DAE-ConvBiLSTM: End-to-end learning single-lead electrocardiogram signal for heart abnormalities detection.
<h4>Background</h4>The electrocardiogram (ECG) is a widely used diagnostic that observes the heart activities of patients to ascertain a heart abnormality diagnosis. The artifacts or noises are primarily associated with the problem of ECG signal processing. Conventional denoising techniq...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2022-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0277932 |
| _version_ | 1797956139133960192 |
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| author | Bambang Tutuko Annisa Darmawahyuni Siti Nurmaini Alexander Edo Tondas Muhammad Naufal Rachmatullah Samuel Benedict Putra Teguh Firdaus Firdaus Ade Iriani Sapitri Rossi Passarella |
| author_facet | Bambang Tutuko Annisa Darmawahyuni Siti Nurmaini Alexander Edo Tondas Muhammad Naufal Rachmatullah Samuel Benedict Putra Teguh Firdaus Firdaus Ade Iriani Sapitri Rossi Passarella |
| author_sort | Bambang Tutuko |
| collection | DOAJ |
| description | <h4>Background</h4>The electrocardiogram (ECG) is a widely used diagnostic that observes the heart activities of patients to ascertain a heart abnormality diagnosis. The artifacts or noises are primarily associated with the problem of ECG signal processing. Conventional denoising techniques have been proposed in previous literature; however, some lacks, such as the determination of suitable wavelet basis function and threshold, can be a time-consuming process. This paper presents end-to-end learning using a denoising auto-encoder (DAE) for denoising algorithms and convolutional-bidirectional long short-term memory (ConvBiLSTM) for ECG delineation to classify ECG waveforms in terms of the PQRST-wave and isoelectric lines. The denoising reconstruction using unsupervised learning based on the encoder-decoder process can be proposed to improve the drawbacks. First, The ECG signals are reduced to a low-dimensional vector in the encoder. Second, the decoder reconstructed the signals. The last, the reconstructed signals of ECG can be processed to ConvBiLSTM. The proposed architecture of DAE-ConvBiLSTM is the end-to-end diagnosis of heart abnormality detection.<h4>Results</h4>As a result, the performance of DAE-ConvBiLSTM has obtained an average of above 98.59% accuracy, sensitivity, specificity, precision, and F1 score from the existing studies. The DAE-ConvBiLSTM has also experimented with detecting T-wave (due to ventricular repolarisation) morphology abnormalities.<h4>Conclusion</h4>The development architecture for detecting heart abnormalities using an unsupervised learning DAE and supervised learning ConvBiLSTM can be proposed for an end-to-end learning algorithm. In the future, the precise accuracy of the ECG main waveform will affect heart abnormalities detection in clinical practice. |
| first_indexed | 2024-04-10T23:44:12Z |
| format | Article |
| id | doaj.art-eaba3f9f9834414a9bb8162c4e6b42fc |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-04-10T23:44:12Z |
| publishDate | 2022-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-eaba3f9f9834414a9bb8162c4e6b42fc2023-01-11T05:32:18ZengPublic Library of Science (PLoS)PLoS ONE1932-62032022-01-011712e027793210.1371/journal.pone.0277932DAE-ConvBiLSTM: End-to-end learning single-lead electrocardiogram signal for heart abnormalities detection.Bambang TutukoAnnisa DarmawahyuniSiti NurmainiAlexander Edo TondasMuhammad Naufal RachmatullahSamuel Benedict Putra TeguhFirdaus FirdausAde Iriani SapitriRossi Passarella<h4>Background</h4>The electrocardiogram (ECG) is a widely used diagnostic that observes the heart activities of patients to ascertain a heart abnormality diagnosis. The artifacts or noises are primarily associated with the problem of ECG signal processing. Conventional denoising techniques have been proposed in previous literature; however, some lacks, such as the determination of suitable wavelet basis function and threshold, can be a time-consuming process. This paper presents end-to-end learning using a denoising auto-encoder (DAE) for denoising algorithms and convolutional-bidirectional long short-term memory (ConvBiLSTM) for ECG delineation to classify ECG waveforms in terms of the PQRST-wave and isoelectric lines. The denoising reconstruction using unsupervised learning based on the encoder-decoder process can be proposed to improve the drawbacks. First, The ECG signals are reduced to a low-dimensional vector in the encoder. Second, the decoder reconstructed the signals. The last, the reconstructed signals of ECG can be processed to ConvBiLSTM. The proposed architecture of DAE-ConvBiLSTM is the end-to-end diagnosis of heart abnormality detection.<h4>Results</h4>As a result, the performance of DAE-ConvBiLSTM has obtained an average of above 98.59% accuracy, sensitivity, specificity, precision, and F1 score from the existing studies. The DAE-ConvBiLSTM has also experimented with detecting T-wave (due to ventricular repolarisation) morphology abnormalities.<h4>Conclusion</h4>The development architecture for detecting heart abnormalities using an unsupervised learning DAE and supervised learning ConvBiLSTM can be proposed for an end-to-end learning algorithm. In the future, the precise accuracy of the ECG main waveform will affect heart abnormalities detection in clinical practice.https://doi.org/10.1371/journal.pone.0277932 |
| spellingShingle | Bambang Tutuko Annisa Darmawahyuni Siti Nurmaini Alexander Edo Tondas Muhammad Naufal Rachmatullah Samuel Benedict Putra Teguh Firdaus Firdaus Ade Iriani Sapitri Rossi Passarella DAE-ConvBiLSTM: End-to-end learning single-lead electrocardiogram signal for heart abnormalities detection. PLoS ONE |
| title | DAE-ConvBiLSTM: End-to-end learning single-lead electrocardiogram signal for heart abnormalities detection. |
| title_full | DAE-ConvBiLSTM: End-to-end learning single-lead electrocardiogram signal for heart abnormalities detection. |
| title_fullStr | DAE-ConvBiLSTM: End-to-end learning single-lead electrocardiogram signal for heart abnormalities detection. |
| title_full_unstemmed | DAE-ConvBiLSTM: End-to-end learning single-lead electrocardiogram signal for heart abnormalities detection. |
| title_short | DAE-ConvBiLSTM: End-to-end learning single-lead electrocardiogram signal for heart abnormalities detection. |
| title_sort | dae convbilstm end to end learning single lead electrocardiogram signal for heart abnormalities detection |
| url | https://doi.org/10.1371/journal.pone.0277932 |
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