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Roediger DJ, Butts J, Falke C, Fiecas MB, Klimes-Dougan B, Mueller BA, Cullen KR. Optimizing the measurement of sample entropy in resting-state fMRI data. Front Neurol 2024; 15:1331365. [PMID: 38426165 PMCID: PMC10902163 DOI: 10.3389/fneur.2024.1331365] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Accepted: 01/31/2024] [Indexed: 03/02/2024] Open
Abstract
Introduction The complexity of brain signals may hold clues to understand brain-based disorders. Sample entropy, an index that captures the predictability of a signal, is a promising tool to measure signal complexity. However, measurement of sample entropy from fMRI signals has its challenges, and numerous questions regarding preprocessing and parameter selection require research to advance the potential impact of this method. For one example, entropy may be highly sensitive to the effects of motion, yet standard approaches to addressing motion (e.g., scrubbing) may be unsuitable for entropy measurement. For another, the parameters used to calculate entropy need to be defined by the properties of data being analyzed, an issue that has frequently been ignored in fMRI research. The current work sought to rigorously address these issues and to create methods that could be used to advance this field. Methods We developed and tested a novel windowing approach to select and concatenate (ignoring connecting volumes) low-motion windows in fMRI data to reduce the impact of motion on sample entropy estimates. We created utilities (implementing autoregressive models and a grid search function) to facilitate selection of the matching length m parameter and the error tolerance r parameter. We developed an approach to apply these methods at every grayordinate of the brain, creating a whole-brain dense entropy map. These methods and tools have been integrated into a publicly available R package ("powseR"). We demonstrate these methods using data from the ABCD study. After applying the windowing procedure to allow sample entropy calculation on the lowest-motion windows from runs 1 and 2 (combined) and those from runs 3 and 4 (combined), we identified the optimal m and r parameters for these data. To confirm the impact of the windowing procedure, we compared entropy values and their relationship with motion when entropy was calculated using the full set of data vs. those calculated using the windowing procedure. We then assessed reproducibility of sample entropy calculations using the windowed procedure by calculating the intraclass correlation between the earlier and later entropy measurements at every grayordinate. Results When applying these optimized methods to the ABCD data (from the subset of individuals who had enough windows of continuous "usable" volumes), we found that the novel windowing procedure successfully mitigated the large inverse correlation between entropy values and head motion seen when using a standard approach. Furthermore, using the windowed approach, entropy values calculated early in the scan (runs 1 and 2) are largely reproducible when measured later in the scan (runs 3 and 4), although there is some regional variability in reproducibility. Discussion We developed an optimized approach to measuring sample entropy that addresses concerns about motion and that can be applied across datasets through user-identified adaptations that allow the method to be tailored to the dataset at hand. We offer preliminary results regarding reproducibility. We also include recommendations for fMRI data acquisition to optimize sample entropy measurement and considerations for the field.
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Affiliation(s)
- Donovan J. Roediger
- Department of Psychiatry and Behavioral Sciences, Medical School, University of Minnesota (UMN), Minneapolis, MN, United States
| | - Jessica Butts
- Division of Biostatistics and Health Data Science, School of Public Health, UMN, Minneapolis, MN, United States
| | - Chloe Falke
- Division of Biostatistics and Health Data Science, School of Public Health, UMN, Minneapolis, MN, United States
| | - Mark B. Fiecas
- Division of Biostatistics and Health Data Science, School of Public Health, UMN, Minneapolis, MN, United States
| | - Bonnie Klimes-Dougan
- Psychology Department, College of Liberal Arts, UMN, Minneapolis, MN, United States
| | - Bryon A. Mueller
- Department of Psychiatry and Behavioral Sciences, Medical School, University of Minnesota (UMN), Minneapolis, MN, United States
| | - Kathryn R. Cullen
- Department of Psychiatry and Behavioral Sciences, Medical School, University of Minnesota (UMN), Minneapolis, MN, United States
