[2N3-J-13] AI application: medical diagnosis

[2N3-J-13-01] Denoising autoencoder-based modification method for RRI data with premature atrial contraction

〇Shota Miyatani¹, Koichi Fujiwara², Manabu Kano¹ (1. Kyoto University, 2. Nagoya University)

Keywords:heart rate variability, arrhythmia, wearable devices, neural network, autoencoder

The fluctuation of an RR interval (RRI) on an electrocardiogram (ECG) is called heart rate variability (HRV). Since HRV reflects the activities of the autonomous nervous system, HRV analysis has been used for health monitoring systems. However, the performance of health monitoring systems using HRV features is easily deteriorated by arrhythmias. The present work focuses on premature atrial contraction (PAC) that many healthy people have. To modify RRI data with PAC, the present work proposes a new method based on a denoising autoencoder (DAE), referred to as DAE-based RRI modification (DAE-RM). The proposed method aims to correct the disturbed RRI data by regarding PAC as artifacts. The performance of DAE-RM was evaluated by its application to RRI data which contains artificial PAC (PAC-RRI). The result showed that DAE-RM successfully modified PAC-RRI data. The root means squared error (RMSE) of the modified RRI was improved by 27.4% from the PAC-RRI. The proposed DAE-RM has a potential for realizing precise health monitoring systems which use HRV analysis.