Impact of pediatric epilepsy on sleep patterns and behaviors in children and parents.

PURPOSE: Disrupted sleep patterns in children with epilepsy and their parents are commonly described clinically. A number of studies have shown increased frequency of sleep disorders among pediatric epilepsy patients; however, few have characterized the association between epilepsy and parental sleep quality and household sleeping arrangements. The purpose of this study was to explore the effect of pediatric epilepsy on child sleep, parental sleep and fatigue, and parent-child sleeping arrangements, including room sharing and cosleeping. METHODS: Parents of children 2 to 10 years of age with and without epilepsy completed written questionnaires assessing seizure history, child and parent sleep, and household sleeping arrangements. Children's Sleep Habits Questionnaire (CSHQ) scores were used to evaluate sleep disturbances for the child. The Pittsburgh Sleep Quality Index (PSQI) and the Iowa Fatigue Scale (IFS) were used to evaluate parental sleep and fatigue, respectively. The Early Childhood Epilepsy Severity Scale (E-Chess) was used to assess epilepsy severity. KEY FINDINGS: One hundred five households with a child with epilepsy and 79 controls participated in this study. Households with a child with epilepsy reported increased rates of both parent-child room sharing (p < 0.001) and cosleeping (p = 0.005) compared to controls. Children with epilepsy were found to have greater sleep disturbance by total CSHQ score (p < 0.001) and the following subscores: parasomnias (p < 0.001), night wakings (p < 0.001), sleep duration (p < 0.001), daytime sleepiness (<0.001), sleep onset delay (p = 0.009), and bedtime resistance (p = 0.023). Parents of children with epilepsy had increased sleep dysfunction (p = 0.005) and were more fatigued (p < 0.001). Severity of epilepsy correlated positively with degree of child sleep dysfunction (0.192, p = 0.049), parental sleep dysfunction (0.273, p = 0.005), and parental fatigue (0.324, p = 0.001). Antiepileptic drug polytherapy was predictive of greater childhood sleep disturbances. Nocturnal seizures were associated with parental sleep problems, whereas room sharing and cosleeping behavior were associated with child sleep problems. Within the epilepsy cohort, 69% of parents felt concerned about night seizures and 44% reported feeling rested rarely or never. Finally, 62% of parents described decreased sleep quality and/or quantity with cosleeping. SIGNIFICANCE: Pediatric epilepsy can significantly affect sleep patterns for both the affected child and his or her parents. Parents frequently room share or cosleep with their child, adaptations which may have detrimental effects for many households. Clinicians must not only be attentive to the sleep issues occurring in pediatric patients with epilepsy, but also for the household as a whole. These data provide evidence of a profound clinical need for improved epilepsy therapeutics and the development of nocturnal seizure monitoring technologies.

A machine-learning algorithm for detecting seizure termination in scalp EEG.

Efforts to develop algorithms that can robustly detect the cessation of seizure activity within scalp EEGs are now underway. Such algorithms can facilitate novel clinical applications such as the estimation of a seizure's duration; the delivery of therapies designed to mitigate postictal period symptoms; or detection of the presence of status epilepticus. In this article, we present and evaluate a novel, machine learning-based method for detecting the termination of electrographic seizure activity. When tested on 133 seizures from a public database, our method successfully detected the end of 132 seizures within 10.3 ± 5.5 seconds of the time determined by an electroencephalographer to represent the electrographic end of seizure. Furthermore, by pairing our seizure end detector with a previously published seizure onset detector, we could automatically estimate the duration of 85% of test electrographic seizures within a 15-second error margin compared with electroencephalographer determinations. This article is part of a Supplemental Special Issue entitled The Future of Automated Seizure Detection and Prediction.

An algorithm for seizure onset detection using intracranial EEG.

This article addresses the problem of real-time seizure detection from intracranial EEG (IEEG). One difficulty in creating an approach that can be used for many patients is the heterogeneity of seizure IEEG patterns across different patients and even within a patient. In addition, simultaneously maximizing sensitivity and minimizing latency and false detection rates has been challenging as these are competing objectives. Automated machine learning systems provide a mechanism for dealing with these hurdles. Here we present and evaluate an algorithm for real-time seizure onset detection from IEEG using a machine-learning approach that permits a patient-specific solution. We extract temporal and spectral features across all intracranial EEG channels. A pattern recognition component is trained using these feature vectors and tested against unseen continuous data from the same patient. When tested on more than 875 hours of IEEG data from 10 patients, the algorithm detected 97% of 67 test seizures of several types with a median detection delay of 5 seconds and a median false alarm rate of 0.6 false alarms per 24-hour period. The sensitivity was 100% for 8 of 10 patients. These results indicate that a sensitive, specific, and relatively short-latency detection system based on machine learning can be employed for seizure detection from EEG using a full set of intracranial electrodes to individual patients. This article is part of a Supplemental Special Issue entitled The Future of Automated Seizure Detection and Prediction.

