Non-invasive diagnosis of fetal arrhythmia based on multi-domain feature and hierarchical extreme learning machine. (January 2023)

• Record Type: Journal Article
• Authors: Liu, Jie; Xu, Huoyao; Wang, Junlang; Peng, Xiangyu; He, Chaoming
• Is Part Of: Biomedical signal processing and control. Volume 79(2023)Part 2
• Publication Date: 2023-01
• Subjects: Non-invasive fetal electrocardiography -- Multi-domain feature -- NCA -- Hierarchical extreme learning machine -- Fetal ARR prediction; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2022.104191
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: A novel deep learning framework based on H-ELM is proposed for fetal arrhythmia diagnosis. A novel multi-domain feature extraction technique is proposed to characterize the fetal arrhythmia. Neighborhood component analysis is adopted to screen sensitive features. Abstract: Heart disease is one of the major causes of affecting the health of newborns. Detecting the presence or potential heart disease of the fetus as soon as possible, and adopting relevant treatment plans in a timely manner, which has a profound impact on doctors and patients. This study aims to develop an accurate screening method with arrhythmia (ARR) to assist physicians to further diagnose whether them have heart disease. Therefore, this paper proposes a multi-domain feature extraction technique and a hierarchical extreme learning machine (H-ELM) network for the prediction of fetal ARR. Firstly, the multi-domain feature extraction technology is used to extract abundant high-dimensional feature for representing the original signal. Secondly, neighborhood component analysis (NCA) is used to screen the sensitive features from the high dimensional feature vectors. Then, the obtained sensitive features are input into stacked extreme learning machine sparse autoencoder (ELM-SAE), which extract high-level fusion features by layer-by-layer unsupervised learning manner. Finally, an original ELM was connected on the end of the ELM-SAE network for the prediction of fetal ARR. The experimental results illustrate that the proposed method can achieve sensitivity of 99.11%, specificity of 93.91%, precision of 93.52%, and accuracy of 96.33%. Furthermore, the proposed method comprehensive performance outperforms the compared models. Therefore, the proposed method can be effectively used for the prediction of fetal ARR, and with the continuous improved the research, it is expected to be considered as an auxiliary diagnostic tool for physicians in the future.