Using Electrocardiogram Signal Features and Heart Rate Variability to Predict Epileptic Attacks
Since the increase in neuronal activity during an epileptic attack affects the voluntary nervous system, and the voluntary nervous system also affects the heart rate variability, it can be concluded that seizures can be predicted by monitoring heart rate variability. In this study, a new method for...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Tamkang University Press
2025-01-01
|
| Series: | Journal of Applied Science and Engineering |
| Subjects: | |
| Online Access: | http://jase.tku.edu.tw/articles/jase-202508-28-08-0017 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832575979997888512 |
|---|---|
| author | Ying Jiang Yuan Feng Danni Lu Lin Yang Qun Zhang Haiyan Yang Ning Li |
| author_facet | Ying Jiang Yuan Feng Danni Lu Lin Yang Qun Zhang Haiyan Yang Ning Li |
| author_sort | Ying Jiang |
| collection | DOAJ |
| description | Since the increase in neuronal activity during an epileptic attack affects the voluntary nervous system, and the voluntary nervous system also affects the heart rate variability, it can be concluded that seizures can be predicted by monitoring heart rate variability. In this study, a new method for predicting epilepsy through the analysis of heart rate variability is proposed. In the proposed method, 12 features are extracted from the heart rate variability signal in time, frequency, time-frequency, and nonlinear domains to predict epileptic seizures. We used a multivariate statistical process control algorithm for abnormality
detection. The presented algorithm was evaluated on a dataset consisting of 17 patients, where the obtained results show that the proposed method can predict epileptic attacks with an accuracy of 88.2%. From a practical point of view, due to the ease of obtaining
the heart rate variability signal, the proposed algorithm is more promising than the algorithms that use brain signal processing to predict epilepsy. |
| format | Article |
| id | doaj-art-1bfc28b1aa7a4913a6e26e10e7599d61 |
| institution | Kabale University |
| issn | 2708-9967 2708-9975 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Tamkang University Press |
| record_format | Article |
| series | Journal of Applied Science and Engineering |
| spelling | doaj-art-1bfc28b1aa7a4913a6e26e10e7599d612025-01-31T15:21:39ZengTamkang University PressJournal of Applied Science and Engineering2708-99672708-99752025-01-012881805181510.6180/jase.202508_28(8).0017Using Electrocardiogram Signal Features and Heart Rate Variability to Predict Epileptic AttacksYing Jiang0Yuan Feng1Danni Lu2Lin Yang3Qun Zhang4Haiyan Yang5Ning Li6Department of Neurology, Ruikang Hospital affiliated to Guangxi University of Chinese Medicine, Nanning, Guangxi, 530011, ChinaDepartment of Neurology, Ruikang Hospital affiliated to Guangxi University of Chinese Medicine, Nanning, Guangxi, 530011, ChinaDepartment of Neurology, Ruikang Hospital affiliated to Guangxi University of Chinese Medicine, Nanning, Guangxi, 530011, ChinaDepartment of Neurology, Ruikang Hospital affiliated to Guangxi University of Chinese Medicine, Nanning, Guangxi, 530011, ChinaDepartment of Neurology, Ruikang Hospital affiliated to Guangxi University of Chinese Medicine, Nanning, Guangxi, 530011, ChinaDepartment of Neurology, Ruikang Hospital affiliated to Guangxi University of Chinese Medicine, Nanning, Guangxi, 530011, ChinaDepartment of Neurology, Ruikang Hospital affiliated to Guangxi University of Chinese Medicine, Nanning, Guangxi, 530011, ChinaSince the increase in neuronal activity during an epileptic attack affects the voluntary nervous system, and the voluntary nervous system also affects the heart rate variability, it can be concluded that seizures can be predicted by monitoring heart rate variability. In this study, a new method for predicting epilepsy through the analysis of heart rate variability is proposed. In the proposed method, 12 features are extracted from the heart rate variability signal in time, frequency, time-frequency, and nonlinear domains to predict epileptic seizures. We used a multivariate statistical process control algorithm for abnormality detection. The presented algorithm was evaluated on a dataset consisting of 17 patients, where the obtained results show that the proposed method can predict epileptic attacks with an accuracy of 88.2%. From a practical point of view, due to the ease of obtaining the heart rate variability signal, the proposed algorithm is more promising than the algorithms that use brain signal processing to predict epilepsy.http://jase.tku.edu.tw/articles/jase-202508-28-08-0017disease diagnosisepilepsyheart ratesignal processingmultivariate statistical process |
| spellingShingle | Ying Jiang Yuan Feng Danni Lu Lin Yang Qun Zhang Haiyan Yang Ning Li Using Electrocardiogram Signal Features and Heart Rate Variability to Predict Epileptic Attacks Journal of Applied Science and Engineering disease diagnosis epilepsy heart rate signal processing multivariate statistical process |
| title | Using Electrocardiogram Signal Features and Heart Rate Variability to Predict Epileptic Attacks |
| title_full | Using Electrocardiogram Signal Features and Heart Rate Variability to Predict Epileptic Attacks |
| title_fullStr | Using Electrocardiogram Signal Features and Heart Rate Variability to Predict Epileptic Attacks |
| title_full_unstemmed | Using Electrocardiogram Signal Features and Heart Rate Variability to Predict Epileptic Attacks |
| title_short | Using Electrocardiogram Signal Features and Heart Rate Variability to Predict Epileptic Attacks |
| title_sort | using electrocardiogram signal features and heart rate variability to predict epileptic attacks |
| topic | disease diagnosis epilepsy heart rate signal processing multivariate statistical process |
| url | http://jase.tku.edu.tw/articles/jase-202508-28-08-0017 |
| work_keys_str_mv | AT yingjiang usingelectrocardiogramsignalfeaturesandheartratevariabilitytopredictepilepticattacks AT yuanfeng usingelectrocardiogramsignalfeaturesandheartratevariabilitytopredictepilepticattacks AT dannilu usingelectrocardiogramsignalfeaturesandheartratevariabilitytopredictepilepticattacks AT linyang usingelectrocardiogramsignalfeaturesandheartratevariabilitytopredictepilepticattacks AT qunzhang usingelectrocardiogramsignalfeaturesandheartratevariabilitytopredictepilepticattacks AT haiyanyang usingelectrocardiogramsignalfeaturesandheartratevariabilitytopredictepilepticattacks AT ningli usingelectrocardiogramsignalfeaturesandheartratevariabilitytopredictepilepticattacks |