019.06.010] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/20/2019] [Revised: 06/18/2019] [Accepted: 06/18/2019] [Indexed: 01/08/2023]
Abstract
BACKGROUND Little is known about neural oscillatory dynamics in first-episode psychosis. Pathophysiology of functional connectivity can be measured through network activity of alpha oscillations, reflecting long-range communication between distal brain regions. METHODS Resting magnetoencephalographic activity was collected from 31 individuals with first-episode schizophrenia spectrum psychosis and 22 healthy control individuals. Activity was projected to the realistic cortical surface, based on structural magnetic resonance imaging. The first principal component of activity in 40 Brodmann areas per hemisphere was Hilbert transformed within the alpha range. Non-negative matrix factorization was applied to single-trial alpha phase-locking values from all subjects to determine alpha networks. Within networks, energy and entropy were compared. RESULTS Four cortical alpha networks were pathological in individuals with first-episode schizophrenia spectrum psychosis. The networks involved the bilateral anterior and posterior cingulate; left auditory, medial temporal, and cingulate cortex; right inferior frontal gyrus and widespread areas; and right posterior parietal cortex and widespread areas. Energy and entropy were associated with the Positive and Negative Syndrome Scale total and thought disorder factors for the first three networks. In addition, the left posterior temporal network was associated with positive and negative factors, and the right inferior frontal network was associated with the positive factor. CONCLUSIONS Machine learning network analysis of resting alpha-band neural activity identified several aberrant networks in individuals with first-episode schizophrenia spectrum psychosis, including the left temporal, right inferior frontal, right posterior parietal, and bilateral cingulate cortices. Abnormal long-range alpha communication is evident at the first presentation for psychosis and may provide clues about mechanisms of dysconnectivity in psychosis and novel targets for noninvasive brain stimulation.
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Affiliation(s)
- Henry Phalen
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Brian A Coffman
- Clinical Neurophysiology Research Laboratory, Western Psychiatric Institute and Clinic, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
| | - Avniel Ghuman
- Laboratory of Cognitive Neurodynamics, Department of Neurological Surgery, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
| | - Ervin Sejdić
- Department of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Dean F Salisbury
- Clinical Neurophysiology Research Laboratory, Western Psychiatric Institute and Clinic, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania.
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27
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Zhang Z, Sejdić E. Radiological images and machine learning: Trends, perspectives, and prospects. Comput Biol Med 2019; 108:354-370. [PMID: 31054502 PMCID: PMC6531364 DOI: 10.1016/j.compbiomed.2019.02.017] [Citation(s) in RCA: 63] [Impact Index Per Article: 12.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2018] [Revised: 02/19/2019] [Accepted: 02/19/2019] [Indexed: 01/18/2023]
Abstract
The application of machine learning to radiological images is an increasingly active research area that is expected to grow in the next five to ten years. Recent advances in machine learning have the potential to recognize and classify complex patterns from different radiological imaging modalities such as x-rays, computed tomography, magnetic resonance imaging and positron emission tomography imaging. In many applications, machine learning based systems have shown comparable performance to human decision-making. The applications of machine learning are the key ingredients of future clinical decision making and monitoring systems. This review covers the fundamental concepts behind various machine learning techniques and their applications in several radiological imaging areas, such as medical image segmentation, brain function studies and neurological disease diagnosis, as well as computer-aided systems, image registration, and content-based image retrieval systems. Synchronistically, we will briefly discuss current challenges and future directions regarding the application of machine learning in radiological imaging. By giving insight on how take advantage of machine learning powered applications, we expect that clinicians can prevent and diagnose diseases more accurately and efficiently.
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Affiliation(s)
- Zhenwei Zhang
- Department of Electrical and Computer Engineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA, 15261, USA
| | - Ervin Sejdić
- Department of Electrical and Computer Engineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA, 15261, USA.
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28
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Sejdić E, Malandraki GA, Coyle JL. Computational deglutition: Signal and image processing methods to understand swallowing and associated disorders. IEEE Signal Process Mag 2019; 36:138-146. [PMID: 31631954 PMCID: PMC6800740 DOI: 10.1109/msp.2018.2875863] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
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29
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Abstract
The displacement of the hyoid bone is one of the key components evaluated in the swallow study, as its motion during swallowing is related to overall swallowing integrity. In daily research settings, experts visually detect the hyoid bone in the video frames and manually plot hyoid bone position frame by frame. This study aims to develop an automatic method to localize the location of the hyoid bone in the video sequence. To automatically detect the location of the hyoid bone in a frame, we proposed a single shot multibox detector, a deep convolutional neural network, which is employed to detect and classify the location of the hyoid bone. We also evaluated the performance of two other state-of-art detection methods for comparison. The experimental results clearly showed that the single shot multibox detector can detect the hyoid bone with an average precision of 89.14% and outperform other auto-detection algorithms. We conclude that this automatic hyoid bone tracking system is accurate enough to be widely applied as a pre-processing step for image processing in dysphagia research, as well as a promising development that may be useful in the diagnosis of dysphagia.
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Affiliation(s)
- Zhenwei Zhang
- Department of Electrical and Computer Engineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA, 15261, USA
| | - James L Coyle
- Department of Communication Science and Disorders, University of Pittsburgh, Pittsburgh, PA, 15260, USA
| | - Ervin Sejdić
- Department of Electrical and Computer Engineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA, 15261, USA.
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30
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Abstract
Compressive sensing is a computational framework for acquisition and processing of sparse signals at sampling rates below the rates mandated by the Nyquist sampling theorem. In this paper, we present seven MATLAB functions for compressive sensing based time-frequency processing of sparse nonstationary signals. These functions are developed to reproduce figures in our companion review paper.
