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This project develops and trains a Recurrent Neural Network (RNN) thatmonitors sleeping infants from an auxiliary microphone for cases of SuddenInfant Death Syndrome (SIDS), manifested in sudden or gradual respiratoryarrest. To minimize invasiveness and maximize economic viability, an electretmicrophone, and parabolic concentrator, paired with a specially designed andtuned amplifier circuit, was used as a very sensitive audio monitoring device,which fed data to the RNN model. This RNN was trained and operated in thefrequency domain, where the respiratory activity is most unique from noise. Inboth training and operation, a Fourier transform and an autoencoder compressionwere applied to the raw audio, and this transformed audio data was fed into themodel in 1/8 second time steps. In operation, this model flagged each perceivedbreath, and the time between breaths was analyzed through a statistical T-testfor slope, which detected dangerous trends. The entire model achieved 92.5%accuracy on continuous data and had an 11.25-second response rate on data thatemulated total respiratory arrest. Because of the compatibility of the trainedmodel with many off-the-shelf devices like Android phones and Raspberry Pi's,free-standing processing hardware deployment is a very feasible future goal.

Application of Autoencoder-Assisted Recurrent Neural Networks to Prevent Cases of Sudden Infant Death Syndrome