Inter-patient ECG characteristic wave detection based on convolutional neural network combined with transformer. (March 2023)

• Record Type: Journal Article
• Authors: Wang, Duoduo; Qiu, Lishen; Zhu, Wenliang; Dong, Yanfang; Zhang, Huimin; Chen, Yuhang; Wang, lirong
• Is Part Of: Biomedical signal processing and control. Volume 81(2023)
• Publication Date: 2023-03
• Subjects: ECG -- CNN -- Transformer -- Characteristic wave delineation -- ECT-net -- Deep learning; 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.2022.104436
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: An end-to-end method based on convolutional neural network and transformer (ECT-net) is proposed to detect ECG characteristic waveform under the inter-patient pattern. Transformer is applied to detect ECG characteristic waveform for the first time. The proposed method shows excellent performance in different pathological ECG datasets. Abstract: Objective: The electrocardiogram (ECG) characteristic waveforms include P wave, QRS wave, and T wave. The detection of ECG characteristic waveforms is particularly important in automatic heart disease diagnosis. Although researchers have proposed many detection methods, the detection under the inter-patient pattern is still a challenge. Method: This paper proposed an end-to-end ECG waveform detection method (ECT-net) based on convolutional neural network (CNN) and transformer. We constructed the feature extractor by a group of convolutional layers to extract the local information of ECG signals and generate the input embedding of the transformer encoder. Meanwhile, we adopted the transformer encoder to extract the long-term time-dependent representation between heartbeats, which can make up for the limitation of the CNN in obtaining global information. This structure ensures that both spatial and temporal features from ECG signals are achieved. For precise detection, we employed a corresponding decoder and skip structure to integrate low-level features and high-level features. The model was trained and verified on the China Physiological Signal Challenge 2018 database (CPSC-DB). Result: Our method performed best among comparison methods and achieved F1 scores of 94.27% on the P wave, 97.32% on the QRS wave, and 93.92% on the T wave. The ablation result shows that the transformer encoder is beneficial for detecting each characteristic waveform. To assess the generalization ability of the method, it was also evaluated on inter-patient datasets with different types of cardiac diseases, and experimental results demonstrated that the proposed method is effective for all of them. Significance: The proposed method applies the transformer encoder to the detection of ECG characteristic waveforms for the first time and achieves competitive performance.