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Goal: The gold standard for detecting the presence of apneic events is a time and effort-consuming manual evaluation of type I polysomnographic recordings by experts, often not error-free. Such acquisition protocol requires dedicated facilities resulting in high costs and long waiting lists. The usage of artificial intelligence models assists the clinician's evaluation overcoming the aforementioned limitations and increasing healthcare quality. Methods: The present work proposes a machine learning-based approach for automatically recognizing apneic events in subjects affected by sleep apnea-hypopnea syndrome. It embraces a vast and diverse pool of subjects, the Wisconsin Sleep Cohort (WSC) database. Results: An overall accuracy of 87.2[Formula: see text]1.8% is reached for the event detection task, significantly higher than other works in literature performed over the same dataset. The distinction between different types of apnea was also studied, obtaining an overall accuracy of 62.9[Formula: see text]4.1%. Conclusions: The proposed approach for sleep apnea events recognition, validated over a wide pool of subjects, enlarges the landscape of possibilities for sleep apnea events recognition, identifying a subset of signals that improves State-of-the-art performance and guarantees simple interpretation., (© 2024 The Authors.)

Objectives: Rapid eye movement sleep behavior disorder (RBD) is a potentially harmful, often overlooked sleep disorder affecting up to 70% of Parkinson's disease patients. Current diagnosis relies on nocturnal video-polysomnography, which is an expensive and cumbersome examination requiring specific clinical expertise. Here, we explored the use of wrist actigraphy to enable automatic RBD diagnoses in home settings., Methods: A total of 26 Parkinson's disease patients underwent 2-week home wrist actigraphy, followed by two in-laboratory evaluations. Patients were classified as RBD versus non-RBD based on dream enactment history and video-polysomnography. We comprehensively characterized patients' movement patterns during sleep using actigraphic signals. We then trained machine learning classification algorithms to discriminate patients with or without RBD using the most relevant features. Classification performance was quantified with respect to clinical diagnosis, separately for in-laboratory and at-home recordings. Performance was further validated in a control group of non-Parkinson's disease patients with other sleep conditions., Results: To characterize RBD, actigraphic features extracted from both (1) individual movement episodes and (2) global nocturnal activity were critical. RBD patients were more active overall, and showed movements that were shorter, of higher magnitude, and more scattered in time. Using these features, our classification algorithms reached an accuracy of 92.9 ± 8.16% during in-clinic tests. When validated on home recordings in Parkinson's disease patients, accuracy reached 100% over a 2-week window, and was 94.4% in non-Parkinson's disease control patients. Features showed robustness across tests and conditions., Interpretation: These results open new perspectives for faster, cheaper, and more regular screening of sleep disorders, both for routine clinical practice and clinical trials. ANN NEUROL 2023;93:317-329., (© 2022 American Neurological Association.)

Disruption of subthalamic nucleus dynamics in Parkinson's disease leads to impairments during walking. Here, we aimed to uncover the principles through which the subthalamic nucleus encodes functional and dysfunctional walking in people with Parkinson's disease. We conceived a neurorobotic platform embedding an isokinetic dynamometric chair that allowed us to deconstruct key components of walking under well-controlled conditions. We exploited this platform in 18 patients with Parkinson's disease to demonstrate that the subthalamic nucleus encodes the initiation, termination, and amplitude of leg muscle activation. We found that the same fundamental principles determine the encoding of leg muscle synergies during standing and walking. We translated this understanding into a machine learning framework that decoded muscle activation, walking states, locomotor vigor, and freezing of gait. These results expose key principles through which subthalamic nucleus dynamics encode walking, opening the possibility to operate neuroprosthetic systems with these signals to improve walking in people with Parkinson's disease.

