Electronic Stethoscope Design with IoT (Internet of Things)-based Disease Symptom Detection

Abstract

Auscultation is a technique or method most often used by medical personnel in the initial examination of patients. One way is to use a stethoscope. However, this method has its drawbacks because the diagnosis is carried out subjectively and cannot be relied on with the accuracy to diagnose the symptoms of heart defects. Thus, the purpose of this study is to create an IoT system for electronic stethoscopes with BPM value output and make analog filters to eliminate noise interference which was a major obstacle in previous studies. The contribution to this study is to make it easier for medical users to monitor vital conditions, namely BPM remotely and produce BPM values in real-time. The method used in this study was to use a mic condensor placed on the patient's chest to detect pressure changes that occurred. This change in pressure causes a change in the voltage output value on the condensor mic. Output dari mic condenser masuk dan diproses di rangkaian PSA. Output sinyal dari PSA masuk ke mikrokontroler yang telah diprogram. Hasil yang dipeoleh dari pengukuran mengasilkan nilai error pengukuran nilai BPM dari 5 responden dan diperoleh nilai error yang dihasilkan dari responden 1 diperoleh error sebesar 0.33 BPM, responden 2 diperoleh nilai error sebesar 0,67 BPM, responden 3 memiliki nilai error sebesar 0,5 BPM, responden 4 nilai error sebesar 0,67 dan responden 5 mempunyai nilai error sebesar 0,67 BPM. The results of the statistical test were also obtained P-Value>0.05 which explained that the resulting value did not have a significant difference and could be used for medical purposes. This research can help make it easier for doctors to analyze and diagnose symptoms of heart defects because this system is equipped with the detection of disease symptoms.

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Published
2022-11-24
How to Cite
[1]
T. Hamzah, E. Setioningsih, S. Sumber, and N. Ragimova, “Electronic Stethoscope Design with IoT (Internet of Things)-based Disease Symptom Detection”, Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics, vol. 4, no. 4, pp. 168-173, Nov. 2022.
Section
Research Article