Machine Learning Based Hardware Model for a Biomedical System for Prediction of Respiratory Failure

This paper proposes a hardware design model of a machine learning based fully connected neural network for detection of respiratory failure among neonates in the Neonatal Intensive Care Unit (NICU). The model has been developed for a diagnostic system comprised of pyroelectric transducer based breathing monitor and a pulse oximeter that can detect apneic events. The input signal of the proposed system is a digitally converted sensory data from the sensors which is processed using machine learning model to detect if apnea condition has occurred in the patient. The accuracy rate of the proposed model is around 99 percent. The proposed design methodology enables the simplification of the models for future low-cost neural network-on-chip hardware implementation.