Parkinson disease (PD), dementia with Lewy bodies (DLB) and multiple system atrophy are synucleinopathies, characterized by neuronal loss, gliosis and the abnormal deposition of α-synuclein in vulnerable areas of the nervous system. Neurodegeneration begins however several years before clinical onset of motor, cognitive or autonomic symptoms. The isolated form of REM sleep behavior disorder (RBD), a parasomnia with dream enactment behaviors and excessive muscle activity during REM sleep, is an early stage synucleinopathy. The neurophysiological hallmark of RBD is REM sleep without atonia (RWSA), i.e. the loss of physiological muscle atonia during REM sleep. RBD pathophysiology is not fully clarified yet, but clinical and basic science suggest that ɑ-syn pathology begins in the lower brainstem where REM atonia circuits are located, including the sublaterodorsal tegmental/subcoeruleus nucleus and the ventral medulla, then propagates rostrally to brain regions such as the substantia nigra, limbic system, cortex. Genetically, there is only a partial overlap between RBD, PD and DLB, and individuals with iRBD may represent a specific subpopulation. A genome-wide association study identified five loci, which all seem to revolve around the GBA1 pathway. iRBD patients often show subtle motor, cognitive, autonomic and/or sensory signs, neuroimaging alterations as well as biofluid and tissue markers of neurodegeneration (in particular pathologic α-synuclein aggregates), which can be useful for risk stratification. Patients with iRBD represent thus the ideal population for neuroprotective/neuromodulating trials. This review provides insights into these aspects, highlighting and substantiating the central role of iRBD in treatment development strategies for synucleinopathies. [ABSTRACT FROM AUTHOR]

Walking ability is essential for human survival and health. Its basic rhythm is mainly generated by the central pattern generator of the spinal cord. The rhythmic stimulation of music to the auditory center affects the cerebral cortex and other higher nerve centers, and acts on the central pattern generator. By means of rhythm entrainment, the central pattern generator can produce walking rhythm synchronized with music rhythm, control muscle tension, and then regulate human gait. Basal ganglia dysfunction is the main cause of abnormal gait in patients with Parkinson's disease. Music therapy provides external rhythmic stimulation, recruits neural networks to bypass the basal ganglia and synchronizes gait with external rhythms in both time and space through auditory-motor neural networks, helping to promote the improvement of abnormal gait patterns in patients with Parkinson's disease. [ABSTRACT FROM AUTHOR]

We introduce an open-access tool capable of automatically extracting the timing of gait events during unconstrained locomotion across different neuromotor impairments. The gait analysis interactive tool is conceived as an assistant for gait assessment studies, both in healthy participants or in people with motor impairments affecting gait symmetry, regularity, or balance, as usually encountered in patients with neurological disorders. Our open-access pipeline makes it possible to automatically identify the time of key gait events (heel strike, toe off) from a single gyroscope axis (lateral mid-axis), simplifying experimental protocols, and can easily be used in everyday life conditions. The code is user-friendly and interactive. At each stage of analysis, it allows for possible adjustments and manual corrections of undetected or mismatched events. To implement, test, and validate our algorithm, we used three different databases of gait recordings that span from healthy subjects to patients affected by Parkinson's disease. The pipeline consists of three main sections that allow us to segment, identify, and eventually correct the events within the gait cycle. The algorithm achieved an average accuracy of 99.23% over healthy participants, either with average weight or overweight, and a performance of 94.84% over patients with Parkinson's disease. Even if gait analysis is a widely studied problem, so far, no open-source algorithm is available. The present work provides an easy tool capable of working with a minimum set of sensors and without any expensive platform or camera-based system. Employing three databases widely different for the environment, and for the subjects' age and motor impairments highlights the versatility of our approach. [ABSTRACT FROM AUTHOR]

Polysomnography, the gold standard diagnostic tool in sleep medicine, is performed in an artificial environment. This might alter sleep and may not accurately reflect typical sleep patterns. While macro-structures are sensitive to environmental effects, micro-structures remain more stable. In this study we applied semi-automated algorithms to capture REM sleep without atonia (RSWA) and sleep spindles, comparing lab and home measurements. We analyzed 107 full-night recordings from 55 subjects: 24 healthy adults, 28 Parkinson's disease patients (15 RBD), and three with isolated Rem sleep behavior disorder (RBD). Sessions were manually scored. An automatic algorithm for quantifying RSWA was developed and tested against manual scoring. RSWAi showed a 60% correlation between home and lab. RBD detection achieved 83% sensitivity, 79% specificity, and 81% balanced accuracy. The algorithm accurately quantified RSWA, enabling the detection of RBD patients. These findings could facilitate more accessible sleep testing, and provide a possible alternative for screening RBD. [ABSTRACT FROM AUTHOR]

