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Mohagheghian F, Han D, Ghetia O, Peitzsch A, Nishita N, Pirayesh Shirazi Nejad M, Ding EY, Noorishirazi K, Hamel A, Otabil EM, DiMezza D, Dickson EL, Tran KV, McManus DD, Chon KH. Noise Reduction in Photoplethysmography Signals Using a Convolutional Denoising Autoencoder With Unconventional Training Scheme. IEEE Trans Biomed Eng 2024; 71:456-466. [PMID: 37682653 DOI: 10.1109/tbme.2023.3307400] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/10/2023]
Abstract
OBJECTIVE We propose an efficient approach based on a convolutional denoising autoencoder (CDA) network to reduce motion and noise artifacts (MNA) from corrupted atrial fibrillation (AF) and non-AF photoplethysmography (PPG) data segments so that an accurate PPG-signal-derived heart rate can be obtained. Our method's main innovation is the optimization of the CDA performance for both rhythms using more AF than non-AF data for training the AF-specific CDA model and vice versa for the non-AF CDA network. METHODS To evaluate this unconventional training scheme, our proposed network was trained and tested on 25-sec PPG data segments from 48 subjects from two different databases-the Pulsewatch dataset and Stanford University's publicly available PPG dataset. In total, our dataset contains 10,773 data segments: 7,001 segments for training and 3,772 independent segments from out-of-sample subjects for testing. RESULTS Using real-life corrupted PPG segments, our approach significantly reduced the average heart rate root mean square error (RMSE) of the reconstructed PPG segments by 45.74% and 23% compared to the corrupted non-AF and AF data, respectively. Further, our approach exhibited lower RMSE, and higher sensitivity and PPV for detected peaks compared to the reconstructed data produced by the alternative methods. CONCLUSION These results show the promise of our approach as a reliable denoising method, which should be used prior to AF detection algorithms for an accurate cardiac health monitoring involving wearable devices. SIGNIFICANCE PPG signals collected from wearables are vulnerable to MNA, which limits their use as a reliable measurement, particularly in uncontrolled real-life environments.
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Mensah Otabil E, Dai Q, Anzenberg P, Filippaios A, Ding E, Mehawej J, Mathew JE, Lessard D, Wang Z, Noorishirazi K, Hamel A, Paul T, DiMezza D, Han D, Mohagheghian F, Soni A, Lin H, Barton B, Saczynski J, Chon KH, Tran KV, McManus DD. Technology engagement is associated with higher perceived physical well-being in stroke patients prescribed smartwatches for atrial fibrillation detection. Front Digit Health 2023; 5:1243959. [PMID: 38125757 PMCID: PMC10731012 DOI: 10.3389/fdgth.2023.1243959] [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] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Accepted: 11/20/2023] [Indexed: 12/23/2023] Open
Abstract
Background Increasing ownership of smartphones among Americans provides an opportunity to use these technologies to manage medical conditions. We examine the influence of baseline smartwatch ownership on changes in self-reported anxiety, patient engagement, and health-related quality of life when prescribed smartwatch for AF detection. Method We performed a post-hoc secondary analysis of the Pulsewatch study (NCT03761394), a clinical trial in which 120 participants were randomized to receive a smartwatch-smartphone app dyad and ECG patch monitor compared to an ECG patch monitor alone to establish the accuracy of the smartwatch-smartphone app dyad for detection of AF. At baseline, 14 days, and 44 days, participants completed the Generalized Anxiety Disorder-7 survey, the Health Survey SF-12, and the Consumer Health Activation Index. Mixed-effects linear regression models using repeated measures with anxiety, patient activation, physical and mental health status as outcomes were used to examine their association with smartwatch ownership at baseline. Results Ninety-six participants, primarily White with high income and tertiary education, were randomized to receive a study smartwatch-smartphone dyad. Twenty-four (25%) participants previously owned a smartwatch. Compared to those who did not previously own a smartwatch, smartwatch owners reported significant greater increase in their self-reported physical health (β = 5.07, P < 0.05), no differences in anxiety (β = 0.92, P = 0.33), mental health (β = -2.42, P = 0.16), or patient activation (β = 1.86, P = 0.54). Conclusions Participants who own a smartwatch at baseline reported a greater positive change in self-reported physical health, but not in anxiety, patient activation, or self-reported mental health over the study period.