Erratum: Author Correction: Deep learning for automated sleep staging using instantaneous heart rate.

[This corrects the article DOI: 10.1038/s41746-020-0291-x.].

Deep learning for automated sleep staging using instantaneous heart rate.

Clinical sleep evaluations currently require multimodal data collection and manual review by human experts, making them expensive and unsuitable for longer term studies. Sleep staging using cardiac rhythm is an active area of research because it can be measured much more easily using a wide variety of both medical and consumer-grade devices. In this study, we applied deep learning methods to create an algorithm for automated sleep stage scoring using the instantaneous heart rate (IHR) time series extracted from the electrocardiogram (ECG). We trained and validated an algorithm on over 10,000 nights of data from the Sleep Heart Health Study (SHHS) and Multi-Ethnic Study of Atherosclerosis (MESA). The algorithm has an overall performance of 0.77 accuracy and 0.66 kappa against the reference stages on a held-out portion of the SHHS dataset for classifying every 30 s of sleep into four classes: wake, light sleep, deep sleep, and rapid eye movement (REM). Moreover, we demonstrate that the algorithm generalizes well to an independent dataset of 993 subjects labeled by American Academy of Sleep Medicine (AASM) licensed clinical staff at Massachusetts General Hospital that was not used for training or validation. Finally, we demonstrate that the stages predicted by our algorithm can reproduce previous clinical studies correlating sleep stages with comorbidities such as sleep apnea and hypertension as well as demographics such as age and gender.

Non-invasive computerized system for automatically initiating vagus nerve stimulation following patient-specific detection of seizures or epileptiform discharges.

OBJECTIVE: To demonstrate the feasibility of using a computerized system to detect the onset of a seizure and, in response, initiate Vagus nerve stimulation (VNS) in patients with medically refractory epilepsy. METHODS: We designed and built a non-invasive, computerized system that automatically initiates VNS following the real-time detection of a pre-identified seizure or epileptiform discharge. The system detects these events through patient-specific analysis of the scalp electroencephalogram (EEG) and electrocardiogram (ECG) signals. RESULTS: We evaluated the performance of the system on 5 patients (A-E). For patients A and B the computerized system initiated VNS in response to seizures; for patients C and D the system initiated VNS in response to epileptiform discharges; and for patient E neither seizures nor epileptiform discharges were observed during the evaluation period. During the 81 hour clinical test of the system on patient A, the computerized system detected 5/5 seizures and initiated VNS within 5 seconds of the appearance of ictal discharges in the EEG; VNS did not seem to alter the electrographic or behavioral characteristics of the seizures in this case. During the same testing session the computerized system initiated false stimulations at the rate of 1 false stimulus every 2.5 hours while the subject was at rest and not ambulating. During the 26 hour clinical test of the system on patient B, the computerized system detected 1/1 seizures and initiated VNS within 16 seconds of the appearance of ictal discharges; VNS did not alter the electrographic duration of the seizure but decreased anxiety and increased awareness during the post-seizure recovery phase. During the same testing session the computerized system did not declare any false detections. SIGNIFICANCE: Initiating Vagus nerve stimulation soon after the onset of a seizure may abort or ameliorate seizure symptoms in some patients; unfortunately, a significant number of patients cannot initiate VNS by themselves following the start of a seizure. A system that automatically couples automated detection of seizure onset to initiation of VNS may be helpful for seizure treatment.

Quantifying neurologic disease using biosensor measurements in-clinic and in free-living settings in multiple sclerosis.

