## 6 Discussion
In one study [52], a bagging classifier achieved superior accuracy in sleep stage detection when a heuristic approach was applied (80.38 \(\pm\) 8.32% vs. 77.85 \(\pm\) 6.63), while the Cohen's kappa score was improved simultaneously (0.65 \(\pm\) 0.13 vs. 0.59 \(\pm\) 0.11). Another ML-based approach achieved a sensitivity and a specificity of 0.70 and 0.66, respectively, for the same purpose [56]. It is noteworthy that these ML-based applications have been found in our research to be unique to RIP and cannot be implemented with traditional statistical analysis. A binary K-means classifier in [11] achieved an 80% overall accuracy with \(<\)26% overall category-wise confusion for the detection of neonatal apnea and breathing asynchrony. Power output during exercise was studied in [60] with various predictive models, which showed moderate accuracy (R\({}^{2}\) = 0.56, mean absolute percentage error: 0.20-0.24) and need improvement. However, the fact that the data were obtained from a single subject is the greatest limitation of this study. We suggest repeating the same procedure with multiple subjects, including both male and female subjects. Moreover, the inclusion of subjects with respiratory diseases along with healthy subjects would further validate the results. There was a dependency on heart rate in this study to achieve the results, which could be further studied solely using RIP data. Apnea detection through machine learning showed promising results in [61], which were comparable to results obtained with other techniques in [12; 54; 55; 56]. However, the results from abdominal RIP data fell behind the results from nasal pressure data (accuracy: 90.3% vs. 78.5%, respectively). It is our recommendation to experiment with RIP data from both channels and the summation of both channels (RIPsum) for better results. Real-time detection of drowsiness in drivers through machine learning was studied in [62], which produced a high specificity and sensitivity (\(>\)96% and \(>\)90%, respectively). The implementation of such an approach could be implemented in real life along with vision-based drowsiness detection. Determination of the accuracy of a system implementing both methods simultaneously could be a topic for future research. However, the limitation of such implementations is the requirement of wearing an RIP belt while driving, which may be inconvenient and result in less compliance by drivers. Detection of breathing asynchrony in children was studied in [42], which benefits from adding inverse cumulative percentage (ICP) features. The training dataset included data from healthy subjects who breathed through a resistive load to simulate abnormal breathing, which could have been replaced with actual data from patients with neuro-muscular illnesses to represent actual scenarios. Moreover, this study could have been performed with a larger population size. Detection of COPD stages has been studied with machine learning in [63], which utilized instantaneous phase differences as a feature. However, the results suffered from some inaccuracies, which need to be improved for practical implementation. A larger dataset might have contributed to less error in the results. Extubation success prediction was performed in [41] in extreme preterm infants with promising results. As extubation fails in 25% of real-life cases, the >83% accuracy for the failure class results demonstrates the potential of such a system. However, the system produced some false-positive results in predicting extubation failure, so there is scope for improvement for real-life applications. It is our observation that the capability of AI to reveal hidden information from RIP data could be stretched further to replace traditional statistical analysis methods and revolutionize the RIP technique in the future. Moreover, problems related to signal processing, i.e., removal of noise and artifacts, filtering, and discarding of faulty signals, could be solved with the use of AI, as observed in the studies in this review.