1
|
Xie J, Fonseca P, van Dijk J, Overeem S, Long X. A multi-task learning model using RR intervals and respiratory effort to assess sleep disordered breathing. Biomed Eng Online 2024; 23:45. [PMID: 38705982 PMCID: PMC11070105 DOI: 10.1186/s12938-024-01240-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Accepted: 04/23/2024] [Indexed: 05/07/2024] Open
Abstract
BACKGROUND Sleep-disordered breathing (SDB) affects a significant portion of the population. As such, there is a need for accessible and affordable assessment methods for diagnosis but also case-finding and long-term follow-up. Research has focused on exploiting cardiac and respiratory signals to extract proxy measures for sleep combined with SDB event detection. We introduce a novel multi-task model combining cardiac activity and respiratory effort to perform sleep-wake classification and SDB event detection in order to automatically estimate the apnea-hypopnea index (AHI) as severity indicator. METHODS The proposed multi-task model utilized both convolutional and recurrent neural networks and was formed by a shared part for common feature extraction, a task-specific part for sleep-wake classification, and a task-specific part for SDB event detection. The model was trained with RR intervals derived from electrocardiogram and respiratory effort signals. To assess performance, overnight polysomnography (PSG) recordings from 198 patients with varying degree of SDB were included, with manually annotated sleep stages and SDB events. RESULTS We achieved a Cohen's kappa of 0.70 in the sleep-wake classification task, corresponding to a Spearman's correlation coefficient (R) of 0.830 between the estimated total sleep time (TST) and the TST obtained from PSG-based sleep scoring. Combining the sleep-wake classification and SDB detection results of the multi-task model, we obtained an R of 0.891 between the estimated and the reference AHI. For severity classification of SBD groups based on AHI, a Cohen's kappa of 0.58 was achieved. The multi-task model performed better than a single-task model proposed in a previous study for AHI estimation, in particular for patients with a lower sleep efficiency (R of 0.861 with the multi-task model and R of 0.746 with single-task model with subjects having sleep efficiency < 60%). CONCLUSION Assisted with automatic sleep-wake classification, our multi-task model demonstrated proficiency in estimating AHI and assessing SDB severity based on AHI in a fully automatic manner using RR intervals and respiratory effort. This shows the potential for improving SDB screening with unobtrusive sensors also for subjects with low sleep efficiency without adding additional sensors for sleep-wake detection.
Collapse
Affiliation(s)
- Jiali Xie
- Biomedical Diagnostics Lab, Department of Electrical Engineering, Eindhoven University of Technology, 5612 AZ, Eindhoven, The Netherlands.
- Department of Electrical Engineering, Eindhoven University of Technology, PO Box 513, 5600 MB, Eindhoven, The Netherlands.
- Eindhoven MedTech Innovaton Center (e/MTIC), P.O. Box 513, 5600 MB, Eindhoven, The Netherlands.
| | - Pedro Fonseca
- Biomedical Diagnostics Lab, Department of Electrical Engineering, Eindhoven University of Technology, 5612 AZ, Eindhoven, The Netherlands
- Philips Research, High Tech Campus, 5656 AE, Eindhoven, The Netherlands
- Eindhoven MedTech Innovaton Center (e/MTIC), P.O. Box 513, 5600 MB, Eindhoven, The Netherlands
| | - Johannes van Dijk
- Biomedical Diagnostics Lab, Department of Electrical Engineering, Eindhoven University of Technology, 5612 AZ, Eindhoven, The Netherlands
- Sleep Medicine Center Kempenhaeghe, 5591 VE, Heeze, The Netherlands
- Department of Orthodontics, Ulm University, 89081, Ulm, Germany
- Eindhoven MedTech Innovaton Center (e/MTIC), P.O. Box 513, 5600 MB, Eindhoven, The Netherlands
| | - Sebastiaan Overeem
- Biomedical Diagnostics Lab, Department of Electrical Engineering, Eindhoven University of Technology, 5612 AZ, Eindhoven, The Netherlands
- Sleep Medicine Center Kempenhaeghe, 5591 VE, Heeze, The Netherlands
- Eindhoven MedTech Innovaton Center (e/MTIC), P.O. Box 513, 5600 MB, Eindhoven, The Netherlands
| | - Xi Long
- Biomedical Diagnostics Lab, Department of Electrical Engineering, Eindhoven University of Technology, 5612 AZ, Eindhoven, The Netherlands
- Eindhoven MedTech Innovaton Center (e/MTIC), P.O. Box 513, 5600 MB, Eindhoven, The Netherlands
| |
Collapse
|
2
|
Wulterkens BM, Den Teuling NGP, Hermans LWA, Asin J, Duis N, Overeem S, Fonseca P, van Gilst MM. Multi-night home assessment of sleep structure in OSA with and without insomnia. Sleep Med 2024; 117:152-161. [PMID: 38547592 DOI: 10.1016/j.sleep.2024.03.031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/26/2024] [Revised: 03/07/2024] [Accepted: 03/17/2024] [Indexed: 04/16/2024]
Abstract
OBJECTIVE To explore sleep structure in participants with obstructive sleep apnea (OSA) and comorbid insomnia (COMISA) and participants with OSA without insomnia (OSA-only) using both single-night polysomnography and multi-night wrist-worn photoplethysmography/accelerometry. METHODS Multi-night 4-class sleep-staging was performed with a validated algorithm based on actigraphy and heart rate variability, in 67 COMISA (23 women, median age: 51 years) and 50 OSA-only (15 women, median age: 51) participants. Sleep statistics were compared using linear regression models and mixed-effects models. Multi-night variability was explored using a clustering approach and between- and within-participant analysis. RESULTS Polysomnographic parameters showed no significant group differences. Multi-night measurements, during 13.4 ± 5.2 nights per subject, demonstrated a longer sleep onset latency and lower sleep efficiency for the COMISA group. Detailed analysis of wake parameters revealed longer mean durations of awakenings in COMISA, as well as higher numbers of awakenings lasting 5 min and longer (WKN≥5min) and longer wake after sleep onset containing only awakenings of 5 min or longer. Within-participant variance was significantly larger in COMISA for sleep onset latency, sleep efficiency, mean duration of awakenings and WKN≥5min. Unsupervised clustering uncovered three clusters; participants with consistently high values for at least one of the wake parameters, participants with consistently low values, and participants displaying higher variability. CONCLUSION Patients with COMISA more often showed extended, and more variable periods of wakefulness. These observations were not discernible using single night polysomnography, highlighting the relevance of multi-night measurements to assess characteristics indicative for insomnia.
Collapse
Affiliation(s)
- Bernice M Wulterkens
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands; Philips Sleep and Respiratory Care, Eindhoven, the Netherlands.
