[An algorithm based on ECG signal for sleep apnea syndrome detection].
The diagnosis of sleep apnea syndrome (SAS) has a significant importance in clinic for preventing diseases of hypertention, coronary heart disease, arrhythmia and cerebrovascular disorder, etc. This study presents a novel method for SAS detection based on single-channel electrocardiogram (ECG) signal. The method preprocessed ECG and detected QRS waves to get RR signal and ECG-derived respiratory (EDR) signal. Then 40 time- and spectral-domain features were extracted to normalize the signals. After that support vector machine (SVM) was used to classify the signals as "apnea" or "normal". Finally, the performance of the method was evaluated by the MIT-BIH Apnea-ECG database, and an accuracy of 95% in train sets and an accuracy of 88% in test sets were achieved.
Department of Biomedical Engineering, Biomedical Engineering and Instrumentation Science College, Zhejiang University, Hangzhou 310027, China.
Xiaomin
X
Yu
Department of Biomedical Engineering, Biomedical Engineering and Instrumentation Science College, Zhejiang University, Hangzhou 310027, China.
Yuewen
Y
Tu
Department of Biomedical Engineering, Biomedical Engineering and Instrumentation Science College, Zhejiang University, Hangzhou 310027, China.
Chao
C
Huang
Department of Biomedical Engineering, Biomedical Engineering and Instrumentation Science College, Zhejiang University, Hangzhou 310027, China.
Shuming
S
Ye
Department of Biomedical Engineering, Biomedical Engineering and Instrumentation Science College, Zhejiang University, Hangzhou 310027, China.
Hang
H
Chen
D000465:Algorithms; D004562:Electrocardiography / Q000379:methods*; D006801:Humans; D012815:Signal Processing, Computer-Assisted; D012891:Sleep Apnea Syndromes / Q000175:diagnosis*; D060388:Support Vector Machine