Initial Development IoT-Based of Heart Sound Segmentation and Diagnosis System

Phonocardiogram (PCG) signal is a heart sound recording that represents first (S1) and second (S2) heart sounds which physically defined as the closure of mitral and tricuspid valves, and the closure of semilunar valves, respectively. Since the assessment of PCG signal that acquired from stethoscope...

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Bibliographic Details
Main Authors: Musa, Maisarah, Ibrahim, Nabilah, Abd Rahman, Nurul Usna
Format: Conference Proceeding
Language:English
Subjects:
Amazon Web Services (AWS)
Arduino
Feature extraction
Heart
Pathology
Phonocardiogram
Power Spectrum Density
Prototypes
Rhythm
Stethoscope
Tera Term
Web services
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Summary:Phonocardiogram (PCG) signal is a heart sound recording that represents first (S1) and second (S2) heart sounds which physically defined as the closure of mitral and tricuspid valves, and the closure of semilunar valves, respectively. Since the assessment of PCG signal that acquired from stethoscope is essential to be conducted for heart disease early diagnosis, numbers of work have been put into effort including computing aided methods such as classification of PCG signal abnormality, and predictive system of heart condition. This work however, proposed to develop a PCG signal acquisition device that able to segment the signal thus diagnose the heart condition within two parties: user and healthcare professionals. Initially, a heart sound sensor was used to collect the PCG signals that controlled by an Arduino Uno microcontroller. Fast Fourier transform and power spectral density were applied on the signals to find the frequency range of the normal and pathological PCG. It was found that pathological PCG signals produced higher frequency than that of the normal PCG signals. Furthermore, detection of the S1 and S2 peak location in both normal and pathological PCG signals convinced the significant difference with the presence of murmur. Apart from the detection of S1 and S2 peak's location, power spectrum of the PCG signals were stored in Amazon Web Services (AWS) as cloud system which able to be accessed by authorized person. Hence, these outcomes have possibility to contribute to e-health system in sequence for better lifestyle.
ISSN:2643-2447
DOI:10.1109/SCOReD53546.2021.9652682