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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.
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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
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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.
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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
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Sbrollini A, Morettini M, Gambi E, Burattini L. Identification of Respiration Types Through Respiratory Signal Derived From Clinical and Wearable Electrocardiograms. IEEE OPEN JOURNAL OF ENGINEERING IN MEDICINE AND BIOLOGY 2023; 4:268-274. [PMID: 38196981 PMCID: PMC10776097 DOI: 10.1109/ojemb.2023.3343557] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Revised: 10/30/2023] [Accepted: 12/08/2023] [Indexed: 01/11/2024] Open
Abstract
GOAL To evaluate suitability of respiratory signals derived from clinical 12-lead electrocardiograms (ECGs) and wearable 1-lead ECG to identify different respiration types. METHODS ECGs were simultaneously acquired through the M12R ECG Holter by Global Instrumentation and the chest strap BioHarness 3.0 by Zephyr from 42 healthy subjects alternating normal breathing, breath holding, and deep breathing. Respiration signals were derived from the ECGs through the Segmented-Beat Modulation Method (SBMM)-based algorithm and the algorithms by Van Gent, Charlton, Soni and Sarkar, and characterized in terms of breathing rate and amplitude. Respiration classification was performed through a linear support vector machine and evaluated by F1 score. RESULTS Best F1 scores were 86.59%(lead V2) and 80.57%, when considering 12-lead and 1-lead ECGs, respectively, and using SBMM-based algorithm. CONCLUSION ECG-derived respiratory signals allow reliable identification of different respiration types even when acquired through wearable sensors, if associated to appropriate processing algorithms, such as the SBMM-based algorithm.
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Affiliation(s)
- Agnese Sbrollini
- Department of Information EngineeringUniversità Politecnica delle Marche60121AnconaItaly
| | - Micaela Morettini
- Department of Information EngineeringUniversità Politecnica delle Marche60121AnconaItaly
| | - Ennio Gambi
- Department of Information EngineeringUniversità Politecnica delle Marche60121AnconaItaly
| | - Laura Burattini
- Department of Information EngineeringUniversità Politecnica delle Marche60121AnconaItaly
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