1 A NOVEL QRS DETECTION METHOD Rajesh Ghongade, V.I.I.T, Pune, INDIA Dr. Ashok Ghatol, Dr. B.A.T.U., Lonere, INDIA Abstract: The most important step for automated human ECG diagnosis is the reliable detection and extraction of QRS complexes. There are several issues which aggravate the problem of reliable QRS detection and those are; patient to patient variation in signal amplitudes, power-line noise infiltration, baseline or DC shift and muscle activity. Various algorithms are suggested and experimented with in the literature, but still a universal scheme for QRS detection for all types of ECG signals is non-existent. This paper presents a completely novel approach for QRS detection and extraction using energy-histogram technique. The technique was evaluated with all the 48 records from MIT-BIH Arrhythmia database. The method is found to be highly effective and robust and exhibits average detection accuracy of 98.94%, average sensitivity of 0.9894, and average positive predictivity of 0.987 and the average detection error rate as 2.26%. The proposed method is found to be most suitable for off-line ECG QRS detection. Keywords: ECG, QRS detection, wavelet transform, energy-gradient 1 Introduction One of the very first areas in medicine where computer processing was employed was the Electrocardiographic analysis. It is quite evident, however, that the outcome of computer interpretation was critically dependent on the accuracy of the measurements. As a result, the role of signal processing has become increasingly important in yielding accurate measurements, especially when analyzing ECGs recorded under ambulatory or strenuous conditions. In addition, theoretical advances in signal processing have contributed significantly to a new understanding of the ECG signal and, in particular, its dynamic properties. So far, no system offers a "universal" type of ECG signal analysis, but systems are designed to process signals recorded under particular conditions. It is, therefore, customary to speak of systems for resting ECG interpretation, stress testing, ambulatory ECG monitoring, intensive care monitoring, and so on. Common to all these systems is a set of algorithms which