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. In our case-control design, we included recordings from 29 patients with tonic-clonic seizures and 29 non-seizing controls. We spectrally decomposed each signal to identify cycle lengths of interest and compared average spectral power- and period-related markers between groups. Additionally, we related seizure occurrence to the phase of ultradian rhythm in patients with recorded seizures. We observed prominent 2- and 4-hour-long ultradian rhythms of accelerometry, as well as 4-hour-long oscillations in heart rate. Patients with seizures displayed a higher peak power in the 2-hour accelerometry rhythm (U = 287, P = 0.038) and a period-lengthened 4-hour heart rate rhythm (U = 291.5, P = 0.037). Those that seized also displayed greater mean rhythmic electrodermal activity (U = 261; P = 0.013). Most seizures occurred during the falling-to-trough quarter phase of accelerometric rhythms (13 out of 27, χ2 = 8.41, P = 0.038). Fluctuations in seizure risk or the occurrence of seizures may interrelate with ultradian rhythms of movement and autonomic function. Longitudinal assessments of ultradian patterns in larger patient samples may enable us to understand how such rhythms may improve the temporal precision of seizure forecasting models.

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. This study aimed to simulate clinical use cases and identify the necessary level of accuracy for each. METHODS: Using a realistic seizure simulator (CHOCOLATES), a ground truth was produced, which was then sampled to generate signals from simulated seizure detectors of various capabilities. Five use cases were evaluated: (1) randomized clinical trials (RCTs), (2) medication adjustment in clinic, (3) injury prevention, (4) sudden unexpected death in epilepsy (SUDEP) prevention, and (5) treatment of seizure clusters. We considered sensitivity (0%-100%), false alarm rate (FAR; 0-2/day), and device type (external wearable vs. implant) in each scenario. RESULTS: The RCT case was efficient for a wide range of wearable parameters, though implantable devices were preferred. Lower accuracy wearables resulted in subtle changes in the distribution of patients enrolled in RCTs, and therefore higher sensitivity and lower FAR values were preferred. In the clinic case, a wide range of sensitivity, FAR, and device type yielded similar results. For injury prevention, SUDEP prevention, and seizure cluster treatment, each scenario required high sensitivity and yet was minimally influenced by FAR. SIGNIFICANCE: The choice of use case is paramount in determining acceptable accuracy levels for a wearable seizure detection device. We offer simulation results for determining and verifying utility for specific use case and specific wearable parameters.

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. We collected patients' electrodermal activity, accelerometry (ACC), and photoplethysmography, from which blood volume pulse (BVP) is derived. Board-certified epileptologists determined seizure onset, offset, and types using video and EEG recordings per the International League Against Epilepsy 2017 classification. We applied three neural network models-a convolutional neural network (CNN) and a CNN-long short-term memory (LSTM)-based generalized detection model and an autoencoder-based personalized detection model-to the raw time-series sensor data to detect seizures and utilized performance measures, including sensitivity, false positive rate (the number of false alarms divided by the total number of nonseizure segments), number of false alarms per day, and detection delay. We applied a 10-fold patientwise cross-validation scheme to the multisignal biosensor data and evaluated model performance on 28 seizure types. RESULTS: We analyzed 166 patients (47.6% female, median age = 10.0 years) and 900 seizures (13 254 h of sensor data) for 28 seizure types. With a CNN-LSTM-based seizure detection model, ACC, BVP, and their fusion performed better than chance; ACC and BVP data fusion reached the best detection performance of 83.9% sensitivity and 35.3% false positive rate. Nineteen of 28 seizure types could be detected by at least one data modality with area under receiver operating characteristic curve > .8 performance. SIGNIFICANCE: Results from this in-hospital study contribute to a paradigm shift in epilepsy care that entails noninvasive seizure detection, provides time-sensitive and accurate data on additional clinical seizure types, and proposes a novel combination of an out-of-the-box monitoring algorithm with an individualized person-oriented seizure detection approach.

