Implantable Cardioverter Defibrillators
Behzad Ghanavati · InTech eBooks · 2011
In recently years, Artificial Neural Networks have been studied extensively and applied in medical field, and have been demonstrated to have much better pattern recognition ability. In this chapter we present a VLSI chip to be implemented using 0.35 μm CMOS technology which is the implantable cardioverter defibrillator (ICDs). Implantable cardioverter defibrillator is a device which monitors the heart and delivers electrical shock therapy in the event of a life-threatening arrhythmia. At present most ICDs are often using time information from leads to classify rhythms. (Leong, P. H.W J The Sinus Tachycardia (ST) arrhythmia and the Ventricular Tachycardia (VT) arrhythmia. The ST is a safe arrhythmia occurs during vigorous exercises and is characterized with rate of 120beat/minute. The VT is a fatal arrhythmia with the same rate. They can be separated only by detecting the morphology changes in each one. (Acherya,U,R.,2004) Most morphology changes are appeared in the QRS-complex. The QRS-complex for both the ST and VT arrhythmia’s are shown in fig.1. (Dale Dublin, 2000) Since most morphology changes are appeared in the QRS-complex, for classifying the arrhythmias we must separate QRS complexes from ECG, consequently a new circuit for detecting QRS is designed. In this circuit the R-R distance between two QRS complexes and also the pulse width of QRS complex are used to improve the detection algorithm. By using fuzzy logic and some parameters of ECG (pulse width, R-R interval and peak) we can separate QRS complex from ECG and after that apply this part to a Neural Network for classification. The proposed analog VLSI chip can detect such morphology changes. It has the following advantages: It is easily interfaced to the analog signals in an ICD (in contrast to the digital systems which require analog to digital conversion). Analog circuits are generally small in area. Low voltage circuits are used to decrease battery weight and size and to extend battery life time which required for portable and modern wireless equipment. Hamming network did not need to have a training system and the reference vectors determine the weights.