The clinical and genetic spectrum in infants with (an) unprovoked cluster(s) of focal seizures.

BACKGROUND: Self-limited (familial) infantile epilepsy (S(F)IE), formerly known as benign (familial) infantile convulsions (B(F)IC), is an infantile cluster epilepsy with in rule a complete recovery. This form of epilepsy is most often caused by variations in the PRRT2 gene (OMIM #605751). AIM: To describe the clinical and genetic spectrum of sudden onset clusters of focal seizures in infancy. METHODS: We retrospectively reviewed all individuals, who presented with unprovoked infantile seizures and selected all infants who had unprovoked clustered focal seizures between 1 and 20 months of age. We described the clinical and genetic spectrum of this cohort. RESULTS: The data of 23 patients from 21 families were collected. All had an initial diagnosis of S(F)IE which was adjusted in 5 individuals. In 12 individuals a pathogenic variation in PRRT2 gene or complete deletion was identified. Pathogenic variants in PCDH19 and KCNQ2 were found in respectively 3 and 1 individuals. One individual had a non-pathogenic variant in ATP1A3 and in 6 others no variants were identified. The mean cluster duration was 2.9 days (range 1-13) (see Table 1). Twelve infants had only one cluster. All patients had focal motor or non-motor seizures, in 12 (52%) followed by bilateral (tonic)clonic seizures. Positive family history was present in 74% of individuals. In 11/12 (92%) tested families, ≥1 family member carried the pathogenic PRRT2 variant. Age of seizure onset (ASO) averaged 6.2 months (range 2-20 months). Age of latest seizure averaged 16 months (range 2-92). In several interictal EEG (electroencephalogram) recordings multifocal spikes or spike-wave abnormalities were detected. Ictal EEG recordings detected primary focal abnormalities. CONCLUSION: We described 23 individuals with unprovoked cluster(s) of focal seizures at infancy. It appears to be a heterogeneous group. Half of them had a pathogenic variation in PRRT2 gene. Most had only one cluster of seizures. When clusters reoccur frequently, when seizures are more therapy-resistant and when seizures persist beyond the age of 2 years, another diagnosis or causative gene is likely.

Adaptive nocturnal seizure detection using heart rate and low-complexity novelty detection.

PURPOSE: Automated seizure detection at home is mostly done using either patient-independent algorithms or manually personalized algorithms. Patient-independent algorithms, however, lead to too many false alarms, whereas the manually personalized algorithms typically require manual input from an experienced clinician for each patient, which is a costly and unscalable procedure and it can only be applied when the patient had a sufficient amount of seizures. We therefore propose a nocturnal heart rate based seizure detection algorithm that automatically adapts to the patient without requiring seizure labels. METHODS: The proposed method initially starts with a patient-independent algorithm. After a very short initialization period, the algorithm already adapts to the patients' characteristics by using a low-complex novelty detection classifier. The algorithm is evaluated on 28 pediatric patients with 107 convulsive and clinical subtle seizures during 695 h of nocturnal multicenter data in a retrospective study that mimics a real-time analysis. RESULTS: By using the adaptive seizure detection algorithm, the overall performance was 77.6% sensitivity with on average 2.56 false alarms per night. This is 57% less false alarms than a patient-independent algorithm with a similar sensitivity. Patients with tonic-clonic seizures showed a 96% sensitivity with on average 1.84 false alarms per night. CONCLUSION: The proposed method shows a strongly improved detection performance over patient-independent performance, without requiring manual adaptation by a clinician. Due to the low-complexity of the algorithm, it can be easily implemented on wearables as part of a (multimodal) seizure alarm system.

Feature selection methods for accelerometry-based seizure detection in children.

