Accurate detection of myocardial infarction using non linear features with ECG signals

Interrupted blood flow to regions of the heart causes damage to heart muscles, resulting in myocardial infarction (MI). MI is a major source of death worldwide. Accurate and timely detection of MI facilitates initiation of emergency revascularization in acute MI and early secondary prevention therapy in established MI. In both acute and ambulatory settings, the electrocardiogram (ECG) is a standard data type for diagnosis. ECG abnormalities associated with MI can be subtle, and may escape detection upon clinical reading. Experience and training are required to visually extract salient information present in the ECG signals. This process of characterization is manually intensive, and prone to intra-and inter-observer-variability. The clinical problem can be posed as one of diagnostic classification of MI versus no MI on the ECG, which is amenable to computational solutions. Computer Aided Diagnosis (CAD) systems are designed to be automated, rapid, efficient, and ultimately cost-effective systems that can be employed to detect ECG abnormalities associated with MI. In this work, ECGs from 200 subjects were analyzed (52 normal and 148 MI). The proposed methodology involves pre-processing of signals and subsequent detection of R peaks using the Pan-Tompkins algorithm. Nonlinear features were extracted. The extracted features were ranked based on Student's t-test and input to k-Nearest Neighbor (KNN), Support Vector Machine (SVM), Probabilistic Neural Network (PNN), and Decision Tree (DT) classifiers for distinguishing normal versus MI classes. This method yielded the highest accuracy 97.96%, sensitivity 98.89%, and specificity 93.80% using the SVM classifier.

Automated summary

Interrupted blood flow to the heart causes myocardial infarction (MI), a major cause of death worldwide. Detection of MI using ECG is crucial for emergency revascularization and secondary prevention therapy. A computational approach using SVM classifier achieved high accuracy, sensitivity, and specificity in distinguishing normal and MI classes.