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Prospective multicenter study of continuous tonic-clonic seizure monitoring on Apple Watch in epilepsy monitoring units and ambulatory environments
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Objective
To evaluate direct user experience with wearable seizure detection devices in the home environment.
Methods
A structured online questionnaire was completed by 242 users (175 caregivers and 67 persons with epilepsy), most of the patients (87.19%) having tonic–clonic seizures.
Results
The vast majority of the users were overall satisfied with the wearable device, considered that using the device was easy, and agreed that the use of the device improved their quality of life (median = 6 on 7‐point Likert scale). A high retention rate (84.58%) and a long median usage time (14 months) were reported. In the home environment, most users (75.85%) experienced seizure detection sensitivity similar (≥95%) to what was previously reported in validation studies in epilepsy monitoring units. The experienced false alarm rate was relatively low (0–0.43 per day). Due to the alarms, almost one third of persons with epilepsy (PWEs; 30.00%) experienced decrease in the number of seizure‐related injuries, and almost two thirds of PWEs (65.41%) experienced improvement in the accuracy of seizure diaries. Nonvalidated devices had significantly lower retention rate, overall satisfaction, perceived sensitivity, and improvement in quality of life, as compared with validated devices.
Significance
Our results demonstrate the feasibility and usefulness of automated seizure detection in the home environment.
Background: Using machine learning to combine wrist accelerometer (ACM) and electrodermal activity (EDA) has been shown effective to detect primarily and secondarily generalized tonic-clonic seizures, here termed as convulsive seizures (CS). A prospective study was conducted for the FDA clearance of an ACM and EDA-based CS-detection device based on a predefined machine learning algorithm. Here we present its performance on pediatric and adult patients in epilepsy monitoring units (EMUs).
Methods: Patients diagnosed with epilepsy participated in a prospective multi-center clinical study. Three board-certified neurologists independently labeled CS from video-EEG. The Detection Algorithm was evaluated in terms of Sensitivity and false alarm rate per 24 h-worn (FAR) on all the data and on only periods of rest. Performance were analyzed also applying the Detection Algorithm offline, with a less sensitive but more specific parameters configuration (“Active mode”).
Results: Data from 152 patients (429 days) were used for performance evaluation (85 pediatric aged 6–20 years, and 67 adult aged 21–63 years). Thirty-six patients (18 pediatric) experienced a total of 66 CS (35 pediatric). The Sensitivity (corrected for clustered data) was 0.92, with a 95% confidence interval (CI) of [0.85-1.00] for the pediatric population, not significantly different (p > 0.05) from the adult population's Sensitivity (0.94, CI: [0.89–1.00]). The FAR on the pediatric population was 1.26 (CI: [0.87–1.73]), higher (p < 0.001) than in the adult population (0.57, CI: [0.36–0.81]). Using the Active mode, the FAR decreased by 68% while reducing Sensitivity to 0.95 across the population. During rest periods, the FAR's were 0 for all patients, lower than during activity periods (p < 0.001).
Conclusions: Performance complies with FDA's requirements of a lower bound of CI for Sensitivity higher than 0.7 and of a FAR lower than 2, for both age groups. The pediatric FAR was higher than the adult FAR, likely due to higher pediatric activity. The high Sensitivity and precision (having no false alarms) during sleep might help mitigate SUDEP risk by summoning caregiver intervention. The Active mode may be advantageous for some patients, reducing the impact of the FAR on daily life. Future work will examine the performance and usability outside of EMUs.
The objective of this clinical practice guideline (CPG) is to provide recommendations for healthcare personnel working with patients with epilepsy on the use of wearable devices for automated seizure detection in patients with epilepsy, in outpatient, ambulatory settings. The Working Group of the International League Against Epilepsy (ILAE) and the International Federation of Clinical Neurophysiology (IFCN) developed the CPG according to the methodology proposed by the ILAE Epilepsy Guidelines Working Group. We reviewed the published evidence using The Preferred Reporting Items for Systematic Review and Meta‐Analysis (PRISMA) statement and evaluated the evidence and formulated the recommendations following the Grading of Recommendations Assessment, Development and Evaluation (GRADE) system. We found high level of evidence for the accuracy of automated detection of generalized tonic‐clonic seizures (GTCS) and focal‐to‐bilateral tonic‐clonic seizures (FBTCS) and recommend the use of wearable automated seizure detection devices for selected patients when accurate detection of GTCS and FBTCS is recommended as a clinical adjunct. We also found a moderate level of evidence for seizure types without GTCS or FBTCS. However, it was uncertain whether the detected alarms resulted in meaningful clinical outcomes for the patients. We recommend using clinically validated devices for automated detection of GTCS and FBTCS, especially in unsupervised patients, where alarms can result in rapid intervention (weak/conditional recommendation). At present, we do not recommend clinical use of the currently available devices for other seizure types (weak/conditional recommendation). Further research and development are needed to improve the performance of automated seizure detection and to document their accuracy and clinical utility.
In patients with epilepsy, the potential to prevent seizure‐related injuries and to improve the unreliability of seizure self‐report have fostered the development and marketing of numerous seizure detection devices for home use. Understanding the requirements of users (patients and caregivers) is essential to improve adherence and mitigate barriers to the long‐term use of such devices. Here we reviewed the evidence on the needs and preferences of users and provided an overview of currently marketed devices for seizure detection (medically approved or with published evidence for their performance). We then compared devices with known needs. Seizure‐detection devices are expected to improve safety and clinical and self‐management, and to provide reassurance to users. Key factors affecting a device’s usability relate to its design (attractive appearance, low visibility, low intrusiveness), comfort of use, confidentiality of recorded data, and timely support from both technical and clinical ends. High detection sensitivity and low false alarm rates are paramount. Currently marketed devices are focused primarily on the recording of non–electroencephalography (EEG) signals associated with tonic‐clonic seizures, whereas the detection of focal seizures without major motor features remains a clear evidence gap. Moreover, there is paucity of evidence coming from real‐life settings. A joint effort of clinical and nonclinical experts, patients, and caregivers is required to ensure an optimal level of acceptability and usability, which are key aspects for a successful continuous monitoring aimed at seizure detection at home.
