Investigation of HR and QT Variability for Monitoring Sleep Apnea: An Interpretable Machine Learning Approach

Partha Pratim Das Turja; Mohammod Abdul Motin; Sumaiya Kabir; Mufti Mahmud; Dinesh Kumar

doi:10.1007/978-3-031-68639-9_11

Investigation of HR and QT Variability for Monitoring Sleep Apnea: An Interpretable Machine Learning Approach

Partha Pratim Das Turja
, Mohammod Abdul Motin^*
, Sumaiya Kabir
, Mufti Mahmud
, Dinesh Kumar

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

Polysomnography, the gold standard technique for monitoring sleep apnea, is a costly, cumbersome, and time-consuming process that often causes disturbance to sleep and, therefore, is unsuitable for long-term monitoring. This paper investigates the single-channel electrocardiogram (ECG) derived heart rate variability (HRV) and QT variability (QTV) features, which are low-cost and suitable for long-term monitoring for automated sleep apnea monitoring. Using HRV alone and HRV combined with QTV features, different classifiers were trained to distinguish apneic events from healthy sleep events. The proposed model is trained and tested using 70 full-night ECG recordings acquired from the PhysioNet apnea ECG database. The extreme gradient boosting classifier outperformed a series of classifiers with sensitivity, specificity, and accuracy of 82.70%, 76.34%, and 79.38%, respectively, for HRV features. Adding QT features improved the sensitivity, specificity, and accuracy to 84.18%, 82.15%, and 83.16%, respectively. The performance suggests that HRV and QTV features have the potential to detect sleep apnea. Moreover, its non-invasive nature and cost-efficiency make it more suitable for wearable-based sleep apnea monitoring.

Original language	English
Title of host publication	Applied Intelligence and Informatics - 3rd International Conference, AII 2023, Revised Selected Papers
Editors	Mufti Mahmud, Hanene Ben-Abdallah, M. Shamim Kaiser, Muhammad Raisuddin Ahmed, Ning Zhong
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	169-185
Number of pages	17
ISBN (Print)	9783031686382
DOIs	https://doi.org/10.1007/978-3-031-68639-9_11
State	Published - 2024
Externally published	Yes
Event	3rd International Conference on Applied Intelligence and Informatics, AII 2023 - Dubai, United Arab Emirates Duration: 29 Oct 2023 → 31 Oct 2023

Publication series

Name	Communications in Computer and Information Science
Volume	2065 CCIS
ISSN (Print)	1865-0929
ISSN (Electronic)	1865-0937

Conference

Conference	3rd International Conference on Applied Intelligence and Informatics, AII 2023
Country/Territory	United Arab Emirates
City	Dubai
Period	29/10/23 → 31/10/23

Bibliographical note

Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

Keywords

Electrocardiogram signal
Extreme gradient boosting
Heart rate variability
Sleep apnea
Support vector machine

ASJC Scopus subject areas

General Computer Science
General Mathematics

Access to Document

10.1007/978-3-031-68639-9_11

Cite this

Das Turja, P. P., Motin, M. A., Kabir, S., Mahmud, M., & Kumar, D. (2024). Investigation of HR and QT Variability for Monitoring Sleep Apnea: An Interpretable Machine Learning Approach. In M. Mahmud, H. Ben-Abdallah, M. S. Kaiser, M. R. Ahmed, & N. Zhong (Eds.), Applied Intelligence and Informatics - 3rd International Conference, AII 2023, Revised Selected Papers (pp. 169-185). (Communications in Computer and Information Science; Vol. 2065 CCIS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-68639-9_11

Das Turja, Partha Pratim ; Motin, Mohammod Abdul ; Kabir, Sumaiya et al. / Investigation of HR and QT Variability for Monitoring Sleep Apnea : An Interpretable Machine Learning Approach. Applied Intelligence and Informatics - 3rd International Conference, AII 2023, Revised Selected Papers. editor / Mufti Mahmud ; Hanene Ben-Abdallah ; M. Shamim Kaiser ; Muhammad Raisuddin Ahmed ; Ning Zhong. Springer Science and Business Media Deutschland GmbH, 2024. pp. 169-185 (Communications in Computer and Information Science).

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title = "Investigation of HR and QT Variability for Monitoring Sleep Apnea: An Interpretable Machine Learning Approach",

abstract = "Polysomnography, the gold standard technique for monitoring sleep apnea, is a costly, cumbersome, and time-consuming process that often causes disturbance to sleep and, therefore, is unsuitable for long-term monitoring. This paper investigates the single-channel electrocardiogram (ECG) derived heart rate variability (HRV) and QT variability (QTV) features, which are low-cost and suitable for long-term monitoring for automated sleep apnea monitoring. Using HRV alone and HRV combined with QTV features, different classifiers were trained to distinguish apneic events from healthy sleep events. The proposed model is trained and tested using 70 full-night ECG recordings acquired from the PhysioNet apnea ECG database. The extreme gradient boosting classifier outperformed a series of classifiers with sensitivity, specificity, and accuracy of 82.70\%, 76.34\%, and 79.38\%, respectively, for HRV features. Adding QT features improved the sensitivity, specificity, and accuracy to 84.18\%, 82.15\%, and 83.16\%, respectively. The performance suggests that HRV and QTV features have the potential to detect sleep apnea. Moreover, its non-invasive nature and cost-efficiency make it more suitable for wearable-based sleep apnea monitoring.",

