ECG_SegNet: An ECG delineation model based on the encoder-decoder structure

With the increasing usage of wearable electrocardiogram (ECG) 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...

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Veröffentlicht in:Computers in biology and medicine Jg. 145; S. 105445
Hauptverfasser: Liang, Xiaohong, Li, Liping, Liu, Yuanyuan, Chen, Dan, Wang, Xinpei, Hu, Shunbo, Wang, Jikuo, Zhang, Huan, Sun, Chengfa, Liu, Changchun
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States Elsevier Ltd 01.06.2022
Elsevier Limited
Schlagworte:
Algorithms
Arrhythmias, Cardiac
Bidirectional long short-term memory (BiLSTM)
Cardiology
Classification
Coders
Coronary artery disease
Decoding
Deep learning
Delineation
ECG delineation
EKG
Electrocardiogram (ECG)
Electrocardiography
Electrocardiography - methods
Encoder-decoder structure
Encoders-Decoders
Feature extraction
Heart diseases
Humans
Internal Medicine
Long short-term memory
Noise
Other
P waves
Real time
Reproducibility of Results
Semantics
Signal processing
Signal Processing, Computer-Assisted
Temporal variations
Waveforms
Wavelet transforms
ECG delineation
Bidirectional long short-term memory (BiLSTM)
Electrocardiogram (ECG)
Encoder-decoder structure
ISSN:0010-4825, 1879-0534, 1879-0534
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Zusammenfassung:With the increasing usage of wearable electrocardiogram (ECG) 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 (NW), as well as the onset and offset of these waveforms. First, the introduction of a standard dilated convolution module (SDCM) into the encoder path enabled the model to extract more useful ECG signal-informative features. Subsequently, bidirectional long short-term memory (BiLSTM) 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. •A model ECG_SegNet based on the encoder-decoder structure is proposed for ECG delineation.•The SDCM and the BiLSTM into the encoder part can extract additional contributing features to ECG delineation.•The ECG_SegNet can effectively restore the original ECG signal coarse-to-fine information by using multi-scale decoding.•The proposed model achieves better performance than other state-of-the-art methods.
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ISSN:0010-4825
1879-0534
1879-0534
DOI:10.1016/j.compbiomed.2022.105445