# Energy-Efficient Respiratory Anomaly Detection in Premature Newborn Infants
## 1 Introduction  
A premature newborn infant is one who is born more than three weeks before the estimated due date. Common health problems of these infants include Apnea of Prematurity (AOP), which is a pause in breathing for 15 to 20 s or more [1], and Neonatal Respiratory Distress Syndrome (NRDS), which is shallow breathing and a sharp pulling in of the chest below and between the ribs with each breath [2]. Precise respiratory monitoring is often necessary to detect AOP and NRDS in premature newborn infants and initiate medical interventions as required [3]. Wired monitoring techniques are invasive and can be obtrusive to the patient. Therefore, non-invasive respiratory monitoring techniques are recommended by pediatricians to increase the comfort of infants and facilitate continuous home monitoring [4]. We have studied the use of wearable technologies in the respiratory monitoring of infants. To this end, we use the Bellypatch (see Figure 1), a wearable smart garment thatutilizes a knitted fabric antenna and passively reflects wireless signals without requiring a battery or wired connection [5; 6; 7]. The Bellypatch fabric stretches and moves as the infant breathes, contracts muscles, and moves about in space; the physical properties of the radio frequency (RF) energy reflected by the antenna change with these movements. These perturbations in RF-reflected properties enable detection and estimation of the infant's respiration rate [8]. There are other possibilities of collecting heart rate [9], movement of the extremities [10], and detection of diaper moisture [11] using RF for medical practice. The Bellypatch operates in the 900 MHz Industrial, Scientific, and Medical (ISM) frequency band using Radio Frequency Identification (RFID) interrogation. An RFID interrogator emits an interrogation signal multiple times per second (often 30-100 interrogations per second). In typical RFID technology deployments, only a single interrogation response is needed for inventory purposes; however, many interrogations are sent to overcome collisions among the responding RFID tags and other signal interference or loss. We exploit this redundant interrogation to sample the state of the antenna each time it is successfully interrogated. Specifically, we observe changes in the received signal strength indicator (RSSI), phase angle, and successful interrogation rate to estimate respiratory properties of the wearer's state as well as other biosignals. The use of passive RFID enables a wireless and unobtrusive wearable device that requires no batteries to operate; the interrogation signal itself is sufficient to power the worn RFID tag for each interrogation. However, path loss, multipath fading, and collision mitigation among the tags in the field require signal denoising and interpolation of the received signal as well as intelligent algorithms for signal processing and estimation. Because these biomedical estimates require real-time or near-real-time processing and may take place on low-power embedded or portable systems, it is desirable to utilize techniques that place minimal constraints on power consumption, online training, and processing latency. To this end, we propose a deep-learning-enabled respiratory classification approach. At the core of this approach is a five stage pipeline involving data collection and labeling, feature selection, deep learning model selection with hyperparameter optimization, model training and validation, and model testing and deployment. We use a Laerdal SimBaby programmable infant mannequin (see Figure 1) to collect respiratory data using the sensors attached on the Bellypatch. We use a 1-D convolutional neural network (1 DCNN) for classification of the features extracted from the SimBaby sensor data. The 1 DCNN model consists of one convolution layer, one polling layer, and three fully connected layers, achieving 97.15% classification accuracy (see Section 6.1).