Background: Sleep spindles have emerged as valuable biomarkers for assessing cognitive abilities and related disorders, underscoring the importance of their detection in clinical research. However, template matching-based algorithms using fixed templates may not be able to fully adapt to spindles of different durations. Moreover, inspired by the multiscale feature extraction of images, the use of multiscale feature extraction methods can be used to better adapt to spindles of different frequencies and durations. Methods: Therefore, this study proposes a novel automatic spindle detection algorithm based on elastic time windows and spatial pyramid pooling (SPP) for extracting multiscale features. The algorithm utilizes elastic time windows to segment electroencephalogram (EEG) signals, enabling the extraction of features across multiple scales. This approach accommodates significant variations in spindle duration and polarization positioning during different EEG epochs. Additionally, spatial pyramid pooling is integrated into a depthwise separable convolutional (DSC) network to perform multiscale pooling on the segmented spindle signal features at different scales. Results: Compared with existing template matching algorithms, this algorithm's spindle wave polarization positioning is more consistent with the real situation. Experimental results conducted on the public dataset DREAMS show that the average accuracy of this algorithm reaches 95.75%, with an average negative predictive value (NPV) of 96.55%, indicating its advanced performance. Conclusions: The effectiveness of each module was verified through thorough ablation experiments. More importantly, the algorithm shows strong robustness when faced with changes in different experimental subjects. This feature makes the algorithm more accurate at identifying sleep spindles and is expected to help experts automatically detect spindles in sleep EEG signals, reduce the workload and time of manual detection, and improve efficiency. [ABSTRACT FROM AUTHOR]

Digital biomarkers that remotely monitor symptoms have the potential to revolutionize outcome assessments in future disease-modifying trials in Parkinson's disease (PD), by allowing objective and recurrent measurement of symptoms and signs collected in the participant's own living environment. This biomarker field is developing rapidly for assessing the motor features of PD, but the non-motor domain lags behind. Here, we systematically review and assess digital biomarkers under development for measuring non-motor symptoms of PD. We also consider relevant developments outside the PD field. We focus on technological readiness level and evaluate whether the identified digital non-motor biomarkers have potential for measuring disease progression, covering the spectrum from prodromal to advanced disease stages. Furthermore, we provide perspectives for future deployment of these biomarkers in trials. We found that various wearables show high promise for measuring autonomic function, constipation and sleep characteristics, including REM sleep behavior disorder. Biomarkers for neuropsychiatric symptoms are less well-developed, but show increasing accuracy in non-PD populations. Most biomarkers have not been validated for specific use in PD, and their sensitivity to capture disease progression remains untested for prodromal PD where the need for digital progression biomarkers is greatest. External validation in real-world environments and large longitudinal cohorts remains necessary for integrating non-motor biomarkers into research, and ultimately also into daily clinical practice. [ABSTRACT FROM AUTHOR]

Brain-Computer Interfaces (BCIs) aim to establish a pathway between the brain and an external device without the involvement of the motor system, relying exclusively on neural signals. Such systems have the potential to provide a means of communication for patients who have lost the ability to speak due to a neurological disorder. Traditional methodologies for decoding imagined speech directly from brain signals often deploy static classifiers, that is, decoders that are computed once at the beginning of the experiment and remain unchanged throughout the BCI use. However, this approach might be inadequate to effectively handle the non-stationary nature of electroencephalography (EEG) signals and the learning that accompanies BCI use, as parameters are expected to change, and all the more in a real-time setting. To address this limitation, we developed an adaptive classifier that updates its parameters based on the incoming data in real time. We first identified optimal parameters (the update coefficient, UC) to be used in an adaptive Linear Discriminant Analysis (LDA) classifier, using a previously recorded EEG dataset, acquired while healthy participants controlled a binary BCI based on imagined syllable decoding. We subsequently tested the effectiveness of this optimization in a real-time BCI control setting. Twenty healthy participants performed two BCI control sessions based on the imagery of two syllables, using a static LDA and an adaptive LDA classifier, in randomized order. As hypothesized, the adaptive classifier led to better performances than the static one in this real-time BCI control task. Furthermore, the optimal parameters for the adaptive classifier were closely aligned in both datasets, acquired using the same syllable imagery task. These findings highlight the effectiveness and reliability of adaptive LDA classifiers for real-time imagined speech decoding. Such an improvement can shorten the training time and favor the development of multi-class BCIs, representing a clear interest for non-invasive systems notably characterized by low decoding accuracies. [ABSTRACT FROM AUTHOR]

