With sudden and unpredictable nature, seizures lead to great risk of the secondary damage, status epilepticus, and sudden unexpected death in epilepsy. Thus, it is essential to use a wearable device to detect seizure ...
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With sudden and unpredictable nature, seizures lead to great risk of the secondary damage, status epilepticus, and sudden unexpected death in epilepsy. Thus, it is essential to use a wearable device to detect seizure and inform patients' caregivers for assistant to prevent or relieve adverse consequence. In this review, we gave an account of the current state of the field of seizuredetection based on wearable devices from three parts: devices, physiological activities, and algorithms. Firstly, seizure monitoring devices available in the market primarily involve wristband-type devices, patch-type devices, and armband-type devices, which are able to detect motor seizures, focal autonomic seizures, or absence seizures. Secondly, seizure-related physiological activities involve the discharge of brain neurons presented, autonomous nervous activities, and motor. Plenty of studies focus on features from one signal, while it is a lack of evidences about the change of signal coupling along with seizures. Thirdly, the seizure detection algorithms developed from simple threshold method to complicated machine learning and deep learning, aiming at distinguish seizures from normal events. After understanding of some preliminary studies, we will propose our own thought for future development in this field.
Objective: Sharing medical data between institutions is difficult in practice due to data protection laws and official procedures within institutions. Therefore, most existing algorithms are trained on relatively smal...
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Objective: Sharing medical data between institutions is difficult in practice due to data protection laws and official procedures within institutions. Therefore, most existing algorithms are trained on relatively small electroencephalogram (EEG) data sets which is likely to be detrimental to prediction accuracy. In this work, we simulate a case when the data can not be shared by splitting the publicly available data set into disjoint sets representing data in individual institutions. Methods and procedures: We propose to train a (local) detector in each institution and aggregate their individual predictions into one final prediction. Four aggregation schemes are compared, namely, the majority vote, the mean, the weighted mean and the Dawid-Skene method. The method was validated on an independent data set using only a subset of EEG channels. Results: The ensemble reaches accuracy comparable to a single detector trained on all the data when sufficient amount of data is available in each institution. Conclusion: The weighted mean aggregation scheme showed best performance, it was only marginally outperformed by the Dawid-Skene method when local detectors approach performance of a single detector trained on all available data. Clinical impact: Ensemble learning allows training of reliable algorithms for neonatal EEG analysis without a need to share the potentially sensitive EEG data between institutions.
Objective Patients with absence epilepsy sensitivity <10% of their absences. The clinical gold standard to assess absence epilepsy is a 24-h electroencephalographic (EEG) recording, which is expensive, obtrusive, a...
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Objective Patients with absence epilepsy sensitivity <10% of their absences. The clinical gold standard to assess absence epilepsy is a 24-h electroencephalographic (EEG) recording, which is expensive, obtrusive, and time-consuming to review. We aimed to (1) investigate the performance of an unobtrusive, two-channel behind-the-ear EEG-based wearable, the Sensor Dot (SD), to detect typical absences in adults and children;and (2) develop a sensitive patient-specific absence seizure detection algorithm to reduce the review time of the recordings. Methods We recruited 12 patients (median age = 21 years, range = 8-50;seven female) who were admitted to the epilepsy monitoring units of University Hospitals Leuven for a 24-h 25-channel video-EEG recording to assess their refractory typical absences. Four additional behind-the-ear electrodes were attached for concomitant recording with the SD. Typical absences were defined as 3-Hz spike-and-wave discharges on EEG, lasting 3 s or longer. seizures on SD were blindly annotated on the full recording and on the algorithm-labeled file and consequently compared to 25-channel EEG annotations. Patients or caregivers were asked to keep a seizure diary. Performance of the SD and seizure diary were measured using the F1 score. Results We concomitantly recorded 284 absences on video-EEG and SD. Our absence detectionalgorithm had a sensitivity of .983 and false positives per hour rate of .9138. Blind reading of full SD data resulted in sensitivity of .81, precision of .89, and F1 score of .73, whereas review of the algorithm-labeled files resulted in scores of .83, .89, and .87, respectively. Patient self-reporting gave sensitivity of .08, precision of 1.00, and F1 score of .15. Significance Using the wearable SD, epileptologists were able to reliably detect typical absence seizures. Our automated absence detectionalgorithm reduced the review time of a 24-h recording from 1-2 h to around 5-10 min.
Background: Spike-wave discharges (SWD) found in neuroelectrical recordings are pathognomonic to absence epilepsy. The characteristic spike-wave morphology of the spike-wave complex (SWC) constituents of SWDs can be m...
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Background: Spike-wave discharges (SWD) found in neuroelectrical recordings are pathognomonic to absence epilepsy. The characteristic spike-wave morphology of the spike-wave complex (SWC) constituents of SWDs can be mathematically described by a subset of possible spectral power and phase values. Morlet wavelet transform (MWT) generates time-frequency representations well-suited to identifying this SWC-associated subset. New method: MWT decompositions of SWDs reveal spectral power concentrated at harmonic frequencies. The phase relationships underlying SWC morphology were identified by calculating the differences between phase values at SWD fundamental frequency from the 2nd, 3rd, and 4th harmonics, then using the three phase differences as coordinates to generate a density distribution in a {360 degrees x 360 degrees x 360 degrees} phase difference space. Strain-specific density distributions were generated from SWDs of mice carrying the Gria4, Gabrg2, or Scn8a mutations to determine whether SWC morphological variants reliably mapped to the same regions of the distribution, and if distribution values could be used to detect SWD. Comparison with existing methods: To the best of our knowledge, this algorithm is the first to employ spectral phase to quantify SWC morphology, making it possible to computationally distinguish SWC morphological subtypes and detect SWDs. Results/conclusions: Proof-of-concept testing of the SWD finder algorithm shows: (1) a major pattern of variation in SWC morphology maps to one axis of the phase difference distribution, (2) variability between the strain-specific distributions reflects differences in the proportions of SWC subtypes generated during SWD, and (3) regularities in the spectral power and phase profiles of SWCs can be used to detect waveforms possessing SWC-like morphology. (C) 2014 Elsevier B.V. All rights reserved.