The "fixed and frozen" AI-based GTCS detection algorithm complies with FDA requirements (lower bound of CI for PPA>70% and FAR<2) for both pediatric and adult populations. • The FAR for pediatric is significantly (p-value<0.01) higher FAR, most likely because children were more active in the EMU. • During rest, the overall FAR drops dramatically to 0.03 FA/24h, which is as low as 0.01 FA/night (considering 8 hours of sleep per day), i.e. 1 FA every 100 nights of sleep. • Future work will examine the prospective detection performance and overall impact in outpatient settings.
Objective:
We conducted a nationwide case-control study in Sweden to test the hypothesis that specific clinical characteristics are associated with increased risk of sudden unexpected death in epilepsy (SUDEP).
Methods:
The study included 255 SUDEP cases (definite and probable) and 1,148 matched controls. Clinical information was obtained from medical records and the National Patient Register. The association between SUDEP and potential risk factors was assessed by odds ratios (ORs) and 95% confidence intervals (CIs) and interaction assessed by attributable proportion due to interaction (AP).
Results:
Experiencing generalized tonic-clonic seizures (GTCS) during the preceding year was associated with a 27-fold increased risk (OR 26.81, 95% CI 14.86-48.38), whereas no excess risk was seen in those with exclusively non-GTCS seizures (OR 1.15, 95% CI 0.54-48.38). The presence of nocturnal GTCS during the last year of observation was associated with a 15-fold risk (OR 15.31, 95% CI 9.57-24.47). Living alone was associated with a 5-fold increased risk of SUDEP (OR 5.01, 95% CI 2.93-8.57) and interaction analysis showed that the combination of not sharing a bedroom and having GTCS conferred an OR of 67.10 (95% CI 29.66-151.88), with AP estimated at 0.69 (CI 0.53-0.85). Among comorbid diseases, a previous diagnosis of substance abuse or alcohol dependence was associated with excess risk of SUDEP.
Conclusions:
Individuals with GTCS who sleep alone have a dramatically increased SUDEP risk. Our results indicate that 69% of SUDEP cases in patients who have GTCS and live alone could be prevented if the patients were not unattended at night or were free from GTCS.
Objective:
To develop and prospectively evaluate a method of epileptic seizure detection combining heart rate and movement.
Methods:
In this multicenter, in-home, prospective, video-controlled cohort study, nocturnal seizures were detected by heart rate (photoplethysmography) or movement (3-D accelerometry) in persons with epilepsy and intellectual disability. Participants with >1 monthly major seizure wore a bracelet (Nightwatch) on the upper arm at night for 2 to 3 months. Major seizures were tonic-clonic, generalized tonic >30 seconds, hyperkinetic, or others, including clusters (>30 minutes) of short myoclonic/tonic seizures. The video of all events (alarms, nurse diaries) and 10% completely screened nights were reviewed to classify major (needing an alarm), minor (needing no alarm), or no seizure. Reliability was tested by interobserver agreement. We determined device performance, compared it to a bed sensor (Emfit), and evaluated the caregivers' user experience.
Results:
Twenty-eight of 34 admitted participants (1,826 nights, 809 major seizures) completed the study. Interobserver agreement (major/no major seizures) was 0.77 (95% confidence interval [CI] 0.65-0.89). Median sensitivity per participant amounted to 86% (95% CI 77%-93%); the false-negative alarm rate was 0.03 per night (95% CI 0.01-0.05); and the positive predictive value was 49% (95% CI 33%-64%). The multimodal sensor showed a better sensitivity than the bed sensor (n = 14, median difference 58%, 95% CI 39%-80%, p < 0.001). The caregivers' questionnaire (n = 33) indicated good sensor acceptance and usability according to 28 and 27 participants, respectively.
Conclusion:
Combining heart rate and movement resulted in reliable detection of a broad range of nocturnal seizures.
Approximately 3 million American adults reported active epilepsy* in 2015 (1). Active epilepsy, especially when seizures are uncontrolled, poses substantial burdens because of somatic, neurologic, and mental health comorbidity; cognitive and physical dysfunction; side effects of antiseizure medications; higher injury and mortality rates; poorer quality of life; and increased financial cost (2). Thus, prompt diagnosis and seizure control (i.e., seizure-free in the 12 months preceding the survey) confers numerous clinical and social advantages to persons with active epilepsy. To obtain recent and reliable estimates of active epilepsy and seizure control status in the U.S. population, CDC analyzed aggregated data from the 2013 and the 2015 National Health Interview Surveys (NHISs). Overall, an annual estimated 2.6 million (1.1%) U.S. adults self-reported having active epilepsy, 67% of whom had seen a neurologist or an epilepsy specialist in the past year, and 90% of whom reported taking epilepsy medication. Among those taking epilepsy medication, only 44% reported having their seizures controlled. A higher prevalence of active epilepsy and poorer seizure control were associated with low family income, unemployment, and being divorced, separated, or widowed. Use of epilepsy medication was higher among adults who saw an epilepsy specialist in the past year than among those who did not. Health care and public health should ensure that adults with uncontrolled seizures have appropriate care and self-management support in order to promote seizure control, improve health and social outcomes, and reduce health care costs.
Objective:
A prospective multicenter phase III trial was undertaken to evaluate the performance and tolerability in the epilepsy monitoring unit (EMU) of an investigational wearable surface electromyographic (sEMG) monitoring system for the detection of generalized tonic-clonic seizures (GTCSs).
Methods:
One hundred ninety-nine patients with a history of GTCSs who were admitted to the EMU in 11 level IV epilepsy centers for clinically indicated video-electroencephalographic monitoring also received sEMG monitoring with a wearable device that was worn on the arm over the biceps muscle. All recorded sEMG data were processed at a central site using a previously developed detection algorithm. Detected GTCSs were compared to events verified by a majority of three expert reviewers.
