СИСТЕМА АВТОМАТИЧНОГО МОНІТОРИНГУ  ЗВУКОВИХ СИГНАЛІВ ОРГАНІЗМУ

Well-known methods of measuring and analyzing sound and other signals of human organs in a wide time dimension have been reviewed, starting from the ones dated 20th century. It has been shown that even a modern expensive digital stethoscope of the Littmann CORE Digital type has a number of shortcomings - it does not allow to automatically analyze the parameters of the signals and transmit the obtained data to an external device. An experimental system that allows to continuously and for a long time automatically measure the time and frequency characteristics of the sound signals of the body and transmit the data of their analysis to an external device has been created and tested. Signals from human organs are received by microphones hidden in soundproof body capsules. Condenser microphones along with an amplifier of the MAX-9814 type, as well as the Clippy-XLR-EM272Z1 and an amplifier with an external sound card Scarlett Solo have been tested. The amplified signals are fed to the input of a Raspberry Pi Pico microcontroller or an analog-to-digital converter of the Scarlett Solo card. After digitization, the signals are input to minicomputers such as Raspberry Pi Zero or Raspberry Pi 3 via the USB port. Software named ChyPaCha was written using MicroPython, C++, and Python. The program for data analysis on Raspberry Pi 3 contains a call to the fast Fourier transform algorithm. The efficiency of the systems has been tested using compiled software tools that provided continuous processing of heart sound signals depending on the state of the load on the human body in real time and transmission of analysis results. It has been shown that the use of the Clippy-XLR-EM272Z1 and Scarlett Solo complex significantly reduced the impact of the system's own noise (amplifier-ADC) compared to the same noise of the MAX-9814 - Raspberry Pi Pico system and increased the accuracy of measuring the intensity of heart sounds.