Technological advances in passive digital phenotyping present the opportunity to quantify neurological diseases using new approaches that may complement clinical assessments. Here, we studied multiple sclerosis (MS) as a model neurological disease for investigating physiometric and environmental signals. The objective of this study was to assess the feasibility and correlation of wearable biosensors with traditional clinical measures of disability both in clinic and in free-living in MS patients. This is a single site observational cohort study conducted at an academic neurological center specializing in MS. A cohort of 25 MS patients with varying disability scores were recruited. Patients were monitored in clinic while wearing biosensors at nine body locations at three separate visits. Biosensor-derived features including aspects of gait (stance time, turn angle, mean turn velocity) and balance were collected, along with standardized disability scores assessed by a neurologist. Participants also wore up to three sensors on the wrist, ankle, and sternum for 8 weeks as they went about their daily lives. The primary outcomes were feasibility, adherence, as well as correlation of biosensor-derived metrics with traditional neurologist-assessed clinical measures of disability. We used machine-learning algorithms to extract multiple features of motion and dexterity and correlated these measures with more traditional measures of neurological disability, including the expanded disability status scale (EDSS) and the MS functional composite-4 (MSFC-4). In free-living, sleep measures were additionally collected. Twenty-three subjects completed the first two of three in-clinic study visits and the 8-week free-living biosensor period. Several biosensor-derived features significantly correlated with EDSS and MSFC-4 scores derived at visit two, including mobility stance time with MSFC-4 z-score (Spearman correlation -0.546; p = 0.0070), several aspects of turning including turn angle (0.437; p = 0.0372), and maximum angular velocity (0.653; p = 0.0007). Similar correlations were observed at subsequent clinic visits, and in the free-living setting. We also found other passively collected signals, including measures of sleep, that correlated with disease severity. These findings demonstrate the feasibility of applying passive biosensor measurement techniques to monitor disability in MS patients both in clinic and in the free-living setting.

Clinical Tremor Severity Estimation Using an Instrumented Eating Utensil.

We demonstrate the feasibility of estimating clinical tremor scores using an eating utensil with motion-sensing and tremor-cancellation technology in thirteen patients with tremor. Three experts scored hand tremor using the modified Fahn- Tolosa-Marin (FTM) scale. A linear model was trained to estimate tremor severity using the recorded motion signals. The average neurologist FTM score was 1.6±0.7 for PD and 2.6±0.7 for ET patients. The average model score was 1.6±0.7 for PD and 2.6±0.6 for ET. Correlation coefficient between the clinical and model tremor scores was 0.91 (p < 0.001). Motion data from an instrumented eating utensil accurately derived tremor ratings enabling practical, objective daily monitoring.

An electroencephalographic recording platform for real-time seizure detection.

There are currently no clinical devices that can be worn by epilepsy patients who suffer from intractable seizures to warn them of seizure onset. Here we summarize state-of-the-art therapies and devices, and present a second-generation hardware platform in which seizure detection algorithms may be programmed into the device. Bi-polar electrographic data is presented for a prototype device and future implementations are discussed.

A data-driven modeling approach to stochastic computation for low-energy biomedical devices.

Low-power devices that can detect clinically relevant correlations in physiologically-complex patient signals can enable systems capable of closed-loop response (e.g., controlled actuation of therapeutic stimulators, continuous recording of disease states, etc.). In ultra-low-power platforms, however, hardware error sources are becoming increasingly limiting. In this paper, we present how data-driven methods, which allow us to accurately model physiological signals, also allow us to effectively model and overcome prominent hardware error sources with nearly no additional overhead. Two applications, EEG-based seizure detection and ECG-based arrhythmia-beat classification, are synthesized to a logic-gate implementation, and two prominent error sources are introduced: (1) SRAM bit-cell errors and (2) logic-gate switching errors ('stuck-at' faults). Using patient data from the CHB-MIT and MIT-BIH databases, performance similar to error-free hardware is achieved even for very high fault rates (up to 0.5 for SRAMs and 7 × 10(-2) for logic) that cause computational bit error rates as high as 50%.

An algorithm for detecting seizure termination in scalp EEG.

Little effort has been devoted to developing algorithms that can detect the cessation of seizure activity in scalp EEG. Such algorithms could facilitate clinical applications such as the estimation of seizure duration or the delivery of therapies designed to mitigate postictal period symptoms. In this paper, we present a method for detecting the termination of seizure activity. When tested on 133 seizures from a public database, our method detected the end of 132 seizures with a mean absolute error of 10.3 ± 5.5 seconds of the time marked by an electroencephalographer. Furthermore, by pairing our seizure end detector with a previously published seizure onset detector, we could automatically estimate the duration of 85% of test seizures within a 15 second error margin.

Creating symbolic representations of electroencephalographic signals: an investigation of alternate methodologies on intracranial data.

The electroencephalogram (EEG) is widely used in the investigation of neurological disorders. Continuous long-term EEG data offers the opportunity to assess patient health over long periods of time, and to discover previously unknown physiological phenomena. However, the sheer volume of information generated by long-term EEG monitoring also poses a serious challenge for both analysis and visualization. Symbolization has been successful in addressing information overload in many disciplines. In this paper, we present different approaches to transform EEG signals into symbolic sequences. This discrete symbolic representation reduces the amount of EEG data by several orders of magnitude and makes the task of discovering and visualizing interesting activity more manageable. We describe alternate methodologies to symbolize EEG data from patients with epilepsy. When evaluated on long-term intracranial data from 10 patients, our symbolization produced results that were consistent with clinical labels of seizures (for 97% of the seizures and 68% of the seizure segments), and often produced finer-grained distinctions.