| | | | - Lieke W A Hermans
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands
| | - Jerryll Asin
- Center for Sleep Medicine, Amphia Hospital, Breda, the Netherlands
| | - Nanny Duis
- Center for Sleep Medicine, Amphia Hospital, Breda, the Netherlands
| | - Sebastiaan Overeem
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands; Sleep Medicine Center Kempenhaeghe, Heeze, the Netherlands
| | - Pedro Fonseca
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands; Philips Sleep and Respiratory Care, Eindhoven, the Netherlands
| | - Merel M van Gilst
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands; Sleep Medicine Center Kempenhaeghe, Heeze, the Netherlands
| |
Collapse
|
3
|
Wulterkens BM, Hermans LWA, Fonseca P, Janssen HCJP, van Hirtum PV, Overeem S, van Gilst MM. Heart rate response to cortical arousals in patients with isolated obstructive sleep apnea and with comorbid insomnia (COMISA). Sleep Breath 2024; 28:735-744. [PMID: 38062226 DOI: 10.1007/s11325-023-02954-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2023] [Revised: 10/25/2023] [Accepted: 11/20/2023] [Indexed: 05/31/2024]
Abstract
PURPOSE Comorbid insomnia often occurs in patients with obstructive sleep apnea (OSA), referred to as COMISA. Cortical arousals manifest as a common feature in both OSA and insomnia, often accompanied by elevated heart rate (HR). Our objective was to evaluate the heart rate response to nocturnal cortical arousals in patients with COMISA and patients with OSA alone. METHODS We analyzed data from patients with COMISA and from patients with OSA matched for apnea-hypopnea index. Sleep staging and analysis of respiratory events and cortical arousals were performed using the Philips Somnolyzer automatic scoring system. Beat-by-beat HR was analyzed from the onset of the cortical arousal to 30 heartbeats afterwards. HR responses were divided into peak and recovery phases. Cortical arousals were separately evaluated according to subtype (related to respiratory events and spontaneous) and duration (3-6 s, 6-10 s, 10-15 s). RESULTS A total of 72 patients with COMISA and 72 patients with OSA were included in this study. There were no overall group differences in the number of cortical arousals with and without autonomic activation. No significant differences were found for spontaneous cortical arousals. The OSA group had more cortical arousals related to respiratory events (21.0 [14.8-30.0] vs 16.0 [9.0-27.0], p = 0.016). However, the COMISA group had longer cortical arousals (7.2 [6.4-7.8] vs 6.7 [6.2-7.7] s, p = 0.024) and the HR recovery phase was prolonged (52.5 [30.8-82.5] vs 40.0 [21.8-55.5] beats/min, p = 0.017). Both the peak and the recovery phase for longer cortical arousals with a duration of 10-15 s were significantly higher in patients with COMISA compared to patients with OSA (47.0 [27.0-97.5] vs 34.0 [21.0-71.0] beats/min, p = 0.032 and 87.0 [47.0-132.0] vs 71.0 [43.0-103.5] beats/min, p = 0.049, respectively). CONCLUSIONS The HR recovery phase after cortical arousals related to respiratory events is prolonged in patients with COMISA compared to patients with OSA alone. This response could be indicative of the insomnia component in COMISA.
Collapse
Affiliation(s)
- Bernice M Wulterkens
- Department of Electrical Engineering, Eindhoven University of Technology, PO BOX 513, Eindhoven, 5600 MB, The Netherlands.
- Philips Research, Eindhoven, The Netherlands.
| | | | - Pedro Fonseca
- Department of Electrical Engineering, Eindhoven University of Technology, PO BOX 513, Eindhoven, 5600 MB, The Netherlands
- Philips Research, Eindhoven, The Netherlands
| | | | | | - Sebastiaan Overeem
- Department of Electrical Engineering, Eindhoven University of Technology, PO BOX 513, Eindhoven, 5600 MB, The Netherlands
- Sleep Medicine Center Kempenhaeghe, Heeze, The Netherlands
| | - Merel M van Gilst
- Department of Electrical Engineering, Eindhoven University of Technology, PO BOX 513, Eindhoven, 5600 MB, The Netherlands
- Sleep Medicine Center Kempenhaeghe, Heeze, The Netherlands
| |
Collapse
|
4
|
Hermans L, van Meulen F, Anderer P, Ross M, Cerny A, van Gilst M, Overeem S, Fonseca P. Performance of cardiorespiratory-based sleep staging in patients using beta blockers. J Clin Sleep Med 2024; 20:575-581. [PMID: 38063156 PMCID: PMC10985295 DOI: 10.5664/jcsm.10938] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Revised: 11/22/2023] [Accepted: 11/28/2023] [Indexed: 04/04/2024]
Abstract
STUDY OBJECTIVES Automatic sleep staging based on cardiorespiratory signals from home sleep monitoring devices holds great clinical potential. Using state-of-the-art machine learning, promising performance has been reached in patients with sleep disorders. However, it is unknown whether performance would hold in individuals with potentially altered autonomic physiology, for example under the influence of medication. Here, we assess an existing sleep staging algorithm in patients with sleep disorders with and without the use of beta blockers. METHODS We analyzed a retrospective dataset of sleep recordings of 57 patients with sleep disorders using beta blockers and 57 age-matched patients with sleep disorders not using beta blockers. Sleep stages were automatically scored based on electrocardiography and respiratory effort from a thoracic belt, using a previously developed machine-learning algorithm (CReSS algorithm). For both patient groups, sleep stages classified by the model were compared to gold standard manual polysomnography scoring using epoch-by-epoch agreement. Additionally, for both groups, overall sleep parameters were calculated and compared between the two scoring methods. RESULTS Substantial agreement was achieved for four-class sleep staging in both patient groups (beta blockers: kappa = 0.635, accuracy = 78.1%; controls: kappa = 0.660, accuracy = 78.8%). No statistical difference in epoch-by-epoch agreement was found between the two groups. Additionally, the groups did not differ on agreement of derived sleep parameters. CONCLUSIONS We showed that the performance of the CReSS algorithm is not deteriorated in patients using beta blockers. Results do not indicate a fundamental limitation in leveraging autonomic characteristics to obtain a surrogate measure of sleep in this clinically relevant population. CITATION Hermans L, van Meulen F, Anderer P, et al. Performance of cardiorespiratory-based sleep staging in patients using beta blockers. J Clin Sleep Med. 2024;20(4):575-581.
Collapse
Affiliation(s)
- Lieke Hermans
- Philips Research, Eindhoven, The Netherlands
- Department of Electrical Engineering, TU/e Eindhoven, Eindhoven, The Netherlands
| | - Fokke van Meulen
- Department of Electrical Engineering, TU/e Eindhoven, Eindhoven, The Netherlands
- Sleep Medicine Center Kempenhaeghe, Heeze, The Netherlands
| | - Peter Anderer
- Philips Sleep and Respiratory Care, Vienna, Austria
- The Siesta Group Schlafanalyse GmbH, Vienna, Austria
| | - Marco Ross
- Department of Electrical Engineering, TU/e Eindhoven, Eindhoven, The Netherlands
- Philips Sleep and Respiratory Care, Vienna, Austria
- The Siesta Group Schlafanalyse GmbH, Vienna, Austria
| | | | - Merel van Gilst
- Department of Electrical Engineering, TU/e Eindhoven, Eindhoven, The Netherlands
- Sleep Medicine Center Kempenhaeghe, Heeze, The Netherlands
| | - Sebastiaan Overeem
- Department of Electrical Engineering, TU/e Eindhoven, Eindhoven, The Netherlands
- Sleep Medicine Center Kempenhaeghe, Heeze, The Netherlands
| | - Pedro Fonseca
- Philips Research, Eindhoven, The Netherlands
- Department of Electrical Engineering, TU/e Eindhoven, Eindhoven, The Netherlands
| |
Collapse
|
5
|
Schipper F, van Sloun RJG, Grassi A, Brouwer J, van Meulen F, Overeem S, Fonseca P. Maximum a posteriori detection of heartbeats from a chest-worn accelerometer. Physiol Meas 2024; 45:035009. [PMID: 38430565 DOI: 10.1088/1361-6579/ad2f5e] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2023] [Accepted: 03/01/2024] [Indexed: 03/04/2024]
Abstract
Objective. Unobtrusive long-term monitoring of cardiac parameters is important in a wide variety of clinical applications, such as the assesment of acute illness severity and unobtrusive sleep monitoring. Here we determined the accuracy and robustness of heartbeat detection by an accelerometer worn on the chest.Approach. We performed overnight recordings in 147 individuals (69 female, 78 male) referred to two sleep centers. Two methods for heartbeat detection in the acceleration signal were compared: one previously described approach, based on local periodicity, and a novel extended method incorporating maximumaposterioriestimation and a Markov decision process to approach an optimal solution.Main results. The maximumaposterioriestimation significantly improved performance, with a mean absolute error for the estimation of inter-beat intervals of only 3.5 ms, and 95% limits of agreement of -1.7 to +1.0 beats per minute for heartrate measurement. Performance held during posture changes and was only weakly affected by the presence of sleep disorders and demographic factors.Significance. The new method may enable the use of a chest-worn accelerometer in a variety of applications such as ambulatory sleep staging and in-patient monitoring.
