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Objective: Aberrant driving behavior (ADB) decreases road safety and is particularly relevant for urban bus drivers, who are required to drive daily shifts of considerable duration. Although numerous frameworks based on human physiological features have been applied to predict ADB, the research remains at an early stage. This study used heart rate variability (HRV) parameters to establish ADB occurrence prediction models with various machine learning approaches. Methods: Twelve Taiwanese urban bus drivers were recruited for four consecutive days of naturalistic driving data collection (from their routine routes) between March and April 2020; driving behaviors and physiological signals were obtained from provided devices. Weather and traffic congestion information was determined from public data, while sleep quality and professional driving experience were self-reported. To develop the ADB prediction model, several machine learning models—logistic regression, random forest, naive Bayes, support vector machine, and gated recurrent unit (GRU)—were trained and 10-fold cross-validated by using the testing data. Results: Most drivers with ADB reported deficient sleep quality (≤80%), with significantly higher mean scores on the Karolinska Sleepiness Scale and driver behavior questionnaire subcategory of lapses and errors than drivers without ADB. Next, HRV indices significantly differed between the measurement of a pre-ADB event and a baseline. The accuracy of the GRU models ranged from 78.84% ± 1.49% to 89.57% ± 1.31%. Conclusion: Drivers with ADB tend to have inadequate sleep quality, which may increase their fatigue levels and impair driving performance. The established time-series models can be considered for ADB occurrence prediction among urban bus drivers.

Obstructive sleep apnea syndrome (OSAS) has mostly been examined using in-laboratory polysomnography (Lab-PSG), which may overestimate severity. This study compared sleep parameters in different environments and investigated the association between the plasma levels of neurochemical biomarkers and sleep parameters.Thirty Taiwanese participants underwent Lab-PSG while wearing a single-lead electrocardiogram patch. Participants' blood samples were obtained in the morning immediately after the recording. Participants wore the patch for the subsequent three nights at home. Sleep disorder indices were calculated, including the apnea-hypopnea index (AHI), chest effort index, and cyclic variation of heart rate index (CVHRI). The 23 eligible participants' derived data were divided into the normal-to-moderate (N-M) group and the severe group according to American Association of Sleep Medicine (AASM) guidelines (Lab-PSG) and the recommendations of a previous study (Rooti Rx). Spearman's correlation was used to examine the correlations between sleep parameters and neurochemical biomarker levels.The mean T-Tau protein level was positively correlated with the home-based CVHRI (r = 0.53, p 0.05), whereas no significant correlation was noted between hospital-based CVHRI and the mean T-tau protein level (r = 0.25, p = 0.25). The home-based data revealed that the mean T-Tau protein level in the severe group was significantly higher than that in the N-M group (severe group: 24.75 ± 6.16 pg/mL, N-M group: 19.65 ± 3.90 pg/mL; p 0.05). Furthermore, the mean in-hospital CVHRI was higher than the mean at-home values (12.16 ± 13.66 events/h).Severe OSAS patients classified by home-based CVHRI demonstrated the higher T-Tau protein level, and CVHRI varied in different sleep environments.

Purpose: Obstructive sleep apnoea (OSA) is a global health concern, and polysomnography (PSG) is the gold standard for assessing OSA severity. However, the sleep parameters of home-based and in-laboratory PSG vary because of environmental factors, and the magnitude of these discrepancies remains unclear.Methods: We enrolled 125 Taiwanese patients who underwent PSG while wearing a single-lead electrocardiogram patch (RootiRx). After the PSG, all participants were instructed to continue wearing the RootiRx over the 3 subsequent nights. Scores on OSA indexes, namely the apnoea–hypopnea index, chest effort index (CEI), cyclic variation of heart rate index (CVHRI), and combined CVHRI and CEI (Rx index), were determined. The patients were divided into 3 groups based on PSG-determined OSA severity. The variables (various severity groups and environmental measurements) were subjected to mean comparisons and their correlations were examined by Pearson’s correlation coefficient. Results: The hospital-based CVHRI, CEI, and Rx index differed significantly among the severity groups. All 3 groups exhibited a significantly lower percentage of supine sleep time in the home-based assessment relative to in the hospital-based assessment. Significant positive correlations were noted between the variations in the supine percentage (∆Supine%) and the OSA indexes. For the patients with high sleep efficiency (≥ 80%), significant correlations were observed between the ∆Supine% and ∆Rx index.Conclusion: The high supine percentage of sleep may cause OSA indexes’ overestimation in hospital-based PSG. Sleep recording at home with patch-type wearable devices may aid accurate OSA diagnosis.

Dangerous driving behaviours contribute significantly to road accidents. Researchers have developed numerous models for predicting dangerous behaviours. However, these models have remained at the development stage. This paper proposes using a dynamic weight moving average (DWMA) method for processing heart rate variability (HRV) indices and establishing prediction models using long short-term memory (LSTM) networks. The changes in HRV indices between baseline and pre-event stages were also investigated. Thirty-three Taiwanese commercial drivers, which were 19 urban drives and 14 highway drivers, were recruited (between September 2019 and June 2020). Their driving behaviours and physiological signals during tasks were obtained by navigation software and an HRV watch. The DWMA and exponential moving average were applied to process the physiological signals. The derived data set was split into training and testing sets (ratio: 80% to 20%). To establish the models, the LSTM networks were trained using the training set and K-fold cross-validation (K = 10). Prediction performance was evaluated by sensitivity, specificity, and accuracy. For the urban drivers, the significantly raised values in the normalized low-frequency spectrum and the sympathovagal balance index were found. The significantly elevated values in the standard deviation of NN intervals were observed. For the highway drivers, the significantly increased heart rate and root mean square of successive RR interval differences can be observed. Besides, the LSTM models based on DWMA demonstrated the highest accuracy in urban and highway groups (Urban driving group: 80.31%, 95% confidence interval: 84.65-91.71%; Highway driving group: 80.70%, 95% confidence interval: 72.25-87.49%). The authors recommend using these models to prevent dangerous driving behaviours.

Introduction Previous studies indicated the accumulation of neurodegenerative protein may be caused by higher Obstructive sleep apnea syndrome (OSAS) severity. However, the association between arousal-related parameters induced by OSAS and the amyloid burden remains unclear. The aim of this study is to investigate the association between arousal threshold (ArTH) and neurochemical protein accumulation in OSAS patients. Methods Suspected OSAS participants were performed Mini-mental status examination (MMSE) and full-night polysomnography (PSG) in the sleep center of Taipei Medical University Shuang Ho Hospital, Taiwan. On the same morning, the blood samples were obtained from the participants. The concentrations of total Tau (T-Tau) and amyloid beta peptide 42 (Aβ42) were quantified by ultra-sensitive immunomagnetic reduction assays. An overall of 23 participants were enrolled and classified into Low ArTH group (n=12) and High ArTH group (n=11) based on low ArTH criteria. Regarding the statistical methods, for categorical variables and continuous variables, Fisher’s exact test and Mann-Whitney U test were performed to investigate the differences between groups, respectively. The associations between biomarkers concentrations and PSG parameters were assessed by Spearman’s correlation. Results Regarding the demographic characteristics in two subgroups, significantly lower body-mass index and OSAS severity were noted in Low ArTH group (p Conclusion OSAS patients with low ArTH have higher neurochemical biomarker levels. Also, the significantly positive correlations between arousals and biomarkers were observed in that group. Support (if any)