Complex network based models of ECoG signals for detection of induced epileptic seizures in rats

Mohammadpoory, Zeynab; Nasrolahzadeh, Mahda; Mahmoodian, Naghmeh; Sayyah, Mohammad; Haddadnia, Javad

doi:10.1007/s11571-019-09527-y

Cited by 14 publications

(3 citation statements)

References 81 publications

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“…To develop and evaluate the automated epileptic seizure detection systems, several publicly available datasets: CHB-MIT [ 43 , 127 , 144 , 150 ], ECoG Dataset [ 72 , 108 ], Freiburg epilepsy dataset [ 112 , 150 ], Bonn seizure dataset [ 82 , 119 , 128 , 136 ], BERN- Barcelona dataset [ 44 , 97 ], Kaggle dataset, Flint-Hills eplipsiae, Hauz Khas and Zenodo dataset. The signals obtained from these datasets are recorded either intracranially or from the scalp of humans or animals.…”

Section: Eeg Signal Acquisitionmentioning

confidence: 99%

EEG seizure detection: concepts, techniques, challenges, and future trends

Dessouky

El‐Sayed

Hemdan

2023

Multimed Tools Appl

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A central nervous system disorder is usually referred to as epilepsy. In epilepsy brain activity becomes abnormal, leading to times of abnormal behavior or seizures, and at times loss of awareness. Consequently, epilepsy patients face problems in daily life due to precautions they must take to adapt to this condition, particularly when they use heavy equipment, e.g., vehicle derivation. Epilepsy studies rely primarily on electroencephalography (EEG) signals to evaluate brain activity during seizures. It is troublesome and timeconsuming to manually decide the location of seizures in EEG signals. The automatic detection framework is one of the principal tools to help doctors and patients take appropriate precautions. This paper reviews the epilepsy mentality disorder and the types of seizure, preprocessing operations that are performed on EEG data, a generally extracted feature from the signal, and a detailed view on classification procedures used in this problem and provide insights on the difficulties and future research directions in this innovative theme. Therefore, this paper presents a review of work on recent methods for the epileptic seizure process along with providing perspectives and concepts to researchers to present an automated EEG-based epileptic seizure detection system using IoT and machine learning classifiers for remote patient monitoring in the context of smart Multimedia Tools and Applications

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Section: Eeg Signal Acquisitionmentioning

confidence: 99%

EEG seizure detection: concepts, techniques, challenges, and future trends

Dessouky

El‐Sayed

Hemdan

2023

Multimed Tools Appl

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“…Unlike traditional recurrent neural networks (RNNs), such as Long Short-Term Memory (LSTM), that use error backpropagation through time (BPTT) algorithm to train the network, reservoir computing (RC), another type of RNN, only uses the simple linear regression meethod to train its output weights, while other connection weights (like input weights, hidden weights) are fixed once initialized. Due to the advantages of high accuracy, fast training, and global convergence (Ding et al 2005), some typical RC models, such as echo state network (ESN) (Jaeger and Haas 2004) and liquid state machine (LSM) (Maass et al 2002), have been widely applied to time series prediction (Li et al 2015;Ma et al 2009;Jaeger et al 2007;Li et al 2016;Wang et al 2019;Hu et al 2020), pattern classification (Skowronski and Harris 2007;M E et al 2009;Hu et al 2015;Zhang et al 2019), and anomaly detection (Chen et al 2018;Mohammadpoory et al 2019), and so on.…”

Section: Introductionmentioning

confidence: 99%

Chaotic time series prediction using phase space reconstruction based conceptor network

Zhang

Zheng

2020

Cogn Neurodyn

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The Conceptor network is a new framework of reservoir computing (RC), in addition to the features of easy training, global convergence, it can online learn new classes of input patterns without complete re-learning from all the training data. The conventional connection topology and weights of the hidden layer (reservoir) of RC are initialized randomly, and are fixed to be no longer fine-tuned after initialization. However, it has been demonstrated that the reservoir connection of RC plays an important role in the computational performance of RC. Therefore, in this paper, we optimize the Conceptor's reservoir connection and propose a phase space reconstruction (PSR) -based reservoir generation method. We tested the generation method on time series prediction task, and the experiment results showed that the proposed PSR-based method can improve the prediction accuracy of Conceptor networks. Further, we compared the PSR-based Conceptor with two Conceptor networks of other typical reservoir topologies (random connected, cortex-like connected), and found that all of their prediction accuracy showed a nonlinear decline trend with increasing storage load, but in comparison, our proposed PSRbased method has the best accuracy under different storage loads.

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“…It is impractical to perform real time classification with this technique considering the non-stationary nature and huge amount of EEG data. To address this limitation, Mohammadpoor et al applied artificial neural network in his work (Mohammadpoory et al 2019) for automatic detection of seizures from the ECoG signals. However, the traditional neural network is faced with the difficulty of weights initialization.…”

Section: Introductionmentioning

confidence: 99%

A stacked sparse auto-encoder and back propagation network model for sensory event detection via a flexible ECoG

et al. 2020

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Current prostheses are limited in their ability to provide direct sensory feedback to users with missing limb. Several efforts have been made to restore tactile sensation to amputees but the somatotopic tactile feedback often results in unnatural sensations, and it is yet unclear how and what information the somatosensory system receives during voluntary movement. The present study proposes an efficient model of stacked sparse autoencoder and back propagation neural network for detecting sensory events from a highly flexible electrocorticography (ECoG) electrode. During the mechanical stimulation with Von Frey (VF) filament on the plantar surface of rats' foot, simultaneous recordings of tactile afferent signals were obtained from primary somatosensory cortex (S1) in the brain. In order to achieve a model with optimal performance, Particle Swarm Optimization and Adaptive Moment Estimation (Adam) were adopted to select the appropriate number of neurons, hidden layers and learning rate of each sparse auto-encoder. We evaluated the stimulus-evoked sensation by using an automated up-down (UD) method otherwise called UDReader. The assessment of tactile thresholds with VF shows that the right side of the hind-paw was significantly more sensitive at the tibia-(p = 6.50 9 10 -4 ), followed by the saphenous-(p = 7.84 9 10 -4 ), and sural-(p = 8.24 9 10 -4 ). We then validated our proposed model by comparing with the state-ofthe-art methods, and recorded accuracy of 98.8%, sensitivity of 96.8%, and specificity of 99.1%. Hence, we demonstrated the effectiveness of our algorithms in detecting sensory events through flexible ECoG recordings which could be a viable option in restoring somatosensory feedback.

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Complex network based models of ECoG signals for detection of induced epileptic seizures in rats

Cited by 14 publications

References 81 publications

EEG seizure detection: concepts, techniques, challenges, and future trends

EEG seizure detection: concepts, techniques, challenges, and future trends

Chaotic time series prediction using phase space reconstruction based conceptor network

A stacked sparse auto-encoder and back propagation network model for sensory event detection via a flexible ECoG

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