Results:
For all subjects, the detection algorithm detected 35 of 46 (76%, 95% confidence interval [CI] = 0.61-0.87) of the GTCSs, with a positive predictive value (PPV) of 0.03 and a mean false alarm rate (FAR) of 2.52 per 24 h. For data recorded while the device was placed over the midline of the biceps muscle, the system detected 29 of 29 GTCSs (100%, 95% CI = 0.88-1.00), with a detection delay averaging 7.70 s, a PPV of 6.2%, and a mean FAR of 1.44 per 24 h. Mild to moderate adverse events were reported in 28% (55 of 199) of subjects and led to study withdrawal in 9% (17 of 199). These adverse events consisted mostly of skin irritation caused by the electrode patch that resolved without treatment. No serious adverse events were reported.
Significance:
Detection of GTCSs using an sEMG monitoring device on the biceps is feasible. Proper positioning of this device is important for accuracy, and for some patients, minimizing the number of false positives may be challenging.
Objective:
New devices are needed for monitoring seizures, especially those associated with sudden unexpected death in epilepsy (SUDEP). They must be unobtrusive and automated, and provide false alarm rates (FARs) bearable in everyday life. This study quantifies the performance of new multimodal wrist-worn convulsive seizure detectors.
Methods:
Hand-annotated video-electroencephalographic seizure events were collected from 69 patients at six clinical sites. Three different wristbands were used to record electrodermal activity (EDA) and accelerometer (ACM) signals, obtaining 5,928 h of data, including 55 convulsive epileptic seizures (six focal tonic-clonic seizures and 49 focal to bilateral tonic-clonic seizures) from 22 patients. Recordings were analyzed offline to train and test two new machine learning classifiers and a published classifier based on EDA and ACM. Moreover, wristband data were analyzed to estimate seizure-motion duration and autonomic responses.
Results:
The two novel classifiers consistently outperformed the previous detector. The most efficient (Classifier III) yielded sensitivity of 94.55%, and an FAR of 0.2 events/day. No nocturnal seizures were missed. Most patients had <1 false alarm every 4 days, with an FAR below their seizure frequency. When increasing the sensitivity to 100% (no missed seizures), the FAR is up to 13 times lower than with the previous detector. Furthermore, all detections occurred before the seizure ended, providing reasonable latency (median = 29.3 s, range = 14.8-151 s). Automatically estimated seizure durations were correlated with true durations, enabling reliable annotations. Finally, EDA measurements confirmed the presence of postictal autonomic dysfunction, exhibiting a significant rise in 73% of the convulsive seizures.
Significance:
The proposed multimodal wrist-worn convulsive seizure detectors provide seizure counts that are more accurate than previous automated detectors and typical patient self-reports, while maintaining a tolerable FAR for ambulatory monitoring. Furthermore, the multimodal system provides an objective description of motor behavior and autonomic dysfunction, aimed at enriching seizure characterization, with potential utility for SUDEP warning.
This companion paper to the introduction of the International League Against Epilepsy (ILAE) 2017 classification of seizure types provides guidance on how to employ the classification. Illustration of the classification is enacted by tables, a glossary of relevant terms, mapping of old to new terms, suggested abbreviations, and examples. Basic and extended versions of the classification are available, depending on the desired degree of detail. Key signs and symptoms of seizures (semiology) are used as a basis for categories of seizures that are focal or generalized from onset or with unknown onset. Any focal seizure can further be optionally characterized by whether awareness is retained or impaired. Impaired awareness during any segment of the seizure renders it a focal impaired awareness seizure. Focal seizures are further optionally characterized by motor onset signs and symptoms: atonic, automatisms, clonic, epileptic spasms, or hyperkinetic, myoclonic, or tonic activity. Nonmotor-onset seizures can manifest as autonomic, behavior arrest, cognitive, emotional, or sensory dysfunction. The earliest prominent manifestation defines the seizure type, which might then progress to other signs and symptoms. Focal seizures can become bilateral tonic-clonic. Generalized seizures engage bilateral networks from onset. Generalized motor seizure characteristics comprise atonic, clonic, epileptic spasms, myoclonic, myoclonic-atonic, myoclonic-tonic-clonic, tonic, or tonic-clonic. Nonmotor (absence) seizures are typical or atypical, or seizures that present prominent myoclonic activity or eyelid myoclonia. Seizures of unknown onset may have features that can still be classified as motor, nonmotor, tonic-clonic, epileptic spasms, or behavior arrest. This "users' manual" for the ILAE 2017 seizure classification will assist the adoption of the new system.
Purpose:
The study aims to review systematically the quality of evidence supporting seizure detection devices. The unpredictable nature of seizures is distressing and disabling for sufferers and carers. If a seizure can be reliably detected then the patient or carer could be alerted. It could help prevent injury and death.
Methods:
A literature search was completed. Forty three of 120 studies found using relevant search terms were suitable for systematic review which was done applying pre-agreed criteria using PRISMA guidelines. The papers identified and reviewed were those that could have potential for everyday use of patients in a domestic setting. Studies involving long term use of scalp electrodes to record EEG were excluded on the grounds of unacceptable restriction of daily activities.
Results:
Most of the devices focused on changes in movement and/or physiological signs and were dependent on an algorithm to determine cut off points. No device was able to detect all seizures and there was an issue with both false positives and missed seizures. Many of the studies involved relatively small numbers of cases or report on only a few seizures. Reports of seizure alert dogs are also considered.
Conclusion:
Seizure detection devices are at a relatively early stage of development and as yet there are no large scale studies or studies that compare the effectiveness of one device against others. The issue of false positive detection rates is important as they are disruptive for both the patient and the carer. Nevertheless, the development of seizure detection devices offers great potential in the management of epilepsy.