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Affiliation(s)
- Ervin Sejdić
- Department of Electrical and Computer Engineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA, 15261, USA
| | - Irena Orović
- Faculty of Electrical Engineering, University of Montenegro, Podgorica, Montenegro
| | - Srdjan Stanković
- Faculty of Electrical Engineering, University of Montenegro, Podgorica, Montenegro
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31
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Sejdić E, Orović I, Stanković S. Compressive sensing meets time-frequency: An overview of recent advances in time-frequency processing of sparse signals. Digit Signal Process 2018; 77:22-35. [PMID: 29867288 PMCID: PMC5984051 DOI: 10.1016/j.dsp.2017.07.016] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
Compressive sensing is a framework for acquiring sparse signals at sub-Nyquist rates. Once compressively acquired, many signals need to be processed using advanced techniques such as time-frequency representations. Hence, we overview recent advances dealing with time-frequency processing of sparse signals acquired using compressive sensing approaches. The paper is geared towards signal processing practitioners and we emphasize practical aspects of these algorithms. First, we briefly review the idea of compressive sensing. Second, we review two major approaches for compressive sensing in the time-frequency domain. Thirdly, compressive sensing based time-frequency representations are reviewed followed by descriptions of compressive sensing approaches based on the polynomial Fourier transform and the short-time Fourier transform. Lastly, we provide brief conclusions along with several future directions for this field.
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Affiliation(s)
- Ervin Sejdić
- Department of Electrical and Computer Engineering, Swanson School of Enginering, University of Pittsburgh, Pittsburgh, PA, 15261, USA
| | - Irena Orović
- Faculty of Electrical Engineering, University of Montenegro, Podgorica, Montenegro
| | - Srdjan Stanković
- Faculty of Electrical Engineering, University of Montenegro, Podgorica, Montenegro
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32
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Dudik JM, Kurosu A, Coyle JL, Sejdić E. Dysphagia and its effects on swallowing sounds and vibrations in adults. Biomed Eng Online 2018; 17:69. [PMID: 29855309 PMCID: PMC5984479 DOI: 10.1186/s12938-018-0501-9] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2018] [Accepted: 05/17/2018] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND To utilize cervical auscultation as a means of screening for risk of dysphagia, we must first determine how the signal differs between healthy subjects and subjects with swallowing disorders. METHODS In this experiment we gathered swallowing sound and vibration data from 53 (13 with stroke, 40 without) patients referred for imaging evaluation of swallowing function with videofluoroscopy. The analysis was limited to non-aspirating swallows of liquid with either thin (< 5 cps) or viscous ([Formula: see text]) consistency. After calculating a selection of generalized time, frequency, and time frequency features for each swallow, we compared our data against our findings in a previous experiment that investigated identical features for a different group of 56 healthy subjects. RESULTS We found that nearly all of our chosen features for both vibrations and sounds showed significant differences between the healthy and disordered swallows despite the absence of aspiration. We also found only negligible differences between dysphagia as a symptom of stroke and dysphagia as a symptom of another condition. CONCLUSION Non-aspirating swallows from healthy controls and patients with dysphagia have distinct feature patterns. These findings should greatly help the development of the cervical auscultation field and serve as a reference for future investigations into more specialized characterization methods.
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Affiliation(s)
- Joshua M Dudik
- Department of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA, USA
| | - Atsuko Kurosu
- Department of Communication Sciences and Disorders, University of Pittsburgh, Pittsburgh, PA, USA
| | - James L Coyle
- Department of Communication Sciences and Disorders, University of Pittsburgh, Pittsburgh, PA, USA
| | - Ervin Sejdić
- Department of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA, USA.
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Abstract
Cervical auscultation is a method for assessing swallowing performance. However, its ability to serve as a classification tool for a practical clinical assessment method is not fully understood. In this study, we utilized neural network classification methods in the form of Deep Belief networks in order to classify swallows. We specifically utilized swallows that did not result in clinically significant aspiration and classified them on whether they originated from healthy subjects or unhealthy patients. Dual-axis swallowing vibrations from 1946 discrete swallows were recorded from 55 healthy and 53 unhealthy subjects. The Fourier transforms of both signals were used as inputs to the networks of various sizes. We found that single and multi-layer Deep Belief networks perform nearly identically when analyzing only a single vibration signal. However, multi-layered Deep Belief networks demonstrated approximately a 5% to 10% greater accuracy and sensitivity when both signals were analyzed concurrently, indicating that higher-order relationships between these vibrations are important for classification and assessment.
