scispace - formally typeset
Search or ask a question
Journal ArticleDOI

Automated Detection of Convulsive Seizures Using a Wearable Accelerometer Device

TL;DR: The proposed algorithm showed a comparable performance with respect to existing unimodal and multi-modal methods for GTCS detection and shows the potential to build an ambulatory monitoring convulsive seizure detection system.
Abstract: Epileptic seizure detection requires specialized approaches such as video/electroencephalography monitoring. However, these approaches are restricted mainly to hospital setting and requires video/EEG analysis by experts, which makes these approaches resource- and labor-intensive. In contrast, we aim to develop a wireless remote monitoring system based on a single wrist-worn accelerometer device, which is sensitive to multiple types of convulsive seizures and is capable of detecting seizures with short duration. Simple time domain features including a new set of Poincare plot based features were extracted from the active movement events recorded using a wrist-worn accelerometer device. The best features were then selected using the area under the ROC curve analysis. Kernelized support vector data description was then used to classify nonseizure and seizure events. The proposed algorithm was evaluated on $\text{5576}\;\text{h}$ of recordings from 79 patients and detected 40 ( $\text{86.95}\%$ ) of 46 convulsive seizures (generalized tonic-clonic (GTCS), psychogenic nonepileptic, and complex partial seizures) from 20 patients with a total of 270 false alarms ( $\text{1.16/24}\;\text{h}$ ). Furthermore, the algorithm showed a comparable performance (sensitivity $\text{95.23}\%$ and false alarm rate $\text{0.64/24}\;\text{h}$ ) with respect to existing unimodal and multimodal methods for GTCS detection. The promising results shows the potential to build an ambulatory monitoring convulsive seizure detection system. A wearable accelerometer based seizure detection system would aid in continuous assessment of convulsive seizures in a timely and non-invasive manner.
Citations
More filters
Journal ArticleDOI
TL;DR: An Internet of Medical Things (IoMT)-based automated seizure detection system which will detect a seizure from electroencephalography (EEG) signals using a voltage level detector (VLD) and a signal rejection algorithm (SRA).
Abstract: Epilepsy is one of the most common neurological disorders affecting a significant portion of the world’s population and approximately 2.5 million people in the United States. Important biomedical research effort is focused on the development of energy efficient devices for the real-time detection of seizures. In this paper, we propose an Internet of Medical Things (IoMT)-based automated seizure detection system which will detect a seizure from electroencephalography (EEG) signals using a voltage level detector (VLD) and a signal rejection algorithm (SRA). The proposed system analyzes neural signals continuously and extracts the hyper-synchronous pulses for the detection of seizure onset. Within a time frame, if the number of pulses exceeds a predefined threshold value, a seizure is declared. The SRA reduces false detections, which in turn enhances the accuracy of the seizure detector. The design was validated using system-level simulations and consumer electronics proof of concept. The proposed seizure detector reports a sensitivity of 96.9% and specificity of 97.5%. The use of minimal circuitry can lead to reduction of power consumption compared to many contemporary approaches. The proposed approach can be generalized to other sensor modalities and the use of wearable or implantable solutions, or a combination of the two.

46 citations


Cites methods from "Automated Detection of Convulsive S..."

  • ...An alternative approach to EEG is proposed [26], which uses a single wrist-worn accelerometer device for monitoring and detection of convulsive seizures....

    [...]

Journal ArticleDOI
TL;DR: The clinical practice guideline (CPG) as discussed by the authors provides recommendations for healthcare personnel working with patients with epilepsy, on the use of wearable devices for automated seizure detection in patients in outpatient, ambulatory settings.

33 citations

Journal ArticleDOI
TL;DR: A review of textile-based EEGs, useful for monitoring sleep quality and alertness, clinical applications, diagnosis, and treatment of patients with epilepsy, disease of Parkinson and other neurological disorders, as well as continuous monitoring of tiredness/alertness in the field.
Abstract: Electroencephalogram (EEG) is the biopotential recording of electrical signals generated by brain activity. It is useful for monitoring sleep quality and alertness, clinical applications, diagnosis, and treatment of patients with epilepsy, disease of Parkinson and other neurological disorders, as well as continuous monitoring of tiredness/ alertness in the field. We provide a review of textile-based EEG. Most of the developed textile-based EEGs remain on shelves only as published research results due to a limitation of flexibility, stickability, and washability, although the respective authors of the works reported that signals were obtained comparable to standard EEG. In addition, nearly all published works were not quantitatively compared and contrasted with conventional wet electrodes to prove feasibility for the actual application. This scenario would probably continue to give a publication credit, but does not add to the growth of the specific field, unless otherwise new integration approaches and new conductive polymer composites are evolved to make the application of textile-based EEG happen for bio-potential monitoring.

28 citations


Cites background from "Automated Detection of Convulsive S..."

  • ...Movement refers to specific body parts that move in specific ways to detect the disorder, which can be identified using accelerometers [3,4], surface electromyography (sEMG) [5], video monitoring [6], or seizure-alert dogs [7]....

    [...]

  • ...[1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] [18] [19] [20] [21] [22] [23] [24] [25] [26] [27] [28] [29] [30] [31] [32] [33] [34] [35] [36] [37]...

    [...]

Journal ArticleDOI
TL;DR: In this article, a clinical practice guideline (CPG) is provided for healthcare personnel working with patients with epilepsy on the use of wearable devices for automated seizure detection in patients in outpatient, ambulatory settings.
Abstract: 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.

