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

Koichi Fujiwara,Miho Miyajima,Asuka Goda,Manabu Kano,Tetsuo Sasano,Shota Miyatani

doi:10.3390/s21093235

Abstract

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.

Highlights

The autonomous nerve system (ANS) regulates various physiological functions, such as circulation, respiration, digestion, sweating, thermoregulation, and metabolism, and is associated with various types of diseases [1]
To realize precise heart rate variability (HRV) analysis, we propose a new framework of nonhazardous extrasystole treatment utilizing an autoencoder (AE) [36] and a denoising autoencoder (DAE) [37], which are types of neural networks
RMSEextrasystole and RMSEmodified are the root mean squared error (RMSE) between the original R-R interval (RRI) data and the artificial extrasystole RRI data, and the RMSE between the artificial extrasystole RRI data and the RRI data modified by the proposed DAE-based extrasystole modification (DAEM)

Summary

Introduction

The autonomous nerve system (ANS) regulates various physiological functions, such as circulation, respiration, digestion, sweating, thermoregulation, and metabolism, and is associated with various types of diseases [1]. Disease diagnosis or symptom detection for clinical purposes in daily life would be possible with the realization of if real-time ANS activity monitoring. A candidate solution is to use the heart rate variability (HRV), which is derived from an electrocardiogram (ECG). An ECG signal consists of peaks, such as the P, T waves and QRS complex, of which the highest peak is an R wave, and the interval between adjacent. R waves is defined as an R-R interval (RRI) (ms). The heart rate variability (HRV) is a phenomenon in which there is fluctuation in the RRI, reflecting the activities of the ANS [2].

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Discussion

Conclusion