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Manual sleep scoring for research purposes and for the diagnosis of sleep disorders is labor-intensive and often varies significantly between scorers, which has motivated many attempts to design automatic sleep stage classifiers. With the recent introduction of large, publicly available hand-scored polysomnographic data, and concomitant advances in machine learning methods to solve complex classification problems with supervised learning, the problem has received new attention, and a number of new classifiers that provide excellent accuracy. Most of these however have non-trivial barriers to use. We introduce the Greifswald Sleep Stage Classifier (GSSC), which is free, open source, and can be relatively easily installed and used on any moderately powered computer. In addition, the GSSC has been trained to perform well on a large variety of electrode set-ups, allowing high performance sleep staging with portable systems. The GSSC can also be readily integrated into brain-computer interfaces for real-time inference. These innovations were achieved while simultaneously reaching a level of accuracy equal to, or exceeding, recent state of the art classifiers and human experts, making the GSSC an excellent choice for researchers in need of reliable, automatic sleep staging.
Objective: The study aimed to test the reliability of a semi-structured telephone interview for the classification of headache disorders according to the ICHD-3.
Background: Questionnaire-based screening tools are often optimized for single primary headache diagnoses [e.g., migraine (MIG) and tension headache (TTH)] and therefore insufficiently represent the diagnostic precision of the ICHD-3, which limits epidemiological research of rare headache disorders. Brief semi-structured telephone interviews could be an effective alternative to improve classification.
Methods: A patient population representative of different primary and secondary headache disorders (n = 60) was recruited from the outpatient clinic (HSA) of a tertiary care headache center. These patients completed an established population-based questionnaire for the classification of MIG, TTH, or trigeminal autonomic cephalalgia (TAC). In addition, they received a semi-structured telephone interview call from three blinded headache specialists individually. The agreement of diagnoses made either using the questionnaires or interviews with the HSA diagnoses was evaluated.
Results: Of the 59 patients (n = 1 dropout), 24% had a second-order and 5% had a third-order headache disorder. The main diagnoses were as follows: frequent primary headaches with 61% MIG, 10% TAC, 9% TTH, and 5% rare primary and 16% secondary headaches. Second-order diagnosis was chronic migraine throughout, and third-order diagnoses were medication overuse headache and TTH. Agreement between main headaches from the HSA was significantly better for the telephone interview than for the questionnaire (questionnaire: κ = 0.330; interview: κ = 0.822; p < 0.001). Second-order diagnoses were not adequately captured by questionnaires, while there was a trend for good agreement with the telephone interview (κ = 0.433; p = 0.074). Headache frequency and psychiatric comorbidities were independent predictors of HSA and telephone interview agreement. Male sex, headache frequency, severity, and depressive disorders were independently predictive for agreement between the questionnaire and HSA. The telephone interview showed high sensitivity (≥71%) and specificity (≥92%) for all primary headache disorders, whereas the questionnaire was below 50% in either sensitivity or specificity.
Conclusion: The semi-structured telephone interview appears to be a more reliable tool for accurate diagnosis of headache disorders than self-report questionnaires. This offers the potential to improve epidemiological headache research and care even in underserved areas.