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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Abstract	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.
AbstractList	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. 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.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. 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. AbstractWith 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.
ArticleNumber	105445
Author	Hu, Shunbo Wang, Xinpei Wang, Jikuo Zhang, Huan Sun, Chengfa Li, Liping Chen, Dan Liu, Changchun Liu, Yuanyuan Liang, Xiaohong
Author_xml	– sequence: 1 givenname: Xiaohong surname: Liang fullname: Liang, Xiaohong organization: School of Control Science and Engineering, Shandong University, Jinan, Shandong, 250061, China – sequence: 2 givenname: Liping surname: Li fullname: Li, Liping organization: College of Intelligence and Information Engineering, Shandong University of Traditional Chinese Medicine, Jinan, Shandong, 250355, China – sequence: 3 givenname: Yuanyuan surname: Liu fullname: Liu, Yuanyuan email: liuyy@sdu.edu.cn organization: School of Control Science and Engineering, Shandong University, Jinan, Shandong, 250061, China – sequence: 4 givenname: Dan surname: Chen fullname: Chen, Dan organization: Department of Cardiology Electrocardiogram Room, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, 250021, China – sequence: 5 givenname: Xinpei surname: Wang fullname: Wang, Xinpei organization: School of Control Science and Engineering, Shandong University, Jinan, Shandong, 250061, China – sequence: 6 givenname: Shunbo orcidid: 0000-0002-1442-0976 surname: Hu fullname: Hu, Shunbo organization: School of Information Science and Engineering, Linyi University, Linyi, Shandong, 276005, China – sequence: 7 givenname: Jikuo surname: Wang fullname: Wang, Jikuo organization: School of Control Science and Engineering, Shandong University, Jinan, Shandong, 250061, China – sequence: 8 givenname: Huan surname: Zhang fullname: Zhang, Huan organization: School of Control Science and Engineering, Shandong University, Jinan, Shandong, 250061, China – sequence: 9 givenname: Chengfa surname: Sun fullname: Sun, Chengfa organization: School of Control Science and Engineering, Shandong University, Jinan, Shandong, 250061, China – sequence: 10 givenname: Changchun surname: Liu fullname: Liu, Changchun organization: School of Control Science and Engineering, Shandong University, Jinan, Shandong, 250061, China
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Keywords	ECG delineation Bidirectional long short-term memory (BiLSTM) Electrocardiogram (ECG) Encoder-decoder structure
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Snippet	With the increasing usage of wearable electrocardiogram (ECG) monitoring devices, it is necessary to develop models and algorithms that can analyze the large... AbstractWith the increasing usage of wearable electrocardiogram (ECG) monitoring devices, it is necessary to develop models and algorithms that can analyze the...
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SubjectTerms	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
Title	ECG_SegNet: An ECG delineation model based on the encoder-decoder structure
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