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.

Fine motor skills including precise tactile and haptic perception are essential to the manipulation of objects. With increasing age, one's perception decreases; however, little is known about the state of touch perception in middle-aged adults. This study investigated the extent to which the decline in touch perception affects adults throughout their working life. In addition, the influence of work-related expertise on tactile and haptic perception was examined in an attempt to determine whether expertise, in the form of the frequent use of the fingers, affects perception and counters age-related losses. The study was conducted with subjects from three age groups (18-25, 34-46, and 54-65 years) with two levels of expertise. Expertise was classified by the subjects' occupations. Five sensory tasks of touch perception were conducted. The results confirmed age-related changes in tactile perception over the span of one's working life. Older workers were proven to have lower tactile performance than younger adults. However, middle-aged workers were hardly affected by the perception losses and did not differ significantly from younger adults. Work-related expertise was not proven to either affect tactile and haptic perception or counteract age-related declines. We conclude that the age-related decline gets steeper in the late working life and that specific work-related expertise does not lead to generally improved touch perception that would result in lower thresholds and improved performance in non-expertise specific tasks.

It has been repeatedly shown that precise finger force control declines with age. The tasks and evaluation parameters used to reveal age-related differences vary between studies. In order to examine effects of task characteristics, young adults (18-25 years) and late middle-aged adults (55-65 years) performed precision grip tasks with varying speed and force requirements. Different outcome variables were used to evaluate age-related differences. Age-related differences were confirmed for performance accuracy (TWR) and variability (relative root mean square error, rRMSE). The task characteristics, however, influenced accuracy and variability in both age groups: Force modulation performance at higher speed was poorer than at lower speed and at fixed force levels than at force levels adjusted to the individual maximum forces. This effect tended to be stronger for older participants for the rRMSE. A curve fit confirmed the age-related differences for both spatial force tracking parameters (amplitude and intercept) and for one temporal parameter (phase shift), but not for the temporal parameter frequency. Additionally, matching the timing parameters of the sine wave seemed to be more important than matching the spatial parameters in both young adults and late middle-aged adults. However, the effect was stronger for the group of late middle-aged, even though maximum voluntary contraction was not significantly different between groups. Our data indicate that changes in the processing of fine motor control tasks with increasing age are caused by difficulties of late middle-aged adults to produce a predefined amount of force in a short time.

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.

Age-related decline of fine motor control commences even in middle adulthood. Less is known, however, whether age-related changes can be postponed through continuous practice. In this study we tested how age and professional expertise influence fine motor control in middle-aged adults. Forty-eight right-handed novices and experts (35 to 65 years) performed submaximal precision grip force modulation tasks with index or middle finger opposing the thumb, either with the right hand or the left hand. Novices revealed expected age-related differences in all performance measures (force initialization, mean applied force, variability), whereas experts outperformed novices in all outcome measures. Expertise seems to contribute to maintaining manual skills into older age, as indicated by the age and expertise interaction for the force initialization.

The ability to selectively attend to task-relevant information increases throughout childhood and decreases in older age. Here, we intended to investigate these opposing developmental trajectories, to assess whether gains and losses early and late in life are associated with similar or different electrophysiological changes, and to get a better understanding about the development in middle-adulthood. We (re-)analyzed behavioral and electrophysiological data of 211 participants, who performed a colored Flanker task while their Electroencephalography (EEG) was recorded. Participants were subdivided into six groups depending on their age, ranging from 8 to 83 years. We analyzed response speed and accuracy as well as the event replated potential (ERP) components P1 and N1, associated with visual processing and attention, N2 as marker of interference suppression and cognitive control, and P3 as a marker of cognitive updating and stimulus categorization. Response speed and accuracy were low early and later in life, with peak performance in young adults. Similarly, ERP latencies of all components and P1 and N1 amplitudes followed a u-shape pattern with shortest latencies and smallest amplitudes occurring in middle-age. N2 amplitudes were larger in children, and for incongruent stimuli in adults middle-aged and older. P3 amplitudes showed a parietal-to-frontal shift with age. Further, group-wise regression analyses suggested that children’s performance depended on cognitive processing speed, while older adults’ performance depended on cognitive resources. Together these results imply that different mechanisms restrict performance early and late in life and suggest a non-linear relationship between electrophysiological markers and performance in the Flanker task across the lifespan.

Tactile perception declines with age on both behavioral and neurophysiological levels. Less well understood is how neurophysiological changes relate to tactile discrimination performance in middle adulthood. A tactile discrimination task was conducted while ERPs were measured in three groups of healthy adults aged 20 to 66 years. Accuracy was lowest in late middle adulthood (56-66 years) while somatosensory ERP components (P50, N70, P100, N140) were comparable across age groups. The cognitive P300 revealed age-related differences in scalp distribution typical for older adults to already be present in late middle adulthood. Increased recruitment of frontal cognitive processes was positively related to performance in later middle adulthood. Our results further the understanding of age-related differences in tactile perception during middle adulthood and the importance of cognitive processes to compensate for age-related decline.

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.