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Affiliation(s)
- Joshua M Dudik
- Department of Electrical and Computer Engineering, Swanson School of Enginering, University of Pittsburgh, Pittsburgh, PA, USA
| | - James L Coyle
- Department of Communication Science and Disorders, School of Health and Rehabilitation Sciences, University of Pittsburgh, Pittsburgh, PA, USA
| | - Amro El-Jaroudi
- Department of Electrical and Computer Engineering, Swanson School of Enginering, University of Pittsburgh, Pittsburgh, PA, USA
| | - Zhi-Hong Mao
- Department of Electrical and Computer Engineering, Swanson School of Enginering, University of Pittsburgh, Pittsburgh, PA, USA
| | - Mingui Sun
- Department of Neurological Surgery, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Ervin Sejdić
- Department of Electrical and Computer Engineering, Swanson School of Enginering, University of Pittsburgh, Pittsburgh, PA, USA
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34
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Montero-Odasso M, Speechley M, Muir-Hunter SW, Sarquis-Adamson Y, Sposato LA, Hachinski V, Borrie M, Wells J, Black A, Sejdić E, Bherer L, Chertkow H. Motor and Cognitive Trajectories Before Dementia: Results from Gait and Brain Study. J Am Geriatr Soc 2018; 66:1676-1683. [DOI: 10.1111/jgs.15341] [Citation(s) in RCA: 61] [Impact Index Per Article: 10.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Affiliation(s)
- Manuel Montero-Odasso
- Gait and Brain Lab, Parkwood Institute; Lawson Health Research Institute; London Ontario Canada
- Division of Geriatric Medicine, Department of Medicine, Schulich School of Medicine and Dentistry; University of Western Ontario; London Ontario Canada
- Department of Epidemiology and Biostatistics; University of Western Ontario; London Ontario Canada
| | - Mark Speechley
- Gait and Brain Lab, Parkwood Institute; Lawson Health Research Institute; London Ontario Canada
- Department of Epidemiology and Biostatistics; University of Western Ontario; London Ontario Canada
| | - Susan W. Muir-Hunter
- Gait and Brain Lab, Parkwood Institute; Lawson Health Research Institute; London Ontario Canada
- Division of Geriatric Medicine, Department of Medicine, Schulich School of Medicine and Dentistry; University of Western Ontario; London Ontario Canada
| | - Yanina Sarquis-Adamson
- Gait and Brain Lab, Parkwood Institute; Lawson Health Research Institute; London Ontario Canada
| | - Luciano A. Sposato
- Department of Epidemiology and Biostatistics; University of Western Ontario; London Ontario Canada
- Department of Clinical Neurological Sciences; University of Western Ontario; London Ontario Canada
- Department of Anatomy and Cell Biology; University of Western Ontario; London Ontario Canada
- Stroke, Dementia and Heart Disease Laboratory; University of Western Ontario; London Ontario Canada
| | - Vladimir Hachinski
- Department of Clinical Neurological Sciences; University of Western Ontario; London Ontario Canada
| | - Michael Borrie
- Division of Geriatric Medicine, Department of Medicine, Schulich School of Medicine and Dentistry; University of Western Ontario; London Ontario Canada
| | - Jennie Wells
- Division of Geriatric Medicine, Department of Medicine, Schulich School of Medicine and Dentistry; University of Western Ontario; London Ontario Canada
| | - Alanna Black
- Gait and Brain Lab, Parkwood Institute; Lawson Health Research Institute; London Ontario Canada
| | - Ervin Sejdić
- Department of Electrical and Computer Engineering; University of Pittsburgh; Pittsburgh Pennsylvania
| | - Louis Bherer
- Department of Medicine; Université de Montréal and Montreal Heart Institute; Montreal Quebec Canada
| | - Howard Chertkow
- Jewish General Hospital Memory Clinic; McGill University; Montreal Quebec Canada
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Jestrović I, Coyle JL, Perera S, Sejdić E. Influence of attention and bolus volume on brain organization during swallowing. Brain Struct Funct 2018; 223:955-964. [PMID: 29058086 DOI: 10.1007/s00429-017-1535-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2016] [Accepted: 10/03/2017] [Indexed: 10/18/2022]
Abstract
It has been shown that swallowing involves certain attentional and cognitive resources which, when disrupted can influence swallowing function with in dysphagic patient. However, there are still open questions regarding the influence of attention and cognitive demands on brain activity during swallowing. In order to understand how brain regions responsible for attention influence brain activity during swallowing, we compared brain organization during no-distraction swallowing and swallowing with distraction. Fifteen healthy male adults participated in the data collection process. Participants performed ten 1 ml, ten 5 ml, and ten 10 ml water swallows under both no-distraction conditions and during distraction while EEG signals were recorded. After standard pre-processing of the EEG signals, brain networks were formed using the time-frequency based synchrony measure. The brain networks formed were then compared between the two sets of conditions. Results showed that there are differences in the Delta, Theta, Alpha, Beta, and Gamma frequency bands between no-distraction swallowing and swallowing with distraction. Differences in the Delta and Theta frequency bands can be attributed to changes in subliminal processes, while changes in the Alpha and Beta frequency bands are directly associated with the various levels of attention and cognitive demands during swallowing process, and changes in the Gamma frequency band are due to changes in motor activity. Furthermore, we showed that variations in bolus volume influenced the swallowing brain networks in the Delta, Theta, Alpha, Beta, and Gamma frequency bands. Changes in the Delta, Theta, and Alpha frequency bands are due to sensory perturbations evoked by the various bolus volumes. Changes in the Beta frequency band are due to reallocation of cognitive demands, while changes in the Gamma frequency band are due to changes in motor activity produced by variations in bolus volume. These findings could potentially lead to the development of better understanding of the nature of dysphagia and various rehabilitation strategies for patients with neurogenic dysphagia who have altered attention or impaired cognitive functions.
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Affiliation(s)
- Iva Jestrović
- Department of Electrical and Computer Engineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA, USA
| | - James L Coyle
- Department of Communication Science and Disorders, School of Health and Rehabilitation Sciences, University of Pittsburgh, Pittsburgh, PA, USA
| | - Subashan Perera
- Department of Medicine, Division of Geriatric Medicine, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Ervin Sejdić
- Department of Electrical and Computer Engineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA, USA.
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36
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Gatouillat A, Dumortier A, Perera S, Badr Y, Gehin C, Sejdić E. Analysis of the pen pressure and grip force signal during basic drawing tasks: The timing and speed changes impact drawing characteristics. Comput Biol Med 2017; 87:124-131. [PMID: 28582693 DOI: 10.1016/j.compbiomed.2017.05.020] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2017] [Revised: 05/18/2017] [Accepted: 05/19/2017] [Indexed: 11/19/2022]
Abstract
Writing is a complex fine and trained motor skill, involving complex biomechanical and cognitive processes. In this paper, we propose the study of writing kinetics using three angles: the pen-tip normal force, the total grip force signal and eventually writing quality assessment. In order to collect writing kinetics data, we designed a sensor collecting these characteristics simultaneously. Ten healthy right-handed adults were recruited and were asked to perform four tasks: first, they were instructed to draw circles at a speed they considered comfortable; they then were instructed to draw circles at a speed they regarded as fast; afterwards, they repeated the comfortable task compelled to follow the rhythm of a metronome; and eventually they performed the fast task under the same timing constraints. Statistical differences between the tasks were computed, and while pen-tip normal force and total grip force signal were not impacted by the changes introduced in each task, writing quality features were affected by both the speed changes and timing constraint changes. This verifies the already-studied speed-accuracy trade-off and suggest the existence of a timing constraints-accuracy trade-off.
