Time-frequency transformation integrated with a lightweight convolutional neural network for detection of myocardial infarction.
Autor: | Sheth KA; School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, 600127, India., Upreti C; School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, 600127, India., Prusty MR; Centre for Cyber-Physical Systems, Vellore Institute of Technology, Chennai, 600127, India. manas.ranjan@vit.ac.in., Satapathy SK; Department of Computer Science, Yonsei University, Seodaemun, Seoul, 03722, South Korea., Mishra S; Centre for Advanced Data Science, Vellore Institute of Technology, Chennai, Tamil Nadu, 600127, India., Cho SB; Department of Computer Science, Yonsei University, Seodaemun, Seoul, 03722, South Korea. |
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Jazyk: | angličtina |
Zdroj: | BMC medical imaging [BMC Med Imaging] 2024 Dec 02; Vol. 24 (1), pp. 326. Date of Electronic Publication: 2024 Dec 02. |
DOI: | 10.1186/s12880-024-01502-2 |
Abstrakt: | Myocardial infarction (MI) is a life-threatening medical condition that necessitates both timely and precise diagnosis. The enhancement of automated method to detect MI diseases from Normal patients can play a crucial role in healthcare. This paper presents a novel approach that utilizes the Discrete Wavelet Transform (DWT) for the detection of myocardial signals. The DWT is employed to break down ECG signals into distinct frequency components and subsequently to selectively filter out noise by thresholding the high-frequency details, resulting in denoised ECG signals for myocardial signal detection. These denoised signals are fed into lightweight one-dimensional Convolutional Neural Networks (CNN) for binary classification into Myocardial Infarction (MI) and Normal categories. The paper explores three distinct approaches: utilizing all signals, incorporating under-sampling and up-sampling to address class imbalances, with both noised and denoised signals. Evaluation of the suggested model is done with the help of two publicly available datasets: PTB-XL, a large publicly available electrocardiography dataset and PTB Diagnostic ECG Database. Results obtained through 5-fold cross-validation on the trained model show that the model has achieved an accuracy of 96%, precision of 97% and F1 score of 95%. On cross-validation with the PTB-ECG dataset, this paper achieved an accuracy of 91.18%. Competing Interests: Declarations. Ethics approval and consent to participate: All the authors have gone through the paper before submission. Consent for publication: This work does not contain any new generated data. Clinical trial number: Not applicable. Competing interests: The authors declare no competing interests. (© 2024. The Author(s).) |
Databáze: | MEDLINE |
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