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. METHODS: Pediatric patients admitted to the epilepsy monitoring unit wore a wearable that recorded the heart rate (HR), heart rate variability (HRV), electrodermal activity (EDA), and peripheral body temperature. We utilized short recordings from 9:00 to 9:15 pm and compared mean values between patients with and without impending seizures. In addition, we collected clinical data: age, sex, age at first seizure, generalized slowing, focal slowing, and spikes on EEG, magnetic resonance imaging findings, and antiseizure medication reduction. We used conventional machine learning techniques with cross-validation to classify patients with and without impending seizures. RESULTS: We included 139 patients: 78 had no seizures and 61 had at least one seizure after 9 pm during the concurrent video-EEG and E4 recordings. HR (P < 0.01) and EDA (P < 0.01) were lower and HRV (P = 0.02) was higher for patients with than for patients without impending seizures. The average accuracy of group classification was 66%, and the mean area under the receiver operating characteristics was 0.72. CONCLUSIONS: Short-term wearable recordings in combination with clinical data have great potential as an easy-to-use seizure likelihood assessment tool.

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. Understanding the utility of these prolonged signatures for seizure count estimation and what factors generally determine seizure logging performance, including the role of data quality vs. algorithm performance, is thus crucial for improving wearables-based epilepsy monitoring and determining which patients benefit most from this technology. METHODS: In this retrospective study we examined 76 pediatric epilepsy patients during multiday video-EEG monitoring equipped with a wearable (Empatica E4; records of electrodermal activity, EDA, accelerometry, ACC, heart rate, HR; 1983 h total recording time; 45 tonic-clonic seizures). To log seizures on prolonged data trends, we applied deep learning on continuous overlapping 1-hour segments of multimodal data in a leave-one-subject-out approach. We systematically examined factors influencing logging performance, including patient age, antiseizure medication (ASM) load, seizure type and duration, and data artifacts. To gain insights into algorithm function and feature importance we applied Uniform Manifold Approximation and Projection (UMAP, to represent the separability of learned features) and SHapley Additive exPlanations (SHAP, to represent the most informative data signatures). RESULTS: Performance for tonic-clonic seizure logging increased systematically with patient age (AUC 0.61 for patients 〈 11 years, AUC 0.77 for patients between 11-15 years, AUC 0.85 for patients 〉 15 years). Across all ages, AUC was 0.75 corresponding to a sensitivity of 0.52 and a false alarm rate of 0.28/24 h. Seizures under high ASM load or with shorter duration were detected worse (P=.025, P=.033, respectively). UMAP visualized discriminatory power at the individual patient level, SHAP analyses identified clonic motor activity and peri/postictal increases in HR and EDA as most informative. In contrast, in missed seizures, these features were absent indicating that recording quality but not the algorithm caused the low sensitivity in these patients. SIGNIFICANCE: Our results demonstrate the utility of prolonged, postictal data segments for seizure logging, contribute to algorithm explainability and point to influencing factors, including high ASM dose and short seizure duration. Collectively, these results may help to identify patients who particularly benefit from such technology.

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. Based on single-trial EEG recordings during a flanker task, we used support vector machines to predict the age group as well as to determine the presented stimulus type (i.e., congruent, or incongruent stimulus). The classification of group membership was highly above chance level (accuracy: 55%, chance level: 17%). Early EEG responses were found to play an important role, and a grouped pattern of classification performance emerged corresponding to age structure. There was a clear cluster of individuals after retirement, i.e., misclassifications mostly occurred within this cluster. The stimulus type could be classified above chance level in ~ 95% of subjects. We identified time windows relevant for classification performance that are discussed in the context of early visual attention and conflict processing. In children and older adults, a high variability and latency of these time windows were found. We were able to demonstrate differences in neuronal dynamics at the level of individual trials. Our analysis was sensitive to mapping gross changes, e.g., at retirement age, and to differentiating components of visual attention across age groups, adding value for the diagnosis of cognitive status across the lifespan. Overall, the results highlight the use of machine learning in the study of brain activity over a lifetime.