Collapse
Affiliation(s)
- Fons Schipper
- Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
- Philips Research, Philips, Eindhoven, The Netherlands
| | - Ruud J G van Sloun
- Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
- Philips Research, Philips, Eindhoven, The Netherlands
| | - Angela Grassi
- Philips Research, Philips, Eindhoven, The Netherlands
| | - Jan Brouwer
- Philips Research, Philips, Eindhoven, The Netherlands
| | - Fokke van Meulen
- Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
- Kempenhaeghe Center for Sleep Medicine, Heeze, The Netherlands
| | - Sebastiaan Overeem
- Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
- Kempenhaeghe Center for Sleep Medicine, Heeze, The Netherlands
| | - Pedro Fonseca
- Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
- Philips Research, Philips, Eindhoven, The Netherlands
| |
Collapse
|
6
|
Gouveia C, Soares B, Albuquerque D, Barros F, Soares SC, Pinho P, Vieira J, Brás S. Remote Emotion Recognition Using Continuous-Wave Bio-Radar System. SENSORS (BASEL, SWITZERLAND) 2024; 24:1420. [PMID: 38474953 DOI: 10.3390/s24051420] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/09/2024] [Revised: 02/08/2024] [Accepted: 02/20/2024] [Indexed: 03/14/2024]
Abstract
The Bio-Radar is herein presented as a non-contact radar system able to capture vital signs remotely without requiring any physical contact with the subject. In this work, the ability to use the proposed system for emotion recognition is verified by comparing its performance on identifying fear, happiness and a neutral condition, with certified measuring equipment. For this purpose, machine learning algorithms were applied to the respiratory and cardiac signals captured simultaneously by the radar and the referenced contact-based system. Following a multiclass identification strategy, one could conclude that both systems present a comparable performance, where the radar might even outperform under specific conditions. Emotion recognition is possible using a radar system, with an accuracy equal to 99.7% and an F1-score of 99.9%. Thus, we demonstrated that it is perfectly possible to use the Bio-Radar system for this purpose, which is able to be operated remotely, avoiding the subject awareness of being monitored and thus providing more authentic reactions.
Collapse
Affiliation(s)
- Carolina Gouveia
- Instituto de Engenharia Electrónica e Telemática de Aveiro, Departamento de Electrónica, Telecomunicações e Informática, Intelligent Systems Associate Laboratory, University of Aveiro, 3810-193 Aveiro, Portugal
- Colab Almascience, Madan Parque, 2829-516 Caparica, Portugal
| | - Beatriz Soares
- Instituto de Telecomunicações, 3810-193 Aveiro, Portugal
- Departamento de Electrónica, Telecomunicações e Informática, University of Aveiro, 3810-193 Aveiro, Portugal
| | - Daniel Albuquerque
- Instituto de Engenharia Electrónica e Telemática de Aveiro, Departamento de Electrónica, Telecomunicações e Informática, Intelligent Systems Associate Laboratory, University of Aveiro, 3810-193 Aveiro, Portugal
- Instituto de Telecomunicações, 3810-193 Aveiro, Portugal
- Escola Superior de Tecnologia e Gestão de Águeda, University of Aveiro, 3810-193 Aveiro, Portugal
| | - Filipa Barros
- Center for Health Technology and Services Research, Department of Education and Psychology, University of Aveiro, 3810-193 Aveiro, Portugal
- William James Center for Research, Department of Education and Psychology, University of Aveiro, 3810-193 Aveiro, Portugal
| | - Sandra C Soares
- William James Center for Research, Department of Education and Psychology, University of Aveiro, 3810-193 Aveiro, Portugal
| | - Pedro Pinho
- Instituto de Telecomunicações, 3810-193 Aveiro, Portugal
- Departamento de Electrónica, Telecomunicações e Informática, University of Aveiro, 3810-193 Aveiro, Portugal
| | - José Vieira
- Instituto de Engenharia Electrónica e Telemática de Aveiro, Departamento de Electrónica, Telecomunicações e Informática, Intelligent Systems Associate Laboratory, University of Aveiro, 3810-193 Aveiro, Portugal
- Instituto de Telecomunicações, 3810-193 Aveiro, Portugal
| | - Susana Brás
- Instituto de Engenharia Electrónica e Telemática de Aveiro, Departamento de Electrónica, Telecomunicações e Informática, Intelligent Systems Associate Laboratory, University of Aveiro, 3810-193 Aveiro, Portugal
| |
Collapse
|
7
|
Bark B, Nam B, Kim IY. SelANet: decision-assisting selective sleep apnea detection based on confidence score. BMC Med Inform Decis Mak 2023; 23:190. [PMID: 37735681 PMCID: PMC10514955 DOI: 10.1186/s12911-023-02292-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Accepted: 09/08/2023] [Indexed: 09/23/2023] Open
Abstract
BACKGROUND One of the most common sleep disorders is sleep apnea syndrome. To diagnose sleep apnea syndrome, polysomnography is typically used, but it has limitations in terms of labor, cost, and time. Therefore, studies have been conducted to develop automated detection algorithms using limited biological signals that can be more easily diagnosed. However, the lack of information from limited signals can result in uncertainty from artificial intelligence judgments. Therefore, we performed selective prediction by using estimated respiratory signals from electrocardiogram and oxygen saturation signals based on confidence scores to classify only those sleep apnea occurrence samples with high confidence. In addition, for samples with high uncertainty, this algorithm rejected them, providing a second opinion to the clinician. METHOD Our developed model utilized polysomnography data from 994 subjects obtained from Massachusetts General Hospital. We performed feature extraction from the latent vector using the autoencoder. Then, one dimensional convolutional neural network-long short-term memory (1D CNN-LSTM) was designed and trained to measure confidence scores for input, with an additional selection function. We set a confidence score threshold called the target coverage and performed optimization only on samples with confidence scores higher than the target coverage. As a result, we demonstrated that the empirical coverage trained in the model converged to the target coverage. RESULT To confirm whether the model has been optimized according to the objectives, the coverage violation was used to measure the difference between the target coverage and the empirical coverage. As a result, the value of coverage violation was found to be an average of 0.067. Based on the model, we evaluated the classification performance of sleep apnea and confirmed that it achieved 90.26% accuracy, 91.29% sensitivity, and 89.21% specificity. This represents an improvement of approximately 7.03% in all metrics compared to the performance achieved without using a selective prediction. CONCLUSION This algorithm based on selective prediction utilizes confidence measurement method to minimize the problem caused by limited biological information. Based on this approach, this algorithm is applicable to wearable devices despite low signal quality and can be used as a simple detection method that determine the need for polysomnography or complement it.
Collapse
Affiliation(s)
- Beomjun Bark
- Department of Biomedical Engineering, Hanyang University, 222, Wangsimni-Ro, Seongdong-Gu, 04763, Seoul, Republic of Korea
| | - Borum Nam
- Department of Electronic Engineering, Hanyang University, Seoul, Republic of Korea
| | - In Young Kim
- Department of Biomedical Engineering, Hanyang University, 222, Wangsimni-Ro, Seongdong-Gu, 04763, Seoul, Republic of Korea.
| |
Collapse
|
8
|
Xie J, Fonseca P, van Dijk JP, Long X, Overeem S. The Use of Respiratory Effort Improves an ECG-Based Deep Learning Algorithm to Assess Sleep-Disordered Breathing. Diagnostics (Basel) 2023; 13:2146. [PMID: 37443540 DOI: 10.3390/diagnostics13132146] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Revised: 06/14/2023] [Accepted: 06/19/2023] [Indexed: 07/15/2023] Open
Abstract
BACKGROUND Sleep apnea is a prevalent sleep-disordered breathing (SDB) condition that affects a large population worldwide. Research has demonstrated the potential of using electrocardiographic (ECG) signals (heart rate and ECG-derived respiration, EDR) to detect SDB. However, EDR may be a suboptimal replacement for respiration signals. METHODS We evaluated a previously described ECG-based deep learning algorithm in an independent dataset including 198 patients and compared performance for SDB event detection using thoracic respiratory effort versus EDR. We also evaluated the algorithm in terms of apnea-hypopnea index (AHI) estimation performance, and SDB severity classification based on the estimated AHI. RESULTS Using respiratory effort instead of EDR, we achieved an improved performance in SDB event detection (F1 score = 0.708), AHI estimation (Spearman's correlation = 0.922), and SDB severity classification (Cohen's kappa of 0.62 was obtained based on AHI). CONCLUSION Respiratory effort is superior to EDR to assess SDB. Using respiratory effort and ECG, the previously described algorithm achieves good performance in a new dataset from an independent laboratory confirming its adequacy for this task.