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Affiliation(s)
| | - Antoine Dumortier
- Department of Electrical and Computer Engineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA, USA
| | - Subashan Perera
- Department of Medicine, Division of Geriatric Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Youakim Badr
- Univ Lyon, INSA-Lyon, LIRIS, UMR5205, F-69621, France
| | | | - Ervin Sejdić
- Department of Electrical and Computer Engineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA, USA.
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Al-Zaiti S, Sejdić E, Nemec J, Callaway C, Soman P, Lux R. Spatial indices of repolarization correlate with non-ST elevation myocardial ischemia in patients with chest pain. Med Biol Eng Comput 2017. [PMID: 28626854 DOI: 10.1007/s11517-017-1659-1] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Abstract
Mild-to-moderate ischemia does not result in ST segment elevation on the electrocardiogram (ECG), but rather non-specific changes in the T wave, which are frequently labeled as non-diagnostic for ischemia. Robust methods to quantify such T wave heterogeneity can have immediate clinical applications. We sought to evaluate the effects of spontaneous ischemia on the evolution of spatial T wave changes, based on the eigenvalues of the spatial correlation matrix of the ECG, in patients undergoing nuclear cardiac imaging for evaluating intermittent chest pain. We computed T wave complexity (TWC), the ratio of the second to the first eigenvalue of repolarization, from 5-min baseline and 5-min peak-stress Holter ECG recordings. Our sample included 30 males and 20 females aged 63 ± 11 years. Compared to baseline, significant changes in TWC were only seen in patients with ischemia (n = 10) during stress testing, but not among others. The absolute changes in TWC were significantly larger in the ischemia group compared to others, with a pattern that seemed to depend on the severity or anatomic distribution of ischemia. Our results demonstrate that ischemia-induced changes in T wave morphology can be meaningfully quantified from the surface 12-lead ECG, suggesting an important opportunity for improving diagnostics in patients with chest pain.
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Affiliation(s)
- Salah Al-Zaiti
- Department of Acute & Tertiary Care Nursing, School of Nursing, University of Pittsburgh, 336 Victoria Building, 3500 Victoria St, Pittsburgh, PA, 15261, USA. .,Department of Emergency Medicine, University of Pittsburgh, Pittsburgh, PA, USA.
| | - Ervin Sejdić
- Department of Computer & Electrical Engineering, University of Pittsburgh, Pittsburgh, PA, USA
| | - Jan Nemec
- Department of Cardiac Electrophysiology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Clifton Callaway
- Department of Emergency Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Prem Soman
- Department of Nuclear Cardiology, University of Pittsburgh, Pittsburgh,, PA, USA
| | - Robert Lux
- Department of Cardiovascular Medicine, University of Utah, Salt Lake, UT, USA
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38
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Movahedi F, Kurosu A, Coyle JL, Perera S, Sejdić E. A comparison between swallowing sounds and vibrations in patients with dysphagia. Comput Methods Programs Biomed 2017; 144:179-187. [PMID: 28495001 PMCID: PMC5455149 DOI: 10.1016/j.cmpb.2017.03.009] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/09/2016] [Revised: 01/27/2017] [Accepted: 03/09/2017] [Indexed: 06/07/2023]
Abstract
The cervical auscultation refers to the observation and analysis of sounds or vibrations captured during swallowing using either a stethoscope or acoustic/vibratory detectors. Microphones and accelerometers have recently become two common sensors used in modern cervical auscultation methods. There are open questions about whether swallowing signals recorded by these two sensors provide unique or complementary information about swallowing function; or whether they present interchangeable information. This study aims to compare of swallowing signals recorded by a microphone and a tri-axial accelerometer from 72 patients (mean age 63.94 ± 12.58 years, 42 male, 30 female), who had videofluoroscopic examination. The participants swallowed one or more boluses of thickened liquids of different consistencies, including thin liquids, nectar-thick liquids, and pudding. A comfortable self-selected volume from a cup or a controlled volume by the examiner from a 5 ml spoon was given to the participants. A broad feature set was extracted in time, information-theoretic, and frequency domains from each of 881 swallows presented in this study. The swallowing sounds exhibited significantly higher frequency content and kurtosis values than the swallowing vibrations. In addition, the Lempel-Ziv complexity was lower for swallowing sounds than those for swallowing vibrations. To conclude, information provided by microphones and accelerometers about swallowing function are unique and these two transducers are not interchangeable. Consequently, the selection of transducer would be a vital step in future studies.
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Affiliation(s)
- Faezeh Movahedi
- Department of Electrical and Computer Engineering, Swanson School of Enginering, University of Pittsburgh, Pittsburgh, PA, USA
| | - Atsuko Kurosu
- Department of Communication Science and Disorders, School of Health and Rehabilitation Sciences, University of Pittsburgh, Pittsburgh, PA, USA
| | - James L Coyle
- Department of Communication Science and Disorders, School of Health and Rehabilitation Sciences, University of Pittsburgh, Pittsburgh, PA, USA
| | - Subashan Perera
- Department of Medicine, Division of Geriatric Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Ervin Sejdić
- Department of Electrical and Computer Engineering, Swanson School of Enginering, University of Pittsburgh, Pittsburgh, PA, USA.