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Affiliation(s)
- Edith Mensah Otabil
- Division of Cardiovascular Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, United States
| | - Qiying Dai
- Division of Cardiovascular Medicine, Department of Medicine, Saint Vincent Hospital, Worcester, MA, United States
| | - Paula Anzenberg
- Division of Cardiovascular Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, United States
| | - Andreas Filippaios
- Division of Cardiovascular Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, United States
| | - Eric Ding
- Division of Cardiovascular Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, United States
| | - Jordy Mehawej
- Division of Cardiovascular Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, United States
| | - Joanne E. Mathew
- Department of Internal Medicine, Central Michigan University, Mount Pleasant, MI, United States
| | - Darleen Lessard
- Department of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester, MA, United States
| | - Ziyue Wang
- Department of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester, MA, United States
| | - Kamran Noorishirazi
- Division of Cardiovascular Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, United States
| | - Alexander Hamel
- Division of Cardiovascular Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, United States
| | - Tenes Paul
- Division of Cardiovascular Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, United States
| | - Danielle DiMezza
- Division of Cardiovascular Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, United States
| | - Dong Han
- Department of Biomedical Engineering, University of Connecticut, Storrs, CT, United States
| | - Fahimeh Mohagheghian
- Department of Biomedical Engineering, University of Connecticut, Storrs, CT, United States
| | - Apurv Soni
- Division of Cardiovascular Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, United States
| | - Honghuang Lin
- Division of Cardiovascular Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, United States
| | - Bruce Barton
- Department of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester, MA, United States
| | - Jane Saczynski
- Department of Pharmacy and Health Systems Sciences, School of Pharmacy, Northeastern University, Boston, MA, United States
| | - Ki H. Chon
- Department of Biomedical Engineering, University of Connecticut, Storrs, CT, United States
| | - Khanh-Van Tran
- Division of Cardiovascular Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, United States
| | - David D. McManus
- Division of Cardiovascular Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, United States
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Ding EY, Tran KV, Lessard D, Wang Z, Han D, Mohagheghian F, Mensah Otabil E, Noorishirazi K, Mehawej J, Filippaios A, Naeem S, Gottbrecht MF, Fitzgibbons TP, Saczynski JS, Barton B, Chon K, McManus DD. Accuracy, Usability, and Adherence of Smartwatches for Atrial Fibrillation Detection in Older Adults After Stroke: Randomized Controlled Trial. JMIR Cardio 2023; 7:e45137. [PMID: 38015598 DOI: 10.2196/45137] [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] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Revised: 05/31/2023] [Accepted: 06/19/2023] [Indexed: 11/29/2023] Open
Abstract
BACKGROUND Atrial fibrillation (AF) is a common cause of stroke, and timely diagnosis is critical for secondary prevention. Little is known about smartwatches for AF detection among stroke survivors. We aimed to examine accuracy, usability, and adherence to a smartwatch-based AF monitoring system designed by older stroke survivors and their caregivers. OBJECTIVE This study aims to examine the feasibility of smartwatches for AF detection in older stroke survivors. METHODS Pulsewatch is a randomized controlled trial (RCT) in which stroke survivors received either a smartwatch-smartphone dyad for AF detection (Pulsewatch system) plus an electrocardiogram patch or the patch alone for 14 days to assess the accuracy and usability of the system (phase 1). Participants were subsequently rerandomized to potentially 30 additional days of system use to examine adherence to watch wear (phase 2). Participants were aged 50 years or older, had survived an ischemic stroke, and had no major contraindications to oral anticoagulants. The accuracy for AF detection was determined by comparing it to cardiologist-overread electrocardiogram patch, and the usability was assessed with the System Usability Scale (SUS). Adherence was operationalized as daily watch wear time over the 30-day monitoring period. RESULTS A total of 120 participants were enrolled (mean age 65 years; 50/120, 41% female; 106/120, 88% White). The Pulsewatch system demonstrated 92.9% (95% CI 85.3%-97.4%) accuracy for AF detection. Mean usability score was 65 out of 100, and on average, participants wore the watch for 21.2 (SD 8.3) of the 30 days. CONCLUSIONS Our findings demonstrate that a smartwatch system designed by and for stroke survivors is a viable option for long-term arrhythmia detection among older adults at risk for AF, though it may benefit from strategies to enhance adherence to watch wear. TRIAL REGISTRATION ClinicalTrials.gov NCT03761394; https://clinicaltrials.gov/study/NCT03761394. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) RR2-10.1016/j.cvdhj.2021.07.002.
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Affiliation(s)
- Eric Y Ding
- Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, United States
| | - Khanh-Van Tran
- Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, United States
| | - Darleen Lessard
- Department of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester, MA, United States
| | - Ziyue Wang
- Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, United States
| | - Dong Han
- Department of Bioengineering, University of Connecticut, Storrs, CT, United States
| | - Fahimeh Mohagheghian
- Department of Bioengineering, University of Connecticut, Storrs, CT, United States
| | - Edith Mensah Otabil
- Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, United States
| | - Kamran Noorishirazi
- Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, United States
| | - Jordy Mehawej
- Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, United States
| | - Andreas Filippaios
- Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, United States
| | - Syed Naeem
- Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, United States
| | - Matthew F Gottbrecht
- Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, United States
| | - Timothy P Fitzgibbons
- Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, United States
| | - Jane S Saczynski
- Department of Pharmacy and Health Systems Sciences, Northeastern University, Boston, MA, United States
| | - Bruce Barton
- Department of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester, MA, United States
| | - Ki Chon
- Department of Bioengineering, University of Connecticut, Storrs, CT, United States
| | - David D McManus
- Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, United States
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Tran KV, Filippaios A, Noorishirazi K, Ding E, Han D, Mohagheghian F, Dai Q, Mehawej J, Wang Z, Lessard D, Otabil EM, Hamel A, Paul T, Gottbrecht MF, Fitzgibbons TP, Saczynski J, Chon KH, McManus DD. False Atrial Fibrillation Alerts from Smartwatches are Associated with Decreased Perceived Physical Well-being and Confidence in Chronic Symptoms Management. Cardiol Cardiovasc Med 2023; 7:97-107. [PMID: 37476150 PMCID: PMC10358285 DOI: 10.26502/fccm.92920314] [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] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 07/22/2023]
Abstract
Wrist-based wearables have been FDA approved for AF detection. However, the health behavior impact of false AF alerts from wearables on older patients at high risk for AF are not known. In this work, we analyzed data from the Pulsewatch (NCT03761394) study, which randomized patients (≥50 years) with history of stroke or transient ischemic attack to wear a patch monitor and a smartwatch linked to a smartphone running the Pulsewatch application vs to only the cardiac patch monitor over 14 days. At baseline and 14 days, participants completed validated instruments to assess for anxiety, patient activation, perceived mental and physical health, chronic symptom management self-efficacy, and medicine adherence. We employed linear regression to examine associations between false AF alerts with change in patient-reported outcomes. Receipt of false AF alerts was related to a dose-dependent decline in self-perceived physical health and levels of disease self-management. We developed a novel convolutional denoising autoencoder (CDA) to remove motion and noise artifacts in photoplethysmography (PPG) segments to optimize AF detection, which substantially reduced the number of false alerts. A promising approach to avoid negative impact of false alerts is to employ artificial intelligence driven algorithms to improve accuracy.
