This paper presents a comprehensive approach for the detailed analysis of ECG waveforms including various morphologies to aid clinical diagnosis. Clinical judgment is often based on observing various features which may occur simultaneously on the ECG. Thus, to automate diagnosis, a comprehensive tool capable of detecting all these features is required.
Parabolic curve fitting, adaptive thresholds and synchronicity across leads are utilized to detect the various waves of the QRS complex namely Q,R,S,R’ and S’. Onset of the QRS complex and the J point are detected using a ‘modified second derivative’ approach. The isoelectric level is detected using linearity and slope conditions. P and T waves are detected using ‘area under curve’ approach. Measurements such as peak-to-peak intervals and ST elevation/depression are numerically calculated from the points obtained. Curve fitting and change in slope are utilized for obtaining morphology of the ST segment. Presence of significant Q waves and abnormal T waves are inferred using clinical guidelines and numerical calculations.
The performance of the algorithm is validated on 40 sample patient data — 20 healthy and 20 with Myocardial Infarction. Average accuracy shown in detecting all points of interest is 98.5%. All measurements are successfully calculated from these points. Along with this reliable performance, the approach proves to be simple and computationally fast.