Automatic detection of generalized tonic-clonic seizures (GTCS) will facilitate patient monitoring and early intervention to prevent comorbidities, recurrent seizures, or death. Brain Sentinel (San Antonio, Texas, USA) developed a seizure-detection algorithm evaluating surface electromyography (sEMG) signals during GTCS. This study aims to validate the seizure-detection algorithm using inpatient video-electroencephalography (EEG) monitoring.
sEMG was recorded unilaterally from the biceps/triceps muscles in 33 patients (17white/16 male) with a mean age of 40 (range 14-64) years who were admitted for video-EEG monitoring. Maximum voluntary biceps contraction was measured in each patient to set up the baseline physiologic muscle threshold. The raw EMG signal was recorded using conventional amplifiers, sampling at 1,024 Hz and filtered with a 60 Hz noise detection algorithm before it was processed with three band-pass filters at pass frequencies of 3-40, 130-240, and 300-400 Hz. A seizure-detection algorithm utilizing Hotelling's T-squared power analysis of compound muscle action potentials was used to identify GTCS and correlated with video-EEG recordings.
In 1,399 h of continuous recording, there were 196 epileptic seizures (21 GTCS, 96 myoclonic, 28 tonic, 12 absence, and 42 focal seizures with or without loss of awareness) and 4 nonepileptic spells. During retrospective, offline evaluation of sEMG from the biceps alone, the algorithm detected 20 GTCS (95%) in 11 patients, averaging within 20 s of electroclinical onset of generalized tonic activity, as identified by video-EEG monitoring. Only one false-positive detection occurred during the postictal period following a GTCS, but false alarms were not triggered by other seizure types or spells.
Brain Sentinel's seizure detection algorithm demonstrated excellent sensitivity and specificity for identifying GTCS recorded in an epilepsy monitoring unit. Further studies are needed in larger patient groups, including children, especially in the outpatient setting.
© 2015 The Authors. Epilepsia published by Wiley Periodicals Inc. on behalf of International League Against Epilepsy.
Many statistical methods rely on an underlying mathematical model of probability based on a simple approximation, one that is simultaneously well-known and yet frequently misunderstood. The Normal approximation to the Binomial distribution underpins a range of statistical tests and methods, including the calculation of accurate confidence intervals, performing goodness of fit and contingency tests, line- and model-fitting, and computational methods based upon these. A common mistake is in assuming that, since the probable distribution of error about the “true value” in the population is approximately Normally distributed, the same can be said for the error about an observation.This paper is divided into two parts: fundamentals and evaluation. First, we examine the estimation of confidence intervals using three initial approaches: the “Wald” (Normal) interval, the Wilson score interval and the “exact” Clopper-Pearson Binomial interval. Whereas the first two can be calculated directly from formulae, the Binomial interval must be approximated towards by computational search, and is computationally expensive. However this interval provides the most precise significance test, and therefore will form the baseline for our later evaluations. We also consider two further refinements: employing log-likelihood in intervals (also requiring search) and the effect of adding a continuity correction.Second, we evaluate each approach in three test paradigms. These are the single proportion interval or 2 × 1 goodness of fit test, and two variations on the common 2 × 2 contingency test. We evaluate the performance of each approach by a “practitioner strategy”. Since standard advice is to fall back to “exact” Binomial tests in conditions when approximations are expected to fail, we report the proportion of instances where one test obtains a significant result when the equivalent exact test does not, and vice versa, across an exhaustive set of possible values.We demonstrate that optimal methods are based on continuity-corrected versions of the Wilson interval or Yates’ test, and that commonly-held beliefs about weaknesses of tests are misleading. Log-likelihood, often proposed as an improvement on , performs disappointingly. Finally we note that at this level of precision we may distinguish two types of 2 2 test according to whether the independent variable partitions data into independent populations, and we make practical recommendations for their use.
Sudden unexpected death in epilepsy (SUDEP) is the leading cause of death in people with chronic refractory epilepsy. Very rarely, SUDEP occurs in epilepsy monitoring units, providing highly informative data for its still elusive pathophysiology. The MORTEMUS study expanded these data through comprehensive evaluation of cardiorespiratory arrests encountered in epilepsy monitoring units worldwide.
Between Jan 1, 2008, and Dec 29, 2009, we did a systematic retrospective survey of epilepsy monitoring units located in Europe, Israel, Australia, and New Zealand, to retrieve data for all cardiorespiratory arrests recorded in these units and estimate their incidence. Epilepsy monitoring units from other regions were invited to report similar cases to further explore the mechanisms. An expert panel reviewed data, including video electroencephalogram (VEEG) and electrocardiogram material at the time of cardiorespiratory arrests whenever available.
147 (92%) of 160 units responded to the survey. 29 cardiorespiratory arrests, including 16 SUDEP (14 at night), nine near SUDEP, and four deaths from other causes, were reported. Cardiorespiratory data, available for ten cases of SUDEP, showed a consistent and previously unrecognised pattern whereby rapid breathing (18-50 breaths per min) developed after secondary generalised tonic-clonic seizure, followed within 3 min by transient or terminal cardiorespiratory dysfunction. Where transient, this dysfunction later recurred with terminal apnoea occurring within 11 min of the end of the seizure, followed by cardiac arrest. SUDEP incidence in adult epilepsy monitoring units was 5·1 (95% CI 2·6-9·2) per 1000 patient-years, with a risk of 1·2 (0·6-2·1) per 10 000 VEEG monitorings, probably aggravated by suboptimum supervision and possibly by antiepileptic drug withdrawal.
SUDEP in epilepsy monitoring units primarily follows an early postictal, centrally mediated, severe alteration of respiratory and cardiac function induced by generalised tonic-clonic seizure, leading to immediate death or a short period of partly restored cardiorespiratory function followed by terminal apnoea then cardiac arrest. Improved supervision is warranted in epilepsy monitoring units, in particular during night time.
Commission of European Affairs of the International League Against Epilepsy.
