A Machine Learning Approach for Ecg Analysis for Emotions
Rubina Jahangir Khan1, Raj Kulkarni2, Jagannath Jadhav3
1Ms Rubina Jahangir Khan*, Department of Electronics and Telecomm, Bharat Ratna Indira Gandhi College of Engineering, Solapur, India.
2Raj Kulkarni, Department of Electronics and Telecomm, Belgavi, India.
3Mr Jagannath Jadhav, Department of Electronics and Telecomm, Belgavi, India.
Manuscript received on August 01, 2020. | Revised Manuscript received on August 05, 2020. | Manuscript published on September 30, 2020. | PP: 243-246 | Volume-9 Issue-3, September 2020. | Retrieval Number: 100.1/ijrte.A2592059120 | DOI: 10.35940/ijrte.A2592.099320
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Emotions are feelings which one can feel and are hard to be put in a form by a person .However they reflect the mental state of a person. Emotions like joy and sadness can be somehow detected from the facial expressions or through the body language. But these emotions do have an impact upon our system. An individual’s electrocardiogram is a way through which one can know the impact of different parameters such as stress, joy, sadness, anger on the mechanism of our body. The emotions such as anger, sadness have an adverse effect on the cardio system and is seen in the form of abnormal ECG which can be a good pointer to a counselor when finding out the reasons and diagnosis. The decomposition technique along with the Hilbert transform can be used for feature retrieval. The different emotions are detected through the binary classification technique
Keywords: Denoised, mean frequency, fission, fusion, decomposition, classifier