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Objective Evaluation Metrics for Automatic Classification of EEG Events - 2021

Objective Evaluation Metrics For Automatic Classification Of Eeg Events

Research Area:  Machine Learning

Abstract:

The evaluation of machine learning algorithms in biomedical fields for applications involving sequential data lacks both rigor and standardization. Common quantitative scalar evaluation metrics such as sensitivity and specificity can often be misleading and not accurately integrate application requirements. Evaluation metrics must ultimately reflect the needs of users yet be sufficiently sensitive to guide algorithm development. For example, feedback from critical care clinicians who use automated event detection software in clinical applications has been overwhelmingly emphatic that a low false alarm rate, typically measured in units of the number of errors per 24 h, is the single most important criterion for user acceptance. Though using a single metric is not often as insightful as examining performance over a range of operating conditions, there is, nevertheless, a need for a single scalar figure of merit. In this chapter, we discuss the deficiencies of existing metrics for a seizure detection task and propose several new metrics that offer a more balanced view of performance. We demonstrate these metrics on a seizure detection task based on the TUH EEG Seizure Corpus. We introduce two promising metrics: (1) a measure based on a concept borrowed from the spoken term detection literature, actual term-weighted value, and (2) a new metric, time-aligned event scoring (TAES), which accounts for the temporal alignment of the hypothesis to the reference annotation. We demonstrate that state-of-the-art technology based on deep learning, though impressive in its performance, still needs significant improvement before it will meet very strict user acceptance guidelines.

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Author(s) Name:  Vinit Shah, Meysam Golmohammadi, Iyad Obeid & Joseph Picone

Journal name:  Biomedical Signal Processing

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Publisher name:  Springer

DOI:  10.1007/978-3-030-67494-6_8

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