Key result
A deep learning model using DWT filtering and CNN feature extraction for ECG-based heart disease classification achieved 96.23% accuracy, 98.23% sensitivity, and 94.75% F-measure.
Why the study?
Nearly 50 million people worldwide have cardiac problems, creating a need for advanced deep learning studies to overcome the limitations of outdated optimization methods in ECG signal-based heart problem detection.
Do deep learning techniques improve the classification and detection of prior heart attacks from ECG signals?
Do deep learning techniques improve the classification and detection of prior heart attacks from ECG signals?
Answers are grounded in this paper and cite their sources.
Deep learning techniques, specifically using CNNs and DWT filters, demonstrate high accuracy and sensitivity in detecting prior heart attacks from ECG signals.
Deep learning technology has performed an advanced ECG signal-based heart monitor data. In this research prior heart attacks detection model’s analysis has been performed. Nearly 50 million people worldwide have cardiac problems, and 200,000 are detected annually. Therefore, an advanced study is required to handle future ECG signal-based heart problem detection. At the initial stage, pre-processing can be performed through the DWT filter. The second stage consists of CNN feature extraction, and the final step involves the classification of heart disease on an ECG graph. The ECG consists of alpha, beta, gamma, and delta waves in which all amplitudes notice and identify spikes. The performance measures like sensitivity 98.23%, accuracy 96.23%, Recall 97.34%, and F measure 94.75% have been attained. Atom Search optimization (ASO), Gray Wolf optimizer (GWO), RFO, and many more methods are outdated. Therefore, advanced deep learning technology is necessary to cross over the limitations of the present study. The proposed model attains more improvement and competes with current technology.
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Vadlamudi et al. (2023) conducted a review in Heart disease / prior heart attacks. Deep learning model (DWT filter and CNN feature extraction) vs. Outdated optimization methods (ASO, GWO, RFO) was evaluated on Heart disease classification performance. A deep learning model using DWT filtering and CNN feature extraction for ECG-based heart disease classification achieved 96.23% accuracy, 98.23% sensitivity, and 94.75% F-measure.
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