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An Automated Framework for Prediction of Falls in Cardiomyopathy People

  • Pasupuleti Megana SanthoshiEmail author
  • Mythili Thirugnanam
Conference paper

Abstract

Purpose In medical field, cardiomyopathy is one of the heart muscle diseases associated with blood pumping that causes heart complications like heart failures, cardiac arrest, and sudden death. According to the WHO, globally at least one in 500 is suffering from cardiomyopathy. It can be identified by symptoms such as chest pain, dizziness, syncope which causes falling. At present, in the cardiomyopathy field, issues such as poor accuracy in detection of cardiomyopathy and no emphasis on classification of cardiomyopathy types are addressed. Especially, no work is concentrated on prediction of fall due to cardiomyopathy. Hence, this work aims to propose an automated framework for prediction of fall in cardiomyopathy patients. Procedure and Conclusion This framework consists of five phases, first and second focused on improving the ECG signal quality to resolve accuracy problems. Third is to detect and classify the cardiomyopathy type as well as prediction of fall. In fourth and fifth, prediction details will be transmitted to Web application and then to personal devices.

Keywords

Pre-fall detection Cardiomyopathy detection Classification of cardiomyopathy ECG data analysis Body sensors Fall with health abnormality 

Notes

Conflict of Interest

The authors declare that they have no conflict of interest.

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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Pasupuleti Megana Santhoshi
    • 1
    Email author
  • Mythili Thirugnanam
    • 1
  1. 1.School of Computer Science and EngineeringVIT UniversityVelloreIndia

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