Nowadaysthe formula to calculate the sample size for estimate a proportion (as prevalence) is based on the Normal distribution, however it would be based on a Binomial distribution which confidence interval was possible to be calculated using the Wilson Score method. By comparing the two formulae (Normal and Binomial distributions), the variation of the amplitude of the confidence intervals is relevant in the tails and the center of the curves. In order to calculate the needed sample size we have simulated an iterative sampling procedure, which shows an underestimation of the sample size for values of prevalence closed to 0 or 1, and also an overestimation for values closed to 0.5. Attending to these results we proposed an algorithm based on Wilson Score method that provides similar values for the sample size than empirically obtained by simulation.
Wearable automated seizure detection devices offer a high potential to improve seizure management, through continuous ambulatory monitoring, accurate seizure counts, and real-time alerts for prompt intervention. More importantly, these devices can be a life-saving help for people with a higher risk of sudden unexpected death in epilepsy (SUDEP), especially in case of generalized tonic-clonic seizures (GTCS). The Embrace and E4 wristbands (Empatica) are the first commercially available multimodal wristbands that were designed to sense the physiological hallmarks of ongoing GTCS: while Embrace only embeds a machine learning-based detection algorithm, both E4 and Embrace devices are equipped with motion (accelerometers, ACC) and electrodermal activity (EDA) sensors and both the devices received medical clearance (E4 from EU CE, Embrace from EU CE and US FDA). The aim of this contribution is to provide updated evidence of the effectiveness of GTCS detection and monitoring relying on the combination of ACM and EDA sensors.
A machine learning algorithm able to recognize ACC and EDA signatures of GTCS-like events has been developed on E4 data, labeled using gold-standard video-EEG examined by epileptologists in clinical centers, and has undergone continuous improvement. While keeping an elevated sensitivity to GTCS (92-100%), algorithm improvements and growing data availability led to lower false alarm rate (FAR) from the initial ˜2 down to 0.2-1 false alarms per day, as showed by retrospective and prospective analyses in inpatient settings. Algorithm adjustment to better discriminate real-life physical activities from GTCS, has brought the initial FAR of ˜6 on outpatient real life settings, down to values comparable to best-case clinical settings (FAR < 0.5), with comparable sensitivity. Moreover, using multimodal sensing, it has been possible not only to detect GTCS but also to quantify seizure-induced autonomic dysfunction, based on automatic features of abnormal motion and EDA. The latter biosignal correlates with the duration of post-ictal generalized EEG suppression, a biomarker observed in 100% of monitored SUDEP cases.
To increase the quality of studies on seizure detection devices, we propose standards for testing and clinical validation of such devices. We identified 4 key features that are important for studies on seizure detection devices: subjects, recordings, data analysis and alarms, and reference standard. For each of these features, we list the specific aspects that need to be addressed in the studies, and depending on these, studies are classified into 5 phases (0-4). We propose a set of outcome measures that need to be reported, and we propose standards for reporting the results. These standards will help in designing and reporting studies on seizure detection devices, they will give readers clear information on the level of evidence provided by the studies, and they will help regulatory bodies in assessing the quality of the validation studies. These standards are flexible, allowing classification of the studies into one of the 5 phases. We propose actions that can facilitate development of novel methods and devices.
Clinical validation studies of seizure detection devices conducted in epilepsy monitoring units (EMUs) can be biased by the artificial environment. We report a field (phase 4) study of a wearable accelerometer device (Epi-Care) that has previously been validated in EMUs for detecting bilateral tonic-clonic seizures (BTCS). Seventy-one patients using the device (or their caregivers) completed the modified Post-Study System Usability Questionnaire. Median time patients had been using the device was 15 months (range = 24 days-6 years). In 10% of cases, patients stopped using the device due to reasons related to the device. The median sensitivity (90%) and false alarm rate (0.1/d) were similar to what had been determined in EMUs. Patients and caregivers were overall satisfied with the device (median = 5.5 on the 7-point Likert scale), considered the technical aspects satisfactory, and considered the device comfortable and efficient. Adverse effects occurred in 11%, but were only mild: skin irritation at the wrist and interference with home electronic appliances. In 55% the device influenced the number of seizures logged into the seizure diary, and in 40% it contributed to fewer seizure-related injuries. This field study demonstrates the applicability and usability of the wearable accelerometer device for detecting BTCS.
Introduction:
Epilepsy is a common neurological condition. Seizure diary reports and patient- or caregiver-reported seizure counts are often inaccurate and underestimated. Many caregivers express stress and anxiety about the patient with epilepsy having seizures when they are not present. Therefore, a need exists for the ability to recognize and/or detect a seizure in the home setting. However, few studies have inquired on detection device features that are important to patients and their caregivers.
Methods:
A survey instrument utilizing a population of patients and caregivers was created to obtain information on the design criteria most desired for patients with epilepsy in regard to wearable devices.
Results:
One thousand one hundred sixty-eight responses were collected. Respondents thought that sensors for muscle signal (61.4%) and heart rate (58.0%) would be most helpful followed by the O2 sensor (41.4%). There was more interest in these three sensor types than for an accelerometer (25.5%). There was very little interest in a microphone (8.9%), galvanic skin response sensor (8.0%), or a barometer (4.9%). Based on a rating scale of 1-5 with 5 being the most important, respondents felt that "detecting all seizures" (4.73) is the most important device feature followed by "text/email alerts" (4.53), "comfort" (4.46), and "battery life" (4.43) as an equally important group of features. Respondents felt that "not knowing device is for seizures" (2.60) and "multiple uses" (2.57) were equally the least important device features. Average ratings differed significantly across age groups for the following features: button, multiuse, not knowing device is for seizures, alarm, style, and text ability. The p-values were all<0.002. Eighty-two point five percent of respondents [95% confidence interval: 80.0%, 84.7%] were willing to pay more than 200.
