A PHONOCARDIOGRAM-BASED NOISE-ROBUST REAL-TIME HEART RATE MONITORING ALGORITHM FOR OUTPATIENTS DURING NORMAL ACTIVITIESA phonocardiogram-based noise-robust real-time heart rate monitoring algorithm for outpatients during normal activities
- Other Titles
- A phonocardiogram-based noise-robust real-time heart rate monitoring algorithm for outpatients during normal activities
- Authors
- Nam, Kyoung Won; Ahn, Ji Min; Hwang, Young Jun; Jeon, Gye Rok; Jang, Dong Pyo; Kim, In Young
- Issue Date
- Aug-2018
- Publisher
- WORLD SCIENTIFIC PUBL CO PTE LTD
- Keywords
- Heart rate monitoring; phonocardiogram; cardiovascular disease; biosignal processing; wearable
- Citation
- JOURNAL OF MECHANICS IN MEDICINE AND BIOLOGY, v.18, no.5
- Indexed
- SCIE
SCOPUS
- Journal Title
- JOURNAL OF MECHANICS IN MEDICINE AND BIOLOGY
- Volume
- 18
- Number
- 5
- URI
- https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/149558
- DOI
- 10.1142/S0219519418500446
- ISSN
- 0219-5194
- Abstract
- For outpatients who need continuous monitoring of heart rate (HR) variation, it is important that HR can be monitored during normal activities such as speaking and walking. In this study, a noise-robust real-time HR monitoring algorithm based on phonocardiogram (PCG) signals is proposed. PCG signals were recorded using an electronic stethoscope; electrocardiogram (ECG) signals were recorded simultaneously with HR references. The proposed algorithm consisted of pre-processing, peak/nonpeak classification, voice noise processing, walking noise processing, and HR calculation. The performance of the algorithm was evaluated using PCG/ECG signals from 11 healthy participants. For comparison, the absolute errors between manually extracted ECG-based HR values and automatically calculated PCG-based HR values were calculated for the proposed algorithm and the comparison algorithm in two different test protocols. Experimental results showed that the average absolute errors of the proposed algorithm were 72.03%, 22.92%, and 36.39% of the values of the comparison algorithm for resting-state, speaking-state, and walking-state data, respectively, in protocol-1. In protocol-2, the average absolute error was 36.99% of that of the comparison algorithm. A total of 1102 cases in protocol-1 and 783 in protocol-2 had an absolute error > 5 beats per minute (BPM) using the comparison algorithm and an absolute error < 5 BPM using the proposed algorithm. On the basis of these results, we anticipate that the proposed algorithm can potentially improve the performance of continuous real-time HR monitoring during activities of normal life, thereby improving the safety of outpatients with cardiovascular diseases.
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