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Real-time identification of ischemic events in high risk cardiac patients

Publication Type : Conference Paper

Thematic Areas : Wireless Network and Application

Publisher : IEEE International Conference on Computational Intelligence and Computing Research, Chennai, India.

Source : IEEE International Conference on Computational Intelligence and Computing Research, Chennai, India (2016)

Url : https://www.researchgate.net/publication/316902644_Real-time_identification_alert_of_ischemic_events_in_high_risk_cardiac_patients

Campus : Amritapuri

School : School of Engineering

Center : Amrita Center for Wireless Networks and Applications (AmritaWNA)

Department : Wireless Networks and Applications (AWNA)

Verified : No

Year : 2016

Abstract : There is a worldwide trend of increase in cardiac related deaths. One of the major reasons is the condition of cardiac ischemia, which implies inadequacy of blood supply to heart leading to myocardial infarction. One of the main techniques used for detection of ischemia is 12-lead ECG test. However, on most occasions the patient may not be attached to any such devices so as to provide immediate medical help. This emphasizes the need for real time detection of such events. With advances in the field of communication and smartphone-based computations, we are now able to use body attached sensors and smartphone based solutions for real-Time detection of diseases. In our work, we introduce a real-Time smartphone based ischemia detection system, which combines ECG signals from patients along with their activity for identification of ischemia. As an initial step, the impact of patient activity on ischemia is studied, with a comparison between severity threshold method and contextual severity threshold technique. We also present initial test results of this system. Initial results suggest that activities of patient needs to be considered in any ischemia detection system. © 2016 IEEE.

Cite this Research Publication : Durga P, Ekanath Srihari Rangan, and Rahul K Pathinarupothi, “Real-time identification of ischemic events in high risk cardiac patients”, in IEEE International Conference on Computational Intelligence and Computing Research, Chennai, India, 2016.

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