A Systematic Review of Time Series Classification Techniques Used in Biomedical Applications

Digital clinical measures collected via various digital sensing technologies such as smartphones, smartwatches, wearables, and ingestible and implantable sensors are increasingly used by individuals and clinicians to capture the health outcomes or behavioral and physiological characteristics of indi...

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Bibliographic Details
Published in:	Sensors (Basel, Switzerland) Vol. 22; no. 20; p. 8016
Main Authors:	Wang, Will Ke, Chen, Ina, Hershkovich, Leeor, Yang, Jiamu, Shetty, Ayush, Singh, Geetika, Jiang, Yihang, Kotla, Aditya, Shang, Jason Zisheng, Yerrabelli, Rushil, Roghanizad, Ali R, Shandhi, Md Mobashir Hasan, Dunn, Jessilyn
Format:	Journal Article
Language:	English
Published:	Switzerland MDPI AG 01-10-2022 MDPI
Subjects:	Algorithms Biomedical engineering Biomedical materials Classification Data processing Data science Datasets Deep learning digital clinical measures Electroencephalography Electronic implants feature engineering Health aspects Human subjects Humans Machine Learning Medical electronics Review Smartphone Smartphones Smartwatches Statistical models systematic review Technology application Time Factors Time series time series classification feature engineering machine learning systematic review digital clinical measures time series classification
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Summary:	Digital clinical measures collected via various digital sensing technologies such as smartphones, smartwatches, wearables, and ingestible and implantable sensors are increasingly used by individuals and clinicians to capture the health outcomes or behavioral and physiological characteristics of individuals. Time series classification (TSC) is very commonly used for modeling digital clinical measures. While deep learning models for TSC are very common and powerful, there exist some fundamental challenges. This review presents the non-deep learning models that are commonly used for time series classification in biomedical applications that can achieve high performance. We performed a systematic review to characterize the techniques that are used in time series classification of digital clinical measures throughout all the stages of data processing and model building. We conducted a literature search on PubMed, as well as the Institute of Electrical and Electronics Engineers (IEEE), Web of Science, and SCOPUS databases using a range of search terms to retrieve peer-reviewed articles that report on the academic research about digital clinical measures from a five-year period between June 2016 and June 2021. We identified and categorized the research studies based on the types of classification algorithms and sensor input types. We found 452 papers in total from four different databases: PubMed, IEEE, Web of Science Database, and SCOPUS. After removing duplicates and irrelevant papers, 135 articles remained for detailed review and data extraction. Among these, engineered features using time series methods that were subsequently fed into widely used machine learning classifiers were the most commonly used technique, and also most frequently achieved the best performance metrics (77 out of 135 articles). Statistical modeling (24 out of 135 articles) algorithms were the second most common and also the second-best classification technique. In this review paper, summaries of the time series classification models and interpretation methods for biomedical applications are summarized and categorized. While high time series classification performance has been achieved in digital clinical, physiological, or biomedical measures, no standard benchmark datasets, modeling methods, or reporting methodology exist. There is no single widely used method for time series model development or feature interpretation, however many different methods have proven successful.
Bibliography:	ObjectType-Article-2 SourceType-Scholarly Journals-1 ObjectType-Undefined-1 ObjectType-Feature-3 ObjectType-Review-4 content type line 23
ISSN:	1424-8220 1424-8220
DOI:	10.3390/s22208016