Search Results (598)

Background: Parkinson’s disease (PD) in advanced stages becomes, over time, a significant challenge, as oral medication becomes ineffective, and it may become necessary to switch to device-assisted therapy (DAT). This should be personalized according to the stage of the disease, the cognitive status of the patients, the association of frailty syndrome or other comorbidities, the support in care from the family, etc. Levodopa–carbidopa–entacapone intestinal gel can significantly improve the status of patients, provided that they are correctly selected for this type of treatment. Materials and Methods: We conducted a single-center prospective study including 20 advanced PD patients, who received a levodopa–carbidopa–entacapone gel through an intestinal pump, within the Parkinson’s Disease Multimodal Treatment Center of the Neurology Clinic of the “St. Ap. Andrew” County Emergency Clinical Hospital in Galați, Romania. The evaluations were performed at baseline (T0), after intestinal pump insertion (T1), and 6 months after the procedure (T2). Results: In the study group, the administration of the levodopa–carbidopa–entacapone intestinal gel, using the device for intestinal administration, had significant benefits, especially for motor symptoms. The periods of off, no-on, freezing, sudden-off, as well as dyskinesia and morning akinesia, were significantly reduced. Among the non-motor symptoms, depression and sleep disorders improved, with no changes in cognitive status and psychotic disorders. Conclusions: Adding new data for the use of device-assisted therapy in advanced PD, our study also highlights the need to further research this challenging patient profile. Full article

Background/Objectives: Sleep plays a key role in female fertility. Sleep disturbances (SDis) during pregnancy are common and may negatively affect maternal health, contributing to an increased risk of perinatal depression and anxiety. Aim: The present prospective study aimed to examine the interplay of sleep, anxiety, and depression during the pregnancy and postpartum stages, comparing women who conceived naturally (NC) with those who conceived through assisted reproductive treatment (ART). Methods: The study included five timepoints: pre-pregnancy (t0), the end of each trimester (t1–t3), and the postpartum period (t4). SDis were assessed using the Pittsburgh Sleep Quality Index (PSQI), the Athens Insomnia Scale (AIS), the Epworth Sleepiness Scale (ESS), the Fatigue Severity Scale (FFS); perinatal depressive and anxiety symptoms were assessed using the Edinburgh Postnatal Depression Scale (EPDS). Demographic and clinical characteristics were also collected. Given the imbalance in group size and the dispersion of values, a negative binomial regression model with robust variances and Satterthwaite approximation for the degrees of freedom was applied. Results: Compared to women with NC (N = 37), those undergoing ART (N = 57) were more likely to be older (p < 0.001), married (p < 0.001), unemployed (p < 0.001), and have a history of thyroid disease (p = 0.008). Significant differences between different time points were observed in both NC (N = 37) and successfully conceived ART groups (N = 9) in all sleep, fatigue, and well-being parameters. Notably, at the end of the first trimester (t1), the ART group reported more severe insomnia symptoms (p = 0.02). Conclusions: SDis are common in pregnancy, but more pronounced during the first trimester among women on ART. These findings highlight the need for early screening and targeted psychological support during perinatal care. Full article

The increasing demand for portable health monitoring has highlighted the need for automated sleep staging systems that are both accurate and computationally efficient. However, most existing deep learning models for electroencephalogram (EEG)-based sleep staging suffer from parameter redundancy, fixed dilation rates, and limited generalization, restricting their applicability in real-time and resource-constrained scenarios. In this paper, we propose ADG-SleepNet, a novel lightweight symmetry-aware multi-scale dilation-gated temporal convolutional network enhanced with adaptive attention mechanisms for EEG-based sleep staging. ADG-SleepNet features a structurally symmetric, parallel multi-branch architecture utilizing various dilation rates to comprehensively capture multi-scale temporal patterns in EEG signals. The integration of adaptive gating and channel attention mechanisms enables the network to dynamically adjust the contribution of each branch based on input characteristics, effectively breaking architectural symmetry when necessary to prioritize the most discriminative features. Experimental results on the Sleep-EDF-20 and Sleep-EDF-78 datasets demonstrate that ADG-SleepNet achieves accuracy rates of 87.1% and 85.1%, and macro F1 scores of 84.0% and 81.1%, respectively, outperforming several state-of-the-art lightweight models. These findings highlight the strong generalization ability and practical potential of ADG-SleepNet for EEG-based health monitoring applications. Full article

