Objective evaluation metrics for automatic classification of EEG events

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 concept borrowed from the spoken term detection literature, Actual Term-Weighted Value (ATWV), and a new metric, Time-Aligned Event Scoring (TAES), that accounts for the temporal alignment of the hypothesis to the reference annotation. We also demonstrate that state of the art technology based on deep learning, though impressive in its performance, still needs significant improvement before it meets very strict user acceptance criteria.Comment: 22 pages, 11 figures, 9 table

Acceptance testing of a spoken language translation system

This paper describes an acceptance test procedure for evaluating a spoken language translation system between Catalan and Spanish. The procedure consists of two independent tests. The first test was an utterance-oriented evaluation for determining how the use of speech benefits communication. This test allowed for comparing relative performance of the different system components, explicitly: source text to target text, source text to target speech, source speech to target text, and source speech to target speech. The second test was a task-oriented experiment for evaluating if users could achieve some predefined goals for a given task with the state of the technology. Eight subjects familiar with the technology and four subjects not familiar with the technology participated in the tests. From the results we can conclude that state of technology is getting closer to provide effective speech-to-speech translation systems but there is still lot of work to be done in this area. No significant differences in performance between users that are familiar with the technology and users that are not familiar with the technology was evidenced. This constitutes, as far as we know, the first evaluation of a Spoken Translation System that considers performance at both, the utterance level and the task level. 1