Key result
Wearable sensors feature in ~79% of IoT heart sound studies for cardiovascular disease identification.
Systematic Review (n=58)
This systematic review highlights a growing trend in using wearable sensors, digital stethoscopes, and machine learning algorithms for the identification and prediction of cardiovascular disorders through heart sounds.
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Wearables and ML predominate in heart sound CVD detection literature; supports tech adoption trends while leaving clinical validation open.
This article presents a systematic mapping study dedicated to conduct a literature review on machine learning and IoT applied in the identification of diseases through heart sounds. This research was conducted between January 2010 and July 2021, considering IEEE Xplore, PubMed Central, ACM Digital Library, JMIR—Journal of Medical Internet Research, Springer Library, and Science Direct. The initial search resulted in 4372 papers, and after applying the inclusion and exclusion criteria, 58 papers were selected for full reading to answer the research questions. The main results are: of the 58 articles selected, 46 (79.31%) mention heart rate observation methods with wearable sensors and digital stethoscopes, and 34 (58.62%) mention care with machine learning algorithms. The analysis of the studies based on the bibliometric network generated by the VOSviewer showed in 13 studies (22.41%) a trend related to the use of intelligent services in the prediction of diagnoses related to cardiovascular disorders.
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Brites et al. (2021) conducted a systematic review in Cardiovascular diseases (n=58). Machine learning and IoT was evaluated on Use of wearable sensors, digital stethoscopes, and machine learning algorithms. Machine learning and IoT applications for cardiovascular disease identification through heart sounds were reviewed in 58 articles, with 79.31% using wearable sensors and 58.62% using machine learning.
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