Improving the decoding accuracy of biological signals has been a research focus for decades to advance health, automation, and robotic industries. However, challenges like inter-subject variability, data scarcity, and multifunctional variability cause low decoding accuracy, thus hindering the practical deployment of biological signal paradigms. This paper proposes a multifunctional biological signals network (Multi-BioSig-Net) that addresses the aforementioned issues by devising a novel blind few-shot learning (FSL) technique to quickly adapt to multiple target domains without needing a pre-trained model. Specifically, our proposed multimodal similarity extractor (MMSE) and self-multiple domain adaptation (SMDA) modules address data scarcity and inter-subject variability issues by exploiting and enhancing the similarity between multimodal samples and quickly adapting the target domains by adaptively adjusting the parameters’ weights and position, respectively. For multifunctional learning, we proposed inter-function discriminator (IFD) that discriminates the classes by extracting inter-class common features and then subtracts them from both classes to avoid false prediction of the proposed model due to overfitting on the common features. Furthermore, we proposed a holistic-local fusion (HLF) module that exploits contextual-detailed features to adapt the scale-varying features across multiple functions. In addition, fractal dimension estimation (FDE) was employed for the classification of left-hand motor imagery (LMI) and right-hand motor imagery (RMI), confirming that proposed method can effectively extract the discriminative features for this task. The effectiveness of our proposed algorithm was assessed quantitatively and statistically against competent state-of-the-art (SOTA) algorithms utilizing three public datasets, demonstrating that our proposed algorithm outperformed SOTA algorithms. Full article

Obstructive sleep apnea–hypopnea syndrome (OSAHS) is a prevalent sleep disorder strongly linked to increased cardiovascular and metabolic risk. While prior studies have explored snore-based analysis for OSAHS, they have largely focused on either detection or classification in isolation. Here, we present a two-stage framework that integrates precise snoring event detection with deep learning-based classification. In the first stage, we develop an Adaptive Multi-Feature Fusion Endpoint Detection algorithm (AMFF-ED), which leverages short-time energy, spectral entropy, zero-crossing rate, and spectral centroid to accurately isolate snore segments following spectral subtraction noise reduction. Through adaptive statistical thresholding, joint decision-making, and post-processing, our method achieves a segmentation accuracy of 96.4%. Building upon this, we construct a balanced dataset comprising 6830 normal and 6814 OSAHS-related snore samples, which are transformed into Mel spectrograms and input into ERBG-Net—a hybrid deep neural network combining ECA-enhanced ResNet18 with bidirectional GRUs. This architecture captures both spectral patterns and temporal dynamics of snoring sounds. The experimental results demonstrate a classification accuracy of 95.84% and an F1 score of 94.82% on the test set, highlighting the model’s robust performance and its potential as a foundation for automated, at-home OSAHS screening. Full article

The relationship between sleep and epilepsy involves complex interactions between thalamocortical circuits, circadian mechanisms, and sleep architecture that fundamentally influence seizure susceptibility and cognitive outcomes. Epileptic activity disrupts essential sleep oscillations, particularly sleep spindles generated by thalamic circuits. Thalamic epileptic spikes actively compete with physiological sleep spindles, impairing memory consolidation and contributing to cognitive dysfunction in epileptic encephalopathy. This disruption explains why patients with epilepsy often experience learning difficulties despite adequate seizure control. Sleep stages show differential seizure susceptibility. REM sleep provides robust protection through enhanced GABAergic inhibition and motor neuron suppression, while non-REM sleep, particularly slow-wave sleep, increases seizure risk. These observations reveal fundamental mechanisms of seizure control within normal brain physiology. Circadian clock genes (BMAL1, CLOCK, PER, CRY) play crucial roles in seizure modulation. Dysregulation of these molecular timekeepers creates permissive conditions for seizure generation while being simultaneously disrupted by epileptic activity, establishing a bidirectional relationship. These mechanistic insights are driving chronobiological therapeutic approaches, including precisely timed antiseizure medications, sleep optimization strategies, and orexin/hypocretin system interventions. This understanding enables a paradigm shift from simple seizure suppression toward targeted restoration of physiological brain rhythms, promising transformative epilepsy management through sleep-informed precision medicine. Full article