We investigate the application of feature selection methods and their influence on distinguishing nocturnal motor seizures in epileptic children from normal nocturnal movements using accelerometry signals. We studied two feature selection methods applied one after the other to reduce the complexity and computation costs of least-squares support vector machine (LS-SVM) models. Simultaneous feature selection analyses were performed for each seizure type individually and jointly. Starting from 140 features, a filter method based on mutual information was applied to remove irrelevant and redundant features. The obtained subset was further reduced through a wrapper feature selection strategy using an LS-SVM classifier with both forward search and backward elimination. The discriminative power of each feature subset was evaluated on the test data in terms of the area under the receiver operating characteristic curve, sensitivity, and false detection rate per hour. We showed that, by using only a filter method for feature selection, it was possible to obtain classification results of comparable or slightly reduced performance with respect to the complete feature set. The attained results could facilitate further development of accelerometry-based seizure detection and alarm systems.

Non-EEG seizure detection systems and potential SUDEP prevention: State of the art: Review and update.

PURPOSE: Detection of, and alarming for epileptic seizures is increasingly demanded and researched. Our previous review article provided an overview of non-invasive, non-EEG (electro-encephalography) body signals that can be measured, along with corresponding methods, state of the art research, and commercially available systems. Three years later, many more studies and devices have emerged. Moreover, the boom of smart phones and tablets created a new market for seizure detection applications. METHOD: We performed a thorough literature review and had contact with manufacturers of commercially available devices. RESULTS: This review article gives an updated overview of body signals and methods for seizure detection, international research and (commercially) available systems and applications. Reported results of non-EEG based detection devices vary between 2.2% and 100% sensitivity and between 0 and 3.23 false detections per hour compared to the gold standard video-EEG, for seizures ranging from generalized to convulsive or non-convulsive focal seizures with or without loss of consciousness. It is particularly interesting to include monitoring of autonomic dysfunction, as this may be an important pathophysiological mechanism of SUDEP (sudden unexpected death in epilepsy), and of movement, as many seizures have a motor component. CONCLUSION: Comparison of research results is difficult as studies focus on different seizure types, timing (night versus day) and patients (adult versus pediatric patients). Nevertheless, we are convinced that the most effective seizure detection systems are multimodal, combining for example detection methods for movement and heart rate, and that devices should especially take into account the user's seizure types and personal preferences.

Automated non-EEG based seizure detection: Do users have a say?

PURPOSE: Quality of life of patients with epilepsy depends largely upon unpredictability of seizure occurrence and would improve by predicting seizures or at least by detecting seizures (after their clinical onset) and react timely. Detection systems are available and researched, but little is known about the actual need and user preferences. The first indicates the market potential; the second allows us to incorporate user requirements into the engineering process. METHODS: We questioned 20 pediatric and young adult patients, 114 caregivers, and 21 involved medical doctors and described, analyzed, and compared their experiences with systems for seizure detection, their opinions on usefulness and purpose of seizure detection, and their requirements for such a device. RESULTS: Experience with detection systems is limited, but 65% of patients and caregivers and 85% of medical doctors express the usefulness, more so during night than day. The need is higher in patients with more severe intellectual disability. The higher the seizure frequency, the higher the need, opinions in the seizure-free group being more divided. Most patients and caregivers require 100% correct detection, and on average, one false alarm per seizure (one per week for those seizure-free) is accepted. Medical doctors allow 90% correct detections and between two false alarms per week and one per month depending on seizure frequency. Detection of seizures involving heavy movement and falls is judged most important by patients and caregivers and second to most by medical doctors. The latter judge heart rate monitoring most relevant, both towards seizure detection and SUDEP (sudden unexpected death in epilepsy) prevention. CONCLUSIONS: The results, including a goal of 90% correct detections and one false alarm per seizure, should be considered in development of seizure detectors.

Long-term accelerometry-triggered video monitoring and detection of tonic-clonic and clonic seizures in a home environment: Pilot study.

