Real-time data analysis in health monitoring systems: A comprehensive systematic literature review

• Published in: Journal of biomedical informatics Vol. 127; p. 104009
• Main Authors: Paganelli, Antonio Iyda, Mondéjar, Abel González, da Silva, Abner Cardoso, Silva-Calpa, Greis, Teixeira, Mateus F., Carvalho, Felipe, Raposo, Alberto, Endler, Markus
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.03.2022
• Subjects: Algorithms; Data Analysis; Data mining; Data Mining - methods; Health care; Health monitoring systems; Machine Learning; Machine learning technique; Medical informatics; Monitoring, Physiologic; Real-time; Health care; Data analysis; Real-time; 0000; 1111; Health monitoring systems; Data mining; Machine learning technique; Medical informatics
• ISSN: 1532-0464; 1532-0480; 1532-0480
• DOI: 10.1016/j.jbi.2022.104009

[Display omitted] •A comprehensive and in-deep review of studies on health monitoring systems that performed data mining tasks in real-time.•A description of applied data analysis methods and their application domains.•A clear view of used sensors, extracted features, data sources, algorithms, and results for each data-mining task and application domain.•A critical review of the real-time features, results achieved, benefits, and limitations reported by the studies.•A set of directions for future research. Health monitoring systems (HMSs) capture physiological measurements through biosensors (sensing), obtain significant properties and measures from the output signal (perceiving), use algorithms for data analysis (reasoning), and trigger warnings or alarms (acting) when an emergency occurs. These systems have the potential to enhance health care delivery in different application domains, showing promising benefits for health diagnosis, early symptom detection, disease prediction, among others. However, the implementation of HMS presents challenges for sensing, perceiving, reasoning, and acting based on monitored data, mainly when data processing should be performed in real time. Thus, the quality of these diagnoses relies heavily on the data and data analysis methods applied. Data mining techniques have been broadly investigated in health systems; however, it is not clear what real-time data analysis techniques are best suited for each context. This work carries out a search in five scientific electronic databases to identify recent studies that investigated HMS using real-time data analysis techniques. Thirty-six research studies were selected after screening 2,822 works. Applied data analysis methods, application domains, utilized sensors, physiological parameters, extracted features, claimed benefits, limitations, datasets used, and published results were described, compared and analyzed. The findings indicate that machine learning methods are trending in such studies. There is no universal solution for all health domains; however, support vector machines are a predominant method. Among the application domains, cardiovascular disease is the most investigated. Most reviewed studies reported improvements in performing data mining tasks or operational modes of solutions. Although studies tested algorithms and presented promising results, those are particular for each experiment. This review gives a comprehensive overview of HMS real-time data analysis and points to directions for future research.