On the detection of Cardiac Arrhythmia with Principal Component Analysis

The Electrocardiogram (ECG) signal is used to record the electrical activity of heart. The subtle variations in ECG attributes are used by cardiologists for diagnosis of heart anomalies. But, for prognosis of cardiac ailments feature extraction from electrocardiographic signal becomes extremely difficult due to presence of noise. With the aim of noise reduction, a hybrid technique involving Extended Kalman filter along with Discrete Wavelet transform for effectively improving signal quality is focused as a powerful tool. The performance of denoising algorithm is evaluated in terms of signal to noise ratio and mean square error. On denoised signal, a quick, simple and effectual approach based on Principal Component Analysis is proposed for R-peak and QRS complex detection. The beat detector performance is validated with MIT-BIH arrhythmia database, yielding a sensitivity of 99.93%, positive predictivity of 99.98% and a 0.079% detection error rate, being a positive outcome in comparison with recent researches. Later, different types of arrhythmias are detected on the basis of heart rate and morphological characteristics of ECG waveform.