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Affiliation(s)
- Khanh-Van Tran
- Division of Cardiovascular Medicine, Department of Medicine, University of Massachusetts, Chan Medical School, 55 Lake Avenue North, Worcester, MA 01655, USA
| | - Andreas Filippaios
- Division of Cardiovascular Medicine, Department of Medicine, University of Massachusetts, Chan Medical School, 55 Lake Avenue North, Worcester, MA 01655, USA
| | - Kamran Noorishirazi
- Division of Cardiovascular Medicine, Department of Medicine, University of Massachusetts, Chan Medical School, 55 Lake Avenue North, Worcester, MA 01655, USA
| | - Eric Ding
- Division of Cardiovascular Medicine, Department of Medicine, University of Massachusetts, Chan Medical School, 55 Lake Avenue North, Worcester, MA 01655, USA
- Department of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, 55 Lake Avenue North, Worcester, MA 01655, USA
| | - Dong Han
- Department of Biomedical Engineering, University of Connecticut, 260 Glenbrook Road, Storrs, CT 06269, USA
| | - Fahimeh Mohagheghian
- Department of Biomedical Engineering, University of Connecticut, 260 Glenbrook Road, Storrs, CT 06269, USA
| | - Qiying Dai
- Division of Cardiovascular Medicine, Department of Medicine, Saint Vincent Hospital, 123 Summer Street, Worcester, MA 01608, USA
| | - Jordy Mehawej
- Division of Cardiovascular Medicine, Department of Medicine, University of Massachusetts, Chan Medical School, 55 Lake Avenue North, Worcester, MA 01655, USA
- Department of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, 55 Lake Avenue North, Worcester, MA 01655, USA
| | - Ziyue Wang
- Division of Cardiovascular Medicine, Department of Medicine, University of Massachusetts, Chan Medical School, 55 Lake Avenue North, Worcester, MA 01655, USA
| | - Darleen Lessard
- Division of Cardiovascular Medicine, Department of Medicine, University of Massachusetts, Chan Medical School, 55 Lake Avenue North, Worcester, MA 01655, USA
- Department of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, 55 Lake Avenue North, Worcester, MA 01655, USA
| | - Edith Mensah Otabil
- Division of Cardiovascular Medicine, Department of Medicine, University of Massachusetts, Chan Medical School, 55 Lake Avenue North, Worcester, MA 01655, USA
| | - Alex Hamel
- Division of Cardiovascular Medicine, Department of Medicine, University of Massachusetts, Chan Medical School, 55 Lake Avenue North, Worcester, MA 01655, USA
| | - Tenes Paul
- Division of Cardiovascular Medicine, Department of Medicine, University of Massachusetts, Chan Medical School, 55 Lake Avenue North, Worcester, MA 01655, USA
| | - Matthew F Gottbrecht
- Division of Cardiovascular Medicine, Department of Medicine, University of Massachusetts, Chan Medical School, 55 Lake Avenue North, Worcester, MA 01655, USA
| | - Timothy P Fitzgibbons
- Division of Cardiovascular Medicine, Department of Medicine, University of Massachusetts, Chan Medical School, 55 Lake Avenue North, Worcester, MA 01655, USA
| | - Jane Saczynski
- Department of Pharmacy and Health Systems Sciences, Northeastern University, Boston, Massachusetts, USA
| | - Ki H Chon
- Department of Biomedical Engineering, University of Connecticut, 260 Glenbrook Road, Storrs, CT 06269, USA
| | - David D McManus
- Division of Cardiovascular Medicine, Department of Medicine, University of Massachusetts, Chan Medical School, 55 Lake Avenue North, Worcester, MA 01655, USA
- Department of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, 55 Lake Avenue North, Worcester, MA 01655, USA
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Leung T, Ding EY, Cho C, Jung H, Dickson EL, Mohagheghian F, Peitzsch AG, DiMezza D, Tran KV, McManus DD, Chon KH. A Smartwatch System for Continuous Monitoring of Atrial Fibrillation in Older Adults After Stroke or Transient Ischemic Attack: Application Design Study. JMIR Cardio 2023; 7:e41691. [PMID: 36780211 PMCID: PMC9972205 DOI: 10.2196/41691] [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] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2022] [Revised: 11/21/2022] [Accepted: 12/31/2022] [Indexed: 01/02/2023] Open
Abstract
BACKGROUND The prevalence of atrial fibrillation (AF) increases with age and can lead to stroke. Therefore, older adults may benefit the most from AF screening. However, older adult populations tend to lag more than younger groups in the adoption of, and comfort with, the use of mobile health (mHealth) apps. Furthermore, although mobile apps that can detect AF are available to the public, most are designed for intermittent AF detection and for younger users. No app designed for long-term AF monitoring has released detailed system design specifications that can handle large data collections, especially in this age group. OBJECTIVE This study aimed to design an innovative smartwatch-based AF monitoring mHealth solution in collaboration with older adult participants and clinicians. METHODS The Pulsewatch system is designed to link smartwatches and smartphone apps, a website for data verification, and user data organization on a cloud server. The smartwatch in the Pulsewatch system is designed to continuously monitor the pulse rate with embedded AF detection algorithms, and the smartphone in the Pulsewatch system