| Title |
Detection of Arrhythmia using 1D Convolution Neural Network with LSTM Model |
| Authors |
(Seungwoo Han) ; (Wongyu Lee) ; (Heesang Eom) ; (Juhyeong Kim) ; (Cheolsoo Park) |
| DOI |
https://doi.org/10.5573/IEIESPC.2020.9.4.261 |
| Keywords |
Arrhythmia; Heartbeat detection; Deep learning |
| Abstract |
Considering the high death rate from cardiovascular diseases, it is important to detect an irregular heart rhythm in order to prevent potential tragedy. The purpose of this paper is to present automatic detection of arrhythmia based on electrocardiography. We suggest a one-dimensional convolutional neural network (1D CNN) with long short-term memory (LSTM). The suggested architecture is compared with two other deep learning methods: the 1D CNN and the multi-layer perceptron (MLP) model. To evaluate performance, we measured the overall accuracy, macro-averaged precision, and macro-averaged recall of our proposed method as being 92.03%, 90.98%, and 86.15%, respectively. The results demonstrate that the 1D CNN-with-LSTM model outperforms the two other models. |