Epilepsy is a chronic disorder of the CNS that predisposes individuals to recurrent seizures. Computerized seizure detection algorithms will enable alerting systems that may decrease the harm of the seizures. This pap...
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Epilepsy is a chronic disorder of the CNS that predisposes individuals to recurrent seizures. Computerized seizure detection algorithms will enable alerting systems that may decrease the harm of the seizures. This paper attempts to provide a comprehensive survey of different types of seizure detection algorithms and their potential role in diagnostic and therapeutic applications. Major recent algorithms use electroencephalogram (EEG) and electrocardiogram (ECG) signals to detect the seizure onset and seizure event. In these algorithms, various features are extracted from the EEG signal alone or in concert with the ECG signal until the patients are classified into two classes, seizure and non-seizure. We identify three major categories for seizure detectors;EEG-based seizure-event detectors, EEG based seizure-onset detectors, and EEG/ECG-based seizure-onset detectors. In addition, some other related issues, such as dataset and evaluation measures, are also discussed. Finally, the performance of algorithms is evaluated, and their capabilities and limitations are described.
Brain-computer interfaces (BCIs) require real-time feature extraction for translating input EEG signals recorded from a subject into an output command or decision. Owing to the inherent difficulties in EEG signal proc...
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ISBN:
(纸本)9781479940844
Brain-computer interfaces (BCIs) require real-time feature extraction for translating input EEG signals recorded from a subject into an output command or decision. Owing to the inherent difficulties in EEG signal processing and neural decoding, many of the feature extraction algorithms are complex and computationally demanding. Presently, software does exist to perform real-time feature extraction and classification of EEG signals. However, the requirement of a personal computer is a major obstacle in bringing these technologies to the home and mobile user affording ease of use. We present the FPGA design and novel architecture of a generalized platform that provides a set of predefined features and preprocessing steps that can be configured by a user for BCI applications. The preprocessing steps include power line noise cancellation and baseline removal while the feature set includes a combination of linear and nonlinear, univariate and bivariate measures commonly utilized in BCIs. We provide a comparison of our results with software and also validate the platform by implementing a seizure detection algorithm on a standard dataset and obtained a classification accuracy of over 96%. A gradual transition of BCI systems to hardware would prove beneficial in terms of compactness, power consumption and much faster response to stimuli.
Epilepsy is a chronic neurological disorder of the brain that affects around 50 million people worldwide. The early detection of epileptic seizures using electroencephalogram (EEG) signals is a useful tool for several...
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Epilepsy is a chronic neurological disorder of the brain that affects around 50 million people worldwide. The early detection of epileptic seizures using electroencephalogram (EEG) signals is a useful tool for several applications in epilepsy diagnosis. Many techniques have been developed for unscrambling the underlying features of seizures present in EEGs. This article reviews the seizure detection algorithms developed in the last decade. In general terms, techniques based on the wavelet transform, entropy, tensors, empirical mode decomposition, chaos theory, and dynamic analysis are surveyed in the field of epilepsy detection. A performance comparison of the reviewed algorithms is also conducted. The needs for a reliable practical implementation are highlighted and some future prospectives in the area are given. Epilepsy detection research is oriented to develop non-invasive and precise methods to allow precise and quick diagnoses Finally, the lack of standardization of the methods in the epileptic seizuredetection field is an emerging problem that has to be solved to allow homogenous comparisons of detector performance.
In this paper, we present a work in progress of a novel tablet-based Android application for a multi-modal seizuredetection system platform. We first provide an overview of the multi-modality platform and then detail...
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ISBN:
(纸本)9781467327466;9781467327459
In this paper, we present a work in progress of a novel tablet-based Android application for a multi-modal seizuredetection system platform. We first provide an overview of the multi-modality platform and then detail the usage of the Android application, including a description of its features. We then present preliminary results of the platform's holistic performance, including the app.
Compression of biosignals is an important means of conserving power in wireless body area networks and ambulatory monitoring systems. In contrast to lossless compression techniques, lossy compression algorithms can ac...
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ISBN:
(纸本)9781424441242
Compression of biosignals is an important means of conserving power in wireless body area networks and ambulatory monitoring systems. In contrast to lossless compression techniques, lossy compression algorithms can achieve higher compression ratios and hence, higher power savings, at the expense of some degradation of the reconstructed signal. In this paper, a variant of the lossy JPEG2000 algorithm is applied to Electroencephalogram (EEG) data from the Freiburg epilepsy database. By varying compression parameters, a range of reconstructions of varying signal fidelity is produced. Although lossy compression has been applied to EEG data in previous studies, it is unclear what level of signal degradation, if any, would be acceptable to a clinician before diagnostically significant information is lost. In this paper, the reconstructed EEG signals are applied to REACT, a state-of-the-art seizure detection algorithm, in order to determine the effect of lossy compression on its seizuredetection ability. By using REACT in place of a clinician, many hundreds of hours of reconstructed EEG data are efficiently analysed, thereby allowing an analysis of the amount of EEG signal distortion that can be tolerated. The corresponding compression ratios that can be achieved are also presented.
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