is designed to serve as the data-transferring hub to the cloud storage server. RESULTS We implemented the Pulsewatch system based on the functionality that patients and caregivers recommended. The user interfaces of the smartwatch and smartphone apps were specifically designed for older adults at risk for AF. We improved our Pulsewatch system based on feedback from focus groups consisting of patients with stroke and clinicians. The Pulsewatch system was used by the intervention group for up to 6 weeks in the 2 phases of our randomized clinical trial. At the conclusion of phase 1, 90 trial participants who had used the Pulsewatch app and smartwatch for 14 days completed a System Usability Scale to assess the usability of the Pulsewatch system; of 88 participants, 56 (64%) endorsed that the smartwatch app is "easy to use." For phases 1 and 2 of the study, we collected 9224.4 hours of smartwatch recordings from the participants. The longest recording streak in phase 2 was 21 days of consecutive recordings out of the 30 days of data collection. CONCLUSIONS This is one of the first studies to provide a detailed design for a smartphone-smartwatch dyad for ambulatory AF monitoring. In this paper, we report on the system's usability and opportunities to increase the acceptability of mHealth solutions among older patients with cognitive impairment. TRIAL REGISTRATION ClinicalTrials.gov NCT03761394; https://www.clinicaltrials.gov/ct2/show/NCT03761394. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) RR2-10.1016/j.cvdhj.2021.07.002.
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Affiliation(s)
| | - Eric Y Ding
- Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, United States
| | - Chaeho Cho
- Zebra Technologies Inc, Holtsville, NY, United States
| | - Haewook Jung
- SSP, Seoul, Republic of Korea.,Mediporte Co, Ltd, Gyeonggi-do, Republic of Korea
| | - Emily L Dickson
- College of Osteopathic Medicine, Des Moines University, Des Moines, IA, United States
| | - Fahimeh Mohagheghian
- Department of Biomedical Engineering, University of Connecticut, Storrs, CT, United States
| | - Andrew G Peitzsch
- Department of Biomedical Engineering, University of Connecticut, Storrs, CT, United States
| | | | - Khanh-Van Tran
- Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, United States
| | - David D McManus
- Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, United States
| | - Ki H Chon
- Department of Biomedical Engineering, University of Connecticut, Storrs, CT, United States
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Mohagheghian F, Han D, Peitzsch A, Nishita N, Ding E, Dickson EL, DiMezza D, Otabil EM, Noorishirazi K, Scott J, Lessard D, Wang Z, Whitcomb C, Tran KV, Fitzgibbons TP, McManus DD, Chon KH. Optimized Signal Quality Assessment for Photoplethysmogram Signals Using Feature Selection. IEEE Trans Biomed Eng 2022; 69:2982-2993. [PMID: 35275809 PMCID: PMC9478959 DOI: 10.1109/tbme.2022.3158582] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
OBJECTIVE With the increasing use of wearable healthcare devices for remote patient monitoring, reliable signal quality assessment (SQA) is required to ensure the high accuracy of interpretation and diagnosis on the recorded data from patients. Photoplethysmographic (PPG) signals non-invasively measured by wearable devices are extensively used to provide information about the cardiovascular system and its associated diseases. In this study, we propose an approach to optimize the quality assessment of the PPG signals. METHODS We used an ensemble-based feature selection scheme to enhance the prediction performance of the classification model to assess the quality of the PPG signals. Our approach for feature and subset size selection yielded the best-suited feature subset, which was optimized to differentiate between the clean and artifact corrupted PPG segments. CONCLUSION A high discriminatory power was achieved between two classes on the test data by the proposed feature selection approach, which led to strong performance on all dependent and independent test datasets. We achieved accuracy, sensitivity, and specificity rates of higher than 0.93, 0.89, and 0.97, respectively, for dependent test datasets, independent of heartbeat type, i.e., atrial fibrillation (AF) or non-AF data including normal sinus rhythm (NSR), premature atrial contraction (PAC), and premature ventricular contraction (PVC). For independent test datasets, accuracy, sensitivity, and specificity rates were greater than 0.93, 0.89, and 0.97, respectively, on PPG data recorded from AF and non-AF subjects. These results were found to be more accurate than those of all of the contemporary methods cited in this work. SIGNIFICANCE As the results illustrate, the advantage of our proposed scheme is its robustness against dynamic variations in the PPG signal during long-term 14-day recordings accompanied with different types of physical activities and a diverse range of fluctuations and waveforms caused by different individual hemodynamic characteristics, and various types of recording devices. This robustness instills confidence in the application of the algorithm to various kinds of wearable devices as a reliable PPG signal quality assessment approach.
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Samadzadehaghdam N, MakkiAbadi B, Eqlimi E, Mohagheghian F, Khajehpoor H, Harirchian MH. Developing a Multi-channel Beamformer by Enhancing Spatially Constrained ICA for Recovery of Correlated EEG Sources. J Biomed Phys Eng 2021; 11:205-214. [PMID: 33937127 PMCID: PMC8064133 DOI: 10.31661/jbpe.v0i0.801] [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] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2017] [Accepted: 10/14/2017] [Indexed: 11/23/2022]
Abstract
Background: Brain source imaging based on electroencephalogram (EEG) data aims to recover the neuron populations’ activity producing the scalp potentials. This procedure is known as the EEG inverse problem. Recently, beamformers have gained a lot of consideration in the EEG inverse problem. Objective: Beamformers lack acceptable performance in the case of correlated brain sources. These sources happen when some regions of the brain have simultaneous or correlated activities such as auditory stimulation or moving left and right extremities of the body at the same time. In this paper, we have developed a multichannel beamformer robust to correlated sources. Material and Methods: In this simulation study, we have looked at the problem of brain source imaging and beamforming from a blind source separation point of view. We focused on the spatially constraint independent component analysis (scICA) algorithm, which generally benefits from the pre-known partial information of mixing matrix, and modified the steps of the algorithm in a way that makes it more robust to correlated sources. We called the modified scICA algorithm Multichannel ICA based EEG Beamformer (MIEB). Results: We evaluated the proposed algorithm on simulated EEG data and compared its performance quantitatively with three algorithms scICA, linearly-constrained minimum-variance (LCMV) and Dual-Core beamformers; it is considered that the latter is specially designed to reconstruct correlated sources. Conclusion: The MIEB algorithm has much better performance in terms of normalized mean squared error in recovering the correlated/uncorrelated sources both in noise free and noisy synthetic EEG signals. Therefore, it could be used as a robust beamformer in recovering correlated brain sources.
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Affiliation(s)
- Nasser Samadzadehaghdam
- PhD, Department of Medical Physics and Biomedical Engineering, School of Medicine, Tehran University of Medical Sciences (TUMS), Tehran, Iran
- PhD, Department of Medical Bioengineering, Faculty of Advanced Medical Sciences, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Bahador MakkiAbadi
- PhD, Department of Medical Physics and Biomedical Engineering, School of Medicine, Tehran University of Medical Sciences (TUMS), Tehran, Iran
- PhD, Research Center for Biomedical Technology and Robotics (RCBTR), Institute of Advanced Medical Technologies (IAMT), Tehran University of Medical Sciences (TUMS), Tehran, Iran
| | - Ehsan Eqlimi
- PhD Candidate, Department of Medical Physics and Biomedical Engineering, School of Medicine, Tehran University of Medical Sciences (TUMS), Tehran, Iran
- PhD Candidate, Research Center for Biomedical Technology and Robotics (RCBTR), Institute of Advanced Medical Technologies (IAMT), Tehran University of Medical Sciences (TUMS), Tehran, Iran
| | - Fahimeh Mohagheghian
- PhD, Department of Medical Physics and Biomedical Engineering, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Hassan Khajehpoor
- PhD, Department of Medical Physics and Biomedical Engineering, School of Medicine, Tehran University of Medical Sciences (TUMS), Tehran, Iran
- PhD, Research Center for Biomedical Technology and Robotics (RCBTR), Institute of Advanced Medical Technologies (IAMT), Tehran University of Medical Sciences (TUMS), Tehran, Iran
| | - Mohammad Hossein Harirchian
- MD, Iranian Centre of Neurological Research, Neuroscience Institute, Tehran University of Medical Sciences, Tehran, Iran
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Mohagheghian F, Khajehpour H, Samadzadehaghdam N, Eqlimi E, Jalilvand H, Makkiabadi B, Deevband MR. Altered effective brain network topology in tinnitus: An EEG source connectivity analysis. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2020.102331] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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Han D, Bashar SK, Mohagheghian F, Ding E, Whitcomb C, McManus DD, Chon KH. Premature Atrial and Ventricular Contraction Detection using Photoplethysmographic Data from a Smartwatch. Sensors (Basel) 2020; 20:E5683. [PMID: 33028000 PMCID: PMC7582300 DOI: 10.3390/s20195683] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [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] [Subscribe] [Scholar Register] [Received: 08/26/2020] [Revised: 09/19/2020] [Accepted: 09/30/2020] [Indexed: 12/12/2022]
Abstract
