获取途径

作者

Xiaohong Liang Liping Li Yuanyuan Liu Dan Chen Xinpei Wang Shunbo Hu Jikuo Wang Huan Zhang Chengfa Sun Changchun Liu

作者单位

Department of Cardiology Electrocardiogram Room, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, 250021, China School of Control Science and Engineering, Shandong University, Jinan, Shandong, 250061, China School of Information Science and Engineering, Linyi University, Linyi, Shandong, 276005, China College of Intelligence and Information Engineering, Shandong University of Traditional Chinese Medicine, Jinan, Shandong, 250355, China

刊名

Computers in Biology and Medicine

年份

2022

页码

105445

ISSN

0010-4825

关键词

Electrocardiogram (ECG) Encoder-decoder structure ECG delineation Bidirectional long short-term memory (BiLSTM)

摘要

With the increasing usage of wearable electrocardiogram monitoring devices, it is necessary to develop models and algorithms that can analyze the large amounts of ECG data obtained in real-time. Accurate ECG delineation is key to assisting cardiologists in diagnosing cardiac diseases. The main objective of this study is to design a delineation model based on the encoder-decoder structure to detect different heartbeat waveforms, including P-waves, QRS complexes, T-waves, and No waves , as well a...更多

With the increasing usage of wearable electrocardiogram monitoring devices, it is necessary to develop models and algorithms that can analyze the large amounts of ECG data obtained in real-time. Accurate ECG delineation is key to assisting cardiologists in diagnosing cardiac diseases. The main objective of this study is to design a delineation model based on the encoder-decoder structure to detect different heartbeat waveforms, including P-waves, QRS complexes, T-waves, and No waves , as well as the onset and offset of these waveforms. First, the introduction of a standard dilated convolution module into the encoder path enabled the model to extract more useful ECG signal-informative features. Subsequently, bidirectional long short-term memory was added to the encoding structure to obtain numerous temporal features. Moreover, the feature sets of the ECG signals at each level in the encoder path were connected to the decoder part for multi-scale decoding to mitigate the information loss caused by the pooling operation in the encoding process. Finally, the proposed model was trained and tested on both QT and LU databases, and it achieved accurate results compared to other state-of-the-art methods. Regarding the QT database, the average accuracy of ECG waveform classification was 96.90%, and an average classification accuracy of 95.40% was obtained on the LU database. In addition, average F1 values of 99.58% and 97.05% were achieved in the ECG delineation task of the QT and LU databases, respectively. The results show that the proposed ECG_SegNet model has good flexibility and reliability when applied to ECG delineation, and it is a reliable method for analyzing ECG signals in real-time.收起

文献类型

期刊