Collapse
Affiliation(s)
- Jiali Xie
- Biomedical Diagnostics Lab, Department of Electrical Engineering, Eindhoven University of Technology, 5612 AZ Eindhoven, The Netherlands
| | - Pedro Fonseca
- Biomedical Diagnostics Lab, Department of Electrical Engineering, Eindhoven University of Technology, 5612 AZ Eindhoven, The Netherlands
- Philips Research, High Tech Campus, 5656 AE Eindhoven, The Netherlands
| | - Johannes P van Dijk
- Biomedical Diagnostics Lab, Department of Electrical Engineering, Eindhoven University of Technology, 5612 AZ Eindhoven, The Netherlands
- Sleep Medicine Center Kempenhaeghe, 5591 VE Heeze, The Netherlands
- Department of Orthodontics, Ulm University, 89081 Ulm, Germany
| | - Xi Long
- Biomedical Diagnostics Lab, Department of Electrical Engineering, Eindhoven University of Technology, 5612 AZ Eindhoven, The Netherlands
- Philips Research, High Tech Campus, 5656 AE Eindhoven, The Netherlands
| | - Sebastiaan Overeem
- Biomedical Diagnostics Lab, Department of Electrical Engineering, Eindhoven University of Technology, 5612 AZ Eindhoven, The Netherlands
- Sleep Medicine Center Kempenhaeghe, 5591 VE Heeze, The Netherlands
| |
Collapse
|
9
|
Fonseca P, Ross M, Cerny A, Anderer P, van Meulen F, Janssen H, Pijpers A, Dujardin S, van Hirtum P, van Gilst M, Overeem S. A computationally efficient algorithm for wearable sleep staging in clinical populations. Sci Rep 2023; 13:9182. [PMID: 37280297 DOI: 10.1038/s41598-023-36444-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Accepted: 06/03/2023] [Indexed: 06/08/2023] Open
Abstract
This study describes a computationally efficient algorithm for 4-class sleep staging based on cardiac activity and body movements. Using an accelerometer to calculate gross body movements and a reflective photoplethysmographic (PPG) sensor to determine interbeat intervals and a corresponding instantaneous heart rate signal, a neural network was trained to classify between wake, combined N1 and N2, N3 and REM sleep in epochs of 30 s. The classifier was validated on a hold-out set by comparing the output against manually scored sleep stages based on polysomnography (PSG). In addition, the execution time was compared with that of a previously developed heart rate variability (HRV) feature-based sleep staging algorithm. With a median epoch-per-epoch κ of 0.638 and accuracy of 77.8% the algorithm achieved an equivalent performance when compared to the previously developed HRV-based approach, but with a 50-times faster execution time. This shows how a neural network, without leveraging any a priori knowledge of the domain, can automatically "discover" a suitable mapping between cardiac activity and body movements, and sleep stages, even in patients with different sleep pathologies. In addition to the high performance, the reduced complexity of the algorithm makes practical implementation feasible, opening up new avenues in sleep diagnostics.
Collapse
Affiliation(s)
- Pedro Fonseca
- Philips Research Eindhoven, High Tech Campus 34, 5656AE, Eindhoven, The Netherlands.
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands.
| | - Marco Ross
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
- Sleep and Respiratory Care, Philips Austria GmbH, Vienna, Austria
| | - Andreas Cerny
- Sleep and Respiratory Care, Philips Austria GmbH, Vienna, Austria
| | - Peter Anderer
- Sleep and Respiratory Care, Philips Austria GmbH, Vienna, Austria
| | - Fokke van Meulen
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
- Sleep Medicine Center Kempenhaeghe, Heeze, The Netherlands
| | - Hennie Janssen
- Sleep Medicine Center Kempenhaeghe, Heeze, The Netherlands
| | | | | | | | - Merel van Gilst
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
- Sleep Medicine Center Kempenhaeghe, Heeze, The Netherlands
| | - Sebastiaan Overeem
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
- Sleep Medicine Center Kempenhaeghe, Heeze, The Netherlands
| |
Collapse
|
10
|
van der Stam JA, Mestrom EHJ, Scheerhoorn J, Jacobs FENB, Nienhuijs S, Boer AK, van Riel NAW, de Morree HM, Bonomi AG, Scharnhorst V, Bouwman RA. The Accuracy of Wrist-Worn Photoplethysmogram-Measured Heart and Respiratory Rates in Abdominal Surgery Patients: Observational Prospective Clinical Validation Study. JMIR Perioper Med 2023; 6:e40474. [PMID: 36804173 PMCID: PMC9989911 DOI: 10.2196/40474] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2022] [Revised: 01/12/2023] [Accepted: 01/31/2023] [Indexed: 02/22/2023] Open
Abstract
BACKGROUND Postoperative deterioration is often preceded by abnormal vital parameters. Therefore, vital parameters of postoperative patients are routinely measured by nursing staff. Wrist-worn sensors could potentially provide an alternative tool for the measurement of vital parameters in low-acuity settings. These devices would allow more frequent or even continuous measurements of vital parameters without relying on time-consuming manual measurements, provided their accuracy in this clinical population is established. OBJECTIVE This study aimed to assess the accuracy of heart rate (HR) and respiratory rate (RR) measures obtained via a wearable photoplethysmography (PPG) wristband in a cohort of postoperative patients. METHODS The accuracy of the wrist-worn PPG sensor was assessed in 62 post-abdominal surgery patients (mean age 55, SD 15 years; median BMI 34, IQR 25-40 kg/m2). The wearable obtained HR and RR measurements were compared to those of the reference monitor in the postanesthesia or intensive care unit. Bland-Altman and Clarke error grid analyses were performed to determine agreement and clinical accuracy. RESULTS Data were collected for a median of 1.2 hours per patient. With a coverage of 94% for HR and 34% for RR, the device was able to provide accurate measurements for the large majority of the measurements as 98% and 93% of the measurements were within 5 bpm or 3 rpm of the reference signal. Additionally, 100% of the HR and 98% of the RR measurements were clinically acceptable on Clarke error grid analysis. CONCLUSIONS The wrist-worn PPG device is able to provide measurements of HR and RR that can be seen as sufficiently accurate for clinical applications. Considering the coverage, the device was able to continuously monitor HR and report RR when measurements of sufficient quality were obtained. TRIAL REGISTRATION ClinicalTrials.gov NCT03923127; https://www.clinicaltrials.gov/ct2/show/NCT03923127.