We developed an algorithm to detect premature atrial contraction (PAC) and premature ventricular contraction (PVC) using photoplethysmographic (PPG) data acquired from a smartwatch. Our PAC/PVC detection algorithm is composed of a sequence of algorithms that are combined to discriminate various arrhythmias. A novel vector resemblance method is used to enhance the PAC/PVC detection results of the Poincaré plot method. The new PAC/PVC detection algorithm with our automated motion and noise artifact detection approach yielded a sensitivity of 86% for atrial fibrillation (AF) subjects while the overall sensitivity was 67% when normal sinus rhythm (NSR) subjects were also included. The specificity, positive predictive value (PPV), negative predictive value (NPV) and accuracy values for the combined data consisting of both NSR and AF subjects were 97%, 81%, 94% and 92%, respectively, for PAC/PVC detection combined with our automated motion and noise artifact detection approach. Moreover, when AF detection was compared with and without PAC/PVC, the sensitivity and specificity increased from 94.55% to 98.18% and from 95.75% to 97.90%, respectively. For additional independent testing data, we used two datasets: a smartwatch PPG dataset that was collected in our ongoing clinical study, and a pulse oximetry PPG dataset from the Medical Information Mart for Intensive Care III database. The PAC/PVC classification results of the independent testing on these two other datasets are all above 92% for sensitivity, specificity, PPV, NPV, and accuracy. The proposed combined approach to detect PAC and PVC can ultimately lead to better accuracy in AF detection. This is one of the first studies involving detection of PAC and PVC using PPG recordings from a smartwatch. The proposed method can potentially be of clinical importance as this enhanced capability can lead to fewer false positive detections of AF, especially for those NSR subjects with frequent episodes of PAC/PVC.
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Affiliation(s)
- Dong Han
- Department of Biomedical Engineering, University of Connecticut, Storrs, CT 06269, USA; (D.H.); (S.K.B.); (F.M.)
| | - Syed Khairul Bashar
- Department of Biomedical Engineering, University of Connecticut, Storrs, CT 06269, USA; (D.H.); (S.K.B.); (F.M.)
| | - Fahimeh Mohagheghian
- Department of Biomedical Engineering, University of Connecticut, Storrs, CT 06269, USA; (D.H.); (S.K.B.); (F.M.)
| | - Eric Ding
- Division of Cardiology, University of Massachusetts Medical School, Worcester, MA 01655, USA; (E.D.); (C.W.); (D.D.M.)
| | - Cody Whitcomb
- Division of Cardiology, University of Massachusetts Medical School, Worcester, MA 01655, USA; (E.D.); (C.W.); (D.D.M.)
| | - David D. McManus
- Division of Cardiology, University of Massachusetts Medical School, Worcester, MA 01655, USA; (E.D.); (C.W.); (D.D.M.)
| | - Ki H. Chon
- Department of Biomedical Engineering, University of Connecticut, Storrs, CT 06269, USA; (D.H.); (S.K.B.); (F.M.)
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10
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Mohagheghian F, Makkiabadi B, Jalilvand H, Khajehpoor H, Samadzadehaghdam N, Eqlimi E, Deevband MR. Computer-Aided Tinnitus Detection based on Brain Network Analysis of EEG Functional Connectivity. J Biomed Phys Eng 2020; 9:687-698. [PMID: 32039100 PMCID: PMC6943854 DOI: 10.31661/jbpe.v0i0.937] [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] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2018] [Accepted: 06/30/2018] [Indexed: 01/04/2023]
Abstract
Background Tinnitus known as a central nervous system disorder is correlated with specific oscillatory activities within auditory and non-auditory brain areas. Several studies in the past few years have revealed that in the most tinnitus cases, the response pattern of neurons in auditory system is changed due to auditory deafferentation, which leads to variation and disruption of the brain networks. Objective In this paper, we introduce an approach to automatically distinguish tinnitus individuals from healthy controls based on whole-brain functional connectivity and network analysis. Material and Methods The functional connectivity analysis was applied to the resting state electroencephalographic (EEG) data of both groups using Weighted Phase Lag Index (WPLI) for various frequency bands in 2-44 Hz frequency range. In this case- control study, the classification was performed on graph theoretical measures using support vector machine (SVM) as a robust classification method. Results Experimental results showed promising classification performance with a high accuracy, sensitivity, and specificity in all frequency bands, specifically in the beta2 frequency band. Conclusion The current study provides substantial evidence that tinnitus network can be successfully detected by consistent measures of the brain networks based on EEG functional connectivity.
