## 6 Discussion
RIP has been found to be effective in detecting sleep apnea, as apnea-hypopnea index (AHI) figures measured from RIP data have an acceptable agreement with the AHI figures derived from polysomnography. The breathing asynchrony produced by partial or complete cessation of breathing produces a relatively \"flat\" signal in RIPsum channels, which represents the summation of signals from thoracic and abdominal RIP belts. RIP appeared to be less sensitive for detection of apnea and hypopnea (AHI scoring) when compared to manual scoring in [12; 13; 54], which did not mention the BMIs of the subjects. However, RIP provided more sensitivity and specificity than sleep study results for AHI scoring in study [53], which was conducted exclusively on obese people. The reason for such differences in results may lie in the experimental and signal processing methodologies used, as well as subject BMIs, sample sizes and demographics, and criteria of manual scoring. Detection of sleep stages from RIP signals has also been studied in the literature, and results have been found to be moderately to strongly correlated with those of polysomnography. However, sleep stage classification with RIP is still in the experimental phase and requires further study. Frequency-based analysis focusing on lower frequencies (<1 Hz) might offer more insights in this regard, as suggested in [52; 55]. Based on the analysis of the results from the selected studies, we recommend RIP for AHI scoring in sleep studies, with RIP devices being embedded in wearable garments in cases where patient comfort and convenience are a priority. RIP is well-tolerated in infants for monitoring respiratory activities to identify postoperative apnea (POA) and respiratory distress, due its non-invasive nature. Phase shifting between thoracic and abdominal RIP channels is observed in respiratory distress in preterm infants, which indicates increased respiratory effort due to breathing insufficiency, which can be detected with RIP. Studies have been carried out on the validation of RIP measurements [48]; detection of POA [11]; observation of respiratory effort to adjust respiratory support [49], i.e., high-flow nasal cannula (HFNC) and continuous positive airway pressure (CPAP); and examination of infant positioning to reduce breathing effort [40] in infants. Though [50] demonstrated a strong agreement of RIP with esophageal manometry, signals with artifacts were excluded from the analysis. The classification of breathing in [11] takes movement of the infant into account but requires pre-processing of collected data prior to analysis, which negatively affects the feasibility of implementing such a system for real-time monitoring. In [51], 78% of the collected data were included in the final assessment, while the rest of the data were discarded due to poor signal quality. The necessity of including selected data indicates that the implementation of RIP could be challenging for continuous monitoring of breathing parameters due to movement and positioning of the infant. Removing movement-induced artifacts from raw data could resolve this issue to a certain extent, which might be addressed in future research. RIP has been studied in children to measure work of breathing (WoB) in normal subjects [10] and patients with neuromuscular illnesses [23] with the PneuRIPTM system. Though RIP measurements were validated in [10], the study only experimented on healthy subjects and simulated breathing insufficiency by adding a resistive load to the respiratory pathway. This method does not fulfil the criterion of representing patients with obstructive lung diseases in our eyes, as the vital capacity remains unchanged in a healthy subject even if additional resistance is added to the respiratory pathway. However, integration with handheld smart devices is an advantage of the PneuRIPTM system in the latter study, which provides conventionality with respect to the target user group.