Self‐Attention‐Based Deep Convolutional Sparse Dense Autoencoder Model With Chebyshev‐Based Osprey Algorithm for Cardiovascular Disease Detection
ABSTRACT Cardiovascular disease (CVD) refers to disorders affecting the heart and blood arteries. Automated screening methods can be used to identify CVDs, the leading cause of death worldwide. Electrocardiogram (ECG)‐based techniques are widely utilized to detect CVDs since they are both noninvasiv...
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| Veröffentlicht in: | Transactions on emerging telecommunications technologies Jg. 36; H. 9 |
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John Wiley & Sons, Ltd
01.09.2025
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| Abstract | ABSTRACT
Cardiovascular disease (CVD) refers to disorders affecting the heart and blood arteries. Automated screening methods can be used to identify CVDs, the leading cause of death worldwide. Electrocardiogram (ECG)‐based techniques are widely utilized to detect CVDs since they are both noninvasive and efficient. This paper presents a deep convolutional neural network (CNN) for categorizing five CVDs using conventional 12‐lead ECG data. The proposed approach comprises three steps: preprocessing, feature extraction, and classification. Initially, input signals are collected from a publicly available dataset; then, pre‐processing is performed using a windowed infinite impulse response notch filter (W‐I2RNF) to remove unwanted noise. The appropriate features are extracted from the preprocessed signals using the mel frequency cepstrum coefficient (MFCC) and modified discrete wavelet transform (Mod‐DWT). CVDs are detected and classified based on the retrieved features using a new self‐attention‐based deep convolutional sparse dense autoencoder (SA_DC_SDAE) model. A deep CNN is combined with a sparse dense autoencoder (AE) technique to enable classification tasks. Also, the parameters of the proposed deep learning model are optimized using the Chebyshev‐based Osprey Algorithm (C‐OA). Therefore, the proposed model efficiently classifies CVDs with an accuracy range of 98.75%, sensitivity of 97.9%, precision of 95%, F1 score of 96%, and specificity of 99% for the PTB‐XL dataset. The proposed model outperforms the state‐of‐the‐art models in terms of performance.
To reduce the unwanted noises and thereby enhance the input signal's quality via the windowed infinite impulse response notch filter (W‐I2RNF). To enhance the classification accuracy and reduce the complexity issues, needed features are extracted using the mel frequency cepstrum coefficient (MFCC) and modified discrete wavelet transform (Mod‐DWT) methods. To perform disease classification, a self‐attention based deep convolutional sparse dense autoencoder (SA_DC_SDAE) model is proposed. |
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| AbstractList | Cardiovascular disease (CVD) refers to disorders affecting the heart and blood arteries. Automated screening methods can be used to identify CVDs, the leading cause of death worldwide. Electrocardiogram (ECG)‐based techniques are widely utilized to detect CVDs since they are both noninvasive and efficient. This paper presents a deep convolutional neural network (CNN) for categorizing five CVDs using conventional 12‐lead ECG data. The proposed approach comprises three steps: preprocessing, feature extraction, and classification. Initially, input signals are collected from a publicly available dataset; then, pre‐processing is performed using a windowed infinite impulse response notch filter (W‐I 2 RNF) to remove unwanted noise. The appropriate features are extracted from the preprocessed signals using the mel frequency cepstrum coefficient (MFCC) and modified discrete wavelet transform (Mod‐DWT). CVDs are detected and classified based on the retrieved features using a new self‐attention‐based deep convolutional sparse dense autoencoder (SA_DC_SDAE) model. A deep CNN is combined with a sparse dense autoencoder (AE) technique to enable classification tasks. Also, the parameters of the proposed deep learning model are optimized using the Chebyshev‐based Osprey Algorithm (C‐OA). Therefore, the proposed model efficiently classifies CVDs with an accuracy range of 98.75%, sensitivity of 97.9%, precision of 95%, F 1 score of 96%, and specificity of 99% for the PTB‐XL dataset. The proposed model outperforms the state‐of‐the‐art models in terms of performance. ABSTRACT Cardiovascular disease (CVD) refers to disorders affecting the heart and blood arteries. Automated screening methods can be used to identify CVDs, the leading cause of