proposal of Prediction method of ECG via 1D-CNN

Abstract

Recently, the ECG (Electro Cardio Gram) has attracted to estimate human condition, such as fatigue and stress. Measuring the human biological data for a long time it takes physical and mental load. In this paper, we propose a method to predict long-term ECG data based on short-term data. The typical methods such as LSTM are generally used to predict time series data. The target ECG is characterized by fluctuation in period and voltage, and it is required to predict fluctuating data. So, we evaluated a 1 Dimension Convolution Neural Networks method using a filter size that matches the frequency characteristic of ECG. We showed that the method can be predicted more accurately than the LSTM. This result suggests that it can be an effective means when predicting long-term data.