Depression Analysis using ECG Signal

Authors

  • Prof. Shamla Mantri MITCOE, Pune
  • Dr. Pankaj Agrawal RCOEM, Nagpur
  • Prof. Dipti Patil MITCOE, Pune
  • Dr. V. M. Wadhai MITCOE, Pune

DOI:

https://doi.org/10.24297/ijct.v11i7.3470

Keywords:

ECG, Adaptive filtering, the least mean square (LMS) algorithm, ST segment.

Abstract

ECG is a bio-medical signal which records the electrical activity of the heart versus time. They are important for diagnostic and research purposes of the human heart. In this paper we discuss a method of feature extraction which is an inevitable step in most approaches in diagnosing abnormalities in the heart. A web application is developed which extracts features of ECG signal like ST segment, QRS wave, etc. and use these features for identifying whether a person suffers from any of the four levels of stress, that is, Hyper Acute stress (Myocardial Infarction), Acute stress (Type A), Hyper Chronic stress (Ischemia) or Chronic Stress (Type B). The application is built using a decision support system formed by extensive learning of behavior of the signals of various persons. 

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Author Biographies

Prof. Shamla Mantri, MITCOE, Pune

Asst. Professor, Computer Engg. Dept.

Dr. Pankaj Agrawal, RCOEM, Nagpur

Associate Professor, Dept. of ECE

Prof. Dipti Patil, MITCOE, Pune

Asst. Professor, Computer Engg. Dept.

Dr. V. M. Wadhai, MITCOE, Pune

Professor

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Published

2013-11-17

How to Cite

Mantri, P. S., Agrawal, D. P., Patil, P. D., & Wadhai, D. V. M. (2013). Depression Analysis using ECG Signal. INTERNATIONAL JOURNAL OF COMPUTERS &Amp; TECHNOLOGY, 11(7), 2746–2751. https://doi.org/10.24297/ijct.v11i7.3470

Issue

Section

Research Articles