death worldwide. Electrocardiogram (ECG)‐based techniques are widely utilized to detect CVDs since they are both noninvasive and efficient. This paper presents a deep convolutional neural network (CNN) for categorizing five CVDs using conventional 12‐lead ECG data. The proposed approach comprises three steps: preprocessing, feature extraction, and classification. Initially, input signals are collected from a publicly available dataset; then, pre‐processing is performed using a windowed infinite impulse response notch filter (W‐I2RNF) to remove unwanted noise. The appropriate features are extracted from the preprocessed signals using the mel frequency cepstrum coefficient (MFCC) and modified discrete wavelet transform (Mod‐DWT). CVDs are detected and classified based on the retrieved features using a new self‐attention‐based deep convolutional sparse dense autoencoder (SA_DC_SDAE) model. A deep CNN is combined with a sparse dense autoencoder (AE) technique to enable classification tasks. Also, the parameters of the proposed deep learning model are optimized using the Chebyshev‐based Osprey Algorithm (C‐OA). Therefore, the proposed model efficiently classifies CVDs with an accuracy range of 98.75%, sensitivity of 97.9%, precision of 95%, F1 score of 96%, and specificity of 99% for the PTB‐XL dataset. The proposed model outperforms the state‐of‐the‐art models in terms of performance. To reduce the unwanted noises and thereby enhance the input signal's quality via the windowed infinite impulse response notch filter (W‐I2RNF). To enhance the classification accuracy and reduce the complexity issues, needed features are extracted using the mel frequency cepstrum coefficient (MFCC) and modified discrete wavelet transform (Mod‐DWT) methods. To perform disease classification, a self‐attention based deep convolutional sparse dense autoencoder (SA_DC_SDAE) model is proposed. |
| Author | Divya, N. J. Kumar, N. Suresh Devi, R. Kanniga |
| Author_xml | – sequence: 1 givenname: N. J. orcidid: 0000-0002-8383-8866 surname: Divya fullname: Divya, N. J. email: divya.ssmcse@gmail.com organization: SSM Institute of Engineering and Technology – sequence: 2 givenname: N. Suresh orcidid: 0000-0001-9484-4965 surname: Kumar fullname: Kumar, N. Suresh organization: School of Computing, Kalasalingam Academy of Research and Education – sequence: 3 givenname: R. Kanniga orcidid: 0000-0001-6288-5607 surname: Devi fullname: Devi, R. Kanniga organization: School of Computer Science and Engineering, Vellore Institute of Technology |
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| Cites_doi | 10.1016/S0140-6736(21)00684-X 10.1093/eurheartj/ehab892 10.2991/978-94-6463-496-9_17 10.1038/s41569-020-00503-2 10.1007/s00521-021-06399-4 10.1016/j.snb.2020.129336 10.1007/s42452-021-04185-4 10.3390/math11194107 10.1016/j.bspc.2021.102968 10.1161/CIRCULATIONAHA.120.050686 10.1038/s41598-024-72382-3 10.1016/j.jacasi.2021.04.007 10.1109/TCSS.2022.3151643 10.1016/j.ygeno.2020.10.024 10.3390/e23091121 10.1016/j.cmpb.2021.106035 10.1016/j.compbiomed.2021.104457 10.1038/s41598-022-15374-5 10.1007/s00521-020-05542-x 10.1007/s12553-021-00552-8 10.1007/s13369-020-05105-1 10.1016/j.metabol.2021.154766 10.1007/s12559-021-09914-w 10.1016/j.icte.2021.08.021 10.1016/j.cmpb.2021.105940 10.1109/ACCESS.2021.3066365 10.1093/europace/euab021 10.1109/ACCESS.2020.2979256 10.3390/electronics11152292 10.1007/s00521-022-07064-0 10.32604/csse.2022.021698 10.52810/TIOT.2021.100035 10.3390/s21030951 10.1111/coin.12405 10.1038/s41746-023-00869-w 10.1109/JIOT.2021.3053420 10.3390/s22072431 10.1016/j.neucom.2019.10.007 10.1109/JIOT.2023.3240536 10.1038/s41569-021-00607-3 10.1002/hsr2.1802 10.1201/9781003144694-7 |
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| Snippet | ABSTRACT
Cardiovascular disease (CVD) refers to disorders affecting the heart and blood arteries. Automated screening methods can be used to identify CVDs, the... Cardiovascular disease (CVD) refers to disorders affecting the heart and blood arteries. Automated screening methods can be used to identify CVDs, the leading... |
| SourceID | crossref wiley |
| SourceType | Index Database Publisher |
| SubjectTerms | artificial intelligence (AI) cardiovascular disease (CVD) convolutional neural network (CNN) coronary heart disease (CHD) World Health Organization (WHO) |
| Title | Self‐Attention‐Based Deep Convolutional Sparse Dense Autoencoder Model With Chebyshev‐Based Osprey Algorithm for Cardiovascular Disease Detection |
| URI | https://onlinelibrary.wiley.com/doi/abs/10.1002%2Fett.70229 |
| Volume | 36 |
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