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

ABSTRACT: Background: 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. Objective: We performed a systematic review to characterize the techniques that are used in time s

SUBMITTER: Wang WK 

PROVIDER: S-EPMC9611376 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature