**4. ECG abnormality classification using polyphase**

The main motivations behind the use of HOS are their ability to (i) preserve the phase of the signal frequency components (ii) suppress Gaussian noise, (iii) characterise and separate motion artefact (Nikias and Petropulu, 1993), and other non-Gaussian low frequency components, and (iv) detect and classify non-linearities. HOS algorithms are employed to develop discriminant contour patterns in the multi-dimensional phase of the polyspectra (polyphase) of normal looking ECGs in outpatients having weariness and general malaise or have recently had suspected angina. Similar polyphase patterns have been found in the ECGs of acute myocardial infarct patients with or without diagnostic S-T segment and T-wave changes. The polyphase computation is done in milliseconds but a temporal window of 10 ECG cycles is necessary in each of the polyspectral averaging process. The polyphase patterns can be displayed every 10 seconds. A high resolution three-lead ECG is adequate for polyphase discrimination. The results of a pilot study show that this is a potential diagnostic technique. Particularly in those 50% Pre-Infarction Syndrome cases which show perfectly normal 12-lead ECGs and will not be identified early enough to prevent them from developing acute myocardial infarction (AMI), (Struebe and Strube, 1989). The standard 12-lead ECG has poor sensitivity for the early detection for (AMI). Only 40-50% patients presenting with AMI show S-T segment and T-wave changes on the initial ECG. The rest might not receive the benefit of acute interventional therapies in the first hours after the event. The application of HOS to ECGs has shown fruition in positively identifying ischemic heart diseases (Strube and Strube, 1989, Zgallai, 2011a, Zgallai, 2011b, and Zgallai 2011c). The methodology involved decomposing the ECGs into linear and several non-linear component waves prior to identification and classification. Methods of validation employed clinical diagnoses and other apriori clinical information spanned over a number of years. Using other HOS discriminant patterns, the focus is on the identification of coronary artery disease, solely from its polyphase contour patterns. Polyphase patterns are obtained using the standard three-lead ECG, a multi-channel low-noise amplifier, an interface card and a PC. Sampling frequency is 0.5 KHz and a resolution of 12 bits. Recurrent neural networks have been used to classify the patterns in Fig 4.1 c and d, respectively (**Rizk et al., 1999b**). The employed neural network has an input layer, a middle layer, and an output layer. The input layer has 64 neurons, the middle layer has 16 neurons, and the output layer has 8 neurons. The network size has been optimised using trial and error with an MSE threshold of 0.001. The learning rate and momentum constant are, respectively, 0.7 and 0.9.

Fig. 4.1. Typical electrocardiogram biphase signature of an Acute myocardial infarction (AMI) presented with a normal looking 12-lead ECG without S-T segment and T-wave changes and normal sinus rhythm. (a) and (b) are ECGs of normal and AMI subjects, respectively. (c) and (d) are the corresponding bispectral phases using the direct method with frequency domain smoothing and Kaiser window. The bispectrum is the average of 10 ST bispectra, the S-T segment is 150 samples starting from the R peak (**Rizk et al., 1999b**).
