Abstract
In this work, arrhythmia detection and classification from ECG signals has been performed using a digital signal processor- TMS320C6713. Two of the predominant ECG arrhythmias- premature ventricular contraction (PVC) and atrial fibrillation (AF) have been addressed in this work. In order to distinguish the PVC and AF beats from normal ECG beats, algorithms based on the morphological characteristics of arrhythmias have been applied. The PVC and AF beats present in ECG signals have been classified using correlation-based algorithm, in which a PVC or AF beats are compared/correlated with a normal ECG beat. The correlation coefficient value for normal ECG beats for a particular ECG signal is above 0.9 (highly correlated) whereas for a PVC or AF beats its value is in the range of 0.09 to 0.3 (highly uncorrelated). Another algorithm, based on slope/amplitude, has been implemented for detecting the PVC beats from ECG signals. The slope/ amplitude-based algorithm detects the PVC beats with 98.94% accuracy as compared to 65.20% accuracy by correlation-based algorithm. Thus, slope/amplitude-based algorithm outperforms the correlation-based algorithm as two parameters -the slope of QRS complex and R wave amplitude- are considered for detecting the abnormal beats. This work presents a DSP processor-based system, ideal for use in real time applications, for detecting PVC and AF beats from ECG signals.
Highlights
In India, chronic diseases are projected to account for 53% of all deaths
The value of the correlation coefficient for an abnormal beat with atrial fibrillation comes in the range of 0.09 to 0.126
The range for correlation coefficient for premature ventricular contraction (PVC) beat and normal beat is in the range of 0.28 to 0.30
Summary
In India, chronic diseases are projected to account for 53% of all deaths. WHO has projected that over the 10 years in India, over 60 million people will die form a chronic disease. Deaths from chronic diseases will increase by 18%. The electrocardiogram (ECG) is one of the simplest and oldest cardiac investigations available. It provides a wealth of information about the heart of the patient. The ECG feature extraction system provides fundamental features (amplitudes and intervals) to be used in subsequent automatic analysis [2]. The ECG features can be extracted in time domain [6] or in frequency domain [7]
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