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Affiliation(s)
- F Mohagheghian
- PhD, Department of Medical Physics and Biomedical engineering, School of Medicine, Shahid Beheshti University of Medical Sciences (SBMU), Tehran, Iran
| | - B Makkiabadi
- PhD, Department of Medical Physics and Biomedical engineering, School of Medicine, Tehran University of Medical Sciences (TUMS), Tehran, Iran
- PhD, Research Center for Biomedical Technology and Robotics (RCBTR), Institute of Advanced Medical Technologies (IAMT), Tehran University of Medical Sciences (TUMS), Tehran, Iran
| | - H Jalilvand
- PhD, Department of Audiology, School of Rehabilitation, Shahid Beheshti University of Medical Sciences (SBMU), Tehran, Iran
| | - H Khajehpoor
- MSc, Department of Medical Physics and Biomedical engineering, School of Medicine, Tehran University of Medical Sciences (TUMS), Tehran, Iran
- MSc, Research Center for Biomedical Technology and Robotics (RCBTR), Institute of Advanced Medical Technologies (IAMT), Tehran University of Medical Sciences (TUMS), Tehran, Iran
| | - N Samadzadehaghdam
- MSc, Department of Medical Physics and Biomedical engineering, School of Medicine, Tehran University of Medical Sciences (TUMS), Tehran, Iran
- MSc, Research Center for Biomedical Technology and Robotics (RCBTR), Institute of Advanced Medical Technologies (IAMT), Tehran University of Medical Sciences (TUMS), Tehran, Iran
| | - E Eqlimi
- MSc, Department of Medical Physics and Biomedical engineering, School of Medicine, Tehran University of Medical Sciences (TUMS), Tehran, Iran
- MSc, Research Center for Biomedical Technology and Robotics (RCBTR), Institute of Advanced Medical Technologies (IAMT), Tehran University of Medical Sciences (TUMS), Tehran, Iran
| | - M R Deevband
- PhD, Department of Medical Physics and Biomedical engineering, School of Medicine, Shahid Beheshti University of Medical Sciences (SBMU), Tehran, Iran
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Khajehpour H, Makkiabadi B, Ekhtiari H, Bakht S, Noroozi A, Mohagheghian F. Disrupted resting-state brain functional network in methamphetamine abusers: A brain source space study by EEG. PLoS One 2019; 14:e0226249. [PMID: 31825996 PMCID: PMC6906079 DOI: 10.1371/journal.pone.0226249] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2019] [Accepted: 11/15/2019] [Indexed: 01/03/2023] Open
Abstract
This study aimed to examine the effects of chronic methamphetamine use on the topological organization of whole-brain functional connectivity network (FCN) by reconstruction of neural-activity time series at resting-state. The EEG of 36 individuals with methamphetamine use disorder (IWMUD) and 24 normal controls (NCs) were recorded, pre-processed and source-reconstructed using standardized low-resolution tomography (sLORETA). The brain FCNs of participants were constructed and between-group differences in network topological properties were investigated using graph theoretical analysis. IWMUD showed decreased characteristic path length, increased clustering coefficient and small-world index at delta and gamma frequency bands compared to NCs. Moreover, abnormal changes in inter-regional connectivity and network hubs were observed in all the frequency bands. The results suggest that the IWMUD and NCs have distinct FCNs at all the frequency bands, particularly at the delta and gamma bands, in which deviated small-world brain topology was found in IWMUD.
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Affiliation(s)
- Hassan Khajehpour
- Department of Medical Physics and Biomedical Engineering, School of Medicine, Tehran University of Medical Sciences (TUMS), Tehran, Iran
- Research Center for Biomedical Technology and Robotics (RCBTR), Institute of Advanced Medical Technologies (IAMT), Tehran University of Medical Sciences (TUMS), Tehran, Iran
| | - Bahador Makkiabadi
- Department of Medical Physics and Biomedical Engineering, School of Medicine, Tehran University of Medical Sciences (TUMS), Tehran, Iran
- Research Center for Biomedical Technology and Robotics (RCBTR), Institute of Advanced Medical Technologies (IAMT), Tehran University of Medical Sciences (TUMS), Tehran, Iran
| | - Hamed Ekhtiari
- Laureate Institute for Brain Research (LIBR), Tulsa, OK, United States of America
- Iranian National Center for Addiction Studies (INCAS), Tehran University of Medical Sciences (TUMS), Tehran, Iran
| | - Sepideh Bakht
- Department of Cognitive Psychology, Institute for Cognitive Sciences Studies (ICSS), Tehran, Iran
| | - Alireza Noroozi
- Iranian National Center for Addiction Studies (INCAS), Tehran University of Medical Sciences (TUMS), Tehran, Iran
- Neuroscience and Addiction Studies Department, School of Advanced Technologies in Medicine (SATiM), Tehran University of Medical Sciences (TUMS), Tehran, Iran
| | - Fahimeh Mohagheghian
- Department of Biomedical Engineering, University of Connecticut, Storrs, CT, United States of America
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12
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Khajehpour H, Mohagheghian F, Ekhtiari H, Makkiabadi B, Jafari AH, Eqlimi E, Harirchian MH. Computer-aided classifying and characterizing of methamphetamine use disorder using resting-state EEG. Cogn Neurodyn 2019; 13:519-530. [PMID: 31741689 PMCID: PMC6825232 DOI: 10.1007/s11571-019-09550-z] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2019] [Revised: 07/18/2019] [Accepted: 08/01/2019] [Indexed: 12/17/2022] Open
Abstract
Methamphetamine (meth) is potently addictive and is closely linked to high crime rates in the world. Since meth withdrawal is very painful and difficult, most abusers relapse to abuse in traditional treatments. Therefore, developing accurate data-driven methods based on brain functional connectivity could be helpful in classifying and characterizing the neural features of meth dependence to optimize the treatments. Accordingly, in this study, computation of functional connectivity using resting-state EEG was used to classify meth dependence. Firstly, brain functional connectivity networks (FCNs) of 36 meth dependent individuals and 24 normal controls were constructed by weighted phase lag index, in six frequency bands: delta (1-4 Hz), theta (4-8 Hz), alpha (8-15 Hz), beta (15-30 Hz), gamma (30-45 Hz) and wideband (1-45 Hz).Then, significant differences in graph metrics and connectivity values of the FCNs were used to distinguish the two groups. Support vector machine classifier had the best performance with 93% accuracy, 100% sensitivity, 83% specificity and 0.94 F-score for differentiating between MDIs and NCs. The best performance yielded when selected features were the combination of connectivity values and graph metrics in the beta frequency band.