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Jestrović I, Coyle JL, Sejdić E. Differences in brain networks during consecutive swallows detected using an optimized vertex-frequency algorithm. Neuroscience 2017; 344:113-123. [PMID: 27989520 PMCID: PMC5303679 DOI: 10.1016/j.neuroscience.2016.11.047] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2016] [Revised: 11/30/2016] [Accepted: 11/30/2016] [Indexed: 11/19/2022]
Abstract
Patients with dysphagia can have higher risks of aspiration after repetitive swallowing activity due to the "fatigue effect". However, it is still unknown how consecutive swallows affect brain activity. Therefore, we sought to investigate differences in swallowing brain networks formed during consecutive swallows using a signal processing on graph approach. Data were collected from 55 healthy people using electroencephalography (EEG) signals. Participants performed dry swallows (i.e., saliva swallows) and wet swallows (i.e., water, nectar-thick, and honey thick swallows). After standard pre-processing of the EEG time series, brain networks were formed using the time-frequency-based synchrony measure, while signals on graphs were formed as a line graph of the brain networks. For calculating the vertex frequency information from the signals on graphs, the proposed algorithm was based on the optimized window size for calculating the windowed graph Fourier transform and the graph S-transform. The proposed algorithms were tested using synthetic signals and showed improved energy concentration in comparison to the original algorithm. When applied to EEG swallowing data, the optimized windowed graph Fourier transform and the optimized graph S-transform showed that differences exist in brain activity between consecutive swallows. In addition, the results showed higher differences between consecutive swallows for thicker liquids.
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Affiliation(s)
- Iva Jestrović
- Department of Electrical and Computer Engineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA 15261, USA
| | - James L Coyle
- Department of Communication Science and Disorders, School of Health and Rehabilitation Sciences, University of Pittsburgh, Pittsburgh, PA 15260, USA
| | - Ervin Sejdić
- Department of Electrical and Computer Engineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA 15261, USA.
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Rothfuss MA, Unadkat JV, Gimbel ML, Mickle MH, Sejdić E. Totally Implantable Wireless Ultrasonic Doppler Blood Flowmeters: Toward Accurate Miniaturized Chronic Monitors. Ultrasound Med Biol 2017; 43:561-578. [PMID: 28038789 DOI: 10.1016/j.ultrasmedbio.2016.11.005] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/10/2016] [Revised: 10/27/2016] [Accepted: 11/12/2016] [Indexed: 06/06/2023]
Abstract
Totally implantable wireless ultrasonic blood flowmeters provide direct-access chronic vessel monitoring in hard-to-reach places without using wired bedside monitors or imaging equipment. Although wireless implantable Doppler devices are accurate for most applications, device size and implant lifetime remain vastly underdeveloped. We review past and current approaches to miniaturization and implant lifetime extension for wireless implantable Doppler devices and propose approaches to reduce device size and maximize implant lifetime for the next generation of devices. Additionally, we review current and past approaches to accurate blood flow measurements. This review points toward relying on increased levels of monolithic customization and integration to reduce size. Meanwhile, recommendations to maximize implant lifetime should include alternative sources of power, such as transcutaneous wireless power, that stand to extend lifetime indefinitely. Coupling together the results will pave the way for ultra-miniaturized totally implantable wireless blood flow monitors for truly chronic implantation.
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Affiliation(s)
- Michael A Rothfuss
- Department of Electrical and Computer Engineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Jignesh V Unadkat
- Department of Plastic Surgery, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Michael L Gimbel
- Department of Plastic Surgery, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Marlin H Mickle
- Department of Electrical and Computer Engineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Ervin Sejdić
- Department of Electrical and Computer Engineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.
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41
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Abstract
The windowed Fourier transform (short time Fourier transform) and the S-transform are widely used signal processing tools for extracting frequency information from non-stationary signals. Previously, the windowed Fourier transform had been adopted for signals on graphs and has been shown to be very useful for extracting vertex-frequency information from graphs. However, high computational complexity makes these algorithms impractical. We sought to develop a fast windowed graph Fourier transform and a fast graph S-transform requiring significantly shorter computation time. The proposed schemes have been tested with synthetic test graph signals and real graph signals derived from electroencephalography recordings made during swallowing. The results showed that the proposed schemes provide significantly lower computation time in comparison with the standard windowed graph Fourier transform and the fast graph S-transform. Also, the results showed that noise has no effect on the results of the algorithm for the fast windowed graph Fourier transform or on the graph S-transform. Finally, we showed that graphs can be reconstructed from the vertex-frequency representations obtained with the proposed algorithms.
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Affiliation(s)
- Iva Jestrović
- Department of Electrical and Computer Engineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA, USA
| | - James L. Coyle
- Department of Communication Science and Disorders, School of Health and Rehabilitation Sciences, University of Pittsburgh, Pittsburgh, PA, USA
| | - Ervin Sejdić
- Department of Electrical and Computer Engineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA, USA
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42
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Dumortier A, Beckjord E, Shiffman S, Sejdić E. Classifying smoking urges via machine learning. Comput Methods Programs Biomed 2016; 137:203-213. [PMID: 28110725 PMCID: PMC5289882 DOI: 10.1016/j.cmpb.2016.09.016] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/14/2016] [Revised: 08/12/2016] [Accepted: 09/20/2016] [Indexed: 05/30/2023]
Abstract
BACKGROUND AND OBJECTIVE Smoking is the largest preventable cause of death and diseases in the developed world, and advances in modern electronics and machine learning can help us deliver real-time intervention to smokers in novel ways. In this paper, we examine different machine learning approaches to use situational features associated with having or not having urges to smoke during a quit attempt in order to accurately classify high-urge states. METHODS To test our machine learning approaches, specifically, Bayes, discriminant analysis and decision tree learning methods, we used a dataset collected from over 300 participants who had initiated a quit attempt. The three classification approaches are evaluated observing sensitivity, specificity, accuracy and precision. RESULTS The outcome of the analysis showed that algorithms based on feature selection make it possible to obtain high classification rates with only a few features selected from the entire dataset. The classification tree method outperformed the naive Bayes and discriminant analysis methods, with an accuracy of the classifications up to 86%. These numbers suggest that machine learning may be a suitable approach to deal with smoking cessation matters, and to predict smoking urges, outlining a potential use for mobile health applications. CONCLUSIONS In conclusion, machine learning classifiers can help identify smoking situations, and the search for the best features and classifier parameters significantly improves the algorithms' performance. In addition, this study also supports the usefulness of new technologies in improving the effect of smoking cessation interventions, the management of time and patients by therapists, and thus the optimization of available health care resources. Future studies should focus on providing more adaptive and personalized support to people who really need it, in a minimum amount of time by developing novel expert systems capable of delivering real-time interventions.
