Detection of Ventricular Fibrillation Using the Image from Time-Frequency Representation and Combined Classifiers without Feature Extraction

Due the fact that the required therapy to treat Ventricular Fibrillation (VF) is aggressive (electric shock), the lack of a proper detection and recovering therapy could cause serious injuries to the patient or trigger a ventricular fibrillation, or even death. This work describes the development of an automatic diagnostic system for the detection of the occurrence of VF in real time by means of the time-frequency representation (TFR) image of the ECG. The main novelties are the use of the TFR image as input for a classification process, as well as the use of combined classifiers. The feature extraction stage is eliminated and, together with the use of specialized binary classifiers, this method improves the results of the classification. To verify the validity of the method, four different classifiers in different combinations are used: Regression Logistic with L2 Regularization (L2RLR), adaptive neural network (ANNC), Bagging (BAGG), and K-nearest neighbor (KNN). The Hierarchical Method (HM) and Voting Majority Method (VMM) combinations are used. ECG signals used for evaluation were obtained from the standard MIT-BIH and AHA databases. When the classifiers were combined, it was observed that the combination of BAGG, KNN, and ANNC using the Hierarchical Method (HM) gave the best results, with a sensitivity of 95.58 +/- 0.41%, a 99.31 +/- 0.08% specificity, a 98.6 +/- 0.04% of overall accuracy, and a precision of 98.25 +/- 0.29% for VF. Whereas a sensitivity of 94.02 +/- 0.58%, a specificity of 99.31 +/- 0.08%, an overall accuracy of 99.14 +/- 0.43%, and a precision of 98.59 +/- 0.09% was obtained for VT with a run time between 0.07 s and 0.12 s. Results show that the use of TFR image data to feed the combined classifiers yields a reduction in execution time with performance values above to those obtained by individual classifiers. This is of special utility for VF detection in real time.