Ventricular fibrillation detection from ECG surface electrodes using different filtering techniques, window length and artificial neural networks

Medical personnel face many difficulties when diagnosing ventricular fibrillation (VF). Its correct diagnosis allows to decide the right medical treatment and, therefore, it is essential to tell it apart adequately from ventricular tachycardia (VT) and other arrhythmias. If the required therapy is not appropriate, the personnel could cause serious injuries or even induce VF. In this work, a diagnosis automatic system for the detection of VF through feature extraction was developed. To verify the validity of this method, an Artificial Neural Network (ANN) classifier was used. The ECG signals used were obtained from the MIT-BIH Malignant Ventricular Arrhythmia Database and AHA (2000 series) database. Different filtering techniques to remove base line wandering and other noise in the signal is applied before extracting features. Two different classifiers are proposed: two-class (Normal-Abnormal) rhythms, and four-classes (Normal-VT-VF-Other). For the four class classifier and the most difficult separation classes (VF and VT), the classification results shows sensitivity and specificity values of 91,82% and 99,74%, respectively, for VF, and 67,33% and 99,76% values of sensitivity and specificity for VT.