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Obstructive sleep apnea (OSA)-related intermittent hypoxaemia is a potential risk factor for different OSA comorbidities, for example cardiovascular disease. However, conflicting results are found as to whether intermittent hypoxaemia is associated with impaired vigilance. Therefore, we aimed to investigate how desaturation characteris-

SummaryOxygen saturation (SpO2)‐based parameters are more strongly linked to impaired daytime vigilance than the conventional diagnostic metrics in patients with obstructive sleep apnea (OSA). However, whether the association between SpO2‐based parameters and impaired daytime vigilance is modulated by sex, remains unknown. Hence, we investigated the interplay between sex and detailed SpO2‐based metrics and their association with impaired vigilance in patients with OSA. The study population consisted of 855 (473 males, 382 females) patients with suspected OSA who underwent overnight polysomnography and psychomotor vigilance task (PVT). The population was grouped by sex and divided into quartiles (Q1–Q4) based on median reaction times (RTs) in the PVT. In addition to conventional diagnostic metrics, desaturation severity (DesSev), fall severity (FallSev), and recovery severity (RecovSev) were compared between the sexes and between the best (Q1) and worst (Q4) performing quartiles by using cumulative distribution functions (CDFs). Additionally, sex‐specific covariate‐adjusted linear regression models were used to investigate the connection between the parameters and RTs. The CDFs showed significantly higher hypoxic load in Q4 in males compared to females. In addition, the DesSev (β = 8.05, p < 0.01), FallSev (β = 6.48, p = 0.02), RecovSev (β = 9.13, p < 0.01), and Oxygen Desaturation Index (β = 12.29, p < 0.01) were associated with increased RTs only in males. Conversely, the Arousal Index (β = 10.75–11.04, p < 0.01) was associated with impaired vigilance in females. The severity of intermittent hypoxaemia was strongly associated with longer RTs in males whereas the Arousal Index had the strongest association in females. Thus, the impact of hypoxic load on impaired vigilance seems to be stronger in males than females.

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Background: Finding components from multi-channel EEG signal for localizing and detection of onset of seizure is a new approach in biomedical signal analysis. Tensor-based approaches are utilized to fit the components into multi-dimensional array in recent works. Method: We initially decompose EEG signals into Beta band using Discrete Wavelet Transform. We compare patient templates with normal template for cross-wavelet analysis to obtain Wavelet cross spectrum and Wavelet cross coherence coefficients. Next we apply PARAFAC (Parallel Factorization) modeling, a three-way tensor-based representation in channel, frequency and time-points dimensions on features. Finally, we utilize ensemble classifier for detecting seizure-free, onset and seizure classes. Results: The clinical dataset for this work comprises of 5 normal subjects and 6 epileptiform patients. The classification performances of Wavelet cross spectrum features on PARAFAC model for Seizure detection using Ensemble Bagged-Trees classifier obtains highest 82.21% accuracy, while for Wavelet Coherence features it provides 84.76% accuracy. The results have been compared with well-known Fine Gaussian SVM, Weighted KNN and Ensemble Subspace KNN classifiers. Conclusions: The aim is to analyze data over three dimensions i.e., time, frequency and space (channels). This EEG based analysis is effective as an automatic method for detection of seizure before its actual manifestation.

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