Distinguishing between ventricular tachycardia and ventricular fibrillation from compressed ECG signal in wireless Body Sensor Networks

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 (V F) 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 V F in real time by means of the time-frequency representation (T F R) image of the ECG. The main novelties are the use of the T F R 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 (L 2 R L R), adaptive neural network (A N N C), Bagging (B A G G), and K-nearest neighbor (K N N). 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 B A G G , K N N , and A N N C 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 V F . 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 V T with a run time between 0.07 s and 0.12 s. Results show that the use of T F R 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 V F detection in real time.

Feasibility and efficacy of a remote real-time wireless ECG monitoring and stimulation system for management of ventricular arrhythmia in rabbits with myocardial infarction

The purpose of this study was to explore the feasibility of continuous remote monitoring, and the induction and termination of malignant ventricular arrhythmias (VAs) by a novel implantable electronic cardiovascular device (IECD) system in rabbits with myocardial infarction (MI). The IECD was implanted and MI was induced by ligation of the left anterior descending coronary artery in 20 adult rabbits. Internet-based remote electrocardiogram (ECG) monitoring and ventricular stimulation were conducted in remote locations with internet access. The voltage amplitudes of the stimulation signals were recorded synchronously by remote and surface ECG. Programmed stimulation with regular stimuli and regular stimuli with an added extra stimulus were performed prior to and following the MI surgery to induce and terminate VAs. IECD implantation and MI surgery, as well as qualified remote and bidirectional signal communications between the implanted IECD and extracorporeal system, were successfully achieved in 18 rabbits. The voltage of the stimulation signals recorded by the remote and surface ECGs showed a good correlation with the stimulation current (remote ECG, r=0.972 and surface ECG, r=0.988; P<0.001). Sustained ventricular tachycardia (VT) was induced in five rabbits (5/20, 25%) prior to MI induction and in 12 rabbits (12/16, 75%) following MI induction. Of the 17 induced VTs, 16 were successfully terminated by remote ventricular stimulation. The novel IECD system provides qualified remote wireless ECG monitoring and possesses the potential to induce and terminate VAs by remote ventricular pacing in this rabbit model of MI. Thus, this model of MI may be used to test the efficacy of novel drugs and devices for the management of VAs.