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SOUL: An Energy-Efficient Unsupervised Online Learning Seizure Detection Classifier
[article]
2021
arXiv
pre-print
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Sensitivity improved by at most 8.2% on long-term data when compared to a typical seizure detection classifier. ...
For an implantable seizure detection system, a low power, at-the-edge, online learning algorithm can be employed to dynamically adapt to the neural signal drifts, thereby maintaining high accuracy without ...
Dean Freestone for providing the iEEG patient dataset. ...
arXiv:2110.02169v1
fatcat:tvq3eov5kzfldbxt3wfu6altkm
Machine learning applications for electroencephalograph signals in epilepsy: a quick review
2020
Acta Epileptologica
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The present review examines various ML approaches for electroencephalograph (EEG) signal procession in epilepsy research, highlighting applications in the aspect of automated seizure detection, prediction ...
Machine learning (ML) is a fundamental concept in the field of state-of-the-art artificial intelligence (AI). ...
In Tsiouris's study long short-term memory networks were introduced for seizure prediction in EEG signals [48] . ...
doi:10.1186/s42494-020-00014-0
fatcat:xoqhcoppjbfepho7q6ltekbljm
Learning Robust Features using Deep Learning for Automatic Seizure Detection
[article]
2016
arXiv
pre-print
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We present and evaluate the capacity of a deep neural network to learn robust features from EEG to automatically detect seizures. ...
The proposed approach exceeds significantly previous results obtained on cross-patient classifiers both in terms of sensitivity and false positive rate. ...
The authors wish to thank Edith Law and Evgeny Naumov for helpful discussions of this work. Financial support was provided by NSERC and CIHR via the Collaborative Health Research Projects program. ...
arXiv:1608.00220v1
fatcat:zs3j3keczfc7pblskwbytd3fim
Wavelet-based Gaussian-mixture hidden Markov model for the detection of multistage seizure dynamics: A proof-of-concept study
2011
BioMedical Engineering OnLine
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The sensitivity, specificity and optimality index of chronic seizure detection were compared for various HMM topologies. ...
The rationale of this study is to develop an unsupervised algorithm for the detection of seizure states so that it may be implemented along with potential intervention strategies. ...
Gamma
40 -100
Super gamma
100 -250
Fast ripple
250 -400
Table 3 3 Performance measure for supervised and unsupervised seizure detection approach WANN
HMM opt7D
HMM opt14D
Sensitivity (TP ...
doi:10.1186/1475-925x-10-29
pmid:21504608
pmcid:PMC3094216
fatcat:j4ckwf5gxbd33avhcdej2qa3tu
Automatic Change Detection for Real-Time Monitoring of EEG Signals
2018
Frontiers in Physiology
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Meanwhile, we also evaluated the proposed method for real detection of seizures occurrence for a monitoring epilepsy patient. ...
We conducted experiments on the publicly-available Bern-Barcelona EEG database where promising results for terms of detection precision (96.97%), detection recall (97.66%) as well as computational efficiency ...
Hopfengärtner et al. (2007) design an efficient, robust and fast method based on power spectral analysis techniques for the off-line detection of epileptic seizures in long-term scalp EEG recordings. ...
doi:10.3389/fphys.2018.00325
pmid:29670541
pmcid:PMC5893758
fatcat:rkioim7325aa3ef4d6mfpap4ym
Current Status and Future Directions of Neuromonitoring With Emerging Technologies in Neonatal Care
2022
Frontiers in Pediatrics
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Now, the emphasis is directed toward improving long-term neurodevelopmental outcomes and quality of life. Brain-focused care has emerged as a necessity. ...
Non-invasive tools, such as continuous electroencephalography (cEEG), amplitude-integrated electroencephalography (aEEG), and near-infrared spectroscopy (NIRS), allow screening for seizures and continuous ...
The model also showed high sensitivity (100%) and specificity (92.6%) for differentiation of preterm and term birth (127) . Meenakshi et al. ...
doi:10.3389/fped.2021.755144
pmid:35402367
pmcid:PMC8984110
fatcat:35nfcsiesnaedijswqee7jph7i
Automated Epileptic Seizure Detection in Pediatric Subjects of CHB-MIT EEG Database—A Survey
2021
Journal of Personalized Medicine
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Performance metrics such as classification accuracy, sensitivity, and specificity were examined, and challenges in automatic seizure detection using the CHB-MIT database were addressed. ...
The dataset analyzed in this article, collected from Children's Hospital Boston (CHB) and the Massachusetts Institute of Technology (MIT), contains long-term EEG records from 24 pediatric patients. ...
Long short-term memory (LSTM) networks were adopted for the prediction of epileptic seizures by enlarging deep learning algorithms with a CNN [69] . ...
doi:10.3390/jpm11101028
pmid:34683169
pmcid:PMC8537151
fatcat:6hoqpkfzerbnzla7xfvznbgatq
Multi-Centroid Hyperdimensional Computing Approach for Epileptic Seizure Detection
2022
Frontiers in Neurology
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Thus, the proposed multi-centroid approach can be an important element in achieving a high performance of epilepsy detection with real-life data balance or during online learning, where seizures are infrequent ...