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. Inclusion criteria were (1) clinical IS diagnosis, (2) repository corticotropin injection treatment, and (3) consecutive EEG recordings before and after repository corticotropin injection treatment. Patients with tuberous sclerosis complex were excluded. Response to treatment was defined as freedom from IS for at least 7 consecutive days during the treatment and resolution of hypsarrhythmia. The authors defined "relapse" as the recurrence of seizures after an initial response. Electrophysiological biomarker assessment included evaluation of semiautomatic spike counting algorithm, delta power, and delta coherence calculation during non-REM sleep EEG. RESULTS: One hundred fifty patients (83 males; 55%; median age of IS onset: 5.9 months) with complete data were included, including 101 responders (67%, 71 with sustained response, and 30 relapses). Fifty patients (33%) with complete EEG data also underwent advanced EEG analysis. Baseline delta coherence was higher in sustained responders than in nonresponders or patients who relapsed. Greater decreases in semiautomatic spike counting algorithm, delta power, and delta coherence were found in sustained responders compared with nonresponders or patients who relapsed. CONCLUSIONS: Repository corticotropin injection treatment was associated with a 67% response rate in patients with IS. Computational biomarkers beyond hypsarrhythmia may provide additional information during IS treatment, such as early determination of treatment response and outcome assessment.

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. We analyzed data from patients with epilepsy from four epilepsy centers. Patients wore wristbands recording accelerometry, electrodermal activity, blood volume pulse, and skin temperature. We calculated data completeness and assessed the time the device was worn (on-body), and modality-specific signal quality scores. We included 37,166 h from 632 patients in the inpatient and 90,776 h from 39 patients in the outpatient setting. All modalities were affected by artifacts. Data loss was higher when using data streaming (up to 49% among inpatient cohorts, averaged across respective recordings) as compared to onboard device recording and storage (up to 9%). On-body scores, estimating the percentage of time a device was worn on the body, were consistently high across cohorts (more than 80%). Signal quality of some modalities, based on established indices, was higher at night than during the day. A uniformly reported data quality and multimodal signal quality index is feasible, makes study results more comparable, and contributes to the development of devices and evaluation routines necessary for seizure monitoring.

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. In this review, we provide an overview of current wearable devices and mobile health software applications for epilepsy. We focus on clinically validated devices, their clinical applications, the challenges faced when using these devices in real-world settings, and how these devices may be optimized in the future.

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. We divided patients into two groups: those with no seizures and those with at least one seizure during the recording period. We assessed the 24-h modulation level and amplitude of EDA, TEMP, and HR. We performed machine learning including physiological and clinical variables. Subsequently, we determined classifier performance by cross-validated machine learning. Patients with seizures (n = 49) had lower EDA levels (p = 0.031), EDA amplitudes (p = 0.045), and trended toward lower HR levels (p = 0.060) compared to patients without seizures (n = 68). Averaged cross-validated classification accuracy was 69% (AUC-ROC: 0.75). Our results show the potential to monitor and forecast risk for epileptic seizures based on changes in 24-h patterns in wearable recordings in combination with clinical variables. Such biomarkers might be applicable to inform care, such as treatment or seizure injury risk during specific periods, scheduling diagnostic tests, such as admission to the epilepsy monitoring unit, and potentially other neurological and chronic conditions.