Collapse
Affiliation(s)
- Jonna A van der Stam
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands.,Clinical Laboratory, Catharina Hospital, Eindhoven, Netherlands.,Expert Center Clinical Chemistry Eindhoven, Eindhoven, Netherlands
| | - Eveline H J Mestrom
- Department of Anesthesiology, Intensive Care & Pain Medicine, Catharina Hospital, Eindhoven, Netherlands
| | - Jai Scheerhoorn
- Department of Surgery, Catharina Hospital, Eindhoven, Netherlands
| | - Fleur E N B Jacobs
- Department of Medical Physics, Catharina Hospital, Eindhoven, Netherlands
| | - Simon Nienhuijs
- Department of Surgery, Catharina Hospital, Eindhoven, Netherlands
| | - Arjen-Kars Boer
- Clinical Laboratory, Catharina Hospital, Eindhoven, Netherlands.,Expert Center Clinical Chemistry Eindhoven, Eindhoven, Netherlands
| | - Natal A W van Riel
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands.,Expert Center Clinical Chemistry Eindhoven, Eindhoven, Netherlands.,Department of Vascular Medicine, Amsterdam University Medical Centers, Amsterdam, Netherlands
| | - Helma M de Morree
- Patient Care & Monitoring Department, Philips Research, Eindhoven, Netherlands
| | - Alberto G Bonomi
- Patient Care & Monitoring Department, Philips Research, Eindhoven, Netherlands
| | - Volkher Scharnhorst
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands.,Clinical Laboratory, Catharina Hospital, Eindhoven, Netherlands.,Expert Center Clinical Chemistry Eindhoven, Eindhoven, Netherlands
| | - R Arthur Bouwman
- Department of Anesthesiology, Intensive Care & Pain Medicine, Catharina Hospital, Eindhoven, Netherlands.,Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands
| |
Collapse
|
11
|
Ryu J, Torres E. Toward interpretable digital biomarkers of walking and reaching in Parkinson's disease. WEARABLE TECHNOLOGIES 2022; 3:e21. [PMID: 38486899 PMCID: PMC10936352 DOI: 10.1017/wtc.2022.16] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Revised: 07/06/2022] [Accepted: 07/09/2022] [Indexed: 03/17/2024]
Abstract
Multimodal digital data registered with wearable biosensors have emerged as highly complementary of clinical pencil-and-paper criteria, offering new insights in ways to detect and diagnose various aspects of Parkinson's disease (PD). A pressing question is how to combine both the clinical knowledge of PD and the new technology to create interpretable digital biomarkers easily obtainable with off-the-shelf technology. Several challenges concerning disparity in biophysical units, anatomical differences across participants, sensor positioning, and sampling resolution are addressed in this work, along with identification of optimal parameters to automatically differentiate patients with PD from controls. We combine data from a multitude of biosensors registering signals from the central (electroencephalography) and peripheral (magnetometry, kinematics) nervous systems, inclusive of the autonomic nervous system (electrocardiogram), as the participants perform natural tasks requiring different levels of intentional planning and automatic control. We find that magnetometer data during walking, across a variety of amplitude and timing signals, provide optimal separation of PD from neurotypical controls. We conclude that using multimodal signals within the context of actions that bear different levels of intent, can be revealing of features of PD that would scape the naked eye. Further, we add that clinical criteria combined with such optimal digital parameter spaces offer a far more complete picture of PD than using either one of these pieces of data alone.
Collapse
Affiliation(s)
- Jihye Ryu
- Neurosurgery Department, University of California Los Angeles, Los Angeles, California90095, USA
- Psychology Department, Rutgers University, Piscataway, New Jersey, USA
| | - Elizabeth Torres
- Psychology Department, Rutgers University, Piscataway, New Jersey, USA
- Rutgers University Center for Cognitive Science, Piscataway, New Jersey, USA
- Computer Science Department, Computational Biomedicine Imaging and Modeling Center, Rutgers University, Piscataway, New Jersey, USA
| |
Collapse
|
12
|
Ng A, Wei B, Jain J, Ward EA, Tandon SD, Moskowitz JT, Krogh-Jespersen S, Wakschlag LS, Alshurafa N. Predicting the Next-Day Perceived and Physiological Stress of Pregnant Women by Using Machine Learning and Explainability: Algorithm Development and Validation. JMIR Mhealth Uhealth 2022; 10:e33850. [PMID: 35917157 PMCID: PMC9382551 DOI: 10.2196/33850] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2021] [Revised: 02/02/2022] [Accepted: 05/13/2022] [Indexed: 11/30/2022] Open
Abstract
Background Cognitive behavioral therapy–based interventions are effective in reducing prenatal stress, which can have severe adverse health effects on mothers and newborns if unaddressed. Predicting next-day physiological or perceived stress can help to inform and enable pre-emptive interventions for a likely physiologically and perceptibly stressful day. Machine learning models are useful tools that can be developed to predict next-day physiological and perceived stress by using data collected from the previous day. Such models can improve our understanding of the specific factors that predict physiological and perceived stress and allow researchers to develop systems that collect selected features for assessment in clinical trials to minimize the burden of data collection. Objective The aim of this study was to build and evaluate a machine-learned model that predicts next-day physiological and perceived stress by using sensor-based, ecological momentary assessment (EMA)–based, and intervention-based features and to explain the prediction results. Methods We enrolled pregnant women into a prospective proof-of-concept study and collected electrocardiography, EMA, and cognitive behavioral therapy intervention data over 12 weeks. We used the data to train and evaluate 6 machine learning models to predict next-day physiological and perceived stress. After selecting the best performing model, Shapley Additive Explanations were used to identify the feature importance and explainability of each feature. Results A total of 16 pregnant women enrolled in the study. Overall, 4157.18 hours of data were collected, and participants answered 2838 EMAs. After applying feature selection, 8 and 10 features were found to positively predict next-day physiological and perceived stress, respectively. A random forest classifier performed the best in predicting next-day physiological stress (F1 score of 0.84) and next-day perceived stress (F1 score of 0.74) by using all features. Although any subset of sensor-based, EMA-based, or intervention-based features could reliably predict next-day physiological stress, EMA-based features were necessary to predict next-day perceived stress. The analysis of explainability metrics showed that the prolonged duration of physiological stress was highly predictive of next-day physiological stress and that physiological stress and perceived stress were temporally divergent. Conclusions In this study, we were able to build interpretable machine learning models to predict next-day physiological and perceived stress, and we identified unique features that were highly predictive of next-day stress that can help to reduce the burden of data collection.
Collapse
Affiliation(s)
- Ada Ng
- McCormick School of Engineering, Northwestern University, Evanston, IL, United States
| | - Boyang Wei
- McCormick School of Engineering, Northwestern University, Evanston, IL, United States
| | - Jayalakshmi Jain
- McCormick School of Engineering, Northwestern University, Evanston, IL, United States
| | - Erin A Ward
- Northwestern University Feinberg School of Medicine, Chicago, IL, United States
| | - S Darius Tandon
- Northwestern University Feinberg School of Medicine, Chicago, IL, United States
| | - Judith T Moskowitz
- Northwestern University Feinberg School of Medicine, Chicago, IL, United States
| | | | - Lauren S Wakschlag
- Northwestern University Feinberg School of Medicine, Chicago, IL, United States
| | - Nabil Alshurafa
- Northwestern University Feinberg School of Medicine, Chicago, IL, United States
| |
Collapse
|
13
|
Tomas B, Grabovac M, Tomas K. Application of the R-peak detection algorithm for locating noise in ECG signals. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2021.103316] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
|
14
|
Mestrom E, Deneer R, Bonomi AG, Margarito J, Gelissen J, Haakma R, Korsten HHM, Scharnhorst V, Bouwman RA. Validation of Heart Rate Extracted From Wrist-Based Photoplethysmography in the Perioperative Setting: Prospective Observational Study. JMIR Cardio 2021; 5:e27765. [PMID: 34734834 PMCID: PMC8603171 DOI: 10.2196/27765] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2021] [Revised: 06/08/2021] [Accepted: 07/27/2021] [Indexed: 11/29/2022] Open
Abstract
Background Measurement of heart rate (HR) through an unobtrusive, wrist-worn optical HR monitor (OHRM) could enable earlier recognition of patient deterioration in low acuity settings and enable timely intervention. Objective The goal of this study was to assess the agreement between the HR extracted from the OHRM and the gold standard 5-lead electrocardiogram (ECG) connected to a patient monitor during surgery and in the recovery period. Methods In patients undergoing surgery requiring anesthesia, the HR reported by the patient monitor’s ECG module was recorded and stored simultaneously with the photopletysmography (PPG) from the OHRM attached to the patient’s wrist. The agreement between the HR reported by the patient’s monitor and the HR extracted from the OHRM’s PPG signal was assessed using Bland-Altman analysis during the surgical and recovery phase. Results A total of 271.8 hours of data in 99 patients was recorded simultaneously by the OHRM and patient monitor. The median coverage was 86% (IQR 65%-95%) and did not differ significantly between surgery and recovery (Wilcoxon paired difference test P=.17). Agreement analysis showed the limits of agreement (LoA) of the difference between the OHRM and the ECG HR were within the range of 5 beats per minute (bpm). The mean bias was –0.14 bpm (LoA between –3.08 bpm and 2.79 bpm) and –0.19% (LoA between –5 bpm to 5 bpm) for the PPG- measured HR compared to the ECG-measured HR during surgery; during recovery, it was –0.11 bpm (LoA between –2.79 bpm and 2.59 bpm) and –0.15% (LoA between –3.92% and 3.64%). Conclusions This study shows that an OHRM equipped with a PPG sensor can measure HR within the ECG reference standard of –5 bpm to 5 bpm or –10% to 10% in the perioperative setting when the PPG signal is of sufficient quality. This implies that an OHRM can be considered clinically acceptable for HR monitoring in low acuity hospitalized patients.
