Long-term night-shift work is associated with accelerates brain aging and worsens N3 sleep in female nurses.

BACKGROUND: Shift work disrupts circadian rhythms and alters sleep patterns, resulting in various health problems. To quantitatively assess the impact of shift work on brain health, we evaluated the brain age index (BAI) derived from sleep electroencephalography (EEG) results in night-shift workers and compared it with that in daytime workers. METHODS: We studied 45 female night shift nurses (mean age: 28.2 ± 3.3 years) and 44 female daytime workers (30.5 ± 4.7 years). Sleep EEG data were analyzed to calculate BAI. The BAI of night shift workers who were asleep during the daytime with those of daytime workers who were asleep at night were statistically compared to explore associations between BAI, duration of shift work, and sleep quality. RESULTS: Night-shift workers exhibited significantly higher BAI (2.14 ± 6.04 vs. 0 ± 5.35), suggesting accelerated brain aging and altered sleep architecture, including reduced delta and sigma wave frequency activity during non-rapid eye movement sleep than daytime workers. Furthermore, poor deep sleep quality, indicated by a higher percentage of N1, lower percentage of N3, and higher arousal index, was associated with increased BAI among shift workers. Additionally, a longer duration of night-shift work was correlated with increased BAI, particularly in older shift workers. CONCLUSION: Night-shift work, especially over extended periods, may be associated with accelerated brain aging, as indicated by higher BAI and alterations in sleep architecture. Interventions are necessary to mitigate the health impacts of shift work. Further research on the long-term effects and potential strategies for sleep improvement and mitigating brain aging in shift workers is warranted.

Are there effects of light exposure on daytime sleep for rotating shift nurses after night shift?: an EEG power analysis.

Introduction: Night-shift workers often face various health issues stemming from circadian rhythm shift and the consequent poor sleep quality. We aimed to study nurses working night shifts, evaluate the electroencephalogram (EEG) pattern of daytime sleep, and explore possible pattern changes due to ambient light exposure (30 lux) compared to dim conditions (<5 lux) during daytime sleep. Moethods: The study involved 31 participants who worked night shifts and 24 healthy adults who had never worked night shifts. The sleep macro and microstructures were analyzed, and electrophysiological activity was compared (1) between nighttime sleep and daytime sleep with dim light and (2) between daytime sleep with dim and 30 lux light conditions. Results: The daytime sleep group showed lower slow or delta wave power during non-rapid eye movement (NREM) sleep than the nighttime sleep group. During daytime sleep, lower sigma wave power in N2 sleep was observed under light exposure compared to no light exposure. Moreover, during daytime sleep, lower slow wave power in N3 sleep in the last cycle was observed under light exposure compared to no light exposure. Discussion: Our study demonstrated that night shift work and subsequent circadian misalignment strongly affect sleep quality and decrease slow and delta wave activities in NREM sleep. We also observed that light exposure during daytime sleep could additionally decrease N2 sleep spindle activity and N3 waves in the last sleep cycle.

Predicting the impact of CPAP on brain health: A study using the sleep EEG-derived brain age index.

OBJECTIVE: This longitudinal study investigated potential positive impact of CPAP treatment on brain health in individuals with obstructive sleep Apnea (OSA). To allow this, we aimed to employ sleep electroencephalogram (EEG)-derived brain age index (BAI) to quantify CPAP's impact on brain health and identify individually varying CPAP effects on brain aging using machine learning approaches. METHODS: We retrospectively analyzed CPAP-treated (n = 98) and untreated OSA patients (n = 88) with a minimum 12-month follow-up of polysomnography. BAI was calculated by subtracting chronological age from the predicted brain age. To investigate BAI changes before and after CPAP treatment, we compared annual ΔBAI between CPAP-treated and untreated OSA patients. To identify individually varying CPAP effectiveness and factors influencing CPAP effectiveness, machine learning approaches were employed to predict which patient displayed positive outcomes (negative annual ΔBAI) based on their baseline clinical features. RESULTS: CPAP-treated group showed lower annual ΔBAI than untreated (-0.6 ± 2.7 vs. 0.3 ± 2.6 years, p < 0.05). This BAI reduction with CPAP was reproduced independently in the Apnea, Bariatric surgery, and CPAP study cohort. Patients with more severe OSA at baseline displayed more positive annual ΔBAI (=accelerated brain aging) when untreated and displayed more negative annual ΔBAI (=decelerated brain aging) when CPAP-treated. Machine learning models achieved high accuracy (up to 86%) in predicting CPAP outcomes. INTERPRETATION: CPAP treatment can alleviate brain aging in OSA, especially in severe cases. Sleep EEG-derived BAI has potential to assess CPAP's impact on brain health. The study provides insights into CPAP's effects and underscores BAI-based predictive modeling's utility in OSA management.