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van Rossum MC, da Silva PMA, Wang Y, Kouwenhoven EA, Hermens HJ. Missing data imputation techniques for wireless continuous vital signs monitoring. J Clin Monit Comput 2023; 37:1387-1400. [PMID: 36729298 PMCID: PMC9893204 DOI: 10.1007/s10877-023-00975-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Accepted: 01/16/2023] [Indexed: 02/03/2023]
Abstract
Wireless vital signs sensors are increasingly used for remote patient monitoring, but data analysis is often challenged by missing data periods. This study explored the performance of various imputation techniques for continuous vital signs measurements. Wireless vital signs measurements (heart rate, respiratory rate, blood oxygen saturation, axillary temperature) from surgical ward patients were used for repeated random simulation of missing data periods (gaps) of 5-60 min in two-hour windows. Gaps were imputed using linear interpolation, spline interpolation, last observation- and mean carried forwards technique, and cluster-based prognosis. Imputation performance was evaluated using the mean absolute error (MAE) between original and imputed gap samples. Besides, effects on signal features (window's slope, mean) and early warning scores (EWS) were explored. Gaps were simulated in 1743 data windows, obtained from 52 patients. Although MAE ranges overlapped, median MAE was structurally lowest for linear interpolation (heart rate: 0.9-2.6 beats/min, respiratory rate: 0.8-1.8 breaths/min, temperature: 0.04-0.17 °C, oxygen saturation: 0.3-0.7% for 5-60 min gaps) but up to twice as high for other techniques. Three techniques resulted in larger ranges of signal feature bias compared to no imputation. Imputation led to EWS misclassification in 1-8% of all simulations. Imputation error ranges vary between imputation techniques and increase with gap length. Imputation may result in larger signal feature bias compared to performing no imputation, and can affect patient risk assessment as illustrated by the EWS. Accordingly, careful implementation and selection of imputation techniques is warranted.
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Affiliation(s)
- Mathilde C van Rossum
- Biomedical Signals and Systems, University of Twente, Enschede, The Netherlands.
- Cardiovascular and Respiratory Physiology, University of Twente, Postbox 217, 7500 AE, Enschede, The Netherlands.
- Department of Surgery, Hospital Group Twente, Almelo, The Netherlands.
| | - Pedro M Alves da Silva
- Biomedical Signals and Systems, University of Twente, Enschede, The Netherlands
- NOVA School of Science and Technology, NOVA University of Lisbon, Lisbon, Portugal
| | - Ying Wang
- Biomedical Signals and Systems, University of Twente, Enschede, The Netherlands
- ZGT Academy, Hospital group Twente, Almelo, The Netherlands
| | | | - Hermie J Hermens
- Biomedical Signals and Systems, University of Twente, Enschede, The Netherlands
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Babu PRK, Di Martino JM, Chang Z, Perochon S, Carpenter KLH, Compton S, Espinosa S, Dawson G, Sapiro G. Exploring Complexity of Facial Dynamics in Autism Spectrum Disorder. IEEE TRANSACTIONS ON AFFECTIVE COMPUTING 2023; 14:919-930. [PMID: 37266390 PMCID: PMC10231874 DOI: 10.1109/taffc.2021.3113876] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/03/2023]
Abstract
Atypical facial expression is one of the early symptoms of autism spectrum disorder (ASD) characterized by reduced regularity and lack of coordination of facial movements. Automatic quantification of these behaviors can offer novel biomarkers for screening, diagnosis, and treatment monitoring of ASD. In this work, 40 toddlers with ASD and 396 typically developing toddlers were shown developmentally-appropriate and engaging movies presented on a smart tablet during a well-child pediatric visit. The movies consisted of social and non-social dynamic scenes designed to evoke certain behavioral and affective responses. The front-facing camera of the tablet was used to capture the toddlers' face. Facial landmarks' dynamics were then automatically computed using computer vision algorithms. Subsequently, the complexity of the landmarks' dynamics was estimated for the eyebrows and mouth regions using multiscale entropy. Compared to typically developing toddlers, toddlers with ASD showed higher complexity (i.e., less predictability) in these landmarks' dynamics. This complexity in facial dynamics contained novel information not captured by traditional facial affect analyses. These results suggest that computer vision analysis of facial landmark movements is a promising approach for detecting and quantifying early behavioral symptoms associated with ASD.
