Development of a computerized ECG analysis model using the cubic spline interpolation method

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 preprocessing, QRS detection, creation of variables and signal classification. In this method, signal processing and programs implementation are based in Matlab environment. 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. Based on the results of the applied methodology, the categorization of heart disorders without actual clinical examination is possible.