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Affiliation(s)
- Antoine Dumortier
- Department of Electrical and Computer Engineering, University of Pittsburgh, Benedum Hall, Pittsburgh, PA 15260, USA
| | - Ellen Beckjord
- Department of Psychiatry, University of Pittsburgh, 5115 Centre Avenue, Suite 140, Pittsburgh, PA 15232, USA
| | - Saul Shiffman
- Department of Psychology, University of Pittsburgh, 510 BELPB, 130 N. Bellefield Avenue, Pittsburgh, PA 15260, USA
| | - Ervin Sejdić
- Department of Electrical and Computer Engineering, University of Pittsburgh, Benedum Hall, Pittsburgh, PA 15260, USA.
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43
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Jestrović I, Coyle JL, Perera S, Sejdić E. Functional connectivity patterns of normal human swallowing: difference among various viscosity swallows in normal and chin-tuck head positions. Brain Res 2016; 1652:158-169. [PMID: 27693396 PMCID: PMC5102805 DOI: 10.1016/j.brainres.2016.09.041] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2016] [Revised: 09/22/2016] [Accepted: 09/27/2016] [Indexed: 11/26/2022]
Abstract
Consuming thicker fluids and swallowing in the chin-tuck position has been shown to be advantageous for some patients with neurogenic dysphagia who aspirate due to various causes. The anatomical changes caused by these therapeutic techniques are well known, but it is unclear whether these changes alter the cerebral processing of swallow-related sensorimotor activity. We sought to investigate the effect of increased fluid viscosity and chin-down posture during swallowing on brain networks. 55 healthy adults performed water, nectar-thick, and honey thick liquid swallows in the neutral and chin-tuck positions while EEG signals were recorded. After pre-processing of the EEG timeseries, the time-frequency based synchrony measure was used for forming the brain networks to investigate whether there were differences among the brain networks between the swallowing of different fluid viscosities and swallowing in different head positions. We also investigated whether swallowing under various conditions exhibit small-world properties. Results showed that fluid viscosity affects the brain network in the Delta, Theta, Alpha, Beta, and Gamma frequency bands and that swallowing in the chin-tuck head position affects brain networks in the Alpha, Beta, and Gamma frequency bands. In addition, we showed that swallowing in all tested conditions exhibited small-world properties. Therefore, fluid viscosity and head positions should be considered in future swallowing EEG investigations.
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Affiliation(s)
- Iva Jestrović
- Department of Electrical and Computer Engineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA, USA.
| | - James L Coyle
- Department of Communication Science and Disorders, School of Health and Rehabilitation Sciences, University of Pittsburgh, Pittsburgh, PA, USA; Department of Otolaryngology, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA.
| | - Subashan Perera
- Department of Medicine, Division of Geratric Medicine, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA.
| | - Ervin Sejdić
- Department of Electrical and Computer Engineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA, USA.
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Zhang Z, VanSwearingen J, Brach JS, Perera S, Sejdić E. Most suitable mother wavelet for the analysis of fractal properties of stride interval time series via the average wavelet coefficient method. Comput Biol Med 2016; 80:175-184. [PMID: 27960102 DOI: 10.1016/j.compbiomed.2016.11.009] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2016] [Revised: 11/20/2016] [Accepted: 11/23/2016] [Indexed: 10/20/2022]
Abstract
Human gait is a complex interaction of many nonlinear systems and stride intervals exhibiting self-similarity over long time scales that can be modeled as a fractal process. The scaling exponent represents the fractal degree and can be interpreted as a "biomarker" of relative diseases. The previous study showed that the average wavelet method provides the most accurate results to estimate this scaling exponent when applied to stride interval time series. The purpose of this paper is to determine the most suitable mother wavelet for the average wavelet method. This paper presents a comparative numerical analysis of 16 mother wavelets using simulated and real fractal signals. Simulated fractal signals were generated under varying signal lengths and scaling exponents that indicate a range of physiologically conceivable fractal signals. The five candidates were chosen due to their good performance on the mean square error test for both short and long signals. Next, we comparatively analyzed these five mother wavelets for physiologically relevant stride time series lengths. Our analysis showed that the symlet 2 mother wavelet provides a low mean square error and low variance for long time intervals and relatively low errors for short signal lengths. It can be considered as the most suitable mother function without the burden of considering the signal length.
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Affiliation(s)
- Zhenwei Zhang
- Department of Electrical and Computer Engineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA 15261, USA
| | - Jessie VanSwearingen
- Department of Physical Therapy, University of Pittsburgh, Pittsburgh, PA 15260, USA
| | - Jennifer S Brach
- Department of Physical Therapy, University of Pittsburgh, Pittsburgh, PA 15260, USA
| | - Subashan Perera
- Department of Medicine, Division of Geriatrics, University of Pittsburgh, Pittsburgh, PA 15261, USA
| | - Ervin Sejdić
- Department of Electrical and Computer Engineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA 15261, USA.