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Affiliation(s)
| | - J Matias Di Martino
- Department of Electrical and Computer Engineering, Duke University, Durham, NC, USA
| | - Zhuoqing Chang
- Department of Electrical and Computer Engineering, Duke University, Durham, NC, USA
| | - Sam Perochon
- Department of Electrical and Computer Engineering, Duke University, Durham, NC, USA
| | - Kimberly L H Carpenter
- Duke Center for Autism and Brain Development, Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA
| | - Scott Compton
- Duke Center for Autism and Brain Development, Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA
| | - Steven Espinosa
- Office of Information Technology, Duke University, Durham, NC, USA
| | - Geraldine Dawson
- Duke Center for Autism and Brain Development, Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC. USA
| | - Guillermo Sapiro
- Department of Electrical and Computer Engineering, Biomedical Engineering, Mathematics, and Computer Sciences, Duke University, Durham, NC, USA
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Babu PRK, Di Martino JM, Chang Z, Perochon S, Aiello R, Carpenter KL, Compton S, Davis N, Franz L, Espinosa S, Flowers J, Dawson G, Sapiro G. Complexity analysis of head movements in autistic toddlers. J Child Psychol Psychiatry 2023; 64:156-166. [PMID: 35965431 PMCID: PMC9771883 DOI: 10.1111/jcpp.13681] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 07/05/2022] [Indexed: 12/24/2022]
Abstract
BACKGROUND Early differences in sensorimotor functioning have been documented in young autistic children and infants who are later diagnosed with autism. Previous research has demonstrated that autistic toddlers exhibit more frequent head movement when viewing dynamic audiovisual stimuli, compared to neurotypical toddlers. To further explore this behavioral characteristic, in this study, computer vision (CV) analysis was used to measure several aspects of head movement dynamics of autistic and neurotypical toddlers while they watched a set of brief movies with social and nonsocial content presented on a tablet. METHODS Data were collected from 457 toddlers, 17-36 months old, during their well-child visit to four pediatric primary care clinics. Forty-one toddlers were subsequently diagnosed with autism. An application (app) displayed several brief movies on a tablet, and the toddlers watched these movies while sitting on their caregiver's lap. The front-facing camera in the tablet recorded the toddlers' behavioral responses. CV was used to measure the participants' head movement rate, movement acceleration, and complexity using multiscale entropy. RESULTS Autistic toddlers exhibited significantly higher rate, acceleration, and complexity in their head movements while watching the movies compared to neurotypical toddlers, regardless of the type of movie content (social vs. nonsocial). The combined features of head movement acceleration and complexity reliably distinguished the autistic and neurotypical toddlers. CONCLUSIONS Autistic toddlers exhibit differences in their head movement dynamics when viewing audiovisual stimuli. Higher complexity of their head movements suggests that their movements were less predictable and less stable compared to neurotypical toddlers. CV offers a scalable means of detecting subtle differences in head movement dynamics, which may be helpful in identifying early behaviors associated with autism and providing insight into the nature of sensorimotor differences associated with autism.
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Affiliation(s)
| | - J. Matias Di Martino
- Department of Electrical and Computer Engineering, Duke University, Durham, NC, USA
| | - Zhuoqing Chang
- Department of Electrical and Computer Engineering, Duke University, Durham, NC, USA
| | - Sam Perochon
- Department of Electrical and Computer Engineering, Duke University, Durham, NC, USA
- Ecole Normale Supérieure Paris-Saclay, Gif-Sur-Yvette, France
| | - Rachel Aiello
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA
- Duke Center for Autism and Brain Development, Duke University, Durham, NC, USA
| | - Kimberly L.H. Carpenter
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA
- Duke Center for Autism and Brain Development, Duke University, Durham, NC, USA
| | - Scott Compton
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA
| | - Naomi Davis
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA
| | - Lauren Franz
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA
- Duke Center for Autism and Brain Development, Duke University, Durham, NC, USA
- Duke Global Health Institute, Duke University, Durham, NC, USA
| | - Steven Espinosa
- Office of Information Technology, Duke University, Durham, NC, USA
| | - Jacqueline Flowers
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA
- Duke Center for Autism and Brain Development, Duke University, Durham, NC, USA
| | - Geraldine Dawson
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA
- Duke Center for Autism and Brain Development, Duke University, Durham, NC, USA
| | - Guillermo Sapiro
- Department of Electrical and Computer Engineering, Duke University, Durham, NC, USA
- Department of Biomedical Engineering, Mathematics, and Computer Sciences, Duke University, Durham, NC, USA
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A Pragmatic Ensemble Strategy for Missing Values Imputation in Health Records. ENTROPY 2022; 24:e24040533. [PMID: 35455196 PMCID: PMC9030272 DOI: 10.3390/e24040533] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/16/2022] [Revised: 04/02/2022] [Accepted: 04/07/2022] [Indexed: 01/03/2023]
Abstract
