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Study and Analysis of Electronic Stethoscope Signal Using MATLAB & LabVIEW as Effective Tools
Oktivasari P.a, Amy H.S.R.a, Freddy H.b, Suprijadia
a Physics Department, Institut Teknologi Bandung, Bandung, Indonesia
b
[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]© Published under licence by IOP Publishing Ltd.This paper trades effectively with the study and analysis of heart sound signal processing from Electronic Stethoscope by using the Matlab tool. Examination of heart sound signal includes generation and simulation of heart sound signal, acquisition of real-time heart sound data, heart sound signal filtering and processing, extraction of feature, comparison of different techniques for cardiac signal processing. Heart sound signal analysis used FFT and Wavelet transform, identification of normal heart sound, calculating Beat Per Minute (BPM), and so on using the most familiar and multipurpose Matlab functionality with user interface LabVIEW. The reasonable need for Matlab functions can contribute us to work in high accuracy and convenience with heart sound signals for processing and analyzing both in realtime and through simulation.[/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]Cardiac signals,Effective tool,Electronic stethoscope,Heart sound signal,Heart sounds,High-accuracy,Matlab functions,MATLAB tools[/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][/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 research has been supported by the Ministry of Research, Technology, and Higher Education, the Republic of Indonesia under PDUPT Grant (ITB) No.1169z/I1.C01/PL/2019.[/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/10.1088/1742-6596/1505/1/012054[/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]