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45
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Karim HT, Huppert TJ, Erickson KI, Wollam ME, Sparto PJ, Sejdić E, VanSwearingen JM. Motor sequence learning-induced neural efficiency in functional brain connectivity. Behav Brain Res 2016; 319:87-95. [PMID: 27845228 DOI: 10.1016/j.bbr.2016.11.021] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2016] [Revised: 11/03/2016] [Accepted: 11/10/2016] [Indexed: 10/20/2022]
Abstract
Previous studies have shown the functional neural circuitry differences before and after an explicitly learned motor sequence task, but have not assessed these changes during the process of motor skill learning. Functional magnetic resonance imaging activity was measured while participants (n=13) were asked to tap their fingers to visually presented sequences in blocks that were either the same sequence repeated (learning block) or random sequences (control block). Motor learning was associated with a decrease in brain activity during learning compared to control. Lower brain activation was noted in the posterior parietal association area and bilateral thalamus during the later periods of learning (not during the control). Compared to the control condition, we found the task-related motor learning was associated with decreased connectivity between the putamen and left inferior frontal gyrus and left middle cingulate brain regions. Motor learning was associated with changes in network activity, spatial extent, and connectivity.
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Affiliation(s)
- Helmet T Karim
- Department of Bioengineering, University of Pittsburgh, Pittsburgh PA, USA.
| | - Theodore J Huppert
- Department of Radiology and Bioengineering, University of Pittsburgh, Pittsburgh PA, USA.
| | - Kirk I Erickson
- Department of Psychology, University of Pittsburgh, Pittsburgh PA, USA.
| | | | - Patrick J Sparto
- Departments of Physical Therapy and Bioengineering, University of Pittsburgh, Pittsburgh PA, USA.
| | - Ervin Sejdić
- Department of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh PA, USA.
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Sejdić E, Movahedi F, Zhang Z, Kurosu A, Coyle JL. The effects of compressive sensing on extracted features from tri-axial swallowing accelerometry signals. Proc SPIE Int Soc Opt Eng 2016; 9857. [PMID: 27695157 DOI: 10.1117/12.2225466] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
Acquiring swallowing accelerometry signals using a comprehensive sensing scheme may be a desirable approach for monitoring swallowing safety for longer periods of time. However, it needs to be insured that signal characteristics can be recovered accurately from compressed samples. In this paper, we considered this issue by examining the effects of the number of acquired compressed samples on the calculated swallowing accelerometry signal features. We used tri-axial swallowing accelerometry signals acquired from seventeen stroke patients (106 swallows in total). From acquired signals, we extracted typically considered signal features from time, frequency and time-frequency domains. Next, we compared these features from the original signals (sampled using traditional sampling schemes) and compressively sampled signals. Our results have shown we can obtain accurate estimates of signal features even by using only a third of original samples.
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Affiliation(s)
- Ervin Sejdić
- Department of Electrical and Computer Engineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA, 15261, USA
| | - Faezeh Movahedi
- Department of Electrical and Computer Engineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA, 15261, USA
| | - Zhenwei Zhang
- Department of Electrical and Computer Engineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA, 15261, USA
| | - Atsuko Kurosu
- Department of Communication Science and Disorders, School of Health and Rehabilitation Sciences, University of Pittsburgh, PA, 15260, USA
| | - James L Coyle
- Department of Communication Science and Disorders, School of Health and Rehabilitation Sciences, University of Pittsburgh, PA, 15260, USA
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47
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Abstract
Swallowing disorders affect thousands of patients every year. Currently utilized techniques to screen for this condition are questionably reliable and are often deployed in non-standard manners, so efforts have been put forth to generate an instrumental alternative based on cervical auscultation. These physiological signals with low signal-to-noise ratios are traditionally denoised by well-known wavelets in a discrete, single tree wavelet decomposition. We attempt to improve this widely accepted method by designing a matched wavelet for cervical auscultation signals to provide better denoising capabilities and by implementing a dual-tree complex wavelet transform to maintain time invariant properties of this filtering. We found that our matched wavelet did offer better denoising capabilities for cervical auscultation signals compared to several popular wavelets and that the dual tree complex wavelet transform did offer better time invariance when compared to the single tree structure. We conclude that this new method of denoising cervical auscultation signals could benefit applications that can spare the required computation time and complexity.
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Affiliation(s)
- Joshua M Dudik
- Department of Electrical and Computer Engineering, Swanson School of Enginering, University of Pittsburgh, Pittsburgh, PA, USA.
| | - James L Coyle
- Department of Communication Science and Disorders, School of Health and Rehabilitation Sciences, University of Pittsburgh, Pittsburgh, PA, USA.
| | - Amro El-Jaroudi
- Department of Electrical and Computer Engineering, Swanson School of Enginering, University of Pittsburgh, Pittsburgh, PA, USA,
| | - Mingui Sun
- Department of Neurological Surgery, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA.
