Forecasting Ventricular Deviation in Monitoring of Live ECG Signal

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Radha Bheemsen Rao Kalaskar
DR. Bharati Harsoor
Rupam Das


Number of coronary artery disease cases and ventricular arrhythmias has been increasing in India. One of the common forms of cardiac disorder is Ventricular Tachycardia(VT). Due to improper electrical activities in the ventricles, consistent and rapid heart rate occurs, which produces Ventricular Tachycardia disorder. Short time period may not lead to severe heart problem, but the longer duration increases it may be a severe heart issue. In this disorder,for short durations it is possible that there may not be any symptoms or few symptoms with palpitations(increase / decrease in heart beats), dizziness or pain in chest. This disorder may result in cardiac arrest. This may also results into ventricular fibrillation. initially it was found that near about 7% of people in cardiac arrest are caused by Ventricular Tachycardia.  In this work, a novel platform for real time diagnosis of Ventricular Tachyarrhythmia with the help of a portable Single lead ECG device is proposed. The gateway for signal analysis and combined edge and cloud based processing for the diagnosis is used. The biosignal captured by the device in LEAD II configuration is pushed to a cloud based diagnosis API through a mobile gateway. An algorithm in the cloud analyses this signal and finds out P, Q, R, S, T, their amplitude positions, onset and offset. From the onset and offset ST segment slope, elevation, depression, S morphology and ST segment variation statistics is captured and classified using rule based classifier. The work evaluates the performance of the classifier with Physionet dataset.  The accuracy of the system was found to be 90% with accuracy of detecting normal ECG being 100% where as the accuracy of detection of VT being 80%. Results shows that the system is extremely efficient in detecting Ventricular Tachyarrhythmia and many related cardio vascular diseases.

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How to Cite
Kalaskar, R., Harsoor, D. B., & Das, R. (2018). Forecasting Ventricular Deviation in Monitoring of Live ECG Signal. International Journal of Machine Learning and Networked Collaborative Engineering, 2(03), 95-109. Retrieved from