How to evaluate your medical time series classification?

• Main Authors: Wang, Yihe, Li, Taida, Yan, Yujun, Song, Wenzhan, Zhang, Xiang
• Format: Journal Article
• Language: English
• Published: 03.10.2024
• Subjects: Computer Science - Computational Engineering, Finance, and Science
• DOI: 10.48550/arxiv.2410.03057

Medical time series (MedTS) play a critical role in many healthcare applications, such as vital sign monitoring and the diagnosis of brain and heart diseases. However, the existence of subject-specific features poses unique challenges in MedTS evaluation. Inappropriate evaluation setups that either exploit or overlook these features can lead to artificially inflated classification performance (by up to 50% in accuracy on ADFTD dataset): this concern has received little attention in current research. Here, we categorize the existing evaluation setups into two primary categories: subject-dependent and subject-independent. We show the subject-independent setup is more appropriate for different datasets and tasks. Our theoretical analysis explores the feature components of MedTS, examining how different evaluation setups influence the features that a model learns. Through experiments on six datasets (spanning EEG, ECG, and fNIRS modalities) using four different methods, we demonstrate step-by-step how subject-dependent utilizes subject-specific features as a shortcut for classification and leads to a deceptive high performance, suggesting that the subject-independent setup is more precise and practicable evaluation setup in real-world. This comprehensive analysis aims to establish clearer guidelines for evaluating MedTS models in different healthcare applications. Code to reproduce this work in \url{https://github.com/DL4mHealth/MedTS_Evaluation}.