Presentation information

Oral presentation

General Session » [General Session] 3. Data Mining

[2H2] [General Session] 3. Data Mining

Wed. Jun 6, 2018 1:20 PM - 3:00 PM Room H (10F Sky Hall)

座長:檜山 敦(東京大学)

2:20 PM - 2:40 PM

[2H2-04] Extracting Long-term Patterns from Electrocardiogram Waveform using Deep Learning

〇Noriyasu Omata1, Yoshihiro Nakamura2, Susumu Shirayama1 (1. School of Engineering, The University of Tokyo, 2. Faculty of Engineering, The University of Tokyo)

Keywords:time-series analysis, pattern extraction, visualization

Automatic analysis of electrocardiograms (ECG) has been attempted. Although almost all methods pay attention only to short-term waveforms, their changes over time are said often important in diagnosis. In this paper, we focus on such long-term waveform change and propose a method to extract it as a pattern. The proposed method combines a method of expressing time series data as a trajectory in a feature space and feature extraction by an autoencoder. Evaluation experiments suggested the existence of regularity in the pattern and its association with the disease.