Deep learning of sleep apnea-hypopnea events for accurate classification of obstructive sleep apnea and determination of clinical severity.

BACKGROUND: /Objective: Automatic apnea/hypopnea events classification, crucial for clinical applications, often faces challenges, particularly in hypopnea detection. This study aimed to evaluate the efficiency of a combined approach using nasal respiration flow (RF), peripheral oxygen saturation (SpO2), and ECG signals during polysomnography (PSG) for improved sleep apnea/hypopnea detection and obstructive sleep apnea (OSA) severity screening. METHODS: An Xception network was trained using main features from RF, SpO2, and ECG signals obtained during PSG. In addition, we incorporated demographic data for enhanced performance. The detection of apnea/hypopnea events was based on RF and SpO2 feature sets, while the screening and severity categorization of OSA utilized predicted apnea/hypopnea events in conjunction with demographic data. RESULTS: Using RF and SpO2 feature sets, our model achieved an accuracy of 94 % in detecting apnea/hypopnea events. For OSA screening, an exceptional accuracy of 99 % and an AUC of 0.99 were achieved. OSA severity categorization yielded an accuracy of 93 % and an AUC of 0.91, with no misclassification between normal and mild OSA versus moderate and severe OSA. However, classification errors predominantly arose in cases with hypopnea-prevalent participants. CONCLUSIONS: The proposed method offers a robust automatic detection system for apnea/hypopnea events, requiring fewer sensors than traditional PSG, and demonstrates exceptional performance. Additionally, the classification algorithms for OSA screening and severity categorization exhibit significant discriminatory capacity.

Novel neuroelectrophysiological age index associated with imaging features of brain aging and sleep disorders.

Sleep architecture and microstructures alter with aging and sleep disorder-led accelerated aging. We proposed a sleep EEG based brain age prediction model using convolutional neural networks. We then associated the estimated brain age index with brain structural aging features, sleep disorders and various sleep parameters. Our model also showed a higher BAI (predicted brain age minus chronological age) is associated with cortical thinning in various functional areas. We found a higher BAI for sleep disorder groups compared to healthy sleepers, as well as significant differences in the spectral pattern of EEG among different sleep disorders (lower power in slow and ϑ waves for sleep apnea vs. higher power in ß and σ for insomnia), suggesting sleep disorder-dependent pathomechanisms of aging. Our results demonstrate that the new EEG-BAI can be a biomarker reflecting brain health in normal and various sleep disorder subjects, and may be used to assess treatment efficacy.

Predicting brain age based on sleep EEG and DenseNet.

We proposed a sleep EEG-based brain age prediction model which showed higher accuracy than previous models. Six-channel EEG data were acquired for 6 hours sleep. We then converted the EEG data into 2D scalograms, which were subsequently inputted to DenseNet used to predict brain age. We then evaluated the association between brain aging acceleration and sleep disorders such as insomnia and OSA.The correlation between chronological age and expected brain age through the proposed brain age prediction model was 80% and the mean absolute error was 5.4 years. The proposed model revealed brain age increases in relation to the severity of sleep disorders.In this study, we demonstrate that the brain age estimated using the proposed model can be a biomarker that reflects changes in sleep and brain health due to various sleep disorders.Clinical Relevance-Proposed brain age index can be a single index that reflects the association of various sleep disorders and serve as a tool to diagnose individuals with sleep disorders.

Altered regional cerebral blood flow in obstructive sleep apnea is associated with sleep fragmentation and oxygen desaturation.

