Stress detection for cognitive rehabilitation in COVID-19 scenario

Ghosh, Ahona; Das, Sima; Saha, Sriparna

doi:10.1049/pbhe042e_ch12

Cited by 14 publications

(1 citation statement)

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“…Worldwide 12% of the total fatality is reported to be caused by cardiac arrhythmia [2]. Stress can increase risk for heart disease [3][4][5]. Early diagnosis of arrhythmia can lessen the life risk.…”

Section: Introductionmentioning

confidence: 99%

Arrhythmia Bot: Arrhythmia Disease Prediction using Internet of Things with Machine Learning Approaches and Telegram Bot

2022

AJAC

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Cardiac demise as of arrhythmia remains a prime cause of mortality in the world. Arrhythmia patient monitoring is a vital technique that gives customers all vital statistics regarding every day maneuver of a cardiovascular affected person. Arrhythmia is an irregular heartbeat; the troubles arises at the same time as the electric waves that harmonize the coronary heart's beats. The faulty signaling reasons for coronary heart to normal or abnormal beating. In this paper ECG assesses heartbeat rate, 5-50 Hz bandpass filter used for filtering, Stationary wavelet transform used for artifact removing. Age, Cp, Trestbps, Chol, Fbs, Rest ECG, Thlach, Exang, Old Peak, Slope, Thal, Sex, Target are the features extraction by Independent component analysis technique. Finally, support vector machines have been categorized the dataset as healthy or arrhythmia patient with 85% and above accuracy. The proposed system is design for arrhythmia disease prediction and send the result by Telegram Bot. The classification result sends to the user's emergency numbers using Bot. The main objective of our proposed system is to monitoring lonely or paralyzed peoples in their home. The proposed work can have a notable impact on paralyzed persons, old age home, health care and as well as society.

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