Conclusions:
Our survey results demonstrated that patients and caregivers have design features that are important to them in regard to a wearable seizure detection device. Overall, the ability to detect all seizures rated highest among respondents which continues to be an unmet need in the community with epilepsy in regard to seizure detection. Additional uses for a wearable were not as important. Based on our results, it is important that an alert (via test and/or email) for events be a portion of the system. A reasonable price point appears to be around 300. An accelerometer was less important to those surveyed when compared with the use of heart rate, oxygen saturation, or muscle twitches/signals. As further products become developed for use in other health arenas, it will be important to consider patient and caregiver desires in order to meet the need and address the gap in devices that currently exist.
Purpose:
Clinical management of epilepsy and current epilepsy therapy trials rely on paper or electronic diaries often with inaccurate self-reported seizure frequency as the primary outcome. This is the first study addressing the feasibility of detecting and recording generalized tonic-clonic seizures (GTCS) through a biosensor linked to an online seizure database.
Method:
A prospective trial was conducted with video-EEG (vEEG) in an epilepsy monitoring unit. Patients wore a wristwatch accelerometer that detected shaking and transmitted events via Bluetooth® to a bedside electronic tablet and then via Wi-Fi to an online portal. The watch recorded the date, time, audio, duration, frequency and amplitude of events. Events logged by the watch and recorded in a bedside paper diary were measured against vEEG, the "gold standard."
Results:
Thirty patients were enrolled and 62 seizures were recorded on vEEG: 31 convulsive and 31 non-convulsive. Twelve patients had a total of 31 convulsive seizures, and of those, 10 patients had 13 GTCS. The watch captured 12/13 (92.3%) GTCS. Watch audio recordings were consistent with seizures in 11/12 (91.6%). Data were successfully transferred to the bedside tablet in 11/12 (91.6%), and to the online database in 10/12 (83.3%) GTCS. The watch recorded 81 false positives, of which 42/81 (51%) were cancelled by the patients. Patients and caregivers verbally reported 15/62 seizures (24.2% sensitivity) but no seizures were recorded on paper logs.
Conclusion:
Automatic detection and recording of GTCS to an online database is feasible and may be more informative than seizure logging in a paper diary.
Objective:
This study aimed to (1) evaluate available systems and algorithms for ambulatory automatic seizure detection and (2) discuss benefits and disadvantages of seizure detection in epilepsy care.
Methods:
PubMed and EMBASE were searched up to November 2014, using variations and synonyms of search terms "seizure prediction" OR "seizure detection" OR "seizures" AND "alarm".
Results:
Seventeen studies evaluated performance of devices and algorithms to detect seizures in a clinical setting. Algorithms detecting generalized tonic-clonic seizures (GTCSs) had varying sensitivities (11% to 100%) and false alarm rates (0.2-4/24h). For other seizure types, detection rates were low, or devices produced many false alarms. Five studies externally validated the performance of four different devices for the detection of GTCSs. Two devices were promising in both children and adults: a mattress-based nocturnal seizure detector (sensitivity: 84.6% and 100%; false alarm rate: not reported) and a wrist-based detector (sensitivity: 89.7%; false alarm rate: 0.2/24h).
Significance:
Detection of seizure types other than GTCSs is currently unreliable. Two detection devices for GTCSs provided promising results when externally validated in a clinical setting. However, these devices need to be evaluated in the home setting in order to establish their true value. Automatic seizure detection may help prevent sudden unexpected death in epilepsy or status epilepticus, provided the alarm is followed by an effective intervention. Accurate seizure detection may improve the quality of life (QoL) of subjects and caregivers by decreasing burden of seizure monitoring and may facilitate diagnostic monitoring in the home setting. Possible risks are occurrence of alarm fatigue and invasion of privacy. Moreover, an unexpectedly high seizure frequency might be detected for which there are no treatment options. We propose that future studies monitor benefits and disadvantages of seizure detection systems with particular emphasis on QoL, comfort, and privacy of subjects and impact of false alarms.
Purpose:
This study investigated the duration of generalized tonic-clonic seizure (GTCS) and the factors that prolong GTCS duration.
Method:
We retrospectively analyzed clinical data collected from a consecutive group of patients who underwent video-electroencephalography (EEG) and experienced at least one GTCS during monitoring. Each seizure was divided into seven phases. The duration of GTCS was defined as the comprehensive duration of each GTCS phase, particularly those of Phase 3 to Phase 7.
Results:
The mean GTCS duration per patient was 74.6s. The results indicated that patients with an age of seizure onset <2 years exhibited a significantly longer duration than those with an age of seizure onset >2 years (p=0.033). A significant difference was also observed in the duration of GTCS between wakefulness and sleep (wakefulness 76.2±38.5, sleep 66.3±27.8, p=0.017). Our data suggest no significant differences between primary and secondary GTCS. The correlations between the duration of GTCS and many risk factors were also analyzed, including gender, age, neurological examination, cognitive status, family history of epilepsy, location of MRI brain abnormalities, reported seizure frequency at time of admission, number of current AEDs, history of SE, and duration of epilepsy.
Conclusions:
The mean duration of GTCS was<2min. The age of seizure onset and the circadian pattern of seizure are the major factors influencing the duration of GTCS.
This review surveys current seizure detection and classification technologies as they relate to aiding clinical decision-making during epilepsy treatment. Interviews and data collected from neurologists and a literature review highlighted a strong need for better distinguishing between patients exhibiting generalized and partial seizure types as well as achieving more accurate seizure counts. This information is critical for enabling neurologists to select the correct class of antiepileptic drugs (AED) for their patients and evaluating AED efficiency during long-term treatment. In our questionnaire, 100% of neurologists reported they would like to have video from patients prior to selecting an AED during an initial consultation. Presently, only 30% have access to video. In our technology review we identified that only a subset of available technologies surpassed patient self-reporting performance due to high false positive rates. Inertial seizure detection devices coupled with video capture for recording seizures at night could stand to address collecting seizure counts that are more accurate than current patient self-reporting during day and night time use. © 2015 Published by Elsevier Ltd on behalf of British Epilepsy Association.
Objectives:
Patient-reported seizure frequency is essential for therapy management and clinical research but lacks validity mainly due to seizure-induced seizure unawareness. Automated seizure detection by mobile monitoring devices promises to settle this serious methodological issue. Here, we explored attitudes and preferences towards future devices for seizure detection in adult patients with therapy-refractory epilepsies.
