Artificial Intelligence-Based Algorithms and Healthcare Applications of Respiratory Inductance Plethysmography: A Systematic Review

• Published in: Algorithms Vol. 17; no. 6; p. 261
• Main Authors: Rahman, Md. Shahidur, Chowdhury, Sowrav, Rasheduzzaman, Mirza, Doulah, A. B. M. S. U.
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.06.2024
• Subjects: Algorithms; apnea; Artificial intelligence; Breathing; Carbon dioxide; Chronic obstructive pulmonary disease; Cooperation; Data analysis; Health care; Human activity recognition; Illnesses; Inductance; Lung diseases; lung volume; Machine learning; Monitoring; non-invasive; Pandemics; Patient compliance; Patients; Plethysmography; Prognosis; Quality assessment; Research methodology; Respiration; Respiratory diseases; respiratory inductance plethysmography; Respiratory rate; Sensors; Spirometry; Systematic review; Technology application; Telemedicine; Usability; wearable sensors
• ISSN: 1999-4893; 1999-4893
• DOI: 10.3390/a17060261

Respiratory Inductance Plethysmography (RIP) is a non-invasive method for the measurement of respiratory rates and lung volumes. Accurate detection of respiratory rates and volumes is crucial for the diagnosis and monitoring of prognosis of lung diseases, for which spirometry is classically used in clinical applications. RIP has been studied as an alternative to spirometry and shown promising results. Moreover, RIP data can be analyzed through machine learning (ML)-based approaches for some other purposes, i.e., detection of apneas, work of breathing (WoB) measurement, and recognition of human activity based on breathing patterns. The goal of this study is to provide an in-depth systematic review of the scope of usage of RIP and current RIP device developments, as well as to evaluate the performance, usability, and reliability of ML-based data analysis techniques within its designated scope while adhering to the PRISMA guidelines. This work also identifies research gaps in the field and highlights the potential scope for future work. The IEEE Explore, Springer, PLoS One, Science Direct, and Google Scholar databases were examined, and 40 publications were included in this work through a structured screening and quality assessment procedure. Studies with conclusive experimentation on RIP published between 2012 and 2023 were included, while unvalidated studies were excluded. The findings indicate that RIP is an effective method to a certain extent for testing and monitoring respiratory functions, though its accuracy is lacking in some settings. However, RIP possesses some advantages over spirometry due to its non-invasive nature and functionality for both stationary and ambulatory uses. RIP also demonstrates its capabilities in ML-based applications, such as detection of breathing asynchrony, classification of apnea, identification of sleep stage, and human activity recognition (HAR). It is our conclusion that, though RIP is not yet ready to replace spirometry and other established methods, it can provide crucial insights into subjects’ condition associated to respiratory illnesses. The implementation of artificial intelligence (AI) could play a potential role in improving the overall effectiveness of RIP, as suggested in some of the selected studies.