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. DESIGN: Cross-sectional study. SETTING: Laboratory. PATIENTS OR OTHER PARTICIPANTS: Ten men with ACLR (age = 28 ± 4 years, time after surgery = 63 ± 35 months) and 17 healthy men (age = 28 ± 4 years) completed 43 ± 4 preplanned (landing leg shown before takeoff) and 51 ± 5 unplanned (visual cue during flight) countermovement jumps with single-legged landings. MAIN OUTCOME MEASURE(S): Movement-related cortical potentials (MRCPs) and frontal θ frequency power before the jump were analyzed using electroencephalography. Movement-related cortical potentials were subdivided into 3 successive 0.5-second time periods (readiness potential [RP]-1, RP-2, and negative slope [NS]) relative to movement onset, with higher values indicating more motor planning. Theta power was calculated for the last 0.5 second before movement onset, with higher values demonstrating more focused attention. Biomechanical landing stability was measured via peak vertical ground reaction force, time to stabilization, and center of pressure. RESULTS: Both the ACLR and healthy groups evoked MRCPs at all 3 time periods. During the unplanned task analyzed using P values and Cohen d, the ACLR group exhibited slightly higher but not different MRCPs, achieving medium effect sizes (RP-1: P = .25, d = 0.44; RP-2: P = .20, d = 0.53; NS: P = .28, d = 0.47). The ACLR group also showed slightly higher θ power values that were not different during the preplanned (P = .18, d = 0.29) or unplanned (P = .42, d = 0.07) condition, achieving small effect sizes. The groups did not differ in their biomechanical outcomes (P values > .05). No condition × group interactions occurred (P values > .05). CONCLUSIONS: Our jump-landing task evoked MRCPs. Although not different between groups, the observed effect sizes provided the first indication that men with ACLR might have consistently relied on more cortical motor planning associated with unplanned jump landings. Confirmatory studies with larger sample sizes are warranted.

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. Whether wearable device signals may also contain information about the effect of antiseizure medications (ASMs), which may ultimately help to better monitor their efficacy, has not been evaluated yet. Here we systematically investigated the effect of ASMs on different data modalities (electrodermal activity, EDA, heart rate, HR, and heart rate variability, HRV) simultaneously recorded by a wearable device in 48 patients with epilepsy over several days in the epilepsy long-term monitoring unit at a tertiary hospital. All signals exhibited characteristic diurnal variations. HRV, but not HR or EDA-based metrics, were reduced by ASMs. By assessing multiple signals related to the autonomic nervous system simultaneously, our results provide novel insights into the effects of ASMs on the sympathetic and parasympathetic interplay in the setting of epilepsy and indicate the potential of easy-to-wear wearable devices for monitoring ASM action. Future work using longer data may investigate these metrics on multidien cycles and their utility for detecting seizures, assessing seizure risk, or informing treatment interventions.

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. The Haar wavelet transform convolutes a time series with a scaled square wave function to produce an approximation which is equivalent to averaging points. RESULTS: Coarse-graining is mathematically identical to the Haar wavelet transform approximations. Thus, multiscale entropy is entropy computed on sub-signals derived from approximations of the Haar wavelet transform. By describing coarse-graining algorithms properly as Haar wavelet transforms, the meaning of 'scales' as wavelet approximations becomes transparent. The computed value of entropy is different with different wavelet basis functions, suggesting further research is needed to determine optimal methods for computing multiscale entropy. CONCLUSION: Coarse-graining is mathematically identical to Haar wavelet approximations at power-of-two scales. Referring to coarse-graining as a Haar wavelet transform motivates research into the optimal approach to signal decomposition for entropy analysis.

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

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. As a main feature the dominant spatio-temporal EEG pattern was extracted in multiple frequency bands per participant. To deduce a pattern's stability, we calculated its proportion of total variance among all activation patterns over time for each task. By comparing fit (N = 15) and less fit older adults (N = 16) characterized by their performance on a 6-min walking test, we found signs of a lower task specificity of the obtained network features for the less fit compared to the fit group. This was indicated by fewer significant differences between tasks in the theta and high beta frequency band in the less fit group. Repeated measures ANOVA revealed that a significantly lower proportion of total variance can be explained by the main pattern in high beta frequency range for the less fit compared to the fit group [F(1,29) = 12.572, p = .001, partial η2 = .300]. Our results indicate that the dedifferentiation in task-related brain activation is lower in fit compared to less fit older adults. Thus, our study supports the idea that cardiorespiratory fitness influences task-related brain network organization in different task domains. SUPPLEMENTARY INFORMATION: The online version of this article (10.1007/s11571-020-09656-9) contains supplementary material, which is available to authorized users.