Methods:
A total of 102 inpatients enrolled and underwent a newly constructed semistructured 30-minute interview on automated seizure registration.
Results:
Most patients would generally apply and permanently use seizure registration devices. Removable devices were preferred (e.g., wristband sensors), but also patch electrodes at invisible body sites appeared acceptable. Only a minority of patients would accept implantations for seizure registration (especially of depth electrodes). Also, permanent optical or acoustical surveillance were accepted by a few patients only. Most patients were ready to care for the device (e.g., charging battery), to have doctor's appointments for device control, and even to pay for the device. Seizure prediction was evaluated as an essential additional function. Only half of the patients wanted emergency calls in case of a seizure.
Significance:
Patients would accept automated seizure registration if the device had as little as possible negative effect on daily living. High acceptance might, therefore, be expected for hardware equipment as it is nowadays used by many healthy subjects for physiological self-monitoring and life-logging. The proper medical engineering task of the future, therefore, is to optimize sensors in those highly feasible devices and to establish reliable biomarkers and outcome measures for a diversity of diseases (including epilepsy) from data obtained by this generic hardware.
Patients with epilepsy and their caregivers are constantly burdened with the possibility of a seizure and its consequences, such as accidents, injuries, and sudden unexplained death in epilepsy. It is the unpredictable nature of seizures that often affects both patients with seizures and their caregivers, limits independence, and hinders quality of life. There are several types of motion detectors on the market, each with varying degrees of sensitivity.
We prospectively tested the SmartWatch, a wrist-worn monitor, on children, adolescents, and young adults with various types of seizures in an epilepsy monitoring unit. Confirmation of seizure type and if there was rhythmic upper extremity jerking associated with the seizure was determined by review of the video electroencephalograph. This was compared with the standard detection system of the watch.
This study analyzed a total of 191 seizures in 41 patients aged 5-41 years. Fifty-one of the seizures were generalized tonic-clonic. Forty-seven of the seizures had a rhythmic arm movement component. The SmartWatch detected 30 seizures (16%) of the total, 16 (31%) of the generalized tonic-clonic seizures, and 16 (34%) seizures associated with rhythmic arm movements.
Overall, only a minority of generalized tonic-clonic seizures or seizures with rhythmic movements were detected, highlighting the need for an effective seizure detection device.
Copyright © 2015 Elsevier Inc. All rights reserved.
For long-term home monitoring of epileptic seizures, the measurement of extracerebral body signals such as abnormal movement is often easier and less obtrusive than monitoring intracerebral brain waves with electroencephalography (EEG). Non-EEG devices are commercially available but with little scientifically valid information and no consensus on which system works for which seizure type or patient.
We evaluated four systems based on efficiency, comfort, and user-friendliness and compared them in one patient suffering from focal epilepsy with secondary generalization. The Emfit mat, Epi-Care device, and Epi-Care Free bracelet are commercially available alarm systems, while the VARIA (Video, Accelerometry, and Radar-Induced Activity recording) device is being developed by our team and requires offline analysis for seizure detection and does so by presenting the 5% or 10% (patient-specific) most abnormal movement events, irrespective of the number of seizures per night.
As we chose to mimic the home situation, we did not record EEG and compared our results to the seizures reported by experienced staff that were monitoring the patient on a semicontinuous basis. This resulted in a sensitivity (sens) of 78% and false detection rate (FDR) of 0.55 per night for Emfit, sens 40% and FDR 0.41 for Epi-Care, sens 41% and FDR 0.05 for Epi-Care Free, and sens 56% and FDR 20.33 for VARIA.
Good results were obtained by some of the devices, even though, as expected, nongeneralized and nonrhythmic motor seizures (involving the head only, having a tonic phase, or manifesting mainly as sound) were often missed. The Emfit mat was chosen for our patient, also based on user-friendliness (few setup steps), comfort (contactless), and possibility to adjust patient-specific settings.
When in need of a seizure detection system for a patient, a thorough individual search is still required, which suggests the need for a database or overview including results of clinical trials describing the patient and their seizure types.
Purpose:
There is a need for a seizure-detection system that can be used long-term and in home situations for early intervention and prevention of seizure related side effects including SUDEP (sudden unexpected death in epileptic patients). The gold standard for monitoring epileptic seizures involves video/EEG (electro-encephalography), which is uncomfortable for the patient, as EEG electrodes are attached to the scalp. EEG analysis is also labour-intensive and has yet to be automated and adapted for real-time monitoring. It is therefore usually performed in a hospital setting, for a few days at the most. The goal of this article is to provide an overview of body signals that can be measured, along with corresponding methods, state-of-art research, and commercially available systems, as well as to stress the importance of a good detection system.
Method:
Narrative literature review.
Results:
A range of body signals can be monitored for the purpose of seizure detection. It is particularly interesting to include monitoring of autonomic dysfunction, as this may be an important patho-physiological mechanism of SUDEP, and of movement, as many seizures have a motor component.
Conclusion:
The most effective seizure detection systems are multimodal. Such systems should also be comfortable and low-power. The body signals and modalities on which a system is based should take account of the user's seizure types and personal preferences.
Our objective was to assess the clinical reliability of a wrist-worn, wireless accelerometer sensor for detecting generalized tonic-clonic seizures (GTCS). Seventy-three consecutive patients (age 6-68 years; median 37 years) at risk of having GTCS and who were admitted to the long-term video-electroencephalography (EEG) monitoring unit (LTM) were recruited in three centers. The reference standard was considered the seizure time points identified by experienced clinical neurophysiologists, based on the video-EEG recordings and blinded to the accelerometer sensor data. Seizure time points detected real-time by the sensor were compared with the reference standard. Patients were monitored for 17-171 h (mean 66.8; total 4,878). Thirty-nine GTCS were recorded in 20 patients. The device detected 35 seizures (89.7%). In 16 patients all seizures were detected. In three patients more than two thirds of the seizures were detected. The mean of the sensitivity calculated for each patient was 91%. The mean detection latency measured from the start of the focal seizure preceding the secondarily GTCS was 55 s (95% confidence interval [CI] 38-73 s). The rate of false alarms was 0.2/day. Our results suggest that the wireless wrist accelerometer sensor detects GTCS with high sensitivity and specificity. Patients with GTCS have an increased risk for injuries related to seizures and for sudden unexpected death in epilepsy (SUDEP), and many nocturnal seizures remain undetected in unattended patients. A portable automatic seizure detection device will be an important tool for helping these patients.
