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Heart Rate Variability Frequency Domain for Detection of Mental Stress Using Support Vector Machine
Novani N.P.a, Arief L.a, Anjasmara R.a, Prihatmanto A.S.b
a Dept. of Computer Engineering, Andalas University, Padang, Indonesia
b Dept. of Electrical Engineering, Institut Teknologi Bandung, Bandung, Indonesia
[vc_row][vc_column][vc_row_inner][vc_column_inner][vc_separator css=”.vc_custom_1624529070653{padding-top: 30px !important;padding-bottom: 30px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner layout=”boxed”][vc_column_inner width=”3/4″ css=”.vc_custom_1624695412187{border-right-width: 1px !important;border-right-color: #dddddd !important;border-right-style: solid !important;border-radius: 1px !important;}”][vc_empty_space][megatron_heading title=”Abstract” size=”size-sm” text_align=”text-left”][vc_column_text]© 2018 IEEE.The Heart Rate Variability (HRV) analysis become a useful tool for understanding the autonomic nervous system (ANS). HRV analysis include spectral analysis or frequency domain defined two major components of the ANS which is interpreted as different physiological rythms. In this work, HRV as a variation of heart rate is used as a tool that describes the activity of the ANS associated with mental stress. The measurements of the HRV frequency domain is used to classify the condition of mental stress and the health condition of subjects related to influenza. The basic frequency domain measurements include nLF, nHF and LF/HF ratio was used as an efficient measurement for stress and influenza classification using SVM Classifier. SVM Classifier are used as a binary classifier for each condition perceived by the subjects. SVM Classifier performance evaluation testing for two different test subjects resulted 94% and 75% accuracy in the classification of influenza from each subject. For stress classification on each subject, we obtained 81% and 66% accuracy using training and testing data from each subject.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Autonomic nervous system,Basic frequencies,Binary classifiers,Frequency domains,Heart rate variability,Stress classifications,SVM classifiers,Training and testing[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Indexed keywords” size=”size-sm” text_align=”text-left”][vc_column_text]FFT,Frequency domain,Heart rate variability,PSD,SVM Classifier[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Funding details” size=”size-sm” text_align=”text-left”][vc_column_text]This paper is supported by Faculty of Information Technology, Andalas University for publication.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”DOI” size=”size-sm” text_align=”text-left”][vc_column_text]https://doi.org/10.1109/ICITSI.2018.8695938[/vc_column_text][/vc_column_inner][vc_column_inner width=”1/4″][vc_column_text]Widget Plumx[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][/vc_column][/vc_row]