PURPOSE: The aim of our study was to test the efficacy of the VARIA system (video, accelerometry, and radar-induced activity recording) and validation of accelerometry-based detection algorithms for nocturnal tonic-clonic and clonic seizures developed by our team. METHODS: We present the results of two patients with tonic-clonic and clonic seizures, measured for about one month in a home environment with four wireless accelerometers (ACM) attached to wrists and ankles. The algorithms were developed using wired ACM data synchronized with the gold standard video-/electroencephalography (EEG) and then run offline on the wireless ACM signals. Detection of seizures was compared with semicontinuous monitoring by professional caregivers (keeping an eye on multiple patients). RESULTS: The best result for the two patients was obtained with the semipatient-specific algorithm which was developed using all patients with tonic-clonic and clonic seizures in our database with wired ACM. It gave a mean sensitivity of 66.87% and false detection rate of 1.16 per night. This included 13 extra seizures detected (31%) compared with professional caregivers' observations. CONCLUSION: While the algorithms were previously validated in a controlled video/EEG monitoring unit with wired sensors, we now show the first results of long-term, wireless testing in a home environment.

Automated Detection of Tonic-Clonic Seizures Using 3-D Accelerometry and Surface Electromyography in Pediatric Patients.

Epileptic seizure detection is traditionally done using video/electroencephalography monitoring, which is not applicable for long-term home monitoring. In recent years, attempts have been made to detect the seizures using other modalities. In this study, we investigated the application of four accelerometers (ACM) attached to the limbs and surface electromyography (sEMG) electrodes attached to upper arms for the detection of tonic-clonic seizures. sEMG can identify the tension during the tonic phase of tonic-clonic seizure, while ACM is able to detect rhythmic patterns of the clonic phase of tonic-clonic seizures. Machine learning techniques, including feature selection and least-squares support vector machine classification, were employed for detection of tonic-clonic seizures from ACM and sEMG signals. In addition, the outputs of ACM and sEMG-based classifiers were combined using a late integration approach. The algorithms were evaluated on 1998.3 h of data recorded nocturnally in 56 patients of which seven had 22 tonic-clonic seizures. A multimodal approach resulted in a more robust detection of short and nonstereotypical seizures (91%), while the number of false alarms increased significantly compared with the use of single sEMG modality (0.28-0.5/12h). This study also showed that the choice of the recording system should be made depending on the prevailing pediatric patient-specific seizure characteristics and nonepileptic behavior.

Online detection of tonic-clonic seizures in pediatric patients using ECG and low-complexity incremental novelty detection.

Home monitoring of refractory epilepsy patients has become of more interest the last couple of decades. A biomedical signal that can be used for online seizure detection at home is the electrocardiogram. Previous studies have shown that tonic-clonic seizures are most often accompanied with a strong heart rate increase. The main issue however is the strong patient-specific behavior of the ictal heart rate features, which makes it hard to make a patient-independent seizure detection algorithm. A patient-specific algorithm might be a solution, but existing methods require the availability of data of several seizures, which would make them inefficient in case the first seizure only occurs after a couple of days. Therefore an online method is described here that automatically converts from a patient-independent towards a patient-specific algorithm as more patient-specific data become available. This is done by using online feedback from the users to previously given alarms. By using a simplified one-class classifier, no seizure training data needs to be available for a good performance. The method is already able to adapt to the patient-specific characteristics after a couple of hours, and is able to detect 23 of 24 seizures longer than 10s, with an average of 0.38 false alarms per hour. Due to its low-complexity, it can be easily used for wearable seizure detection at home.

Critical evaluation of four different seizure detection systems tested on one patient with focal and generalized tonic and clonic seizures.

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.

Detecting rare events using extreme value statistics applied to epileptic convulsions in children.