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. We selected 45 min of preictal and 60 min of postictal data and time-matched segments for controls. Data were analyzed over 15-min windows. For unimodal analysis, mean values over each time window were calculated for all modalities and analyzed by Friedman's two-way analysis of variance. RESULTS: Twenty-one children with recorded generalized tonic-clonic seizures (GTCS), and 21 age- and gender-matched controls were included. Unimodal results revealed no significant effect for RR and TEMP, but EDA (p = 0.002) and HR (p < 0.001) were elevated 0-15 min after seizures. The averaged bimodal correlation across all pairs of modalities changed for 15-min windows in patients with seizures. The highest correlations were observed immediately before (0.85) and the lowest correlation immediately after seizures. Overall, average correlations for controls were higher. SIGNIFICANCE: Multimodal ANS changes related to GTCS occur within and across autonomic nervous system modalities. While unimodal changes were most prominent during postictal segments, bimodal correlations increased before seizures and decreased postictally. This offers a promising avenue for further research on seizure detection, and potentially risk assessment for seizure recurrence and sudden unexplained death in epilepsy.

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. This study evaluates the seizure detection performance of custom-developed machine learning (ML) algorithms across a broad spectrum of epileptic seizures utilizing wrist- and ankle-worn multisignal biosensors. METHODS: We enrolled patients admitted to the epilepsy monitoring unit and asked them to wear a wearable sensor on either their wrists or ankles. The sensor recorded body temperature, electrodermal activity, accelerometry (ACC), and photoplethysmography, which provides blood volume pulse (BVP). We used electroencephalographic seizure onset and offset as determined by a board-certified epileptologist as a standard comparison. We trained and validated ML for two different algorithms: Algorithm 1, ML methods for developing seizure type-specific detection models for nine individual seizure types; and Algorithm 2, ML methods for building general seizure type-agnostic detection, lumping together all seizure types. RESULTS: We included 94 patients (57.4% female, median age = 9.9 years) and 548 epileptic seizures (11 066 h of sensor data) for a total of 930 seizures and nine seizure types. Algorithm 1 detected eight of nine seizure types better than chance (area under the receiver operating characteristic curve [AUC-ROC] = .648-.976). Algorithm 2 detected all nine seizure types better than chance (AUC-ROC = .642-.995); a fusion of ACC and BVP modalities achieved the best AUC-ROC (.752) when combining all seizure types together. SIGNIFICANCE: Automatic seizure detection using ML from multimodal wearable sensor data is feasible across a broad spectrum of epileptic seizures. Preliminary results show better than chance seizure detection. The next steps include validation of our results in larger datasets, evaluation of the detection utility tool for additional clinical seizure types, and integration of additional clinical information.

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. METHODS: In a 22-week single-blinded randomized controlled trial, elderly people with SMC were allocated to the golf (n = 25, 180 min training/week) or control group (n = 21). Primary outcomes were feasibility (golf exam, adherence, adverse events) and general cognitive function (Alzheimer's Disease Assessment Scale). Secondary outcomes include specific cognitive functions (Response Inhibition, Corsi Block Tapping Test, Trail Making Test), KP metabolites and physical performance (6-Minute-Walk-Test). Baseline-adjusted Analysis-of-Covariance was conducted for each outcome. RESULTS: 42 participants were analyzed. All participants that underwent the golf exam after the intervention passed it (20/23). Attendance rate of the golf intervention was 75 %. No adverse events or drop-outs related to the intervention occurred. A significant time*group interaction (p = 0.012, F = 7.050, Cohen's d = 0.89) was found for correct responses on the Response Inhibition task, but not for ADAS-Cog. Moreover, a significant time*group interaction for Quinolinic acid to Tryptophan ratios (p = 0.022, F = 5.769, Cohen's d = 0.84) in favor of the golf group was observed. An uncorrected negative correlation between attendance rate and delta Quinolinic acid to Kynurenic acid ratios in the golf group (p = 0.039, r=-0.443) was found as well. CONCLUSIONS: The findings indicate that learning golf is feasible and safe for elderly people with cognitive problems. Preliminary results suggest positive effects on attention and the KP. To explore the whole potential of golfing and its effect on cognitive decline, a larger cohort should be studied over a longer period with higher cardiovascular demands. TRIAL REGISTRATION: The trial was retrospectively registered (2nd July 2018) at the German Clinical Trials Register ( DRKS00014921 ).

