# Energy-Efficient Respiratory Anomaly Detection in Premature Newborn Infants

arXiv: https://arxiv.org/abs/2202.10570 · HTML: https://mlsanity.com/paper/2202.10570/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 68 references that are in the map.
- First posted on arXiv 21 Feb 2022.

## Builds on (most cited first, 24 of 24)

- [Deep Convolutional Networks on Graph-Structured Data](https://mlsanity.com/paper/1506.05163/) (2015)
- [Going Deeper in Spiking Neural Networks: VGG and Residual Architectures](https://mlsanity.com/paper/1802.02627/) (2018)
- [Low-bit Quantization of Neural Networks for Efficient Inference](https://mlsanity.com/paper/1902.06822/) (2019)
- [Automatic heterogeneous quantization of deep neural networks for low-latency inference on the edge for particle detectors](https://mlsanity.com/paper/2006.10159/) (2020)
- [A scalable multi-core architecture with heterogeneous memory structures for Dynamic Neuromorphic Asynchronous Processors (DYNAPs)](https://mlsanity.com/paper/1708.04198/) (2017)
- [Mapping Spiking Neural Networks to Neuromorphic Hardware](https://mlsanity.com/paper/1909.01843/) (2019)
- [Unsupervised Heart-rate Estimation in Wearables With Liquid States and A Probabilistic Readout](https://mlsanity.com/paper/1708.05356/) (2017)
- [Mapping of Local and Global Synapses on Spiking Neuromorphic Hardware](https://mlsanity.com/paper/1908.08024/) (2019)
- [Compiling Spiking Neural Networks to Neuromorphic Hardware](https://mlsanity.com/paper/2004.03717/) (2020)
- [NeuroXplorer 1.0: An Extensible Framework for Architectural Exploration with Spiking Neural Networks](https://mlsanity.com/paper/2105.01795/) (2021)
- [Endurance-Aware Mapping of Spiking Neural Networks to Neuromorphic Hardware](https://mlsanity.com/paper/2103.05707/) (2021)
- [Heartbeat Classification in Wearables Using Multi-layer Perceptron and Time-Frequency Joint Distribution of ECG](https://mlsanity.com/paper/1908.06865/) (2019)
- [Enabling Resource-Aware Mapping of Spiking Neural Networks via Spatial Decomposition](https://mlsanity.com/paper/2009.09298/) (2020)
- [Thermal-Aware Compilation of Spiking Neural Networks to Neuromorphic Hardware](https://mlsanity.com/paper/2010.04773/) (2020)
- [Special Session: Reliability Analysis for ML/AI Hardware](https://mlsanity.com/paper/2103.12166/) (2021)
- [On the Role of System Software in Energy Management of Neuromorphic Computing](https://mlsanity.com/paper/2103.12231/) (2021)
- [Dynamic Reliability Management in Neuromorphic Computing](https://mlsanity.com/paper/2105.02038/) (2021)
- [Improving Dependability of Neuromorphic Computing With Non-Volatile Memory](https://mlsanity.com/paper/2006.05868/) (2020)
- [Run-time Mapping of Spiking Neural Networks to Neuromorphic Hardware](https://mlsanity.com/paper/2006.06777/) (2020)
- [A Case for Lifetime Reliability-Aware Neuromorphic Computing](https://mlsanity.com/paper/2007.02210/) (2020)
- [Reliability-Performance Trade-offs in Neuromorphic Computing](https://mlsanity.com/paper/2009.12672/) (2020)
- [Design Methodologies for Reliable and Energy-efficient PCM Systems](https://mlsanity.com/paper/2011.13959/) (2020)
- [Compiling Spiking Neural Networks to Mitigate Neuromorphic Hardware Constraints](https://mlsanity.com/paper/2011.13965/) (2020)
- [Improving Inference Lifetime of Neuromorphic Systems via Intelligent Synapse Mapping](https://mlsanity.com/paper/2106.09104/) (2021)

## Built on by (most cited first, 2 of 2)

- [Review of medical data analysis based on spiking neural networks](https://mlsanity.com/paper/2212.02234/) (2022)
- [Aggregating empirical evidence from data strategy studies: a case on model quantization](https://mlsanity.com/paper/2505.00816/) (2025)

## Abstract (arXiv)

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.