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MI-CSBO: a hybrid system for myocardial infarction classification using deep learning and Bayesian optimization
Evrim Gül1, Aykut Diker2, Engin Avcı3
1Department of Emergency Medicine, Fırat University, Elazig, Turkey.
Insights
A novel hybrid approach, MI-CSBO, accurately classifies Myocardial Infarction (MI) using ECG spectrograms and Bayesian optimization. This method achieved a 100% correct diagnosis rate, improving heart attack detection.
Area of Science:
- Cardiology
- Biomedical Engineering
- Machine Learning
Background:
- Myocardial Infarction (MI) is heart tissue damage from blocked coronary arteries, often due to atherosclerosis.
- Risk factors include hypertension, diabetes, high cholesterol, and genetic predisposition.
- Early and accurate MI detection and classification are critical for patient outcomes.
Purpose of the Study:
- To introduce a new hybrid approach, MI-CSBO, for classifying Myocardial Infarction using Electrocardiogram (ECG) data.
- To enhance the diagnostic accuracy of MI detection through advanced signal processing and machine learning.
Main Methods:
- ECG signals from the PTB Database were transformed into spectrograms (frequency domain).
- A deep residual Convolutional Neural Network (CNN) was applied to the ECG spectrograms.
- Bayesian optimization, NCA feature selection, and various machine learning algorithms (k-NN, SVM, Tree, Bagged, Naïve Bayes, Ensemble) were employed for classification.
Main Results:
- The MI-CSBO method demonstrated a 100% correct diagnosis rate for Myocardial Infarction.
- The hybrid approach effectively integrated time-frequency analysis with deep learning and Bayesian optimization.
Conclusions:
- The MI-CSBO approach offers a highly accurate and reliable method for MI classification from ECG data.
- This technique holds significant potential for improving the early diagnosis and management of heart attacks.
Abstract:
Myocardial Infarction (MI) refers to damage to the heart tissue caused by an inadequate blood supply to the heart muscle due to a sudden blockage in the coronary arteries. This blockage is often a result of the accumulation of fat (cholesterol) forming plaques (atherosclerosis) in the arteries. Over time, these plaques can crack, leading to the formation of a clot (thrombus), which can block the artery and cause a heart attack. Risk factors for a heart attack include smoking, hypertension, diabetes, high cholesterol, metabolic syndrome, and genetic predisposition. Early diagnosis of MI is crucial. Thus, detecting and classifying MI is essential. This paper introduces a new hybrid approach for MI Classification using Spectrogram and Bayesian Optimization (MI-CSBO) for Electrocardiogram (ECG). First, ECG signals from the PTB Database (PTBDB) were converted from the time domain to the frequency domain using the spectrogram method. Then, a deep residual CNN was applied to the test and train datasets of ECG imaging data. The ECG dataset trained using the Deep Residual model was then acquired. Finally, the Bayesian approach, NCA feature selection, and various machine learning algorithms (k-NN, SVM, Tree, Bagged, Naïve Bayes, Ensemble) were used to derive performance measures. The MI-CSBO method achieved a 100% correct diagnosis rate, as detailed in the Experimental Results section.
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