Collapse
Affiliation(s)
- Eveline Mestrom
- Department of Anesthesiology, Catharina Hospital Eindhoven, Eindhoven, Netherlands
| | - Ruben Deneer
- Clinical Laboratory, Catharina Hospital Eindhoven, Eindhoven, Netherlands.,Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands.,Expert Center Clinical Chemistry Eindhoven, Eindhoven, Netherlands
| | - Alberto G Bonomi
- Department of Personal Health, Philips Research, Eindhoven, Netherlands
| | - Jenny Margarito
- Department of Personal Health, Philips Research, Eindhoven, Netherlands
| | - Jos Gelissen
- Department of Personal Health, Philips Research, Eindhoven, Netherlands
| | - Reinder Haakma
- Department of Personal Health, Philips Research, Eindhoven, Netherlands
| | - Hendrikus H M Korsten
- Department of Anesthesiology, Catharina Hospital Eindhoven, Eindhoven, Netherlands.,Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands
| | - Volkher Scharnhorst
- Clinical Laboratory, Catharina Hospital Eindhoven, Eindhoven, Netherlands.,Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands.,Expert Center Clinical Chemistry Eindhoven, Eindhoven, Netherlands
| | - R Arthur Bouwman
- Department of Anesthesiology, Catharina Hospital Eindhoven, Eindhoven, Netherlands.,Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands
| |
Collapse
|
15
|
Jeong DU, Lim KM. Convolutional neural network for classification of eight types of arrhythmia using 2D time-frequency feature map from standard 12-lead electrocardiogram. Sci Rep 2021; 11:20396. [PMID: 34650175 PMCID: PMC8516863 DOI: 10.1038/s41598-021-99975-6] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2021] [Accepted: 09/23/2021] [Indexed: 11/29/2022] Open
Abstract
Electrocardiograms (ECGs) are widely used for diagnosing cardiac arrhythmia based on the deformation of signal shapes due to changes in various heart diseases. However, these abnormal signs may not be observed in some 12 ECG channels, depending on the location, the heart shape, and the type of cardiac arrhythmia. Therefore, it is necessary to closely and comprehensively observe ECG records acquired from 12 channel electrodes to diagnose cardiac arrhythmias accurately. In this study, we proposed a clustering algorithm that can classify persistent cardiac arrhythmia as well as episodic cardiac arrhythmias using the standard 12-lead ECG records and the 2D CNN model using the time–frequency feature maps to classify the eight types of arrhythmias and normal sinus rhythm. The standard 12-lead ECG records were provided by China Physiological Signal Challenge 2018 and consisted of 6877 patients. The proposed algorithm showed high performance in classifying persistent cardiac arrhythmias; however, its accuracy was somewhat low in classifying episodic arrhythmias. If our proposed model is trained and verified using more clinical data, we believe it can be used as an auxiliary device for diagnosing cardiac arrhythmias.
Collapse
Affiliation(s)
- Da Un Jeong
- Kumoh National Institute of Technology, IT Convergence Engineering, Gumi, 39253, Republic of Korea
| | - Ki Moo Lim
- Kumoh National Institute of Technology, IT Convergence Engineering, Gumi, 39253, Republic of Korea. .,Kumoh National Institute of Technology, Medical IT Convergence Engineering, Gumi, 39253, Republic of Korea.
| |
Collapse
|
16
|
Hasnul MA, Aziz NAA, Alelyani S, Mohana M, Aziz AA. Electrocardiogram-Based Emotion Recognition Systems and Their Applications in Healthcare-A Review. SENSORS 2021; 21:s21155015. [PMID: 34372252 PMCID: PMC8348698 DOI: 10.3390/s21155015] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/17/2021] [Revised: 07/15/2021] [Accepted: 07/19/2021] [Indexed: 11/30/2022]
Abstract
Affective computing is a field of study that integrates human affects and emotions with artificial intelligence into systems or devices. A system or device with affective computing is beneficial for the mental health and wellbeing of individuals that are stressed, anguished, or depressed. Emotion recognition systems are an important technology that enables affective computing. Currently, there are a lot of ways to build an emotion recognition system using various techniques and algorithms. This review paper focuses on emotion recognition research that adopted electrocardiograms (ECGs) as a unimodal approach as well as part of a multimodal approach for emotion recognition systems. Critical observations of data collection, pre-processing, feature extraction, feature selection and dimensionality reduction, classification, and validation are conducted. This paper also highlights the architectures with accuracy of above 90%. The available ECG-inclusive affective databases are also reviewed, and a popularity analysis is presented. Additionally, the benefit of emotion recognition systems towards healthcare systems is also reviewed here. Based on the literature reviewed, a thorough discussion on the subject matter and future works is suggested and concluded. The findings presented here are beneficial for prospective researchers to look into the summary of previous works conducted in the field of ECG-based emotion recognition systems, and for identifying gaps in the area, as well as in developing and designing future applications of emotion recognition systems, especially in improving healthcare.
Collapse
Affiliation(s)
- Muhammad Anas Hasnul
- Faculty of Engineering and Technology, Multimedia University, Melaka 75450, Malaysia; (M.A.H.); (A.A.A.)
| | - Nor Azlina Ab. Aziz
- Faculty of Engineering and Technology, Multimedia University, Melaka 75450, Malaysia; (M.A.H.); (A.A.A.)
- Correspondence:
| | - Salem Alelyani
- Center for Artificial Intelligence (CAI), King Khalid University, Abha 61421, Saudi Arabia; (S.A.); (M.M.)
- College of Computer Science, King Khalid University, Abha 61421, Saudi Arabia
| | - Mohamed Mohana
- Center for Artificial Intelligence (CAI), King Khalid University, Abha 61421, Saudi Arabia; (S.A.); (M.M.)
| | - Azlan Abd. Aziz
- Faculty of Engineering and Technology, Multimedia University, Melaka 75450, Malaysia; (M.A.H.); (A.A.A.)
| |
Collapse
|
17
|
The Autonomic Nervous System Differentiates between Levels of Motor Intent and End Effector. J Pers Med 2020; 10:jpm10030076. [PMID: 32751933 PMCID: PMC7563544 DOI: 10.3390/jpm10030076] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2020] [Revised: 07/28/2020] [Accepted: 07/28/2020] [Indexed: 12/22/2022] Open
Abstract
While attempting to bridge motor control and cognitive science, the nascent field of embodied cognition has primarily addressed intended, goal-oriented actions. Less explored, however, have been unintended motions. Such movements tend to occur largely beneath awareness, while contributing to the spontaneous control of redundant degrees of freedom across the body in motion. We posit that the consequences of such unintended actions implicitly contribute to our autonomous sense of action ownership and agency. We question whether biorhythmic activities from these motions are separable from those which intentionally occur. Here we find that fluctuations in the biorhythmic activities of the nervous systems can unambiguously differentiate across levels of intent. More important yet, this differentiation is remarkable when we examine the fluctuations in biorhythmic activity from the autonomic nervous systems. We find that when the action is intended, the heart signal leads the body kinematics signals; but when the action segment spontaneously occurs without instructions, the heart signal lags the bodily kinematics signals. We conclude that the autonomic nervous system can differentiate levels of intent. Our results are discussed while considering their potential translational value.
