Sleep disorder identification using wavelet scattering on ECG signals
Article
Sharma, Manish, Lodhi, Harsh, Yadav, Rishita and Acharya, U. Rajendra. 2024. "Sleep disorder identification using wavelet scattering on ECG signals." International Journal of Imaging Systems and Technology. 34 (1). https://doi.org/10.1002/ima.22980
| Article Title | Sleep disorder identification using wavelet scattering on ECG signals |
|---|---|
| ERA Journal ID | 36561 |
| Article Category | Article |
| Authors | Sharma, Manish, Lodhi, Harsh, Yadav, Rishita and Acharya, U. Rajendra |
| Journal Title | International Journal of Imaging Systems and Technology |
| Journal Citation | 34 (1) |
| Article Number | e22980 |
| Number of Pages | 14 |
| Year | 2024 |
| Publisher | John Wiley & Sons |
| Place of Publication | United States |
| ISSN | 0899-9457 |
| 1098-1098 | |
| Digital Object Identifier (DOI) | https://doi.org/10.1002/ima.22980 |
| Web Address (URL) | https://onlinelibrary.wiley.com/doi/10.1002/ima.22980 |
| Abstract | Sleep affects the functioning of all biological processes in the human body. Sleep disorders disrupt the well-being of an individual and can potentially lead to other health complications. It affects a large part of the population, and their timely and efficient detection is crucial for improving the quality of health. Sleep disorders are generally analyzed using polysomnogram (PSG) signals, which must be captured in sophisticated laboratory settings. However, electrocardiogram (ECG) signals provide information about the electrical activity of the heart, which is strongly linked with disturbances in sleep and can be used as an indicator of disordered sleep. Along with that, the recording of ECG signals is much easier than other signals and can be done remotely without causing any significant disturbance to the subject. This brings practicality to the systems that are already being employed for sleep disorder detection. In this paper, we present a method for identifying types of sleep disorders using ECG signals. The objective of this paper is to investigate the use of ECG signals for the automated identification of insomnia, narcolepsy, periodic leg movement (PLM), rapid eye movement (REM) behavior disorder (RBD), and nocturnal frontal lobe epilepsy (NFLE) against healthy subjects. We aim to develop a machine learning-based algorithm that automatically classifies ECG signals into these sleep disorder categories. The cyclic alternating pattern (CAP) sleep database was used in this study, which contains PSG recordings from individuals with and without sleep disorders, including insomnia, narcolepsy, PLM, RBD, and NFLE. A wavelet scattering network has been used to extract features from the ECG signals. Various classifiers were tested, and the ensemble bag of trees classifier provided the optimum performance. An overall accuracy of 98% was obtained for the identification of sleep disorders, along with a classification accuracy of 99.37%, 99.45%, 99.23%, 99.4%, and 99.65% for insomnia, narcolepsy, NFLE, PLM, and RBD, respectively, for binary classification against healthy subject data. Our results show that wavelet scattering network and ensemble of bagged tree (EbagT) classification can accurately identify various sleep disorders. They provided evidence that ECG signals can be used to identify and diagnose sleep disorders, which could lead to the development of accurate detection of these sleep disorders. Also, our study demonstrated the effectiveness of the proposed algorithm in identifying multiple sleep disorders, which could lead to more efficient and cost-effective diagnosis and treatment of sleep disorders. |
| Keywords | electrocardiogram; ensemble of bagged trees; insomnia; machine learning; narcolepsy; nocturnal frontal lobe epilepsy; periodic leg movement; REM behaviour disorder; sleep disorder identification |
| Contains Sensitive Content | Does not contain sensitive content |
| ANZSRC Field of Research 2020 | 420313. Mental health services |
| Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
| Byline Affiliations | Institute of Infrastructure, Technology, Research and Management (IITRAM), India |
| School of Mathematics, Physics and Computing |
Permalink -
https://research.usq.edu.au/item/z5vv8/sleep-disorder-identification-using-wavelet-scattering-on-ecg-signals
41
total views0
total downloads1
views this month0
downloads this month