An Efficient Neural Network Model for the Identification of Stress using Electrocardiogram
Mithun H R1, Suchitra M2
1Mithun H R, M.Tech Student, Department of ECE, Vidyavardhaka College of Engineering, Mysuru (Karnataka), India.
2Dr. Suchitra M, Associate Professor, and PG-Coordinator, Department of ECE, Vidyavardhaka College of Engineering, Mysuru (Karnataka), India.
Manuscript received on 20 August 2019 | Revised Manuscript received on 30 August 2019 | Manuscript Published on 16 September 2019 | PP: 502-507 | Volume-8 Issue-2S6 July 2019 | Retrieval Number: B10950782S619/2019©BEIESP | DOI: 10.35940/ijrte.B1095.0782S619
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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: People are facing numerous pressures in their daily routine in the latest society. Stress has traditionally has been described as action from a calm state to an emotional state in order to preserve the integrity of organism. Stress observation is very important for mental wellbeing and early identification of stress related disorders. Stress is to learn the body response in stressful state, whenever the body reaction is activated that means the heart rate and blood pressure will raise and several hormones enter our bloodshed. These hormones and bodily changes may increases our performances to a particular extent. Everyone’s response to stress is discreet, and not all stress is bad. Someone may discover a significant condition of pressure to be enjoyable, while others may find it stressful. However, individuals also have different stress symptoms. stress area can also recognize using frequency and excitation of a speech signal, Since the biomedical signals are consistently related to central nervous system, therefore physiological parameters are the best way to understand the human emotions. The present work is focused on stress identification from Electrocardiogram using ECG physiologic net database, then entire environment of ECG signal characteristics i.e. mean heart rate variability (HRV), standard deviation of all R-R interval (SDNN), square root mean of the sum of the square difference between R-R interval (RMSSD) and number of consecutive R-R interval variations greater than 50ms (NN50), these features are extracted using Pan-Tompkins algorithm, then it is trained and validated to machine learning using back-propagation algorithm in neural network model. With the help of these features (mean HRV, SDNN, RMSSD and NN50), the study can be analyzed whether a person is under stress or not. Thus how the suggested technique provides the subjective information which helps the doctor to find out whether the person is under stress or not.
Keywords: Human Emotions, Physiological Signals, Pan-Tompkins Algorithm and Neural Network.
Scope of the Article: Signal Processing