Anouk Van de Vel's research while affiliated with University of Antwerp and other places

Publications (32)

Chapter
In literature, Dravet syndrome (DS) has always been correlated with photosensitivity on electroencephalogram (EEG), and patients with DS have been known to suffer from provoked seizures by photic stimuli. However, in our daily clinical practice, clinical photosensitivity and related convulsive seizures are not major problems. We reviewed the litera...
Article
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 su...
Article
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 cos...
Article
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...
Article
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,...
Article
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 vec...
Article
Full-text available
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 seizu...
Conference Paper
Previous studies have shown that during several types of seizures, the heart rate increases strongly towards a maximal patient-specific epileptic heart rate HRep. This ictal peak heart rate is one of the most important features for classifying epileptic heart rate increases. We therefore try to estimate HRep, which is done by using least squares su...
Conference Paper
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...
Article
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 e...
Article
A seizure detection system in the non-clinical environment would enable long-term monitoring and give better insights into the number of seizures and their characteristics. Moreover, an alarm at seizure onset is important for alerting the parents or care-givers so they could comfort the child and optionally give the treatment. Therefore, we develop...
Article
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 w...
Article
Full-text available
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 b...
Article
Full-text available
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. Supervised methods t...
Article
Full-text available
Nocturnal home monitoring of epileptic children is often not feasible due to the cumbersome manner of seizure detection with the standard method of video/EEG-monitoring. We propose a method for hypermotor seizure detection based on accelerators attached to the extremities. Hypermotor seizures often involve violent movements with the arms or legs, w...
Article
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 u...
Conference Paper
Data of nocturnal movements in epileptic patients is marked by an imbalance due to the relative small number of seizures compared to normal nocturnal movements. This makes developing a robust classifier more difficult, especially with respect to reducing the number of false positives while keeping a high sensitivity. In this paper we evaluated diff...
Article
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 moveme...
Conference Paper
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 increa...
Article
Full-text available
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 s...
Conference Paper
Epileptic seizure detection in a home situation is often not feasible due to the complicated attachment of the EEG-electrodes on the scalp. We propose to detect nocturnal seizures with a motor component in patients by means of a single video camera. To this end we use a combination of optical flow and mean shift clustering to egister moving body pa...
Conference Paper
In this paper we investigate whether it is possible to detect movement out of video images recorded from sleeping patients with epilepsy. This information is used to detect possible epileptic seizures, normal movement, breathing and other kinds of movement. For this we use optical flow and clustering algorithms. As a result, different motion patter...

Citations

... Seizure onset in girls is often in infancy [26,5,7,8]. Seizures may show clustering [17,3,38], and be triggered by fever [38,24,25]. Seizure types include focal and generalized seizures and maybe clonic, tonic-clonic, tonic, atonic, myoclonic and absences. ...
... This is an established strategy but is likely to decrease sensitivity even the threshold is rather moderate (minimal HR change of 20% required to pass the filter). 12 Thirdly, due to the retrospective design only limited interictal ECG data were available for patients of group 2 (in most patients only 24 hours) and group 3 (only 24 or 48 hours), so that the number of false positive alarms per patient tends to be underestimated. Fourthly, our patient groups were not matched with respect to demographic and epilepsy-related features, but the composition of the groups displayed significant differences. ...
... They have the advantage that those measuring devices are more tolerated when being worn for an extended period of time. Combining several of these biosignals can improve seizure detection performance [8,9]. ...
... They identified motion artifacts as the main reason for failed detection, although they did not further specify them as resulting from spontaneous or epileptic activity. They use a set of rules published by De Cooman [61] to analyze their PPG signals and identify IT. However, they mention that 11 of the 47 seizures in their dataset were not associated with IT. ...
... The following seizure types were considered clinically urgent and denoted as major: (1) Generalized tonic clonic and focal to bilateral tonic clonic (TC) seizures; (2) long (> 30 seconds) tonic (T) seizures; (3) hyperkinetic (HK) seizures and (4) other major (OM) seizures, consisting of TC-like seizures with atypical semiology and clusters of minor seizures lasting > 30 minutes. We focused on these motor seizures because parents would want to go to the child for intervention or support, and because of the increased risk of status epilepticus, SUDEP and other complications [3,20,22]. All other seizures were classified as minor and their detection was considered as false positives. ...
... We calculated features using a sliding window of 10 s length and 50% overlap. Aside from standard statistical features (like mean and variance of the values within one window), we chose features from the literature used for the detection of TCS (from [7,20,[23][24][25][26][27]). Additionally, we chose features used for the detection of tonic seizures in literature (from [22]) to increase the detection capabilities regarding the seizure's first phase and features used in activity recognition for better differentiation between seizure and similar non-seizure activities (from [28,29]). ...
... In particular, by using such real-time epileptic seizure detection mechanisms, it is possible to notify family members, caregivers, and emergency units in case of a seizure. Therefore, it should be possible to reduce seizure-related injuries, status epilepticus, and SUDEP [6]. However, realtime epileptic seizure detection is not possible without energy-efficient wearable technologies, which are also key enablers for long-term patient monitoring in ambulatory settings. ...
... Functional disorders such as seizure and vestibular system disorder and neuromuscular disorders such as peripheral neuropathy can be diagnosed through ML approaches [62,104,105]. Ikizoglu et al. [62] compared two-dimensional reduction techniques, including feature selection and feature transformation for SVM, to identify Vestibular System (VS) disorders. As the most used feature transformation method, kernel-modified PCA achieved the best performance, with a 89.2% classification accuracy. ...
... Outpatient setting studies are crucial for testing and validation. [5][6][7] Currently, different research groups adopt various methods and report heterogeneous or incomplete information, leading to inconsistency between studies and hindering study comparison and replication. It is important to note that these studies often face technical and usability challenges frequently not reported, resulting in the acquisition of sub-optimal data sets. ...
... The algorithms developed by Milosevic et al. [6] for tonic-clonic and clonic seizures based on a large database of video/electroencephalography (EEG) data were used. Results of a patient-specific (algorithm trained only on data of the patient itself), a nonpatient-specific (algorithm trained on data not including those of the patient itself), and a semipatientspecific (algorithm trained on data also including those of the patient itself) approach were compared with the notes of professional caregivers. ...