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An Automated Framework for Pre-fall Detections of Arrhythmia in Cardiomyopathy Patients


P. Megana Santhoshi and Mythili Thirugnanam
Abstract

Cardiovascular diseases are considered as most chronic for health. In these types of diseases, cardiomyopathy is one of the most challenging diseases it related to heart complications such as arrhythmia, heart failure, heart blockage, and cardiac arrest. Arrhythmia i.e. irregular heartbeat may deteriorate patient condition to the extent that a fall may cause serious injuries or even sudden death to the patient. Several schemes have been developed in the past for early prediction but achieving the performance accuracy is still a challenging task. The falls caused due to arrhythmia in cardiomyopathy patients are addressed in proposed work by presenting a pre-fall detection mechanism. The proposed work aims to present a machine learning based framework which identifies arrhythmia in cardiomyopathy patients through classification and ultimately generates pre-fall detection alert. Due to its early and accurate prediction capability and instant alert, the proposed lightweight framework can definitely be a life savior for arrhythmia patients.

Volume 11 | Issue 7

Pages: 247-258