OBJECTIVE: Nocturnal home monitoring of epileptic children is often not feasible due to the cumbersome manner of seizure detection with the standard method of video electroencephalography monitoring. The goal of this paper is to propose a method for hypermotor seizure detection based on accelerometers that are attached to the extremities. METHODS: Supervised methods that are commonly used in literature need annotation of data and hence require expert (neurologist) interaction resulting in a substantial cost. In this paper an unsupervised method is proposed that uses extreme value statistics and seizure detection based on a model of normal behavior that is estimated using all recorded and unlabeled data. In this way the expensive interaction can be avoided. RESULTS: When applying this method to a labeled dataset, acquired from 7 patients, all hypermotor seizures are detected in 5 of the 7 patients with an average positive predictive value (PPV) of 53%. For evaluating the performance on an unlabeled dataset, seizure events are presented to the system as normal movement events. Since hypermotor seizures are rare compared to normal movements, the very few abnormal events have a negligible effect on the quality of the model. In this way, it was possible to evaluate the system for 3 of the 7 patients when 3% of the training set was composed of seizure events. This resulted in sensitivity scores of 80%, 22% and 90% and a PPV of 89%, 21% and 44% respectively. These scores are comparable with a state-of-the-art supervised machine learning based approach which requires a labeled dataset. CONCLUSIONS: A person-dependent epileptic seizure detection method has been designed that requires little human interaction. In contrast to traditional machine learning approaches, the imbalance of the dataset does not cause substantial difficulties.

Accelerometry-based home monitoring for detection of nocturnal hypermotor seizures based on novelty detection.

Nocturnal home monitoring of epileptic children is often not feasible due to the cumbersome manner of seizure monitoring with the standard method of video/EEG-monitoring. We propose a method for hypermotor seizure detection based on accelerometers attached to the extremities. From the acceleration signals, multiple temporal, frequency, and wavelet-based features are extracted. After determining the features with the highest discriminative power, we classify movement events in epileptic and nonepileptic movements. This classification is only based on a nonparametric estimate of the probability density function of normal movements. Such approach allows us to build patient-specific models to classify movement data without the need for seizure data that are rarely available. If, in the test phase, the probability of a data point (event) is lower than a threshold, this event is considered to be an epileptic seizure; otherwise, it is considered as a normal nocturnal movement event. The mean performance over seven patients gives a sensitivity of 95.24% and a positive predictive value of 60.04%. However, there is a noticeable interpatient difference.

Non-EEG seizure-detection systems and potential SUDEP prevention: state of the art.

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.

Long-term home monitoring of hypermotor seizures by patient-worn accelerometers.

Long-term home monitoring of epileptic seizures is not feasible with the gold standard of video/electro-encephalography (EEG) monitoring. The authors developed a system and algorithm for nocturnal hypermotor seizure detection in pediatric patients based on an accelerometer (ACM) attached to extremities. Seizure detection is done using normal movement data, meaning that the system can be installed in a new patient's room immediately as prior knowledge on the patient's seizures is not needed for the patient-specific model. In this study, the authors compared video/EEG-based seizure detection with ACM data in seven patients and found a sensitivity of 95.71% and a positive predictive value of 57.84%. The authors focused on hypermotor seizures given the availability of this seizure type in the data, the typical occurrence of these seizures during sleep, i.e., when the measurements were done, and the importance of detection of hypermotor seizures given their often refractory nature and the possible serious consequences. To our knowledge, it is the first detection system focusing on this type of seizure in pediatric patients.

Using Spatio-Temporal Interest Points (STIP) for myoclonic jerk detection in nocturnal video.

In this study we introduce a method for detecting myoclonic jerks during the night with video. Using video instead of the traditional method of using EEG-electrodes, permits patients to sleep without any attached sensors. This improves the comfort during sleep and it makes long term home monitoring possible. The algorithm for the detection of the seizures is based on spatio-temporal interest points (STIPs), proposed by Ivan Laptev, which is the state-of-the-art in action recognition.We applied this algorithm on a group of patients suffering from myoclonic jerks. With an optimal parameter setting this resulted in a sensitivity of over 75% and a PPV of over 85%, on the patients' combined data.