Background/Objectives: We have previously shown that motor and non-motor symptoms of patients with Parkinson’s disease (PD) improved after a two-week in-patient multimodal intensive neurorehabilitation and care (iMINC). This program includes five hours/day for five days/week of multimodal neurorehabilitation and drug adjustments, taking advantage of extensive patient observation. In this study, we ascertained whether the improvements observed after iMINC similarly occurred in patients with and without drug adjustments. Methods: With a retrospective approach, the scores of UPDRS Total and Part III, Beck’s Depression Inventory (BDI), PDQ-39, Parkinson’s Disease Sleep Scale (PDSS), and Vocal Volume before and after two weeks of iMINC were compared in two groups of patients with moderate to advanced PD (H&Y Stage 3–4). In one group, drug adjustment was not necessary (PD no drug adjustment, PDnda, 38 patients), and another group underwent drug changes (PD with drug adjustment, PDda, 93 patients). Scores of all tests were compared using ANOVAs (within subject: before iMINC, after iMINC; between subject: PDda, PDnda). Results: Following iMINC, all outcome measures improved in both groups. Conclusions: Pharmacological adjustment is not the major factor that drives the improvement of motor and non-motor outcome scores following iMINC. These findings suggest that this comprehensive in-patient approach addresses most parkinsonian symptoms and that proper medication status may enhance the positive effects of iMINC. Full article

The automatic classification of sleep stages and Cyclic Alternating Pattern (CAP) subtypes from electroencephalogram (EEG) recordings remains a significant challenge in computational sleep research because of the short duration of CAP events and the inherent class imbalance in clinical datasets. Background/Objectives: The research introduces a domain-specific deep learning system that employs an LSTM network optimized through a PSO-Hyperband hybrid hyperparameter tuning method. Methods: The research enhances EEG-based sleep analysis through the implementation of hybrid optimization methods within an LSTM architecture that addresses CAP sequence classification requirements without requiring architectural changes. Results: The developed model demonstrates strong performance on the CAP Sleep Database by achieving 97% accuracy for REM and 96% accuracy for stage S0 and ROC AUC scores exceeding 0.92 across challenging CAP subtypes (A1–A3). The model transparency is improved through the application of SHAP-based interpretability techniques, which highlight the role of spectral and morphological EEG features in classification outcomes. Conclusions: The proposed framework demonstrates resistance to class imbalance and better discrimination between visually similar CAP subtypes. The results demonstrate how hybrid optimization methods improve the performance, generalizability, and interpretability of deep learning models for EEG-based sleep microstructure analysis. Full article

This opinion article aims to highlight the potential mechanisms behind a specific stage of Yoga Nidra (YN) practice, i.e., the formulation and repetition of Sankalpa, encouraging scholars to further study it and providing athletes with suggestions on how to use it for their sport and health. YN can be defined as a meditation practice encompassing a sequence of breathing, guided body awareness, visualization and cognitive restructuring process exercises. According to preliminary results, YN stimulates a hypnagogic state generally associated with improvements in sleep parameters, thus enhancing recovery and health in different populations including athletes. Cognitive restructuring processes can be stimulated by the formulation of Sankalpa, a YN element comparable to positive self-instructions used to counteract dysfunctional cognitions. From a practical standpoint, the formulation of Sankalpa involves expressing an intention that could positively influence body, mind and emotions. For example, Sankalpa might stop or reverse unhealthy thought patterns, resulting in greater mental health. It might also foster intrinsic motivation and enhance emotional intelligence by strengthening mental resilience. In particular, athletes could use Sankalpa as an affirmation to awaken any strength they may feel is necessary to provide them with stress–recovery balance and mental health. However, additional research on this topic is needed to better elucidate Sankalpa’s mechanisms/effects and better integrate its formulation into sport programs. Full article