Altered cerebral perfusion has been reported in obstructive sleep apnea (OSA). Using dynamic susceptibility contrast MRI, we compared cerebral perfusion between male OSA patients and male healthy reference subjects and assessed correlations of perfusion abnormalities of OSA patients with sleep parameters and neuropsychological deficits at 3 T MRI, polysomnography and neuropsychological tests in 68 patients with OSA and 21 reference subjects. We found lower global and regional cerebral blood flow and cerebral blood volume, localized mainly in bilateral parietal and prefrontal cortices, as well as multiple focal cortical and deep structures related to the default mode network and attention network. In the correlation analysis between regional hypoperfusion and parameters of polysomnography, different patterns of regional hypoperfusion were distinctively associated with parameters of intermittent hypoxia and sleep fragmentation, which involved mainly parietal and orbitofrontal cortices, respectively. There was no association between brain perfusion and cognition in OSA patients in areas where significant association was observed in reference subjects, largely overlapping with nodes of the default mode network and attention network. Our results suggest that impaired cerebral perfusion in important areas of functional networks could be an important pathomechanism of neurocognitive deficits in OSA.

Unsupervised automatic seizure detection for focal-onset seizures recorded with behind-the-ear EEG using an anomaly-detecting generative adversarial network.

BACKGROUND AND OBJECTIVE: Epilepsy is a neurological disorder of the brain, which involves recurrent seizures. An encephalogram (EEG) is a gold standard method in the detection and analysis of epileptic seizures. However, the standard EEG recording system is too obstructive to be used in daily life. Behind-the-ear EEG is an alternative approach to record EEG conveniently. Previous researchers applied machine learning to automatically detect seizures with EEG, but the epileptic EEG waveform contains subtle changes that are difficult to be identified. Furthermore, the extremely small proportion of ictal events in the long-term monitoring may cause the imbalance problem and, consequently, poor prediction performance in supervised learning approaches. In this study, we present an automatic seizure detection algorithm with a generative adversarial network (GAN) trained by unsupervised learning and evaluated it with behind-the-ear EEG. METHODS: We recorded behind-the-ear EEGs from 12 patients who have various types of epilepsy. Data were reviewed separately by two epileptologists, who determined the onsets and ends of seizures. First, we conducted unsupervised learning with the normal records for the GAN to learn the representation of normal states. Second, we performed automatic seizure detection with the trained GAN as an anomaly detector. Last, we combined the Gram matrix with other anomaly losses to improve detection performance. RESULTS: The proposed approach achieved detection performance with an area under the receiver operating curve of 0.939 and sensitivity of 96.3% with a false alarm rate of 0.14 per hour in the test dataset. In addition, we confirmed distinguishability with the distribution of the anomaly scores in terms of EEG frequency bands. CONCLUSIONS: It is expected that the proposed anomaly detection via GAN with the behind-the-ear EEG can be effectively used for long-term seizure monitoring in daily life.

Comparison of the efficacy and safety of tolterodine 2 mg and 4 mg combined with an α-blocker in men with lower urinary tract symptoms (LUTS) and overactive bladder: a randomized controlled trial.

OBJECTIVE: To evaluate the efficacy and safety of low-dose (2 mg) tolterodine extended release (ER) with an α-blocker compared with standard-dose (4 mg) tolterodine ER with an α-blocker for the treatment of men with residual storage symptoms after α-blocker monotherapy. PATIENTS AND METHODS: The study was a 12-week, single-blind, randomized, parallel-group, non-inferiority trial that included men with residual storage symptoms despite receiving at least 4 weeks of α-blocker treatment. Inclusion criteria were total International Prostate Symptom Score (IPSS) ≥12, IPSS quality-of-life item score ≥3, and ≥8 micturitions and ≥2 urgency episodes per 24 h. The primary outcome was change in the total IPSS score from baseline. Bladder diary variables, patient-reported outcomes and safety were also assessed. RESULTS: Patients were randomly assigned to addition of either 2 mg tolterodine ER (n = 47) or 4 mg tolterodine ER (n = 48) to α-blocker therapy for 12 weeks. Patients in both treatment groups had a significant improvement in total IPSS score (-5.5 and -6.3, respectively), micturition per 24 h (-1.3 and -1.7, respectively) and nocturia per night (-0.4 and -0.4, respectively). Changes in IPSS, bladder diary variables, and patient-reported outcomes were not significantly different between the treatment groups. All interventions were well tolerated by patients. CONCLUSIONS: These results suggest that 12 weeks of low-dose tolterodine ER add-on therapy is similar to standard-dose tolterodine ER add-on therapy in terms of efficacy and safety for patients experiencing residual storage symptoms after receiving α-blocker monotherapy.