Discrimination of Normal and Abnormal Auscultated Lung Sounds using Edge Devices

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  • エッジデバイスによる聴診された肺音の正常異常判別

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<p>This paper describes the elemental technology of a system that discriminates whether or not sputum is accumulated (whether or not suction is necessary) based on auscultation sounds using edge devices such as microcomputers, in order to support endotracheal suctioning (a procedure to suction sputum accumulated in the trachea), which is one of nursing tasks. Since a typical FFT library for general microcomputers is used, a long-span FFT is required to process a single lung sound. On the high-performance microcomputer SPRESENSE, auscultated lung sounds were FFTed over a long span (approximately 4.16 seconds), and the frequency spectrum was used to discriminate normal from abnormal using a neural network model that was machine-learned on the computer beforehand. As a result, the discrimination accuracy was confirmed to be relatively good, about 96%. </p>

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