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Improved Bat algorithm for the detection of myocardial infarction.

Padmavathi Kora¹, Sri Ramakrishna Kalva²

¹Department of ECE, GRIET, Bachupally, 500090 Hyderabad, India.

|November 12, 2015

Summary

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.

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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.

Keywords:

ECG Improved Bat algorithm Myocardial infarction Neural network classifier

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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.