Software Design Collection and Handling of Signal Sound Body
Son Nguyen Van1, Duc Trinh Quang2, Giang Nguyen Hoai3, Quynh Nguyen Thi Huong4, Khanh Pham Xuan5

1Son Nguyen Van*, Hanoi Open University, Hanoi, Vietnam..
2Duc Trinh Quang, Hanoi University of Science and Technology, Hanoi, Vietnam.
3Giang Nguyen Hoai, Hanoi Open University, Hanoi, Vietnam.
4Quynh Nguyen Thi Hương, Department of Science, Technology and Environment Department, Ministry of Education and Training
5Khanh Pham Xuan, Hanoi Vocationa College of High Technoogy
Manuscript received on November 25, 2019. | Revised Manuscript received on December 08, 2019. | Manuscript published on December 30, 2019. | PP: 260-264 | Volume-9 Issue-2, December, 2019. | Retrieval Number: B2370129219/2019©BEIESP | DOI: 10.35940/ijeat.B2370.129219
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Abstract: We demonstrate the designed software that possibly collects the body sound data to be used for clinical diagnosis applications. Body sound signals are collected and processed through a software designed in Labview to adapt with Arduino-Uno. The analog signals transduced from a piezoelectric microphone are converted to the digital signals by an ADC component integrated in the Uno board and controlled the sampling frequency via the software. The collected signals are observed and visualized in graph panel of the software and the audio sound can play through speakers in real-time then stored the measured values as the audio file format simultaneously. The data can use to analyze by another software or study the analyzed algorithm to extract the disease signals. To evaluate the quality of the system, a series of experiments were examined in hospital environment and asserted with clinical experiences of specified medical doctors. To enhance the scope of the disease signal, the spectrum of the signal can be collected ranged on 5 Hz to 35 kHz corresponding to the full spectrum of the hardware system, with the sampling frequency reached to 100 kHz. Based on this initial system, a series of development applying to clinical diagnosis can be potentially opened in the near future.
Keywords: Digital Stethoscope, Body sound, Labview, signal processing.