Collapse
|
18
|
Jeon E, Oh K, Kwon S, Son H, Yun Y, Jung ES, Kim MS. A Lightweight Deep Learning Model for Fast Electrocardiographic Beats Classification With a Wearable Cardiac Monitor: Development and Validation Study. JMIR Med Inform 2020; 8:e17037. [PMID: 32163037 PMCID: PMC7099397 DOI: 10.2196/17037] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2019] [Revised: 01/21/2020] [Accepted: 02/07/2020] [Indexed: 01/27/2023] Open
Abstract
Background Electrocardiographic (ECG) monitors have been widely used for diagnosing cardiac arrhythmias for decades. However, accurate analysis of ECG signals is difficult and time-consuming work because large amounts of beats need to be inspected. In order to enhance ECG beat classification, machine learning and deep learning methods have been studied. However, existing studies have limitations in model rigidity, model complexity, and inference speed. Objective To classify ECG beats effectively and efficiently, we propose a baseline model with recurrent neural networks (RNNs). Furthermore, we also propose a lightweight model with fused RNN for speeding up the prediction time on central processing units (CPUs). Methods We used 48 ECGs from the MIT-BIH (Massachusetts Institute of Technology-Beth Israel Hospital) Arrhythmia Database, and 76 ECGs were collected with S-Patch devices developed by Samsung SDS. We developed both baseline and lightweight models on the MXNet framework. We trained both models on graphics processing units and measured both models’ inference times on CPUs. Results Our models achieved overall beat classification accuracies of 99.72% for the baseline model with RNN and 99.80% for the lightweight model with fused RNN. Moreover, our lightweight model reduced the inference time on CPUs without any loss of accuracy. The inference time for the lightweight model for 24-hour ECGs was 3 minutes, which is 5 times faster than the baseline model. Conclusions Both our baseline and lightweight models achieved cardiologist-level accuracies. Furthermore, our lightweight model is competitive on CPU-based wearable hardware.
Collapse
Affiliation(s)
- Eunjoo Jeon
- Technology Research, Samsung SDS, Seoul, Republic of Korea
| | - Kyusam Oh
- Technology Research, Samsung SDS, Seoul, Republic of Korea
| | - Soonhwan Kwon
- Technology Research, Samsung SDS, Seoul, Republic of Korea
| | - HyeongGwan Son
- Technology Research, Samsung SDS, Seoul, Republic of Korea
| | - Yongkeun Yun
- Technology Research, Samsung SDS, Seoul, Republic of Korea
| | - Eun-Soo Jung
- Technology Research, Samsung SDS, Seoul, Republic of Korea
| | - Min Soo Kim
- Technology Research, Samsung SDS, Seoul, Republic of Korea
| |
Collapse
|
19
|
Nano M, Fonseca P, Overeem S, Vullings R, Aarts RM. Lying Awake at Night: Cardiac Autonomic Activity in Relation to Sleep Onset and Maintenance. Front Neurosci 2020; 13:1405. [PMID: 32009886 PMCID: PMC6974549 DOI: 10.3389/fnins.2019.01405] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2019] [Accepted: 12/12/2019] [Indexed: 12/18/2022] Open
Abstract
Insomnia, i.e., difficulties initiating and/or maintaining sleep, is one of the most common sleep disorders. To study underlying mechanisms for insomnia, we studied autonomic activity changes around sleep onset in participants without clinical insomnia but with varying problems with initiating or maintaining sleep quantified as increased sleep onset latency (SOL) and wake after sleep onset (WASO), respectively. Polysomnography and electrocardiography were simultaneously recorded in 176 participants during a single night. Cardiac autonomic activity was assessed using frequency domain analysis of RR intervals and results show that the normalized spectral power in the low frequency band (LFnu) after sleep onset was significantly higher in participants with long SOL compared to participants with short SOL. Furthermore, the normalized spectral power in the high frequency band (HFnu) was significantly lower in participants with long SOL as compared to participants with short SOL over 3 time periods (first 10 min in bed intending to sleep, 10 min before, and 10 min after sleep onset). These results suggest that participants with long SOL are more aroused in all three examined time periods when compared to participants with short SOL, especially for young adults (20–40 years). As there is no clear consensus on the cutoff for an increased WASO, we used a data-driven approach to explore different cutoffs to define short WASO and long WASO groups. LFnu, HFnu, and LF/HF differed between the long and the short WASO groups. A higher LFnu and LF/HF and a lower HFnu was observed in participants with long WASO for most cutoffs. The highest effect size was found using the cutoff of 66 min. Our findings suggest that autonomic cardiac activity has predictive value with respect to sleep characteristics pertaining to sleep onset and maintenance.
Collapse
Affiliation(s)
- Marina Nano
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands.,Philips Research, Eindhoven, Netherlands.,Sleep Medicine Centre Kempenhaeghe, Heeze, Netherlands
| | - Pedro Fonseca
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands.,Philips Research, Eindhoven, Netherlands
| | - Sebastiaan Overeem
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands.,Sleep Medicine Centre Kempenhaeghe, Heeze, Netherlands
| | - Rik Vullings
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands
| | - Ronald M Aarts
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands.,Philips Research, Eindhoven, Netherlands
| |
Collapse
|
20
|
John RG, Ramachandran KI. Extraction of foetal ECG from abdominal ECG by nonlinear transformation and estimations. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2019; 175:193-204. [PMID: 31104707 DOI: 10.1016/j.cmpb.2019.04.022] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/18/2019] [Revised: 04/13/2019] [Accepted: 04/20/2019] [Indexed: 06/09/2023]
Abstract
BACKGROUND AND OBJECTIVE This paper proposes a simple yet effective method for the extraction of foetal ECG from abdominal ECG which is necessary due to similar spatial and temporal content of mother and foetal ECG. METHODS The proposed algorithm for extraction of foetal ECG (fECG) from abdominal signal uses single channel. Pre-processing of abdominal ECG (abdECG) has been done to eliminate noise and condition the signal. The maternal ECG R-peaks have been detected based on thresholding, first order Gaussian differentiation and zero cross detection on pre-processed signal. Having identified R-peaks and pre-processed signal as base, using Maximum Likelihood Estimation, one beat including QRS complex morphology of maternal ECG (mECG) has been constructed. Extraction of maternal ECG from abdECG is done based on the constructed beat, R-peak locations and its corresponding QRS complex of abdECG. Extracted mECG has been cancelled from abdECG. This results in foetal ECG with residual noise. The noise has been reduced by Polynomial Approximation and Total Variation (PATV) to improve SNR. This approach ensures no loss of partially or completely overlapped fECG signals due to mECG removal. The algorithm is tested on three database namely daISy (DBI), Physiobank challenge 2013 (DBII) and abdominal and direct foetal ECG database (adfecgdb) of Physiobank (DBIII). RESULTS The algorithm detected no false positives or false negatives with certain channel for DBI, DBII and DBIII which shows that the proposed algorithm can achieve good performance. Overall accuracy and sensitivity of the system is 98.53% and 100% for DBI. Best accuracy and sensitivity of 97.77% and 98.63% are obtained for DBII. Best accuracy of 92.41% and sensitivity of 93.8% are obtained for DBIII. Correlation coefficient between actual foetal heart rate (fHR) and estimated fHR of 0.66 for DBII and 0.59 for DBIII is obtained. The method has obtained overall F1 score of 99.25% for DBI, 96.04% for DBII and 94.25% for DBIII. It has obtained a best MSE of fHR and overall MSE of R-R interval which is 10.8bpm2 and 2.2 ms for DBII, 12bpm2 and 2.14 ms for DBIII. CONCLUSION The results for different public databases show that the proposed method is capable of providing good results. The foetal QRS, R-peaks and R-R intervals have also been obtained in this method. Thus, it gives a significant contribution in the required area of research.
