Precise monitoring of respiratory rate in premature infants is essential to
initiate medical interventions as required. Wired technologies can be invasive
and obtrusive to the patients. We propose a Deep Learning enabled wearable
monitoring system for premature newborn infants, where respiratory cessation is
predicted using signals that are collected wirelessly from a non-invasive
wearable Bellypatch put on infant's body. We propose a five-stage design
pipeline involving data collection and labeling, feature scaling, model
selection with hyperparameter tuning, model training and validation, model
testing and deployment. The model used is a 1-D Convolutional Neural Network
(1DCNN) architecture with 1 convolutional layer, 1 pooling layer and 3
fully-connected layers, achieving 97.15% accuracy. To address energy
limitations of wearable processing, several quantization techniques are
explored and their performance and energy consumption are analyzed. We propose
a novel Spiking-Neural-Network(SNN) based respiratory classification solution,
which can be implemented on event-driven neuromorphic hardware. We propose an
approach to convert the analog operations of our baseline 1DCNN to their
spiking equivalent. We perform a design-space exploration using the parameters
of the converted SNN to generate inference solutions having different accuracy
and energy footprints. We select a solution that achieves 93.33% accuracy with
18 times lower energy compared with baseline 1DCNN model. Additionally the
proposed SNN solution achieves similar accuracy but with 4 times less energy.

本研究提出使用深度学习技术的可穿戴式监测系统，采用无创的 Bellypatch 设备从早产儿的身体上实时采集信号，对呼吸停止等问题进行预测和监测。将传统的 1DCNN 模型转换为基于脉冲神经网络的模型，实现了 93.33% 的准确率和比基础模型低 18 倍的能量消耗。