26 citations

Journal ArticleDOI
TL;DR: In this paper, a leave-one-subject-out cross-validation approach (LOSO-CV) was used to detect seizures based on individual video frames (convolutional neural networks, CNNs) or video sequences (CNN+long short-term memory (LSTM) networks).
Abstract: Timely detection of seizures is crucial to implement optimal interventions, and may help reduce the risk of sudden unexpected death in epilepsy (SUDEP) in patients with generalized tonic-clonic seizures (GTCSs). While video-based automated seizure detection systems may be able to provide seizure alarms in both in-hospital and at-home settings, earlier studies have primarily employed hand-designed features for such a task. In contrast, deep learning-based approaches do not rely on prior feature selection and have demonstrated outstanding performance in many data classification tasks. Despite these advantages, neural network-based video classification has rarely been attempted for seizure detection. We here assessed the feasibility and efficacy of automated GTCSs detection from videos using deep learning. We retrospectively identified 76 GTCS videos from 37 participants who underwent long-term video-EEG monitoring (LTM) along with interictal video data from the same patients, and 10 full-night seizure-free recordings from additional patients. Using a leave-one-subject-out cross-validation approach (LOSO-CV), we evaluated the performance to detect seizures based on individual video frames (convolutional neural networks, CNNs) or video sequences [CNN+long short-term memory (LSTM) networks]. CNN+LSTM networks based on video sequences outperformed GTCS detection based on individual frames yielding a mean sensitivity of 88% and mean specificity of 92% across patients. The average detection latency after presumed clinical seizure onset was 22 seconds. Detection performance increased as a function of training dataset size. Collectively, we demonstrated that automated video-based GTCS detection with deep learning is feasible and efficacious. Deep learning-based methods may be able to overcome some limitations associated with traditional approaches using hand-crafted features, serve as a benchmark for future methods and analyses, and improve further with larger datasets.

23 citations

References
More filters
Journal ArticleDOI
TL;DR: An inventory of 20 items with a set of instructions and response- and computational-conventions is proposed and the results obtained from a young adult population numbering some 1100 individuals are reported.

33,268 citations


"Automated Detection of Convulsive S..." refers background in this paper

  • ...the fact, that most people use right hand for performing their daily activities [37]....

    [...]

Journal ArticleDOI
TL;DR: This paper refines the statistical comparison of the areas under two ROC curves derived from the same set of patients by taking into account the correlation between the areas that is induced by the paired nature of the data.
Abstract: Receiver operating characteristic (ROC) curves are used to describe and compare the performance of diagnostic technology and diagnostic algorithms. This paper refines the statistical comparison of the areas under two ROC curves derived from the same set of patients by taking into account the correlation between the areas that is induced by the paired nature of the data. The correspondence between the area under an ROC curve and the Wilcoxon statistic is used and underlying Gaussian distributions (binormal) are assumed to provide a table that converts the observed correlations in paired ratings of images into a correlation between the two ROC areas. This between-area correlation can be used to reduce the standard error (uncertainty) about the observed difference in areas. This correction for pairing, analogous to that used in the paired t-test, can produce a considerable increase in the statistical sensitivity (power) of the comparison. For studies involving multiple readers, this method provides a measure...

6,836 citations


"Automated Detection of Convulsive S..." refers background in this paper

  • ...a receiver operating characteristic (ROC) analysis [34] where, the area under the curve (AUC) of an individual feature across all patients was determined to asses the class separation....

    [...]

Journal ArticleDOI
TL;DR: The Support Vector Data Description (SVDD) is presented which obtains a spherically shaped boundary around a dataset and analogous to the Support Vector Classifier it can be made flexible by using other kernel functions.
Abstract: Data domain description concerns the characterization of a data set. A good description covers all target data but includes no superfluous space. The boundary of a dataset can be used to detect novel data or outliers. We will present the Support Vector Data Description (SVDD) which is inspired by the Support Vector Classifier. It obtains a spherically shaped boundary around a dataset and analogous to the Support Vector Classifier it can be made flexible by using other kernel functions. The method is made robust against outliers in the training set and is capable of tightening the description by using negative examples. We show characteristics of the Support Vector Data Descriptions using artificial and real data.

2,789 citations


"Automated Detection of Convulsive S..." refers background or methods in this paper

  • ...detection approach based on kernelized support vector data description (SVDD) [28]....

    [...]

  • ...In contrast, a high seizure detection sensitivity is achieved using the proposed approach, where a learning technique (SVDD) that can handle the class imbalance in the data is employed [28]....

    [...]

  • ...SVDD is a state-of-art classifier that can handle the class imbalance in the data [28]....

    [...]

Journal ArticleDOI
TL;DR: This paper shows the use of a data domain description method, inspired by the support vector machine by Vapnik, called the support vectors domain description (SVDD), which can be used for novelty or outlier detection and is compared with other outlier Detection methods on real data.

1,581 citations


"Automated Detection of Convulsive S..." refers methods in this paper

  • ...(11) In our work, we have chosen a radially symmetrical Gaussian kernel function as it has been shown to results in good description of the data using SVDD [35]....

    [...]

Journal ArticleDOI
TL;DR: An overview of seizure detection and related prediction methods is presented and their potential uses in closed-loop warning systems in epilepsy are discussed.

402 citations


"Automated Detection of Convulsive S..." refers background in this paper

  • ...However, HRV is a low specificity signal and the heart rate changes vary from person to person [7]....

    [...]