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2020 | OriginalPaper | Chapter

To Determinate PEP and LVET Through Analyzing LPC of Heart Sounds

Authors : Jin-Hao Ou, Ming-Hao Yang, Ming-Hsien Yu, Wen-Chien Chen

Published in: Future Trends in Biomedical and Health Informatics and Cybersecurity in Medical Devices

Publisher: Springer International Publishing

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Abstract

To determine the pre-ejection period (PEP) and the left ventricular ejection time (LVET) thorough heart sounds and ECG are major tasks in this paper. The first step was to determine the event time of PEP, which detected the feature point of the first heart sound (S1) of PCG about 0.02–0.07 s after the R-peak of the ECG, and then obtained the prominent peaks of PCG during changes of signal slope with drastic change of peak value. The second step was taking R-peak to define a period of sound signal, making LPC and FFT, and moving certain few points to make another section, which was repeated to find out changes from the coefficient and frequency. Taking the sections with changes occurred, and FFT results as references got the time of PEP and LVET. Comparing the proposed results to the annotations, the average error of PEP detection is approximately 36.4 ms, and that of LVET is approximately 6.95 ms. With the error of the PEP, 83.4% and 33.9% accuracy are achieved within the time of 40 ms and 20 ms. With the error of the LVET, 94.2% and 25.8% accuracy are achieved at the time of 80 ms and 60 ms. The advantage of this method is that although the signals are very small, finding out the peak value of PCG, and analyzing the PEP and LVET are not hard tasks. Another advantage is that if the noise of the signal is not big enough to affect the original characteristic, the ECG signal could be applied to find out the peak value perfectly, and could be cut into piece by piece. The “NaN” point is hard to be defined in the testing data. Some issues are required to be improved or verified by other methods.

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Metadata
Title
To Determinate PEP and LVET Through Analyzing LPC of Heart Sounds
Authors
Jin-Hao Ou
Ming-Hao Yang
Ming-Hsien Yu
Wen-Chien Chen
Copyright Year
2020
DOI
https://doi.org/10.1007/978-3-030-30636-6_51