Wavelet based ventricular tachyarrhythmia (VT) detection system

Abstract

The most dangerous of heart diseases ventricular tachyarrhythmia (VT) is very difficult to detect. Our project, an amalgamation of research and application development uses a novel wavelet based algorithm for detecting VT. This project is done in collaboration with Armed Forces Institute of Cardiology, National Institute of Heart Diseases (AFICNIHD) Wavelet transform has emerged over recent years as a powerful time–frequency analysis tool favored for the interrogation of complex non-stationary signals. The proposed algorithm uses an efficient method for detecting VT in wavelet pre-processed ECG signals. A MATLAB routine using built in library functions for pre-processing removes high frequency noise. The preprocessed signal is applied to the Spectral Algorithm (SPEC) which works in frequency domain and analyses the energy content. If the algorithm decides that the ECG part contains VT, the result is accepted as true and no further investigation is required. Otherwise a further investigation is carried out to confirm the result or disprove it. The terminal parts of the ECG signal are processed with a continuous wavelet transform, which leads to a time-frequency representation of the signal. The diagnostic feature vectors are obtained by subdividing the representations into several regions and by processing the sum of the decomposition coefficients belonging to each region. Wavelet based efficient algorithms are used for detection of VT. With these methods, underlying features within the VT waveform are made visible in the wavelet time-scale half space. The proposed algorithms overcome the non-sensitivity of SPEC iii algorithm utilizing its highly specific nature to the fullest. An exhaustive testing exemplified higher sensitivity, predictivity and specificity, enabling the cardiologists and electro physiologists to detect VT with accuracy of more than 85%.