There is a constant search for novel methods of classication and predicting cardiac rhythm disorders or arrhythmias. We prefer to classify them as
wide complex tachyarrhythmia's or ventricular arrhythmias inclusive of malignant ventricular arrhythmias which with hemodynamic compromise
is usually life threatening. Long term and fatality predictions warranting AICD implantation are already available. We have a novel method and
robust algorithm with preprocessing and optimal feature selection from ECG signal analysis for such rhythm disorders. Variability of ECG
recording makes predictability analysis challenging especially when execution time is of prime importance in tackling resuscitative attempts for
MVA. Noisy data needs ltering and preprocessing for effective analysis. Portable devices need more of this ltering prior to data input.
Deterministic probabilistic nite state automata (DPFA) which generates a probability strings from the broad morphologic patterns of an ECG can
generate a classier data for the algorithm without preprocessing for atrial high rate episodes (AHRE). DPFA can be effectively used for atrial
tachyarrhythmias for predictive analysis. The method we suggest is use of optimal classier set for prediction of malignant ventricular arrhythmias
and use of DFPA for atrial arrhythmias. Here traditional practices of heart rate variability based support vector machine (SVM), discrete wavelet
transform (DWT), principal component analysis (PCA), deep neural network (DNN), convoutional neural network (CNN) or CNN with long term
memory (LSTM) can be outperformed.
AICD - automatic implantable cardiac debrillator, MVA - Malignant Ventricular Arrhythmias, VT - ventricular tachycardia, VF - ventricular
brillation,DFPA deterministic probabilistic nite state automata, SVM -Support Vector Machine, DWT discrete wavelet transform, PCA
principal component analysis, DNN deep neural network, CNN convoutional neural network, Convoutional LSTM Long short term
memory,RNN recurrent neural network
Classification analysis using support vector machine, decision tree, and neural network with principal component analysis to determine molecular structure relationship from its biological activity on dipeptidyl peptidase IV inhibitors