Detection and identification of ECG waves by histogram approach

A histogram based simple and novel idea is proposed here for detection and identification of R wave, P wave and T wave from noise removal ECG Signal. The identification of ECG waveforms and their characteristic features is an important task for the diagnosis. In this work, histograms, a graphical demonstration of numerical data of equal size, is used as an estimator of the above mentioned waves of ECG signal. For this purpose the whole signal is divided into few small windows of predefined width having maximum 60 sample values in each. The Histograms are basically generated by measuring the variations of the orientations among these sample values in some quantized directions. After getting the histograms, few zones are depicted as QRS zones having the area more than a pre-defined threshold. The local maxima of these zones are considered as the R-peak. Based on same technique, P and T wave can also be detected. The method is advantageous as it can be used directly for online analysis without using any complex mathematical models. The whole technique has been established to be useful to a variety of ECG records for all the 12 leads taken from CSE Multi-lead ECG database which contains 5000 samples recorded at a sampling frequency of 500Hz. The algorithm is implemented on MATLAB R2010a environment. The performance of the proposed technique is evaluated. The accuracy of the proposed technique is achieved in Sensitivity (Se=99.86%), Positive Predictivity (+p=99.76%) and Detection accuracy (DA 99.8%) and hence we conclude that the proposed technique may be used for ECG analysis and classification.