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Published on: January 14, 2014
Improved Bat algorithm for the detection of myocardial infarction
Padmavathi Kora1, Sri Ramakrishna Kalva2
1Department of ECE, GRIET, Bachupally, 500090 Hyderabad, India.
Insights
This study introduces an Improved Bat algorithm for extracting key features from electrocardiogram (ECG) signals to detect myocardial infarction (MI). Optimized features significantly enhance neural network classifier performance for heart disease detection.
Area of Science:
- Cardiology
- Biomedical Engineering
- Signal Processing
Background:
- Electrocardiogram (ECG) analysis is crucial for detecting heart diseases like myocardial infarction (MI).
- MI occurs due to blocked coronary arteries, leading to characteristic changes in ECG signals.
- Effective MI detection relies on accurate preprocessing and feature extraction from ECG data.
Purpose of the Study:
- To present an Improved Bat algorithm for extracting optimal features from cardiac beats.
- To enhance the accuracy of myocardial infarction detection using ECG signals.
- To evaluate the impact of optimized features on neural network classifier performance.
Main Methods:
- ECG signal preprocessing to remove noise using filters.
- Feature extraction from cardiac beats utilizing an Improved Bat algorithm.
- Inputting optimized features into a neural network classifier for MI detection.
Main Results:
- The Improved Bat algorithm successfully extracted key features from ECG signals.
- Reduced feature sets were generated, improving computational efficiency.
- The neural network classifier demonstrated improved performance with optimized features.
Conclusions:
- The Improved Bat algorithm is effective for ECG feature extraction in MI detection.
- Optimized features enhance the diagnostic accuracy of neural network classifiers.
- This method offers a promising approach for automated heart disease detection.
Abstract:
The medical practitioners study the electrical activity of the human heart in order to detect heart diseases from the electrocardiogram (ECG) of the heart patients. A myocardial infarction (MI) or heart attack is a heart disease, that occurs when there is a block (blood clot) in the pathway of one or more coronary blood vessels (arteries) that supply blood to the heart muscle. The abnormalities in the heart can be identified by the changes in the ECG signal. The first step in the detection of MI is Preprocessing of ECGs which removes noise by using filters. Feature extraction is the next key process in detecting the changes in the ECG signals. This paper presents a method for extracting key features from each cardiac beat using Improved Bat algorithm. Using this algorithm best features are extracted, then these best (reduced) features are applied to the input of the neural network classifier. It has been observed that the performance of the classifier is improved with the help of the optimized features.