| | - Ervin Sejdić
- Department of Electrical and Computer Engineering, Swanson School of Enginering, University of Pittsburgh, Pittsburgh, PA, USA,
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48
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Bocan KN, Sejdić E. Adaptive Transcutaneous Power Transfer to Implantable Devices: A State of the Art Review. Sensors (Basel) 2016; 16:s16030393. [PMID: 26999154 PMCID: PMC4813968 DOI: 10.3390/s16030393] [Citation(s) in RCA: 87] [Impact Index Per Article: 10.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/05/2016] [Revised: 02/26/2016] [Accepted: 03/11/2016] [Indexed: 11/16/2022]
Abstract
Wireless energy transfer is a broad research area that has recently become applicable to implantable medical devices. Wireless powering of and communication with implanted devices is possible through wireless transcutaneous energy transfer. However, designing wireless transcutaneous systems is complicated due to the variability of the environment. The focus of this review is on strategies to sense and adapt to environmental variations in wireless transcutaneous systems. Adaptive systems provide the ability to maintain performance in the face of both unpredictability (variation from expected parameters) and variability (changes over time). Current strategies in adaptive (or tunable) systems include sensing relevant metrics to evaluate the function of the system in its environment and adjusting control parameters according to sensed values through the use of tunable components. Some challenges of applying adaptive designs to implantable devices are challenges common to all implantable devices, including size and power reduction on the implant, efficiency of power transfer and safety related to energy absorption in tissue. Challenges specifically associated with adaptation include choosing relevant and accessible parameters to sense and adjust, minimizing the tuning time and complexity of control, utilizing feedback from the implanted device and coordinating adaptation at the transmitter and receiver.
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Affiliation(s)
- Kara N Bocan
- Department of Electrical and Computer Engineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA 15213, USA.
| | - Ervin Sejdić
- Department of Electrical and Computer Engineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA 15213, USA.
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Bleton H, Perera S, Sejdić E. Cognitive tasks and cerebral blood flow through anterior cerebral arteries: a study via functional transcranial Doppler ultrasound recordings. BMC Med Imaging 2016; 16:22. [PMID: 26969112 PMCID: PMC4788871 DOI: 10.1186/s12880-016-0125-0] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2015] [Accepted: 02/29/2016] [Indexed: 01/12/2023] Open
Abstract
Background Functional transcanial Doppler ultrasound (fTCD) is a convenient approach to examine cerebral blood flow velocity (CBFV) in major cerebral arteries. Methods In this study, the anterior cerebral artery (ACA) was insonated on both sides, that is, right ACA (R-ACA) and left ACA (L-ACA). The envelope signals (the maximum velocity) and the raw signals were analyzed during cognitive processes, i.e. word-generation tasks, geometric tasks and resting state periods separating each task. Data which were collected from 20 healthy participants were used to investigate the changes and the hemispheric functioning while performing cognitive tasks. Signal characteristics were analyzed in time domain, frequency domain and time-frequency domain. Results Significant results have been obtained through the use of both classic/modern methods (i.e. envelope/raw, time and frequency/information-theoretic and time-frequency domains). The frequency features extracted from the raw signals highlighted sex effects on cerebral blood flow which revealed distinct brain response during each process and during resting periods. In the time-frequency analysis, the distribution of wavelet energies on the envelope signals moved around the low frequencies during mental processes and did not experience any lateralization during cognitive tasks. Conclusions Even if no lateralization effects were noticed during resting-state, verbal and geometric tasks, understanding CBFV in ACA during cognitive tasks could complement information extracted from cerebral blood flow in middle cerebral arteries during similar cognitive tasks (i.e. sex effects).
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Affiliation(s)
- Héloïse Bleton
- Department of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA, USA
| | - Subashan Perera
- Division of Geriatric Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Ervin Sejdić
- Department of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA, USA.
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Dudik JM, Kurosu A, Coyle JL, Sejdić E. A statistical analysis of cervical auscultation signals from adults with unsafe airway protection. J Neuroeng Rehabil 2016; 13:7. [PMID: 26801236 PMCID: PMC4722771 DOI: 10.1186/s12984-015-0110-9] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2015] [Accepted: 12/19/2015] [Indexed: 01/16/2023] Open
Abstract
Background Aspiration, where food or liquid is allowed to enter the larynx during a swallow, is recognized as the most clinically salient feature of oropharyngeal dysphagia. This event can lead to short-term harm via airway obstruction or more long-term effects such as pneumonia. In order to non-invasively identify this event using high resolution cervical auscultation there is a need to characterize cervical auscultation signals from subjects with dysphagia who aspirate. Methods In this study, we collected swallowing sound and vibration data from 76 adults (50 men, 26 women, mean age 62) who underwent a routine videofluoroscopy swallowing examination. The analysis was limited to swallows of liquid with either thin (<5 cps) or viscous (≈300 cps) consistency and was divided into those with deep laryngeal penetration or aspiration (unsafe airway protection), and those with either shallow or no laryngeal penetration (safe airway protection), using a standardized scale. After calculating a selection of time, frequency, and time-frequency features for each swallow, the safe and unsafe categories were compared using Wilcoxon rank-sum statistical tests. Results Our analysis found that few of our chosen features varied in magnitude between safe and unsafe swallows with thin swallows demonstrating no statistical variation. We also supported our past findings with regard to the effects of sex and the presence or absence of stroke on cervical ausculation signals, but noticed certain discrepancies with regards to bolus viscosity. Conclusions Overall, our results support the necessity of using multiple statistical features concurrently to identify laryngeal penetration of swallowed boluses in future work with high resolution cervical auscultation.
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Affiliation(s)
- Joshua M Dudik
- Department of Electrical and Computer Engineering, Swanson School of Engineering, University of Pittsburgh, 3700 O'Hara Street, Pittsburgh, PA, 15261, USA.
| | - Atsuko Kurosu
- Department of Communication Science and Disorders, School of Health and Rehabilitation Sciences, University of Pittsburgh, 4028 Forbes Tower, Pittsburgh, PA, 15260, USA.
| | - James L Coyle
- Department of Communication Science and Disorders, School of Health and Rehabilitation Sciences, University of Pittsburgh, 4028 Forbes Tower, Pittsburgh, PA, 15260, USA.
| | - Ervin Sejdić
- Department of Electrical and Computer Engineering, Swanson School of Engineering, University of Pittsburgh, 3700 O'Hara Street, Pittsburgh, PA, 15261, USA.
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