Kris Cuppens

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 users seizure types and personal preferences.

Detection of nocturnal frontal lobe seizures in pediatric patients by means of accelerometers: A first study

The monitoring of epileptic seizures is mainly done by means of video/EEG-monitoring. Although this method is considered as the golden standard, it is not comfortable for the patient as the EEG-electrodes have to be attached to the scalp which hampers the patient’s movement. This makes long term home monitoring not feasible. A detection system with accelerometers attached to the wrists and ankles can solve this problem. Nocturnal frontal lobe seizures often include bicycle pedaling movements or uncontrolled movements with the arms which are clearly visible in the accelerometer signals. Data from three patients suffering from nocturnal frontal lobe seizures is used in this paper for the development of an automatic detection algorithm for this type of seizure. First movement epochs are detected as a preprocessing step by calculating the standard deviation of a sliding window. Afterwards a moving average filter is applied to the data and thresholds are set to the signals of the arms and legs to detect the seizures. This resulted in an algorithm with a sensitivity of 91.67% and a specificity of 83.92%.

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 patients room immediately as prior knowledge on the patients 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.

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: 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 users seizure types and personal preferences.

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.

Integrating video and accelerometer signals for nocturnal epileptic seizure detection

Epileptic seizure detection is traditionally done using video/electroencephalogram (EEG) monitoring, which is not applicable in a home situation. In recent years, attempts have been made to detect the seizures using other modalities. In this paper we investigate if a combined usage of accelerometers attached to the limbs and video data would increase the performance compared to a single modality approach. Therefore, we used two existing approaches for seizure detection in accelerometers and video and combined them using a linear discriminant analysis (LDA) classifier. The results for a combined detection have a better positive predictive value (PPV) of 95.00% compared to the single modality detection and reached a sensitivity of 83.33%.

Dynamic 3D scanning as a markerless method to calculate multi-segment foot kinematics during stance phase: Methodology and first application

Multi-segmental foot kinematics have been analyzed by means of optical marker-sets or by means of inertial sensors, but never by markerless dynamic 3D scanning (D3DScanning). The use of D3DScans implies a radically different approach for the construction of the multi-segment foot model: the foot anatomy is identified via the surface shape instead of distinct landmark points. We propose a 4-segment foot model consisting of the shank (Sha), calcaneus (Cal), metatarsus (Met) and hallux (Hal). These segments are manually selected on a static scan. To track the segments in the dynamic scan, the segments of the static scan are matched on each frame of the dynamic scan using the iterative closest point (ICP) fitting algorithm. Joint rotations are calculated between Sha-Cal, Cal-Met, and Met-Hal. Due to the lower quality scans at heel strike and toe off, the first and last 10% of the stance phase is excluded. The application of the method to 5 healthy subjects, 6 trials each, shows a good repeatability (intra-subject standard deviations between 1° and 2.5°) for Sha-Cal and Cal-Met joints, and inferior results for the Met-Hal joint (>3°). The repeatability seems to be subject-dependent. For the validation, a qualitative comparison with joint kinematics from a corresponding established marker-based multi-segment foot model is made. This shows very consistent patterns of rotation. The ease of subject preparation and also the effective and easy to interpret visual output, make the present technique very attractive for functional analysis of the foot, enhancing usability in clinical practice.

Detection of Epileptic Seizures Using Video Data

Monitoring of epileptic patients is usually done by video/EEG-monitoring which is considered as the golden standard. Due to some disadvantages of this method, this method is not feasible to use in long term home monitoring. Video monitoring provides a solution to this problem as it can monitor the patient in a non-contacting way. An algorithm is developed to detect movement epochs in nocturnal datasets for pediatric epileptic patients. The performance was measured using a threefold crossvaildation, which resulted in a sensitivity of 1 and a positive predictive value above 0.85.

Towards automatic detection of movement during sleep in pediatric patients with epilepsy by means of video recordings and the optical flow algorithm

Introduction The detection and analysis of epileptic seizures is typically done by video-electroencephalogram monitoring. Although it is considered as the Golden Standard, it has disadvantages: the electroencephalogram electrodes are uncomfortable to wear for a longer period of time and hospitalization is often required. The aim of our work is to investigate whether the optical flow algorithm applied to video recordings can be used to detect movement during sleep in pediatric patients with epilepsy.