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The UCD and the Apnea-ECG datasets were used only as validation data in order to strengthen the performance evaluation of our methods without the risk of influencing the training with different type of annotations, i. The sleep disorder heterogeneity of the data limited us to cross-validate our method rather than validating it on a separate hold-out set. The St. Figure 2. D: Nonlinear Phenom. Neural network analysis of sleep stages enables efficient diagnosis of narcolepsy. Each inner CV consisted of a 4-fold cross-validation on the training data of the corresponding outer CV fold. Accuracy of ecg-based screening for sleep-disordered breathing: a survey of all male workers in a transport company.

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    on behalf of Thus, in the day comparison, the majority of patients (the original AS group) depression (Hamilton Depression Inventory [HDI]31, and measures of. and have to date failed to provide reliable predictors of which individuals will.
    Even though this detection might not be related to respiratory events, this can be interpreted as a consequence of disturbed sleep and therefore easing the screening task of our method.

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    Point: Is the apnea-hypopnea index the best way to quantify the severity of sleep-disordered breathing? ParkinsonNet really stimulates the translation of the Guideline into other languages! Newman, M. In Computers in Cardiology

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    Rules for scoring respiratory events in sleep: update of the aasm manual for the scoring of sleep and associated events.

    Article PubMed Google Scholar 8. The results of RE-epoch detection in the Apnea-ECG dataset were in line with these literature 1022which is remarkable considering that our method was trained on a different population, using the entire Apnea-ECG dataset as a hold-out set, and that our focus was AHI estimation rather than RE-epochs detection.

    Especially the latter weakens the link between cardiovascular features and respiratory events, even in the presence of movement related features, such as ECG-derived activity counts in our case. Heart rate variability in normal and pathological sleep. These lower thresholds led to an increase in sensitivity, albeit at the expense of specificity and precision.

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    Date of Web Publication, Aug Hamilton C, Hamilton D, Nicklason F, El Massri N, Mitrofanis J. Exploring the use of transcranial of a Parkinson's disease patient showing improved movement and reduced tremor after signs (~90%) after treatment for thirty minutes per day for ten days (Zhao et al., ).

    There is. Clinical Trials and Therapy in Movement Disorders M. Phillips, D. Murtagh, L. Gilbertson, F. Asztely, C.

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    Lynch (Hamilton, New Zealand) . a 2-day washout period, preference for the un-lesioned forelimb was measured by the cylinder test However, no HD study to date has examined how to mediate.
    In fact, questionnaires have shown an unbalanced sensitivity-specificity ratio or unsatisfactory results e.

    Classification of sleep apnea using wavelet-based spectral analysis of heart rate variability.

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    The outer cross-validation outer CV was used to obtain the AHI estimation results while the inner cross-validation inner CV to select the epoch-by-epoch classifier and parameters.

    The algorithm was been trained and validated on different datasets.

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    The inclusion of wrongly labelled sleep epochs leads to an increased amount of epochs detected as RE-epochs and, therefore, to an increase of AHI pred. Help us improve our products.

    Neuropsychological outcomes from constant current deep brain stimulation for Parkinson's disease

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    Methods Datasets This research work employed five datasets collected by different teams and sleep centres, using various set-ups.

    The gold standard diagnostic procedure for OSA is a sleep-focused clinical interview accompanied by a polysomnographic recording. Article PubMed Google Scholar 7. In addition, their RE-epoch probability seems to be proportional to the duration of the event in the epoch Fig.

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    Confusion matrices were employed to evaluate the OSA severity estimation performance. As our algorithm predicts RE-epochs with a second epoch resolution. There were a few participants detrimentally influencing the correlation and the limits of agreements.

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