Long-term monitoring of patients with epilepsy presents a challenging problem from the engineering perspective of real-time detection and wearable devices design. ...
Thus, it can be an important step forward to achieve high performance in epilepsy detection with real-life data distributions, where seizures are infrequent, especially during online learning. ...
doi:10.3389/fneur.2022.816294
pmid:35432152
pmcid:PMC9008228
fatcat:267y47wwivedfd2oonun56x6oq
The Challenging Path to Developing a Mobile Health Device for Epilepsy: The Current Landscape and Where We Go From Here
2021
Frontiers in Neurology
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Yet, very long-term continuous monitoring of seizure-sensitive biosignals in the ambulatory setting presents a number of challenges. ...
We herein provide an overview of these challenges and current technological landscape of mHealth devices for seizure detection. ...
The overarching goal of these devices is to provide continuous long-term monitoring of non-EEG seizure related signals in order to detect or forecast seizures (2-9). ...
doi:10.3389/fneur.2021.740743
pmid:34659099
pmcid:PMC8517120
fatcat:mlgkcjcwrzdntehkptsyhs6whi
Parental preferences for seizure detection devices: a discrete choice experiment
2022
Epilepsia
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The preferred sensitivity-to-PPV ratio correlated with seizure frequency (r=-0.32) with a preference for relative high sensitivity and low PPV among those with relative low seizure frequency (p=0.04). ...
Previous studies identified essential user preferences for seizure detection devices (SDDs), without addressing their relative strength. ...
Multimodal nocturnal seizure detection in a residential care setting: a long-term prospective trial. Neurology. 2018;91(21):e2010-9. 35. ...
doi:10.1111/epi.17202
pmid:35184284
pmcid:PMC9314803
fatcat:e2kwtunj5nhcph4rief7dxltbm
Multi-Centroid Hyperdimensional Computing Approach for Epileptic Seizure Detection
[article]
2021
arXiv
pre-print
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Thus, the proposed multi-centroid approach can be an important element in achieving a high performance of epilepsy detection with real-life data balance or during online learning, where seizures are infrequent ...
Long-term monitoring of patients with epilepsy presents a challenging problem from the engineering perspective of real-time detection and wearable devices design. ...
Thus, it can be an important step forward to achieve high performance in epilepsy detection with real-life data distributions, where seizures are infrequent, especially during online learning. ...
arXiv:2111.08463v1
fatcat:aqevflp2ijhz3l2ig47grto4du
Online Seizure Prediction Using an Adaptive Learning Approach
2013
IEEE Transactions on Knowledge and Data Engineering
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Index Terms-adaptive online seizure prediction, reinforcement learning, time series pattern recognition ! Shouyi Wang is with the ...
In this study, we propose a new adaptive learning approach for online seizure prediction based on analysis of electroencephalogram (EEG) recordings. ...
Very few studies investigate online seizure prediction algorithms using prospective analysis of continuous long-term EEG recordings [45] , [49] . ...
doi:10.1109/tkde.2013.151
fatcat:qp3khffigfha3gysliefr42aii
Time- and frequency-resolved covariance analysis for detection and characterization of seizures from intracraneal EEG recordings
[article]
2020
arXiv
pre-print
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The area below the resulting receiver-operating characteristic curves was 87\% for the detection of seizures and 91\% for the detection of recruited electrodes. ...
Its simplicity makes it suitable for online implementation. Good sampling of the non-ictal periods is required, while no demands are imposed on the amount of data during ictal activity. ...
Results
An unsupervised method based on PCA of the power in each band Our first goal is to develop an unsupervised method for detecting seizures based on features that physicians are accustomed to work ...
arXiv:1902.11236v2
fatcat:2rhdcb4x2vdcnn5tmrqwqo6ibe
Review on Epileptic Seizure Prediction: Machine Learning and Deep Learning Approaches
2022
Computational and Mathematical Methods in Medicine
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Nowadays, modern computational tools, machine learning, and deep learning methods have been used to predict seizures using EEG. ...
These limitations in automatic detection of interictal spikes and epileptic seizures are preferred, which is an essential tool for examining and scrutinizing the EEG recording more precisely. ...
[26] presented work on the deep neural network, which uses unsupervised feature extraction machine learning algorithms for computerized detection of seizures. ...
doi:10.1155/2022/7751263
pmid:35096136
pmcid:PMC8794701
fatcat:mzlwvlfs35djtcqrfaomcqj2fi
A Deep Learning Approach for Automatic Seizure Detection in Children With Epilepsy
2021
Frontiers in Computational Neuroscience
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Therefore, this article proposes a novel deep-learning approach for detecting seizures in pediatric patients based on the classification of raw multichannel EEG signal recordings that are minimally pre-processed ...
Based on five evaluation metrics, the best performing model was a supervised deep convolutional autoencoder (SDCAE) model that uses a bidirectional long short-term memory (Bi-LSTM) – based classifier, ...
novel deep-learning approach for the detection of seizures in pediatric patients is proposed.The novel approach uses a 2D-SDCAE for the detection of epileptic seizures based on classifying minimally pre-processed ...
doi:10.3389/fncom.2021.650050
pmid:33897397
pmcid:PMC8060463
fatcat:barwq65uw5ctxb54cmrlichcou
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