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Affiliation(s)
- Hassan Khajehpour
- Department of Medical Physics and Biomedical Engineering, School of Medicine, Tehran University of Medical Sciences (TUMS), Tehran, Iran
- Research Center for Biomedical Technology and Robotics (RCBTR), Institute of Advanced Medical Technologies (IAMT), Tehran University of Medical Sciences (TUMS), Tehran, Iran
| | - Fahimeh Mohagheghian
- Department of Medical Physics and Biomedical Engineering, School of Medicine, Shahid Beheshti University of Medical Sciences (SBMU), Tehran, Iran
| | - Hamed Ekhtiari
- Laureate Institute for Brain Research (LIBR), Tulsa, OK USA
- Iranian National Center for Addiction Studies (INCAS), Tehran University of Medical Sciences (TUMS), Tehran, Iran
| | - Bahador Makkiabadi
- Department of Medical Physics and Biomedical Engineering, School of Medicine, Tehran University of Medical Sciences (TUMS), Tehran, Iran
- Research Center for Biomedical Technology and Robotics (RCBTR), Institute of Advanced Medical Technologies (IAMT), Tehran University of Medical Sciences (TUMS), Tehran, Iran
| | - Amir Homayoun Jafari
- Department of Medical Physics and Biomedical Engineering, School of Medicine, Tehran University of Medical Sciences (TUMS), Tehran, Iran
- Research Center for Biomedical Technology and Robotics (RCBTR), Institute of Advanced Medical Technologies (IAMT), Tehran University of Medical Sciences (TUMS), Tehran, Iran
| | - Ehsan Eqlimi
- Department of Medical Physics and Biomedical Engineering, School of Medicine, Tehran University of Medical Sciences (TUMS), Tehran, Iran
- Research Center for Biomedical Technology and Robotics (RCBTR), Institute of Advanced Medical Technologies (IAMT), Tehran University of Medical Sciences (TUMS), Tehran, Iran
| | - Mohammad Hossein Harirchian
- Iranian Center of Neurological Research, Neuroscience Institute, Tehran University of Medical Sciences (TUMS), Tehran, Iran
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Khajehpour H, Mohagheghian F, Bakht S, Samadzadehaghdam N, Eqlimi E, Makkiabadi B. Event-Related Potential Correlates of Biased Cognitive Processing and Control in Substance Abusers: A Review. fbt 2019. [DOI: 10.18502/fbt.v6i1.1102] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
Abstract
Event-Related Potentials (ERPs) have been used in addiction studies to evaluate cognitive performance and craving in individuals with Substance Use Dependence (SUD). This paper reviews studies that used ERPs to investigate cue reactivity, inhibitory control and error processing in SUDs. Five abused substances are included in the investigation, i.e. alcohol, nicotine, cannabis, cocaine, and methamphetamine. For each substance, the main recent findings related to the ERPs are specifically discussed, according to the latency of ERPs.
The results show that individuals with SUDs allocate more attention resources to the cognitive processing of substance-related cues, indexed by increased amplitude of middle and late latency ERPs. Laboratory observations also show amplitude enlargement for early latency ERPs. SUDs reveal a deficiency in the inhibitory control and conscious error processing, indexed by attenuated N2 and Pe amplitude. The cognitive and motor inhibitory component (P3) changes show a controversial result.
This study expands the findings of previous related reviews implying that substance abusers allocate more attentional resources to drug cues indexed by enlarged P3 and LPP amplitude. Regarding P3 elicited in inhibitory control tasks, there is not still convergent results, while N2 and Pe become attenuated as reported in previous reviews. The outcomes also show that the chance of relapse to substance abuse could be predicted by recording ERPs reflecting inhibitory control and error processing.
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Abstract
It is known that the transformations estimated in most of the non-rigid image registration methods are not invertible. This means that the transformation is not one-to-one and also it defines the deformation only in one direction making the registration methods become inconsistent. The symmetric image registration method is a solution to constrain the estimated transformation to become more consistent. This technique promotes the quality of registration significantly. In this paper we present symmetric non-rigid image registration method using multi-scale approach, defining the registration progressively in four different image scales which used result of the previous level as the initial condition for the next level. Our proposed algorithm uses a symmetric cost function which includes forward and backward transformations. Regularization term is used for both forward and reverse deformation fields to control the spatial behavior of the transformation. The results show improving the image similarity and consistency compared to asymmetric methods.
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