Deep Learning for Detection of Fetal ECG from Multi-Channel Abdominal Leads
Journal
2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2018 - Proceedings
Date Issued
2018
Author(s)
La, Fang-Wen
Abstract
In this paper, we propose to use a CNN-based approach for fetal ECG detection from the abdominal ECG recording. Our work flow contains a pre-processing phase and a classification phase. In the pre-processing phase, abdominal ECG waveform is normalized and segmented. Then, short-time Fourier transform is applied to obtain time-frequency representation. The 2D representation is sent to 2D convolutional neural network for classification. Two convolutional layers, two pooling layers, one fully-connected layer are used. The softmax activation function is used at the output layer to compute the probabilities of four events. The classified results from multiple channels are fused to derive the final detection according to the respective detection accuracies. Compared to the K-nearest neighbor algorithm, the CNN-based classifier has better detection accuracy. © 2018 APSIPA organization.
Event(s)
10th Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2018
Subjects
abdominal ECG
classification
convolutional neural network
Electrocardiogram (ECG)
fetal ECG
Description
Honolulu, 12 November 2018 through 15 November 2018
Type
conference paper