침대 패드 형태의 용량성 전극에서 측정된 심전도 신호를처리하기 위한 자동 잡음 제거 및 피크 검출 알고리즘
Automatic Noise Removal and Peak Detection Algorithm for ECG Measured from Capacitively Coupled Electrodes Included within a Cloth Mattress Pad
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초록
Recent technological advances have increased interest in personal health monitoring. Electrocardiogram(ECG) monitoring is a basic healthcare activity and can provide decisive information regarding cardiovascularsystem status. In this study, we developed a capacitive ECG measurement system that can be included within a clothmattress pad. The device permits ECG data to be obtained during sleep by using capacitive electrodes. However,it is difficult to detect R-wave peaks automatically because signals obtained from the system can include a high levelof noise from various sources. Because R-peak detection is important in ECG applications, we developed an algorithmthat can reduce noise and improve detection accuracy under noisy conditions. Algorithm reliability was evaluated bydetermining its sensitivity(Se), positive predictivity(+P), and error rate(Er) by using data from the MIT-BIH PolysomnographicDatabase and from our capacitive ECG system. The results showed that Se = 99.75%, +P = 99.77%,and Er = 0.47% for MIT-BIH Polysomnographic Database while Se = 96.47%, +P = 99.32%, and Er = 4.34% forour capacitive ECG system. Based on those results, we conclude that our R-peak detection method is capable of providinguseful ECG information, even under noisy signal conditions
Abstract
Recent technological advances have increased interest in personal health monitoring. Electrocardiogram(ECG) monitoring is a basic healthcare activity and can provide decisive information regarding cardiovascularsystem status. In this study, we developed a capacitive ECG measurement system that can be included within a clothmattress pad. The device permits ECG data to be obtained during sleep by using capacitive electrodes. However,it is difficult to detect R-wave peaks automatically because signals obtained from the system can include a high levelof noise from various sources. Because R-peak detection is important in ECG applications, we developed an algorithmthat can reduce noise and improve detection accuracy under noisy conditions. Algorithm reliability was evaluated bydetermining its sensitivity(Se), positive predictivity(+P), and error rate(Er) by using data from the MIT-BIH PolysomnographicDatabase and from our capacitive ECG system. The results showed that Se = 99.75%, +P = 99.77%,and Er = 0.47% for MIT-BIH Polysomnographic Database while Se = 96.47%, +P = 99.32%, and Er = 4.34% forour capacitive ECG system. Based on those results, we conclude that our R-peak detection method is capable of providinguseful ECG information, even under noisy signal conditions
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