A Multi-Sensor Based Automatic Sleep Apnea Detection System for Adults Using Neural Network Inference on FPGA

This paper proposes an automatic sleep apnea monitoring device for adults employing of single ECG patch and a pulse oximeter. The device is designed to automatically detect sleep apneic (SA) events with the inference of feedforward neural network (FNN) model embedded in digital hardware. The three-layer (8-6-4) FNN model was trained over several epochs with a 5-fold cross validation technique where the training set had a mini-batch size of 10. Open-source Apnea ECG dataset collected from the PhysioNET bank was used in training, validating, and testing the model. Rectified Linear Unit (ReLU) activation function was used in the input and hidden layers of the network and sigmoid function was used as the output classifier. ADAM optimizer was used for optimization of the model while mean-squared-error (MSE) was used for calculating model loss. The final trained and validated model was implemented onto re-programmable digital hardware called Field-Programmable Gate Array (FPGA). The hardware implementation of the model yielded an accuracy of over 87 percent with a power consumption rate of around 52 W, which is 5x times lower than that of commercially available machine learning hardware accelerators. The proposed system design will be realized in integrated circuits on CMOS platform for developing energy-efficient, smart, wearable, and automated sleep apnea detection and screening device for adults.