Energy-Efficient Respiratory Anomaly Detection in Premature Newborn Infants

• URL: http://arxiv.org/abs/2202.10570v1
• Date: Mon, 21 Feb 2022 23:15:03 GMT
• Title: Energy-Efficient Respiratory Anomaly Detection in Premature Newborn Infants
• Authors: Ankita Paul, Md. Abu Saleh Tajin, Anup Das, William M. Mongan, and Kapil R. Dandekar
• Abstract summary: We propose a Deep Learning enabled wearable monitoring system for premature newborn infants. Respiratory cessation is predicted using signals that are collected wirelessly from a non-invasive wearable Bellypatch put on infant's body. We select a solution that achieves 93.33% accuracy with 18 times lower energy compared with baseline 1DCNN model.
• Score: 1.845415610921548
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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