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. Patients wore a device that continuously recorded EDA and temperature (TEMP). EDA levels, EDA spectral power, and TEMP levels were analyzed. To investigate 24-hour patterns, we performed a nonlinear mixed-effects model analysis. Relative mean pre-ictal (-30 min to seizure onset) and post-ictal (I: 30 min after seizure offset; II: 30 to 60 min after seizure offset) values were compared for SZ subgroups. RESULTS: We included 119 patients (40 SZ, 17 NEEG, 62 EA). EDA level and power group-specific models (SZ, NEEG, EA) (h = 1; P < .01) were superior to the all-patient cohort model. Fifty-nine seizures were analyzed. Pre-ictal EDA values were lower than respective 24-hour modulated SZ group values. Post hoc comparisons following the period-by-seizure type interaction (EDA level: χ2  = 18.50; P < .001, and power: χ2  = 6.73; P = .035) revealed that EDA levels were higher in the post-ictal period I for FIAS and GTCS and in post-ictal period II for GTCS only compared to the pre-ictal period. SIGNIFICANCE: Continuously monitored EDA shows a pattern of change over 24 hours. Curve amplitudes in patients with recorded seizures were lower as compared to patients who did not exhibit seizures during the recording period. Sympathetic skin responses were greater and more prolonged in GTCS compared to FIAS. EDA recordings from wearable devices offer a noninvasive tool to continuously monitor sympathetic activity with potential applications for seizure detection, prediction, and potentially sudden unexpected death in epilepsy (SUDEP) risk estimation.

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 ± 2.2 years, body mass index 24.5 ± 1.8 kg m-2, VO2max 50.4 ± 5.3 ml kg-1 min-1), completed four running tests, each of 60-min duration at individual running speeds (mean running speed 9.5 ± 1.1 km h-1) on a motorized treadmill, after an initial exercise test. Either no load was carried (control) or loads of 1.0 kg, in a handheld water bottle, waist belt, or backpack. Economy was assessed by means of energy cost (CR), oxygen cost (O2 cost), heart rate (HR), and rate of perceived exertion (RPE). CR [F(2,20) = 37.74, p < 0.01, ηp 2 = 0.79], O2 cost [F(2,20) = 37.98, p < 0.01, ηp 2 = 0.79], HR [F(2,18) = 165.62, p < 0.01, ηp 2 = 0.95], and RPE [F(2,18) = 165.62, p < 0.01, ηp 2 = 0.95] increased over time, but no significant differences were found between the systems. Carrying a handheld water bottle, waist belt, or backpack, weighing 1.0 kg, during a 60-min run exhibited similar physiological changes. Runners' choice may be guided by personal preference in the absence of differences in economy (CR, O2 cost, HR, and RPE).

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 (9.9 ± 5.8 years; 27 females; 161 seizures) we investigated differences between inter- and preictal periods in terms of mean, variance, and entropy of these signals. We found that signal mean and variance do not differentiate between inter- and preictal periods in a statistically meaningful way. EDA signal entropy was found to be increased prior to seizures in a small subset of patients. Findings may provide novel insights into the pathophysiology of epileptic seizures with respect to ANS function, and, while further validation and investigation of potential causes of the observed changes are needed, indicate that epilepsy-related state changes may be detectable using peripheral wearable devices. Detection of such changes with wearable devices may be more feasible for everyday monitoring than utilizing an electroencephalogram.