Pristine and trustworthy data are required for efficient computer modelling for medical decision-making, yet data in medical care is frequently missing. As a result, missing values may occur not just in training data but also in testing data that might contain a single undiagnosed episode or a participant. This study evaluates different imputation and regression procedures identified based on regressor performance and computational expense to fix the issues of missing values in both training and testing datasets. In the context of healthcare, several procedures are introduced for dealing with missing values. However, there is still a discussion concerning which imputation strategies are better in specific cases. This research proposes an ensemble imputation model that is educated to use a combination of simple mean imputation, k-nearest neighbour imputation, and iterative imputation methods, and then leverages them in a manner where the ideal imputation strategy is opted among them based on attribute correlations on missing value features. We introduce a unique Ensemble Strategy for Missing Value to analyse healthcare data with considerable missing values to identify unbiased and accurate prediction statistical modelling. The performance metrics have been generated using the eXtreme gradient boosting regressor, random forest regressor, and support vector regressor. The current study uses real-world healthcare data to conduct experiments and simulations of data with varying feature-wise missing frequencies indicating that the proposed technique surpasses standard missing value imputation approaches as well as the approach of dropping records holding missing values in terms of accuracy.
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Iranfar A, Arza A, Atienza D. ReLearn: A Robust Machine Learning Framework in Presence of Missing Data for Multimodal Stress Detection from Physiological Signals . ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:535-541. [PMID: 34891350 DOI: 10.1109/embc46164.2021.9630040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Continuous and multimodal stress detection has been performed recently through wearable devices and machine learning algorithms. However, a well-known and important challenge of working on physiological signals recorded by conventional monitoring devices is missing data due to sensors insufficient contact and interference by other equipment. This challenge becomes more problematic when the user/patient is mentally or physically active or stressed because of more frequent conscious or subconscious movements. In this paper, we propose ReLearn, a robust machine learning framework for stress detection from biomarkers extracted from multimodal physiological signals. ReLearn effectively copes with missing data and outliers both at training and inference phases. ReLearn, composed of machine learning models for feature selection, outlier detection, data imputation, and classification, allows us to classify all samples, including those with missing values at inference. In particular, according to our experiments and stress database, while by discarding all missing data, as a simplistic yet common approach, no prediction can be made for 34% of the data at inference, our approach can achieve accurate predictions, as high as 78%, for missing samples. Also, our experiments show that the proposed framework obtains a cross-validation accuracy of 86.8% even if more than 50% of samples within the features are missing.
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Banerjee S, Singh GK. Deep neural network based missing data prediction of electrocardiogram signal using multiagent reinforcement learning. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.102508] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
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Singha Roy M, Roy B, Gupta R, Das Sharma K. On-Device Reliability Assessment and Prediction of Missing Photoplethysmographic Data Using Deep Neural Networks. IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS 2020; 14:1323-1332. [PMID: 33026985 DOI: 10.1109/tbcas.2020.3028935] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Photoplethysmographic (PPG) measurements from ambulatory subjects may suffer from unreliability due to body movements and missing data segments due to loosening of sensor. This paper describes an on-device reliability assessment from PPG measurements using a stack denoising autoencoder (SDAE) and multilayer perceptron neural network (MLPNN). The missing segments were predicted by a personalized convolutional neural network (CNN) and long-short term memory (LSTM) model using a short history of the same channel data. Forty sets of volunteers' data, consisting of equal share of healthy and cardiovascular subjects were used for validation and testing. The PPG reliability assessment model (PRAM) achieved over 95% accuracy for correctly identifying acceptable PPG beats out of total 5000 using expert annotated data. Disagreement with experts' annotation was nearly 3.5%. The missing segment prediction model (MSPM) achieved a root mean square error (RMSE) of 0.22, and mean absolute error (MAE) of 0.11 for 40 missing beats prediction using only four beat history from the same channel PPG. The two models were integrated in a standalone device based on quad-core ARM Cortex-A53, 1.2 GHz, with 1 GB RAM, with 130 MB memory requirement and latency ∼0.35 s per beat prediction with a 30 s frame. The present method also provides improved performance with published works on PPG quality assessment and missing data prediction using two public datasets, CinC and MIMIC-II under PhysioNet.
