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.
Highlights
Heart diseases are the most important cause of human mortality globally
The proposed Improved Bat algorithm (IBA) is compared against other three myocardial infarction (MI) detection algorithms as shown in the Table 7 such as Cross Wavelet Transform (XWT), Multiple Instance Learning (MIL), Morphological features and SVM in terms of related features selected from the original database and classification accuracy obtained from different classifiers using Matlab software
In our approach IBA features for each beat(MI, normal) are extracted and the results shows that accuracy for the detection of MI has increased
Summary
Heart diseases are the most important cause of human mortality globally. 9.4 million deaths are attributed to heart diseases. This includes 51 % of deaths due to strokes and 45 % of deaths due to coronary heart diseases. Most of the cardiac diseases are caused due to the risk factors such as unhealthy diet, high blood pressure, tobacco usage, obesity, diabetes and physical inactivity. Abnormal cardiac beat identification is a crucial step in the detection of heart diseases. Electrical impulses within the heart muscle stimulate the heart to contract or beat. Our present study describes a procedure for the detection of ECG patterns with MI. There are two types of MI in general. This study focuses on the detection of Type 1 MI
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