Detection of Obstructive Sleep Apnea from ECG Signal Using SVM Based Grid Search

被引：14

作者：

Valavan, K. K. ^{[1
]}

Manoj, S. ^{[1
]}

Abishek, S. ^{[1
]}

Vijay, T. G. Gokull ^{[1
]}

Vojaswwin, A. P. ^{[1
]}

Gini, J. Rolant ^{[1
]}

Ramachandran, K., I ^{[2
]}

机构：

[1] Amrita Vishwa Vidyapeetham, Amrita Sch Engn, Dept Elect & Commun Engn, Coimbatore, Tamil Nadu, India

[2] Amrita Vishwa Vidyapeetham, Amrita Sch Engn, Ctr Computat Engn & Networking CEN, Coimbatore, Tamil Nadu, India

来源：

INTERNATIONAL JOURNAL OF ELECTRONICS AND TELECOMMUNICATIONS | 2021年 / 67卷 / 01期

关键词：

ECG signal; Grid Search; RR intervals; Sleep Apnea; Support Vector Machine;

D O I：

10.24425/ijet.2020.134021

中图分类号：

TN [电子技术、通信技术];

学科分类号：

0809 ;

摘要：

Obstructive Sleep Apnea is one common form of sleep apnea and is now tested by means of a process called Polysomnography which is time-consuming, expensive and also requires a human observer throughout the study of the subject which makes it inconvenient and new detection techniques are now being developed to overcome these difficulties. Heart rate variability has proven to be related to sleep apnea episodes and thus the features from the ECG signal can be used in the detection of sleep apnea. The proposed detection technique uses Support Vector Machines using Grid search algorithm and the classifier is trained using features based on heart rate variability derived from the ECG signal. The developed system is tested using the dataset and the results show that this classification system can recognize the disorder with an accuracy rate of 89%. Further, the use of the grid search algorithm has made this system a reliable and an accurate means for the classification of sleep apnea and can serve as a basis for the future development of its screening.

引用

页码：5 / 12

页数：8

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