An investigation of bio potentials for the pre-diagnosis of heart dysfunction using a novel portable high resolution electronic analyzer and software

An electrocardiogram (ECG) is a bioelectrical signal which records the heart‘s electrical activity versus time. It is an important diagnostic tool for assessing heart functions. The interpretation of ECG signal is an application of pattern recognition. The techniques used in this pattern recognition comprise: signal pre-processing, QRS detection, creation of variables and signal classification. In this research, signal processing and programs implementation are based in Matlab environment. The processed simulated signal source came from the SIMULAIDS® interactive ECG simulator™ device and the actual heart signals came from actual patients that suffer from various heart disorders, as well as healthy persons that hadn‘t recorded any form of heart condition in the past. For the creation of the database in this research, 5 types of ECG waveform were selected from the ECG simulator device. These are normal sinus rhythm (NSR), ventricular tachycardia (VT poly), ventricular fibrillation (VF), Atrial fibrillation (A FIB) and supra ventricular tachycardia (SVT). An essential part of this research was the development of a portable high resolution ECG device, capable of connecting with, either an ECG simulator device, or recording real human data. This device is able to produce higher resolution than normal ECG devices and high values of Signal to Noise Ratio (SNR).
Matlab was used to develop a program that could further examine, analyze and study the ECG samples. Since the heart waveform can be simulated by cubic spline interpolation, this feature was used by the implemented Matlab program. The ECG samples were normalized and processed to produce 4 specific coefficients. These 4 coefficients of cubic spline were used in the applied methodology in order to evaluate and separate the various heart disorders with mathematical terms and equations. The database created was compared with the real human samples that were taken and passed through the same data process. Through this step, the entire data process and implementation was not only confirmed, but also proved that the capability to diagnose heart disorders was possible. Based on the results of the applied methodology, the categorization of heart disorders without actual clinical examination is possible. Further analysis of each group of results, can lead to heart disorder prediction. Also given are further suggestions to plan experiments for future work.