Objectives:
This paper summarizes much of the research that is applicable to the design of auditory alarms in a medical context. It also summarizes research that demonstrates that false alarm rates are unacceptably high, meaning that the proper application of auditory alarm design principles are compromised.
Target audience:
Designers, users, and manufacturers of medical information and monitoring systems that indicate when medical or other parameters are exceeded and that are indicated by an auditory signal or signals.
Scope:
The emergence of alarms as a 'hot topic'; an outline of the issues and design principles, including IEC 60601-1-8; the high incidence of false alarms and its impact on alarm design and alarm fatigue; approaches to reducing alarm fatigue; alarm philosophy explained; urgency in audible alarms; different classes of sound as alarms; heterogeneity in alarm set design; problems with IEC 60601-1-8 and ways of approaching this design problem.
The ability to statistically compare the performance of two computer detection (CD) or computer-aided detection (CAD) algorithms is fundamental for the development and evaluation of medical image analysis tools. Automated detection tools for medical imaging are commonly characterized using free-response receiver operating characteristic (FROC) methods. However, few statistical tools are currently available to estimate statistical significance when comparing two FROC performance curves. In this study, we introduce a permutation and a bootstrap resampling method for the nonparametric estimation of statistical significance of performance metrics when comparing two FROC curves. We then provide an initial validation of the proposed methods using an area under the FROC performance metric and a simulation model for creating CD algorithm prompts. Validation is based on a comparison of the Type I error rate produced by two statistically identical CD algorithms. The results of 104 Monte Carlo trials show that both the permutation and bootstrap methods produced excellent estimates of the expected Type I error rate
Caregivers of people with epilepsy are commonly concerned about unwitnessed seizures causing injury and even death. The goal of this study was to determine if a wrist-worn motion detector could detect tonic-clonic seizures. Individuals admitted for continuous video/EEG monitoring wore a wristwatch-size device that was programmed to detect rhythmic movements such as those that occur during tonic-clonic seizures. When such movement was detected, the device sent a Bluetooth signal to a computer that registered the time and duration of the movements. Recorded detections were compared with the routinely recorded video/EEG data. Six of 40 patients had a total of eight tonic-clonic seizures. Seven of the eight seizures were detected. Nonseizure movements were detected 204 times, with opportunity for canceling transmission by the patient. Only one false detection occurred during sleep. In principle, this device should allow caregivers of people with tonic-clonic seizures to be alerted when a seizure occurs.
The unpredictable and random occurrence of seizures is of the most distressful issue affecting patients and their families. Unattended seizures can have serious consequences including injury or death. The objective of this study is to develop a small, portable, wearable device capable of detecting seizures and alerting patients and families on recognition of specific seizures' motor activity. Ictal data were prospectively obtained in consecutive patients admitted to two video-EEG units. This study included patients with a history of motor seizures, clonic or tonic, or tonic-clonic seizures or patients with complex partial seizures with frequent secondary generalization. A "Motion Sensor" unit mounted on a bracelet was attached to one wrist. The "Sensor" contains a three-axis accelerometer and a transmitter. The three-axis movements' data were transmitted to a portable computer. Algorithm specially developed for this purpose analyzed the recorded data. Seizures' alerts were compared with the video-EEG data. Ictal data were acquired in 15 of the 31 recruited patients. The algorithm correctly identified 20 of 22 (91%) captured seizures and generated an alarm within a median period of 17 seconds. All events lasting >30 seconds (i.e., 19 events) were identified. The system failed to identify 2 of 22 seizures (9%). There were eight false alarms during 1,692 hours of monitoring. Preliminary data suggest that this motion detection device/alarm system can identify most motor seizures with high sensitivity and with a low false alarm rate.
To evaluate the effects of a daily patient reminder on seizure documentation accuracy.
Randomized controlled trial.
Monitoring unit of an academic department of epileptology. Patients Consecutive sample of 91 adult inpatients with focal epilepsies undergoing video-electroencephalographic monitoring. Intervention While all patients were asked to document seizures at the beginning of the monitoring period, patients from the experimental group were reminded each day to document seizures. Main Outcome Measure Documentation accuracy (percentage of documented seizures).
A total of 582 partial seizures were recorded. Patients failed to document 55.5% of all recorded seizures, 73.2% of complex partial seizures, 26.2% of simple partial seizures, 41.7% of secondarily generalized tonic-clonic seizures, 85.8% of all seizures during sleeping, and 32.0% of all seizures during the awake state. The group medians of individual documentation accuracies for overall seizures, simple partial seizures, complex partial seizures, and secondarily generalized tonic-clonic seizures were 33.3%, 66.7%, 0%, and 83.3%, respectively. Neither the patient reminder nor cognitive performance affected documentation accuracy. A left-sided electroencephalographic focus or lesion, but not the site (frontal or temporal), contributed to documentation failure.
Patient seizure counts do not provide valid information. Documentation failures result from postictal seizure unawareness, which cannot be avoided by reminders. Unchanged documentation accuracy is a prerequisite for the use of patient seizure counts in clinical trials and has to be demonstrated in a subsample of patients undergoing electroencephalographic monitoring.
Generalized Seizure Detection And Alerting In The EMU With The Empatica Embrace Watch And Smartphone-Based Alert System
- R W Picard
510(k) Premarket Notification Embrace. FDA
- R Lal