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pmid: 22254656
Since the 1970s, various automatic sleep spindles procedures have been implemented and presented in the literature. Unfortunately, their results are not easily comparable because the databases, the assessment methods and the terminologies employed are often radically different. In this study, we propose a systematic assessment method for any automatic sleep spindles detection algorithm. We apply this assessment method to our own automatic detection process in order to illustrate and legitimate its use. We obtain a global sensitivity of 70.20%, for a false positive proportion (relative to the total number of visually scored sleep spindles) of only 26.44% (False positive rate = 1.38% and specificity = 98.62%).
Sleep Wake Disorders, Male, Adult, Polysomnography, Reproducibility of Results, Brain, Electroencephalography, Middle Aged, Sensitivity and Specificity, Pattern Recognition, Automated, Humans, Female, Diagnosis, Computer-Assisted, Sleep, Algorithms
Sleep Wake Disorders, Male, Adult, Polysomnography, Reproducibility of Results, Brain, Electroencephalography, Middle Aged, Sensitivity and Specificity, Pattern Recognition, Automated, Humans, Female, Diagnosis, Computer-Assisted, Sleep, Algorithms
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 84 | |
popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Top 10% | |
impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |