Ultradian rhythms in accelerometric and autonomic data vary based on seizure occurrence in paediatric epilepsy patients.

Ultradian rhythms are physiological oscillations that resonate with period lengths shorter than 24 hours. This study examined the expression of ultradian rhythms in patients with epilepsy, a disease defined by an enduring seizure risk that may vary cyclically. Using a wearable device, we recorded heart rate, body temperature, electrodermal activity and limb accelerometry in patients admitted to the paediatric epilepsy monitoring unit.

Minimum clinical utility standards for wearable seizure detectors: A simulation study.

Objective: Epilepsy management employs self-reported seizure diaries, despite evidence of seizure underreporting. Wearable and implantable seizure detection devices are now becoming more widely available. There are no clear guidelines about what levels of accuracy are sufficient.

Artificial intelligence-enhanced epileptic seizure detection by wearables.

Objective: Wrist- or ankle-worn devices are less intrusive than the widely used electroencephalographic (EEG) systems for monitoring epileptic seizures. Using custom-developed deep-learning seizure detection models, we demonstrate the detection of a broad range of seizure types by wearable signals.

Methods: Patients admitted to the epilepsy monitoring unit were enrolled and asked to wear wearable sensors on either wrists or ankles.

Development of a Multivariable Seizure Likelihood Assessment Based on Clinical Information and Short Autonomic Activity Recordings for Children With Epilepsy.

Background: Predicting seizure likelihood for the following day would enable clinicians to extend or potentially schedule video-electroencephalography (EEG) monitoring when seizure risk is high. Combining standardized clinical data with short-term recordings of wearables to predict seizure likelihood could have high practical relevance as wearable data is easy and fast to collect. As a first step toward seizure forecasting, we classified patients based on whether they had seizures or not during the following recording.

Explainable AI for wearable seizure logging: Impact of data quality, patient age, and antiseizure medication on performance.

Objective: Objective seizure count estimates are crucial for ambulatory epilepsy management. Wearables have shown promise for the detection of tonic-clonic seizures but may suffer from false alarms and undetected seizures. Seizure signatures recorded by wearables often occur over prolonged periods, including increased levels of electrodermal activity and heart rate long after seizure EEG onset, however, previous detection methods only partially exploited these signatures.

Classification of age groups and task conditions provides additional evidence for differences in electrophysiological correlates of inhibitory control across the lifespan.

The aim of this study was to extend previous findings on selective attention over a lifetime using machine learning procedures. By decoding group membership and stimulus type, we aimed to study differences in the neural representation of inhibitory control across age groups at a single-trial level. We re-analyzed data from 211 subjects from six age groups between 8 and 83 years of age.

Source connectivity patterns in the default mode network differ between elderly golf-novices and non-golfers.

Learning to play golf has high demands on attention and therefore may counteract age-related changes of functional brain networks. This cross-sectional study compared source connectivity in the Default Mode Network (DMN) between elderly golf novices and non-golfers. Four-minute resting-state electroencephalography (128 channels) from 22 elderly people (mean age 67 ± 4.

Data quality evaluation in wearable monitoring.

Wearable recordings of neurophysiological signals captured from the wrist offer enormous potential for seizure monitoring. Yet, data quality remains one of the most challenging factors that impact data reliability. We suggest a combined data quality assessment tool for the evaluation of multimodal wearable data.

The Potential of Wearable Devices and Mobile Health Applications in the Evaluation and Treatment of Epilepsy.

Wearable devices and mobile health software applications have a great potential for improving epilepsy-related health outcomes and contributing to personalized medical care for persons with epilepsy. With limitations and challenges, they can be used for tracking seizure occurrence and for seizure detection, prediction, and forecasting in hospital and ambulatory settings. They can also help promote self-monitoring and self-management and thereby contribute to patient empowerment.

Seizure-related differences in biosignal 24-h modulation patterns.

A seizure likelihood biomarker could improve seizure monitoring and facilitate adjustment of treatments based on seizure risk. Here, we tested differences in patient-specific 24-h-modulation patterns of electrodermal activity (EDA), peripheral body temperature (TEMP), and heart rate (HR) between patients with and without seizures. We enrolled patients who underwent continuous video-EEG monitoring at Boston Children's Hospital to wear a biosensor.

Cortical Motor Planning and Biomechanical Stability During Unplanned Jump Landings in Men With Anterior Cruciate Ligament Reconstruction.

Context: Athletes with anterior cruciate ligament (ACL) reconstruction (ACLR) exhibit increased cortical motor planning during simple sensorimotor tasks compared with healthy athletes serving as control groups. This may interfere with proper decision making during time-constrained movements, elevating the reinjury risk.

Objective: To compare cortical motor planning and biomechanical stability during jump landings between participants with ACLR and healthy individuals.

Classification characteristics of fine motor experts based on electroencephalographic and force tracking data.

The application of machine learning techniques provides a data-driven approach for a deeper understanding of the development and expressions of expertise. In extension to the common procedure of comparing experts' and novices' performances in expertise-domain-related tasks we applied conventional classification algorithms. We distinguished between tasks for each participant and between groups, i.

Wearable device assessments of antiseizure medication effects on diurnal patterns of electrodermal activity, heart rate, and heart rate variability.

Patient-generated health data provide a great opportunity for more detailed ambulatory monitoring and more personalized treatments in many diseases. In epilepsy, robust diagnostics applicable to the ambulatory setting are needed as diagnosis and treatment decisions in current clinical practice are primarily reliant on patient self-reports, which are often inaccurate. Recent work using wearable devices has focused on methods to detect and forecast epileptic seizures.

Coarse-graining and the Haar wavelet transform for multiscale analysis.

Background: Multiscale entropy (MSE) has become increasingly common as a quantitative tool for analysis of physiological signals. The MSE computation involves first decomposing a signal into multiple sub-signal 'scales' using a coarse-graining algorithm.

Methods: The coarse-graining algorithm averages adjacent values in a time series to produce a coarser scale time series.

Generalized tonic-clonic seizures are accompanied by changes of interrelations within the autonomic nervous system.

Purpose: A seizure is a strong central stimulus that affects multiple subsystems of the autonomic nervous system (ANS), and results in different interactions across ANS modalities. Here, we aimed to evaluate whether multimodal peripheral ANS measures demonstrate interactions before and after seizures as compared to controls to provide the basis for seizure detection and forecasting based on peripheral ANS signals.

Methods: Continuous electrodermal activity (EDA), heart rate (HR), peripheral body temperature (TEMP), and respiratory rate (RR) calculated based on blood volume pulse were acquired by a wireless multi-sensor device.

Electrophysiological signatures of dedifferentiation differ between fit and less fit older adults.

Unlabelled: Cardiorespiratory fitness was found to influence age-related changes of resting state brain network organization. However, the influence on dedifferentiated involvement of wider and more unspecialized brain regions during task completion is barely understood. We analyzed EEG data recorded during rest and different tasks (sensory, motor, cognitive) with dynamic mode decomposition, which accounts for topological characteristics as well as temporal dynamics of brain networks.

EEG Biomarkers of Repository Corticotropin Injection Treatment.

Purpose: Hypsarrhythmia is one of the major diagnostic and treatment response criteria in infantile spasms (IS). The clinical and electrophysiological effect of repository corticotropin injection treatment on IS was evaluated using electrophysiological biomarkers.

Methods: Consecutive infants (<24 months) treated with repository corticotropin injection for IS were included in this retrospective descriptive study.

Seizure detection using wearable sensors and machine learning: Setting a benchmark.

Objective: Tracking seizures is crucial for epilepsy monitoring and treatment evaluation. Current epilepsy care relies on caretaker seizure diaries, but clinical seizure monitoring may miss seizures. Wearable devices may be better tolerated and more suitable for long-term ambulatory monitoring.

Classification of visuomotor tasks based on electroencephalographic data depends on age-related differences in brain activity patterns.

Classification of physiological data provides a data driven approach to study central aspects of motor control, which changes with age. To implement such results in real-life applications for elderly it is important to identify age-specific characteristics of movement classification. We compared task-classification based on EEG derived activity patterns related to brain network characteristics between older and younger adults performing force tracking with two task characteristics (sinusoidal; constant) with the right or left hand.

Learning to play golf for elderly people with subjective memory complaints: feasibility of a single-blinded randomized pilot trial.

Background: Subjective Memory Complaints (SMC) in elderly people due to preclinical Alzheimer's Disease may be associated with dysregulation of the Kynurenine Pathway (KP), with an increase in neurotoxic metabolites that affect cognition. Golf is a challenging sport with high demands on motor, sensory, and cognitive abilities, which might bear the potential to attenuate the pathological changes of preclinical AD. This trial investigated the feasibility of learning to play golf for elderly with cognitive problems and its effects on cognitive functions and the KP.

Twenty-four-hour patterns in electrodermal activity recordings of patients with and without epileptic seizures.

Objective: Daytime and nighttime patterns affect the dynamic modulation of brain and body functions and influence the autonomic nervous system response to seizures. Therefore, we aimed to evaluate 24-hour patterns of electrodermal activity (EDA) in patients with and without seizures.

Methods: We included pediatric patients with (a) seizures (SZ), including focal impaired awareness seizures (FIAS) or generalized tonic-clonic seizures (GTCS), (b) no seizures and normal electroencephalography (NEEG), or (c) no seizures but epileptiform activity in the EEG (EA) during vEEG monitoring.

The Optimal Weight Carriage System for Runners: Comparison Between Handheld Water Bottles, Waist Belts, and Backpacks.

In endurance running, where fluid and nutritional support is not always readily available, the carriage of water and nutrition is essential. To compare the economy and physiological demands of different carriage systems, 12 recreational runners (mean age 22.8 ± 2.

Autonomic nervous system changes detected with peripheral sensors in the setting of epileptic seizures.

A better understanding of the early detection of seizures is highly desirable as identification of an impending seizure may afford improved treatments, such as antiepileptic drug chronotherapy, or timely warning to patients. While epileptic seizures are known to often manifest also with autonomic nervous system (ANS) changes, it is not clear whether ANS markers, if recorded from a wearable device, are also informative about an impending seizure with statistically significant sensitivity and specificity. Using statistical testing with seizure surrogate data and a unique dataset of continuously recorded multi-day wristband data including electrodermal activity (EDA), temperature (TEMP) and heart rate (HR) from 66 people with epilepsy (9.