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Sarlabous L, Aquino-Esperanza J, Magrans R, de Haro C, López-Aguilar J, Subirà C, Batlle M, Rué M, Gomà G, Ochagavia A, Fernández R, Blanch L. Development and validation of a sample entropy-based method to identify complex patient-ventilator interactions during mechanical ventilation. Sci Rep 2020; 10:13911. [PMID: 32807815 PMCID: PMC7431581 DOI: 10.1038/s41598-020-70814-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2019] [Accepted: 08/05/2020] [Indexed: 11/28/2022] Open
Abstract
Patient-ventilator asynchronies can be detected by close monitoring of ventilator screens by clinicians or through automated algorithms. However, detecting complex patient-ventilator interactions (CP-VI), consisting of changes in the respiratory rate and/or clusters of asynchronies, is a challenge. Sample Entropy (SE) of airway flow (SE-Flow) and airway pressure (SE-Paw) waveforms obtained from 27 critically ill patients was used to develop and validate an automated algorithm for detecting CP-VI. The algorithm's performance was compared versus the gold standard (the ventilator's waveform recordings for CP-VI were scored visually by three experts; Fleiss' kappa = 0.90 (0.87-0.93)). A repeated holdout cross-validation procedure using the Matthews correlation coefficient (MCC) as a measure of effectiveness was used for optimization of different combinations of SE settings (embedding dimension, m, and tolerance value, r), derived SE features (mean and maximum values), and the thresholds of change (Th) from patient's own baseline SE value. The most accurate results were obtained using the maximum values of SE-Flow (m = 2, r = 0.2, Th = 25%) and SE-Paw (m = 4, r = 0.2, Th = 30%) which report MCCs of 0.85 (0.78-0.86) and 0.78 (0.78-0.85), and accuracies of 0.93 (0.89-0.93) and 0.89 (0.89-0.93), respectively. This approach promises an improvement in the accurate detection of CP-VI, and future study of their clinical implications.
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Affiliation(s)
- Leonardo Sarlabous
- Critical Care Center, Hospital Universitari Parc Taulí, Institut d'Investigació i Innovació Parc Taulí I3PT, Universitat Autònoma de Barcelona, Parc Taulí 1, 08208, Sabadell, Barcelona, Spain.
- Biomedical Research Networking Center in Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Instituto de Salud Carlos III, Madrid, Spain.
| | - José Aquino-Esperanza
- Critical Care Center, Hospital Universitari Parc Taulí, Institut d'Investigació i Innovació Parc Taulí I3PT, Universitat Autònoma de Barcelona, Parc Taulí 1, 08208, Sabadell, Barcelona, Spain
- Biomedical Research Networking Center in Respiratory Disease (CIBERES), Instituto de Salud Carlos III, Madrid, Spain
- Facultat de Medicina, Universitat de Barcelona, Barcelona, Spain
| | | | - Candelaria de Haro
- Critical Care Center, Hospital Universitari Parc Taulí, Institut d'Investigació i Innovació Parc Taulí I3PT, Universitat Autònoma de Barcelona, Parc Taulí 1, 08208, Sabadell, Barcelona, Spain
- Biomedical Research Networking Center in Respiratory Disease (CIBERES), Instituto de Salud Carlos III, Madrid, Spain
| | - Josefina López-Aguilar
- Critical Care Center, Hospital Universitari Parc Taulí, Institut d'Investigació i Innovació Parc Taulí I3PT, Universitat Autònoma de Barcelona, Parc Taulí 1, 08208, Sabadell, Barcelona, Spain
- Biomedical Research Networking Center in Respiratory Disease (CIBERES), Instituto de Salud Carlos III, Madrid, Spain
| | - Carles Subirà
- Department of Intensive Care, Fundació Althaia, Universitat Internacional de Catalunya , Manresa, Spain
| | - Montserrat Batlle
- Department of Intensive Care, Fundació Althaia, Universitat Internacional de Catalunya , Manresa, Spain
| | - Montserrat Rué
