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Article

Autoencoder-Based Extrasystole Detection and Modification of RRI Data for Precise Heart Rate Variability Analysis

1
Department of Material Process Engineering, Nagoya University, Furo-cho, Chikusa-ku, Nagoya, Aichi 464-8601, Japan
2
Department of Systems Science, Kyoto University, Kyoto 606-8501, Japan
3
Department of Liaison Psychiatry and Palliative Medicine, Tokyo Medical and Dental University, Tokyo 113-8510, Japan
*
Author to whom correspondence should be addressed.
Academic Editor: Zongyuan Ge
Sensors 2021, 21(9), 3235; https://doi.org/10.3390/s21093235
Received: 27 February 2021 / Revised: 27 April 2021 / Accepted: 2 May 2021 / Published: 7 May 2021
(This article belongs to the Special Issue Healthcare Monitoring and Management with Artificial Intelligence)
Heart rate variability, which is the fluctuation of the R-R interval (RRI) in electrocardiograms (ECG), has been widely adopted for autonomous evaluation. Since the HRV features that are extracted from RRI data easily fluctuate when arrhythmia occurs, RRI data with arrhythmia need to be modified appropriately before HRV analysis. In this study, we consider two types of extrasystoles—premature ventricular contraction (PVC) and premature atrial contraction (PAC)—which are types of extrasystoles that occur every day, even in healthy persons who have no cardiovascular diseases. A unified framework for ectopic RRI detection and a modification algorithm that utilizes an autoencoder (AE) type of neural network is proposed. The proposed framework consists of extrasystole occurrence detection from the RRI data and modification, whose targets are PVC and PAC. The RRI data are monitored by means of the AE in real time in the detection phase, and a denoising autoencoder (DAE) modifies the ectopic RRI caused by the detected extrasystole. These are referred to as AE-based extrasystole detection (AED) and DAE-based extrasystole modification (DAEM), respectively. The proposed framework was applied to real RRI data with PVC and PAC. The result showed that AED achieved a sensitivity of 93% and a false positive rate of 0.08 times per hour. The root mean squared error of the modified RRI decreased to 31% in PVC and 73% in PAC from the original RRI data by DAEM. In addition, the proposed framework was validated through application to a clinical epileptic seizure problem, which showed that it correctly suppressed the false positives caused by PVC. Thus, the proposed framework can contribute to realizing accurate HRV-based health monitoring and medical sensing systems. View Full-Text
Keywords: heart rate variability analysis; extrasystole; RRI data; machine learning; autoencoder heart rate variability analysis; extrasystole; RRI data; machine learning; autoencoder
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MDPI and ACS Style

Fujiwara, K.; Miyatani, S.; Goda, A.; Miyajima, M.; Sasano, T.; Kano, M. Autoencoder-Based Extrasystole Detection and Modification of RRI Data for Precise Heart Rate Variability Analysis. Sensors 2021, 21, 3235. https://doi.org/10.3390/s21093235

AMA Style

Fujiwara K, Miyatani S, Goda A, Miyajima M, Sasano T, Kano M. Autoencoder-Based Extrasystole Detection and Modification of RRI Data for Precise Heart Rate Variability Analysis. Sensors. 2021; 21(9):3235. https://doi.org/10.3390/s21093235

Chicago/Turabian Style

Fujiwara, Koichi, Shota Miyatani, Asuka Goda, Miho Miyajima, Tetsuo Sasano, and Manabu Kano. 2021. "Autoencoder-Based Extrasystole Detection and Modification of RRI Data for Precise Heart Rate Variability Analysis" Sensors 21, no. 9: 3235. https://doi.org/10.3390/s21093235

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