A micropower support vector machine based seizure detection architecture for embedded medical devices.

Implantable neurostimulators for the treatment of epilepsy that are capable of sensing seizures can enable novel therapeutic applications. However, detecting seizures is challenging due to significant intracranial EEG signal variability across patients. In this paper, we illustrate how a machine-learning based, patient-specific seizure detector provides better performance and lower power consumption than a patient non-specific detector using the same seizure library. The machine-learning based architecture was fully implemented in the micropower domain, demonstrating feasibility for an embedded detector in implantable systems.

Impact of patient-specificity on seizure onset detection performance.

In this paper we quantify the degree to which patient-specificity affects the detection latency, sensitivity, and specificity of a seizure detector using 536 hours of continuously recorded scalp EEG from 16 epilepsy patients. We demonstrate that a detector that knows of an individual's seizure and non-seizure EEG outperforms a detector limited to knowledge of an individual's non-seizure EEG, and a detector limited to knowledge of population seizure and non-seizure EEG.

Building a High-Quality Mobile Telemedicine System using Network Striping over Dissimilar Wireless Wide Area Networks.

This paper describes an approach to building a high-quality mobile telemedicine system that overcomes the limitations of individual public wireless data networks. Public wireless data channels do not have the capacity to handle the high-bandwidth video needed for applications such as remote evaluation of trauma and stroke patients. Network striping allows us to aggregate multiple physical channels to meet the bandwidth requirements for the video. We have developed flexible network-striping software middleware, and are building a telemedicine system using that middleware. Our approach uses existing communications infrastructure and conventional-off-the-shelf components, making the system easy to deploy.

Detecting seizure onset in the ambulatory setting: demonstrating feasibility.

Ambulatory EEG recorders are commercially available. The majority of these recorders are only capable of capturing and storing EEG for later review by clinicians. A few models are equipped with real-time seizure event detectors, but these detectors make no guarantees on when during a seizure a detection is made. This renders current ambulatory EEG recorders unsuitable for activating alarms or initiating therapies to acutely impact seizure progression in the ambulatory setting. Integrating seizure onset detectors into existing ambulatory recorders will make these applications feasible. Successful integration requires that these detectors be executable on the resource-limited digital signal processors found within ambulatory recorders. In this paper we describe the integration of a patient-specific seizure onset detector with a commercially available ambulatory EEG recorder, and demonstrate how such integration could enable the detection of seizure onset in the ambulatory setting.

Patient-specific seizure onset detection.

This article presents an automated, patient-specific method for the detection of epileptic seizure onset from noninvasive electroencephalography. We adopt a patient-specific approach to exploit the consistency of an individual patient's seizure and nonseizure electroencephalograms. Our method uses a wavelet decomposition to construct a feature vector that captures the morphology and spatial distribution of an electroencephalographic epoch, and then determines whether that vector is representative of a patient's seizure or nonseizure electroencephalogram using the support vector machine classification algorithm. Our completely automated method was tested on noninvasive electroencephalograms from 36 pediatric subjects suffering from a variety of seizure types. It detected 131 of 139 seizure events within 8.0+/-3.2 seconds of electrographic onset, and declared 15 false detections in 60 hours of clinical electroencephalography. Our patient-specific method can be used to initiate delay-sensitive clinical procedures following seizure onset, for example, the injection of a functional imaging radiotracer.

Patient-specific seizure onset detection.

This paper presents an automated, patient-specific method for the detection of epileptic seizure onsets from noninvasive EEG. We adopt a patient-specific approach to exploit the consistency of an individual patient's seizure and non-seizure EEG. Our method uses a wavelet decomposition to construct a feature vector that captures the morphology and spatial distribution of an EEG epoch, and then determines whether that vector is representative of a patient's seizure or non-seizure EEG using the support-vector machine classification algorithm. Our completely automated method was tested on non-invasive EEG from thirty-six pediatric subjects suffering from a variety of seizure types. It detected 131 of 139 seizure events within 8.0+/-3.2 seconds following electrographic onset, and declared 15 false-detections in 60 hours of clinical EEG. Our patient-specific method can be used to initiate delay-sensitive clinical procedures following seizure onset; for example, the injection of an imaging radiopharmaceutical or stimulation of the vagus nerve.