- Department of Basic Medical Sciences, Universitat de Lleida-IRBLLEIDA, Lleida, Spain
| | - Gemma Gomà
- Critical Care Center, Hospital Universitari Parc Taulí, Institut d'Investigació i Innovació Parc Taulí I3PT, Universitat Autònoma de Barcelona, Parc Taulí 1, 08208, Sabadell, Barcelona, Spain
| | - Ana Ochagavia
- Critical Care Center, Hospital Universitari Parc Taulí, Institut d'Investigació i Innovació Parc Taulí I3PT, Universitat Autònoma de Barcelona, Parc Taulí 1, 08208, Sabadell, Barcelona, Spain
- Biomedical Research Networking Center in Respiratory Disease (CIBERES), Instituto de Salud Carlos III, Madrid, Spain
| | - Rafael Fernández
- Biomedical Research Networking Center in Respiratory Disease (CIBERES), Instituto de Salud Carlos III, Madrid, Spain
- Department of Intensive Care, Fundació Althaia, Universitat Internacional de Catalunya , Manresa, Spain
| | - Lluís Blanch
- Critical Care Center, Hospital Universitari Parc Taulí, Institut d'Investigació i Innovació Parc Taulí I3PT, Universitat Autònoma de Barcelona, Parc Taulí 1, 08208, Sabadell, Barcelona, Spain
- Biomedical Research Networking Center in Respiratory Disease (CIBERES), Instituto de Salud Carlos III, Madrid, Spain
- BetterCare S.L, Sabadell, Spain
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Humeau-Heurtier A. Multiscale Entropy Approaches and Their Applications. ENTROPY 2020; 22:e22060644. [PMID: 33286416 PMCID: PMC7517182 DOI: 10.3390/e22060644] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 05/28/2020] [Accepted: 06/02/2020] [Indexed: 01/13/2023]
Affiliation(s)
- Anne Humeau-Heurtier
- LARIS-Laboratoire Angevin de Recherche en Ingénierie des Systèmes, University of Angers, 49035 Angers, France
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A Novel Microwave Treatment for Sleep Disorders and Classification of Sleep Stages Using Multi-Scale Entropy. ENTROPY 2020; 22:e22030347. [PMID: 33286121 PMCID: PMC7516818 DOI: 10.3390/e22030347] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/13/2020] [Revised: 03/12/2020] [Accepted: 03/16/2020] [Indexed: 12/21/2022]
Abstract
The aim of this study was to develop an integrated system of non-contact sleep stage detection and sleep disorder treatment for health monitoring. Hence, a method of brain activity detection based on microwave scattering technology instead of scalp electroencephalogram was developed to evaluate the sleep stage. First, microwaves at a specific frequency were used to penetrate the functional sites of the brain in patients with sleep disorders to change the firing frequency of the activated areas of the brain and analyze and evaluate statistically the effects on sleep improvement. Then, a wavelet packet algorithm was used to decompose the microwave transmission signal, the refined composite multiscale sample entropy, the refined composite multiscale fluctuation-based dispersion entropy and multivariate multiscale weighted permutation entropy were obtained as features from the wavelet packet coefficient. Finally, the mutual information-principal component analysis feature selection method was used to optimize the feature set and random forest was used to classify and evaluate the sleep stage. The results show that after four times of microwave modulation treatment, sleep efficiency improved continuously, the overall maintenance was above 80%, and the insomnia rate was reduced gradually. The overall classification accuracy of the four sleep stages was 86.4%. The results indicate that the microwaves with a certain frequency can treat sleep disorders and detect abnormal brain activity. Therefore, the microwave scattering method is of great significance in the development of a new brain disease treatment, diagnosis and clinical application system.