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Das Turja, PP, Motin, MA, Kabir, S, Mahmud, M & Kumar, D 2024, Investigation of HR and QT Variability for Monitoring Sleep Apnea: An Interpretable Machine Learning Approach. in M Mahmud, H Ben-Abdallah, MS Kaiser, MR Ahmed & N Zhong (eds), Applied Intelligence and Informatics - 3rd International Conference, AII 2023, Revised Selected Papers. Communications in Computer and Information Science, vol. 2065 CCIS, Springer Science and Business Media Deutschland GmbH, pp. 169-185, 3rd International Conference on Applied Intelligence and Informatics, AII 2023, Dubai, United Arab Emirates, 29/10/23. https://doi.org/10.1007/978-3-031-68639-9_11

Investigation of HR and QT Variability for Monitoring Sleep Apnea: An Interpretable Machine Learning Approach. / Das Turja, Partha Pratim; Motin, Mohammod Abdul; Kabir, Sumaiya et al.
Applied Intelligence and Informatics - 3rd International Conference, AII 2023, Revised Selected Papers. ed. / Mufti Mahmud; Hanene Ben-Abdallah; M. Shamim Kaiser; Muhammad Raisuddin Ahmed; Ning Zhong. Springer Science and Business Media Deutschland GmbH, 2024. p. 169-185 (Communications in Computer and Information Science; Vol. 2065 CCIS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Investigation of HR and QT Variability for Monitoring Sleep Apnea

T2 - 3rd International Conference on Applied Intelligence and Informatics, AII 2023

AU - Das Turja, Partha Pratim

AU - Motin, Mohammod Abdul

AU - Kabir, Sumaiya

AU - Mahmud, Mufti

AU - Kumar, Dinesh

N1 - Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

PY - 2024

Y1 - 2024

N2 - Polysomnography, the gold standard technique for monitoring sleep apnea, is a costly, cumbersome, and time-consuming process that often causes disturbance to sleep and, therefore, is unsuitable for long-term monitoring. This paper investigates the single-channel electrocardiogram (ECG) derived heart rate variability (HRV) and QT variability (QTV) features, which are low-cost and suitable for long-term monitoring for automated sleep apnea monitoring. Using HRV alone and HRV combined with QTV features, different classifiers were trained to distinguish apneic events from healthy sleep events. The proposed model is trained and tested using 70 full-night ECG recordings acquired from the PhysioNet apnea ECG database. The extreme gradient boosting classifier outperformed a series of classifiers with sensitivity, specificity, and accuracy of 82.70%, 76.34%, and 79.38%, respectively, for HRV features. Adding QT features improved the sensitivity, specificity, and accuracy to 84.18%, 82.15%, and 83.16%, respectively. The performance suggests that HRV and QTV features have the potential to detect sleep apnea. Moreover, its non-invasive nature and cost-efficiency make it more suitable for wearable-based sleep apnea monitoring.

AB - Polysomnography, the gold standard technique for monitoring sleep apnea, is a costly, cumbersome, and time-consuming process that often causes disturbance to sleep and, therefore, is unsuitable for long-term monitoring. This paper investigates the single-channel electrocardiogram (ECG) derived heart rate variability (HRV) and QT variability (QTV) features, which are low-cost and suitable for long-term monitoring for automated sleep apnea monitoring. Using HRV alone and HRV combined with QTV features, different classifiers were trained to distinguish apneic events from healthy sleep events. The proposed model is trained and tested using 70 full-night ECG recordings acquired from the PhysioNet apnea ECG database. The extreme gradient boosting classifier outperformed a series of classifiers with sensitivity, specificity, and accuracy of 82.70%, 76.34%, and 79.38%, respectively, for HRV features. Adding QT features improved the sensitivity, specificity, and accuracy to 84.18%, 82.15%, and 83.16%, respectively. The performance suggests that HRV and QTV features have the potential to detect sleep apnea. Moreover, its non-invasive nature and cost-efficiency make it more suitable for wearable-based sleep apnea monitoring.

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KW - Extreme gradient boosting

KW - Heart rate variability

KW - Sleep apnea

KW - Support vector machine

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SN - 9783031686382

T3 - Communications in Computer and Information Science

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BT - Applied Intelligence and Informatics - 3rd International Conference, AII 2023, Revised Selected Papers

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PB - Springer Science and Business Media Deutschland GmbH

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Das Turja PP, Motin MA, Kabir S, Mahmud M, Kumar D. Investigation of HR and QT Variability for Monitoring Sleep Apnea: An Interpretable Machine Learning Approach. In Mahmud M, Ben-Abdallah H, Kaiser MS, Ahmed MR, Zhong N, editors, Applied Intelligence and Informatics - 3rd International Conference, AII 2023, Revised Selected Papers. Springer Science and Business Media Deutschland GmbH. 2024. p. 169-185. (Communications in Computer and Information Science). doi: 10.1007/978-3-031-68639-9_11

Investigation of HR and QT Variability for Monitoring Sleep Apnea: An Interpretable Machine Learning Approach

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

Fingerprint

Cite this