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Wearable Bowel Sound Monitoring with Quality Enhancement using U-net

Human bowel sounds (BSs) contain rich information on gastrointestinal health status. Recently, the wearable BS monitoring systems has made it possible to record the human BSs in the long term. However, BSs collected by long-term monitoring devices usually contain various types of noises, which significantly affect the detection accuracy on BS events of interest. To filter out the noises in the long-term BS recordings, a U-Net convolutional neural network (CNN) based BS quality enhancement algorithm is proposed. The algorithm features a frequency-domain BS enhancement using U-Net to efficiently suppress the amplitude spectrum noises of BS segments. Experimental results on our BS dataset show that after enhancing with the proposed algorithm, the average SNR improvement of the sound segments is 28.72dB, which is the best among similar works. Moreover, after the enhancement, the accuracy of BS event detection using a threshold-based approach can reach 98.38%, which proves the effectiveness of the proposed enhancement algorithm. To the best of our knowledge, we are the first to utilize the CNN for BS quality enhancement.