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Kafantaris E, Piper I, Lo TYM, Escudero J. Augmentation of Dispersion Entropy for Handling Missing and Outlier Samples in Physiological Signal Monitoring. ENTROPY (BASEL, SWITZERLAND) 2020; 22:E319. [PMID: 33286093 PMCID: PMC7516770 DOI: 10.3390/e22030319] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/28/2020] [Revised: 03/02/2020] [Accepted: 03/09/2020] [Indexed: 12/14/2022]
Abstract
Entropy quantification algorithms are becoming a prominent tool for the physiological monitoring of individuals through the effective measurement of irregularity in biological signals. However, to ensure their effective adaptation in monitoring applications, the performance of these algorithms needs to be robust when analysing time-series containing missing and outlier samples, which are common occurrence in physiological monitoring setups such as wearable devices and intensive care units. This paper focuses on augmenting Dispersion Entropy (DisEn) by introducing novel variations of the algorithm for improved performance in such applications. The original algorithm and its variations are tested under different experimental setups that are replicated across heart rate interval, electroencephalogram, and respiratory impedance time-series. Our results indicate that the algorithmic variations of DisEn achieve considerable improvements in performance while our analysis signifies that, in consensus with previous research, outlier samples can have a major impact in the performance of entropy quantification algorithms. Consequently, the presented variations can aid the implementation of DisEn to physiological monitoring applications through the mitigation of the disruptive effect of missing and outlier samples.
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Affiliation(s)
- Evangelos Kafantaris
- School of Engineering, Institute for Digital Communications, University of Edinburgh, Edinburgh EH9 3FB, UK;
| | - Ian Piper
- MRC Centre for Reproductive Health, Department of Child Life and Health, University of Edinburgh, Edinburgh EH9 1UW, UK;
- Royal Hospital for Sick Children, NHS Lothian, Edinburgh EH9 1LF, UK
| | - Tsz-Yan Milly Lo
- Royal Hospital for Sick Children, NHS Lothian, Edinburgh EH9 1LF, UK
- Usher Institute, Edinburgh Medical School, University of Edinburgh, Edinburgh EH16 4UX, UK;
| | - Javier Escudero
- School of Engineering, Institute for Digital Communications, University of Edinburgh, Edinburgh EH9 3FB, UK;
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Chen C, Sun S, Cao Z, Shi Y, Sun B, Zhang XD. A comprehensive comparison and overview of R packages for calculating sample entropy. Biol Methods Protoc 2019; 4:bpz016. [PMID: 32161808 PMCID: PMC6994089 DOI: 10.1093/biomethods/bpz016] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2019] [Revised: 11/06/2019] [Accepted: 11/17/2019] [Indexed: 11/21/2022] Open
Abstract
Sample entropy is a powerful tool for analyzing the complexity and irregularity of physiology signals which may be associated with human health. Nevertheless, the sophistication of its calculation hinders its universal application. As of today, the R language provides multiple open-source packages for calculating sample entropy. All of which, however, are designed for different scenarios. Therefore, when searching for a proper package, the investigators would be confused on the parameter setting and selection of algorithms. To ease their selection, we have explored the functions of five existing R packages for calculating sample entropy and have compared their computing capability in several dimensions. We used four published datasets on respiratory and heart rate to study their input parameters, types of entropy, and program running time. In summary, NonlinearTseries and CGManalyzer can provide the analysis of sample entropy with different embedding dimensions and similarity thresholds. CGManalyzer is a good choice for calculating multiscale sample entropy of physiological signal because it not only shows sample entropy of all scales simultaneously but also provides various visualization plots. MSMVSampEn is the only package that can calculate multivariate multiscale entropies. In terms of computing time, NonlinearTseries, CGManalyzer, and MSMVSampEn run significantly faster than the other two packages. Moreover, we identify the issues in MVMSampEn package. This article provides guidelines for researchers to find a suitable R package for their analysis and applications using sample entropy.
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Affiliation(s)
- Chang Chen
- Faculty of Health Sciences, University of Macau, Taipa, Macau, China
| | - Shixue Sun
- Faculty of Health Sciences, University of Macau, Taipa, Macau, China
| | - Zhixin Cao
- Department of Respiratory and Critical Care Medicine, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China
- Beijing Institute of Respiratory Medicine, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China
- Beijing Engineering Research Center of Respiratory and Critical Care Medicine, Beijing, China
| | - Yan Shi
- School of Automation Science and Electrical Engineering, Beihang University, Beijing, China
| | - Baoqing Sun
- State Key Laboratory of Respiratory Disease, The 1st Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Xiaohua Douglas Zhang
- Faculty of Health Sciences, University of Macau, Taipa, Macau, China
- Department of Biostatistics, Yale University, New Haven, CT06511, USA
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