Protein-bound uraemic toxins (PBUTs), such as indoxyl sulphate (IS) and p-cresyl sulphate (PCS), are poorly cleared by conventional haemodialysis (HD) or haemodiafiltration (HDF). Haemoadsorption combined with HD (HAHD) using the novel pHA130 cartridge may increase PBUT removal, and this trial aimed to compare its efficacy and safety with HDF in patients with end-stage renal disease (ESRD). In this single-centre, open-label trial, 30 maintenance HD patients were randomized (1:1:1) to HDF once every two weeks (HDF-q2w), HAHD once every two weeks (HAHD-q2w), or HAHD once weekly (HAHD-q1w) for 8 weeks, with the primary endpoint being the single-session reduction ratio (RR) of IS. The combined HAHD group (n = 20) demonstrated a significantly greater IS reduction than the HDF-q2w group (n = 10) (46.9% vs. 31.8%; p = 0.044) and superior PCS clearance (44.6% vs. 31.4%; p = 0.003). Both HAHD regimens significantly reduced predialysis IS levels at Week 8. Compared with HDF, weekly HAHD provided greater relief from pruritus and improved sleep quality, with comparable adverse events among groups. In conclusion, HAHD with the pHA130 cartridge is more effective than HDF for enhancing single-session PBUT removal and alleviating uraemic symptoms in patients with ESRD, with weekly application showing optimal symptomatic benefits. Full article

Sleep is a vital physiological process for recovery and health. In people with knee osteoarthritis (OA), disrupted sleep is common and linked to worse clinical outcomes. Commercial sleep trackers provide an accessible option to monitor sleep in this population, but their accuracy for detecting sleep, wake, and sleep stages remains uncertain. This study compared nighttime sleep data from polysomnography (PSG) and Fitbit Sense in individuals with knee OA and insomnia. Data were collected from 53 participants (60.4% women, mean age 51 ± 8.2 years) over 62 nights using simultaneous PSG and Fitbit recording. Fitbit Sense showed high accuracy (85.76%) and sensitivity (95.95%) for detecting sleep but lower specificity (50.96%), indicating difficulty separating quiet wakefulness from sleep. Agreement with PSG was higher on nights with longer total sleep time, higher sleep efficiency, shorter sleep onset, and fewer awakenings, suggesting better performance when sleep is less fragmented. The device showed limited precision in classifying sleep stages, often misclassifying deep and REM sleep as light sleep. Despite these issues, Fitbit Sense may serve as a useful complementary tool for monitoring sleep duration, timing, and regularity in this population. However, sleep stage and fragmentation data should be interpreted cautiously in both clinical and research settings. Full article

The organization of consciousness is described through increasingly rich theoretical models. We review evidence that working memory capacity—essential to generating consciousness in the cerebral cortex—is supported by dual limbic memory systems. These dorsal (Papez) and ventral (Yakovlev) limbic networks provide the basis for mnemonic processing and prediction in the dorsal and ventral divisions of the human neocortex. Empirical evidence suggests that the dorsal limbic division is (i) regulated preferentially by excitatory feedforward control, (ii) consolidated by REM sleep, and (iii) controlled in waking by phasic arousal through lemnothalamic projections from the pontine brainstem reticular activating system. The ventral limbic division and striatum, (i) organizes the inhibitory neurophysiology of NREM to (ii) consolidate explicit memory in sleep, (iii) operating in waking cognition under the same inhibitory feedback control supported by collothalamic tonic activation from the midbrain. We propose that (i) these dual (excitatory and inhibitory) systems alternate in the stages of sleep, and (ii) in waking they must be balanced—at criticality—to optimize the active inference that generates conscious experiences. Optimal Bayesian belief updating rests on balanced feedforward (excitatory predictive) and feedback (inhibitory corrective) control biases that play the role of prior and likelihood (i.e., sensory) precision. Because the excitatory (E) phasic arousal and inhibitory (I) tonic activation systems that regulate these dual limbic divisions have distinct affective properties, varying levels of elation for phasic arousal (E) and anxiety for tonic activation (I), the dual control systems regulate sleep and consciousness in ways that are adaptively balanced—around the entropic nadir of E–I criticality—for optimal self-regulation of consciousness and psychological health. Because they are emotive as well as motive control systems, these dual systems have unique qualities of feeling that may be registered as subjective experience. Full article

