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Objective evaluation metrics for automatic classification of EEG events by Ziyabari, Saeedeh; Shah, Vinit; Golmohammadi, Meysam; Obeid, Iyad; Picone, Joseph is a scholarly article available to read on EtoBox.

What is Objective evaluation metrics for automatic classification of EEG events about?

The evaluation of machine learning algorithms in biomedical fields for applications involving sequential data lacks standardization. Common quantitative scalar evaluation metrics such as sensitivity and specificity can often be misleading depending on the requirements of the application. Evaluation metrics must ultimately reflect the needs of users yet be sufficiently sensitive to guide algorithm development. 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 hours, 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 a need for a single scalar figure of merit. In this paper, 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 Corpus. We show that two promising metrics are a measure based on a co

Author

Ziyabari, Saeedeh; Shah, Vinit; Golmohammadi, Meysam; Obeid, Iyad; Picone, Joseph

Published

2017

Language

EN