Summary: Sleep disorders are symptomatic hallmarks of a variety of medical conditions. Accurately identifying the specific stage in which these disorders occur is particularly important for the correct diagnosis of non‐rapid eye movement and rapid eye movement parasomnias. In‐lab polysomnography suffers from limited availability and does not reflect habitual sleep conditions, which is especially important in older adults and those with neurodegenerative diseases. We aimed to explore the feasibility and validity of a new wearable system for accurately measuring sleep at home. The system core technology is soft, printed dry electrode arrays and a miniature data acquisition unit with a cloud‐based data storage for offline analysis. The positions of the electrodes allow manual scoring following the American Association of Sleep Medicine guidelines. Fifty participants (21 healthy subjects, mean age 56.6 ± 8.4 years; and 29 patients with Parkinson's disease, 65.4 ± 7.6 years) underwent a polysomnography evaluation with parallel recording with the wearable system. Total agreement between the two systems reached Cohen's kappa (k) of 0.688 with agreement in each stage of: wake k = 0.701; N1 = 0.224; N2 = 0.584; N3 = 0.410; and rapid eye movement = 0.723. Moreover, the system reliably detected rapid eye movement sleep without atonia with a sensitivity of 85.7%. Additionally, a comparison between sleep as measured in the sleep lab with data collected from a night at home showed significantly lower wake after sleep onset at home. The results demonstrate that the system is valid, accurate and allows for the exploration of sleep at home. This new system offers an opportunity to help detect sleep disorders on a larger scale than possible today, fostering better care. [ABSTRACT FROM AUTHOR]

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Processing incoming neural oscillatory signals in real-time and decoding from them relevant behavioral or pathological states is often required for adaptive Deep Brain Stimulation (aDBS) and other brain-computer interface (BCI) applications. Most current approaches rely on first extracting a set of predefined features, such as the power in canonical frequency bands or various time-domain features, and then training machine learning systems that use those predefined features as inputs and infer what the underlying brain state is at each given time point. However, whether this algorithmic approach is best suited to extract all available information contained within the neural waveforms remains an open question. Here, we aim to explore different algorithmic approaches in terms of their potential to yield improvements in decoding performance based on neural activity such as measured through local field potentials (LFPs) recordings or electroencephalography (EEG). In particular, we aim to explore the potential of end-to-end convolutional neural networks, and compare this approach with other machine learning methods that are based on extracting predefined feature sets. To this end, we implement and train a number of machine learning models, based either on manually constructed features or, in the case of deep learning-based models, on features directly learnt from the data. We benchmark these models on the task of identifying neural states using simulated data, which incorporates waveform features previously linked to physiological and pathological functions. We then assess the performance of these models in decoding movements based on local field potentials recorded from the motor thalamus of patients with essential tremor. Our findings, derived from both simulated and real patient data, suggest that end-to-end deep learning-based methods may surpass feature-based approaches, particularly when the relevant patterns within the waveform data are either unknown, difficult to quantify, or when there may be, from the point of view of the predefined feature extraction pipeline, unidentified features that could contribute to decoding performance. The methodologies proposed in this study might hold potential for application in adaptive deep brain stimulation (aDBS) and other brain-computer interface systems. [ABSTRACT FROM AUTHOR]

Parkinson’s disease (PD) is a neurodegenerative disorder that affects millions of people worldwide. Its slow and heterogeneous progression over time makes timely diagnosis challenging. Wrist-worn digital devices, particularly smartwatches, are currently the most popular tools in the PD research field due to their convenience for long-term daily life monitoring. While wrist-worn sensing devices have garnered significant interest, their value for daily practice is still unclear. In this narrative review, we survey demographic, clinical and technological information from 39 articles across four public databases. Wrist-worn technology mainly monitors motor symptoms and sleep disorders of patients in daily life. We find that accelerometers are the most commonly used sensors to measure the movement of people living with PD. There are few studies on monitoring the disease progression compared to symptom classification. We conclude that wrist-worn sensing technology might be useful to assist in the management of PD through an automatic assessment based on patient-provided daily living information. [ABSTRACT FROM AUTHOR]