Reaction time in psychomotor vigilance task is related to hypoxic load in males with sleep apnea.

Oxygen 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 (ß = 8.05, p < 0.01), FallSev (ß = 6.48, p = 0.02), RecovSev (ß = 9.13, p < 0.01), and Oxygen Desaturation Index (ß = 12.29, p < 0.01) were associated with increased RTs only in males. Conversely, the Arousal Index (ß = 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.

Obstructive sleep apnea-related intermittent hypoxaemia is associated with impaired vigilance.

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 characteristics differ between the non-impaired vigilance and impaired vigilance patient groups formed based on psychomotor vigilance task (PVT) performance and compared with traditional OSA severity parameters. The study population comprised 863 patients with suspected OSA who underwent a PVT test before polysomnography. The conventional OSA parameters, for example, the apnea-hypopnea index, oxygen desaturation index, and arousal index were computed. Furthermore, the median desaturation area, fall area, recovery area, and desaturation depth were computed with the pre-event baseline reference and with reference to the 100% oxygen saturation level. Patients were grouped into best- and worst-performing quartiles based on the number of lapses in PVT (Q1: PVT lapses <5 and Q4: PVT lapses >36). The association between parameters and impaired vigilance was evaluated by cumulative distribution functions (CDFs) and binomial logistic regression. Based on the CDFs, patients in Q4 had larger desaturation areas, recovery areas, and deeper desaturations when these were referenced to 100% saturation compared with Q1. The odds ratio (OR) of the median desaturation area (OR = 1.56), recovery area (OR = 1.71), and depth (OR = 1.65) were significantly elevated in Q4 in regression models. However, conventional OSA parameters were not significantly associated with impaired vigilance (ORs: 0.79-1.09). Considering desaturation parameters with a 100% SpO2 reference in the diagnosis of OSA could provide additional information on the severity of OSA and related daytime vigilance impairment.

Epileptic-seizure onset detection using PARAFAC model with cross-wavelet transformation on multi-channel EEG.

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 arrays in recent works. We initially decompose EEG signals into Beta band using discrete wavelet transform (DWT). We compare patient templates with normal template for cross-wavelet analysis to obtain Wavelet cross spectrum (WCS) and Wavelet cross coherence coefficients. Next we apply parallel factorization (PARAFAC) modeling, a three-way tensor-based representation in channel, frequency and time-points dimensions on features. Finally, we utilize the ensemble classifier for detecting seizure-free, onset and seizure classes. The clinical dataset for this work comprises of 5 normal subjects and 6 epileptiform patients. The classification performances of WCS features on PARAFAC model for Seizure detection using Ensemble Bagged-Trees classifier obtains 82.21% accuracy, while for Wavelet Coherence features, it provides higher 84.76% accuracy. The results have been compared with well-known Fine Gaussian SVM, Weighted KNN and Ensemble Subspace KNN classifiers. The aim is to analyze data over three dimensions namely, time, frequency and space (channels). This EEG based analysis is significant and effective as an automatic method for detection of seizure before its actual manifestation.