# Energy-Efficient Respiratory Anomaly Detection in Premature Newborn Infants
## 1 Introduction
This is higher than state-of-the-art accuracy obtained using machine learning techniques such as Support Vector Machine (SVM), Logistic Regression (LR), and Random Forest (RF), all of which are proposed for the respiratory classification of infants. Hyperparameters of this 1 DCNN model are selected using a Grid Search method, which is discussed in Section 3.4, and the model parameters are trained using the Backpropagation Algorithm [12]. Our model is trained using the respiratory data on an external work station. We also test the model in the workstation. Figure 1: (**left**) A smart-fabric Bellyband and (**right**) Bellypatch can be integrated into wearable garments to enable wireless and passive biomedical monitoring in infants. (**a**) top and (**b**) bottom view of the Bellypatch. Since we intend to deploy the trained network on a wearable device, we conduct energy efficiency explorations to find which approach provides the least power consumption and maximum accuracy. We show that the energy consumption of our baseline 1 DCNN model is considerably higher, which makes it difficult to implement the same on the Bellypatch due to its limited power availability. Our goal is to minimize the energy consumption, thus allowing more processing within a given power budget. Therefore, we propose several quantization approaches involving limiting the bit-precision of the model parameters. We show that in order to achieve a significant reduction in energy, the model accuracy can be considerably lower. Therefore, model quantization may not be the best solution to implement respiratory classification on the Bellypatch. Finally, we propose a novel respiratory classification solution enabled by spiking neural network (SNN) [13], which can be implemented on event-driven neuromorphic hardware such as TrueNorth [14], Loihi [15], and DYNAPs [16]. We perform design-space exploration using SNN parameters, obtaining SNN solutions with different accuracy and energy. We select a solution that leads to 93.33% accuracy with 18\(\times\) lower energy than the baseline 1 DCNN model. This SNN-based solution has similar accuracy as the best performing quantized CNN model with 4\(\times\) lower energy. This is particularly useful for wearable devices that are used for bio signals monitoring using less energy such as the Human++ [17]. Overall, the SNN-based approach introduces two additional stages in our design pipeline: model conversion and SNN parameter tuning, making the overall approach a seven-stage pipeline. Using this seven stage design pipeline, we show that the accuracy is significantly higher than all prior solutions, with considerably lower energy, making this solution extremely relevant for the battery-less Bellypatch. The remainder of this paper is organized as follows. Related works on respiratory classification are discussed in Section 2. The five-stage design pipeline is described in Section 3. Model quantization techniques are introduced in Section 4. The SNN approach to respiratory classification is formulated in Section 5. The proposed approach is evaluated in Section 6 and the paper is concluded in Section 7.