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mDLSpiro: Hardware Efficient Deep Learning based Mobile Spirometry

Respiratory diseases such as chronic pulmonary disease (COPD), asthma etc., are diseases related to the lungs which usually cause difficulty in breathing. The most common of all the diseases is COPD. Spirometry is the standard diagnostic test for diagnosing COPD. Like every other disease, COPD negatively impacts the sufferer’s life, which worsens over time. This requires them to have regular spirometry tests, which are time-consuming or to buy expensive portable devices. Therefore either time or money, both are affected and highly utilized. Since there has been much advancement in the computational power of smartphones, in this work, we try to exploit this and, using built-in microphones, provide a cost-effective alternative to traditional spirometry. This paper proposes a classification system that could classify if the person has COPD or not based on the breathing audio signal. The convolutional neural network is trained and evaluated on the Respiratory Sound Database (ICBHI 2017 database). The model is then compressed using the post-training quantization technique so that it can be deployed on an edge device (smartphone, for instance). A training accuracy of 99.05% and a testing accuracy of 95.56% is achieved. The model size is reduced from an initial 44.822 Megabytes to 11.207 Megabytes which is a four times reduction.