Collapse
Affiliation(s)
- Rolant Gini John
- Department of Electronics and Communication Engineering, Amrita School of Engineering, Coimbatore, Amrita Vishwa Vidyapeetham, India.
| | - K I Ramachandran
- Center for Computational Engineering & Networking (CEN), Amrita School of Engineering, Coimbatore, Amrita Vishwa Vidyapeetham, India
| |
Collapse
|
21
|
Abstract
Cardiac signal processing is usually a computationally demanding task as signals are heavily contaminated by noise and other artifacts. In this paper, an effective approach for peak point detection and localization in noisy electrocardiogram (ECG) signals is presented. Six stages characterize the implemented method, which adopts the Hilbert transform and a thresholding technique for the detection of zones inside the ECG signal which could contain a peak. Subsequently, the identified zones are analyzed using the wavelet transform for R point detection and localization. The conceived signal processing technique has been evaluated, adopting ECG signals belonging to MIT-BIH Noise Stress Test Database, which includes specially selected Holter recordings characterized by baseline wander, muscle artifacts and electrode motion artifacts as noise sources. The experimental results show that the proposed method reaches most satisfactory performance, even when challenging ECG signals are adopted. The results obtained are presented, discussed and compared with some other R wave detection algorithms indicated in literature, which adopt the same database as a test bench. In particular, for a signal to noise ratio (SNR) equal to 6 dB, results with minimal interference from noise and artifacts have been obtained, since Se e +P achieve values of 98.13% and 96.91, respectively.
Collapse
|
22
|
Nano MM, Fonseca P, Overeem S, Vullings R, Aarts RM. Autonomic cardiac activity in adults with short and long sleep onset latency. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2018; 2018:1448-1451. [PMID: 30440665 DOI: 10.1109/embc.2018.8512534] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Autonomic cardiac activity during sleep has been widely studied. Research has mostly focused on cardiac activity between different sleep stages and wakefulness as well as between normal and pathological sleep. This work investigates autonomic activity changes during sleep onset in healthy subjects with long and short sleep onset latency (SOL). Polysomnography (PSG) and electrocardiography (ECG) were simultaneously recorded in 186 healthy subjects during a single night. Autonomic activity was assessed based on frequency domain analysis of RR intervals and results show that the analysis of RR intervals differs significantly between the short SOL and the long SOL groups. We found that the spectral power in the low frequency band (LF) was significantly higher in the long SOL group compared to the short SOL group in the first 10 minutes in bed intended to sleep. There was no significant difference for LF and the spectral power in the high frequency band (HF) 10 minutes before and after sleep onset between the two groups. Only in the short SOL group there was a significant increase in HF from the first 10 minutes in bed intended to sleep to 10 minutes before SO, while LF decreased significantly in both groups. The effect of time (5.5-min bin) on the heart rate variability (HRV) features around sleep onset showed that both LF and HF differed significantly during the period surrounding sleep onset only in the short SOL group.
Collapse
|
23
|
Carvalho JM, Brás S, Pratas D, Ferreira J, Soares SC, Pinho AJ. Extended-alphabet finite-context models. Pattern Recognit Lett 2018. [DOI: 10.1016/j.patrec.2018.05.026] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
|
24
|
Ryu J, Torres EB. Characterization of Sensory-Motor Behavior Under Cognitive Load Using a New Statistical Platform for Studies of Embodied Cognition. Front Hum Neurosci 2018; 12:116. [PMID: 29681805 PMCID: PMC5897674 DOI: 10.3389/fnhum.2018.00116] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2017] [Accepted: 03/12/2018] [Indexed: 11/13/2022] Open
Abstract
The field of enacted/embodied cognition has emerged as a contemporary attempt to connect the mind and body in the study of cognition. However, there has been a paucity of methods that enable a multi-layered approach tapping into different levels of functionality within the nervous systems (e.g., continuously capturing in tandem multi-modal biophysical signals in naturalistic settings). The present study introduces a new theoretical and statistical framework to characterize the influences of cognitive demands on biophysical rhythmic signals harnessed from deliberate, spontaneous and autonomic activities. In this study, nine participants performed a basic pointing task to communicate a decision while they were exposed to different levels of cognitive load. Within these decision-making contexts, we examined the moment-by-moment fluctuations in the peak amplitude and timing of the biophysical time series data (e.g., continuous waveforms extracted from hand kinematics and heart signals). These spike-trains data offered high statistical power for personalized empirical statistical estimation and were well-characterized by a Gamma process. Our approach enabled the identification of different empirically estimated families of probability distributions to facilitate inference regarding the continuous physiological phenomena underlying cognitively driven decision-making. We found that the same pointing task revealed shifts in the probability distribution functions (PDFs) of the hand kinematic signals under study and were accompanied by shifts in the signatures of the heart inter-beat-interval timings. Within the time scale of an experimental session, marked changes in skewness and dispersion of the distributions were tracked on the Gamma parameter plane with 95% confidence. The results suggest that traditional theoretical assumptions of stationarity and normality in biophysical data from the nervous systems are incongruent with the true statistical nature of empirical data. This work offers a unifying platform for personalized statistical inference that goes far beyond those used in conventional studies, often assuming a “one size fits all model” on data drawn from discrete events such as mouse clicks, and observations that leave out continuously co-occurring spontaneous activity taking place largely beneath awareness.
Collapse
Affiliation(s)
- Jihye Ryu
- Sensory Motor Integration Laboratory, Department of Psychology, Rutgers University, Piscataway, NJ, United States
| | - Elizabeth B Torres
- Computational Biomedical Imaging and Modeling Center, Department of Psychology and Computer Science, Rutgers University Center for Cognitive Science, Rutgers University, Piscataway, NJ, United States
| |
Collapse
|
25
|
Satija U, Ramkumar B, Manikandan MS. A Review of Signal Processing Techniques for Electrocardiogram Signal Quality Assessment. IEEE Rev Biomed Eng 2018; 11:36-52. [PMID: 29994590 DOI: 10.1109/rbme.2018.2810957] [Citation(s) in RCA: 81] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Electrocardiogram (ECG) signal quality assessment (SQA) plays a vital role in significantly improving the diagnostic accuracy and reliability of unsupervised ECG analysis systems. In practice, the ECG signal is often corrupted with different kinds of noises and artifacts. Therefore, numerous SQA methods were presented based on the ECG signal and/or noise features and the machine learning classifiers and/or heuristic decision rules. This paper presents an overview of current state-of-the-art SQA methods and highlights the practical limitations of the existing SQA methods. Based upon past and our studies, it is noticed that a lightweight ECG noise analysis framework is highly demanded for real-time detection, localization, and classification of single and combined ECG noises within the context of wearable ECG monitoring devices which are often resource constrained.
Collapse
|
26
|
Lui HW, Chow KL. Multiclass classification of myocardial infarction with convolutional and recurrent neural networks for portable ECG devices. INFORMATICS IN MEDICINE UNLOCKED 2018. [DOI: 10.1016/j.imu.2018.08.002] [Citation(s) in RCA: 64] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
|
27
|
Removal of BW and Respiration Noise in abdECG for fECG Extraction. ADVANCES IN INTELLIGENT SYSTEMS AND COMPUTING 2018. [DOI: 10.1007/978-3-319-67934-1_1] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
|
28
|
Herry CL, Frasch M, Seely AJE, Wu HT. Heart beat classification from single-lead ECG using the synchrosqueezing transform. Physiol Meas 2017; 38:171-187. [DOI: 10.1088/1361-6579/aa5070] [Citation(s) in RCA: 53] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
|
29
|
Wavelet-based electrocardiogram signal compression methods and their performances: A prospective review. Biomed Signal Process Control 2014. [DOI: 10.1016/j.bspc.2014.07.002] [Citation(s) in RCA: 80] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
|
30
|
Varghees VN, Ramachandran K. A novel heart sound activity detection framework for automated heart sound analysis. Biomed Signal Process Control 2014. [DOI: 10.1016/j.bspc.2014.05.002] [Citation(s) in RCA: 40] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
|