Optimization of three morphologic algorithms for arrhythmia discrimination in implantable cardioverter defibrillators

Discrimination between ventricular tachycardia (VT) and supraventricular tachycardia (SVT) in implantable cardioverter defibrillators (ICDs) is still an unsolved task due to the low specificity of traditional techniques based in rate, stability and onset. Several morphological published algorithms enhance VT vs. SVT discrimination by increasing algorithm complexity. Three morphological published algorithms with increasing complexity have been selected: time domain (complex peak area comparison), simplified wavelet and frequency domain (Fourier complex power spectra analysis and neural network) algorithms. All them have been reconstructed from published information and programmed in MATLAB. The algorithms has been optimized in order to obtain an improved classification and to work in a 16-bit microcontroller platform (Texas Instruments MSP430 microcontroller). A final test of the optimized algorithms has been accomplished using a classified unipolar and bipolar electrogram (EGM) database. The configurable parameters of the algorithms have been adjusted in order to maximize sensitivity (SE), specificity (SP) and accuracy (AC)