Shift work can have an adverse impact on sleep and wellbeing, as well as negative consequences for workplace safety and productivity. SleepShifters is a co-developed sleep management programme that aims to equip shift workers and employers with the skills needed to manage sleep from the onset of employment, thus preventing sleep problems and their associated consequences from arising. This paper describes the co-development process and resulting programme protocol of SleepShifters, designed in line with the Medical Research Council’s framework for the development and evaluation of complex interventions. Programme components were co-produced in partnership with stakeholders from four organisations across the United Kingdom, following an iterative, four-stage process based on focus groups and interviews. As well as a handbook containing guidance on shift scheduling, workplace lighting, and controlled rest periods, SleepShifters consists of five key components: (1) an annual sleep awareness event; (2) a digital sleep training induction module for new starters; (3) a monthly-themed sleep awareness campaign; (4) a website, hosting a digital Cognitive Behavioural Therapy for insomnia platform and supportive video case studies from shift-working peers; (5) a sleep scheduling app for employees. Future work will implement and assess the effectiveness of delivering SleepShifters in organisational settings. Full article

Background/Objectives: This study shows a lightweight hybrid framework based on a feedforward neural network using a single frontopolar electroencephalography channel, which is a practical configuration for wearable systems combining memory and a sleep stage transition probability matrix. Methods: Motivated by autocorrelation analysis, revealing strong temporal dependencies across sleep stages, we incorporate prior epoch information as additional features. To capture temporal context without requiring long input sequences, we introduce a transition-aware feature derived from the softmax output of the previous epoch, weighted by a learned stage transition matrix. The model combines predictions from memory-based and no-memory networks using a confidence-driven fallback strategy. Results: The proposed model achieves up to 85.4% accuracy and 0.79 Cohen’s kappa, despite using only a single 30 s epoch per prediction. Compared to other models that use a single frontopolar channel, our method outperforms convolutional neural networks, recurrent neural networks, and decision tree approaches. Additionally, confidence-based rejection of low-certainty predictions enhances reliability, since most of the epochs with low confidence in the sleep stage classification contain transitions between sleep stages. Conclusions: These results demonstrate that the proposed method balances performance, interpretability, and computational efficiency, making it well-suited for real-time clinical and wearable sleep staging applications using battery-powered computing devices. Full article

Accurate sleep staging and sleep-disordered breathing (SDB) severity prediction are critical for the early diagnosis and management of sleep disorders. However, real-world polysomnography (PSG) data often suffer from modality heterogeneity, label scarcity, and non-independent and identically distributed (non-IID) characteristics across institutions, posing significant challenges for model generalization and clinical deployment. To address these issues, we propose a federated multi-task learning (FMTL) framework that simultaneously performs sleep staging and SDB severity classification from seven multimodal physiological signals, including EEG, ECG, respiration, etc. The proposed framework is built upon a hybrid deep neural architecture that integrates convolutional layers (CNN) for spatial representation, bidirectional GRUs for temporal modeling, and multi-head self-attention for long-range dependency learning. A shared feature extractor is combined with task-specific heads to enable joint diagnosis, while the FedAvg algorithm is employed to facilitate decentralized training across multiple institutions without sharing raw data, thereby preserving privacy and addressing non-IID challenges. We evaluate the proposed method across three public datasets (APPLES, SHHS, and HMC) treated as independent clients. For sleep staging, the model achieves accuracies of 85.3% (APPLES), 87.1% (SHHS_rest), and 79.3% (HMC), with Cohen’s Kappa scores exceeding 0.71. For SDB severity classification, it obtains macro-F1 scores of 77.6%, 76.4%, and 79.1% on APPLES, SHHS_rest, and HMC, respectively. These results demonstrate that our unified FMTL framework effectively leverages multimodal PSG signals and federated training to deliver accurate and scalable sleep disorder assessment, paving the way for the development of a privacy-preserving, generalizable, and clinically applicable digital sleep monitoring system. Full article