Classifying biosignals with wavelet networks [a method fornoninvasive diagnosis]

In recent years, a particular challenge has arisen in noninvasive medical diagnostic procedures. Because biosignals recorded on the body surface reflect the internal behaviour and status of the organism or its parts, they are ideally suited to provide essential information of these organs to the clinician without any invasive measures. But how are the recorded time courses of the signals to be interpreted with regard to a diagnostic decision? What are the essential features and in what code is the information hidden in the signals? These questions are typical of so-called pattern-recognition tasks. This article reviews pattern recognition as it applies to medical diagnostics and discusses the concept of wavelet networks as a means of biosignal classification. An example is presented in which this approach was used for classifying preprocessed ECG signals to identify patients who were at high-risk of developing ventricular tachycardia (VT)