EEG signal analysis for epileptic seizures detection by applying Data Mining techniques

Zazzaro, Gaetano; Cuomo, Salvatore; Martone, Angelo; Montaquila, Roberto Valentino; Toraldo, Gerardo; Pavone, Luigi

doi:10.1016/j.iot.2019.03.002

Cited by 36 publications

(22 citation statements)

References 22 publications

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Section: Introductionmentioning

confidence: 99%

“…Screening techniques in the diagnosis of epileptic seizures comprise two important categories of functional and structural neuroimaging modalities [ 4 , 5 , 6 , 7 , 8 , 9 ]. The functional neuroimaging modality provides important information about brain function during epileptic seizure occurrence for physicians and neurologists [ 4 , 5 , 6 , 7 , 8 , 9 ]. The structural neuroimaging modalities provide physicians with substantial information about the brain structure of individuals with epileptic seizures [ 4 , 5 , 6 , 7 , 8 , 9 ].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Epileptic Seizures Detection Using Deep Learning Techniques: A Review

Shoeibi

Khodatars

Ghassemi

et al. 2021

IJERPH

254

109

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A variety of screening approaches have been proposed to diagnose epileptic seizures, using electroencephalography (EEG) and magnetic resonance imaging (MRI) modalities. Artificial intelligence encompasses a variety of areas, and one of its branches is deep learning (DL). Before the rise of DL, conventional machine learning algorithms involving feature extraction were performed. This limited their performance to the ability of those handcrafting the features. However, in DL, the extraction of features and classification are entirely automated. The advent of these techniques in many areas of medicine, such as in the diagnosis of epileptic seizures, has made significant advances. In this study, a comprehensive overview of works focused on automated epileptic seizure detection using DL techniques and neuroimaging modalities is presented. Various methods proposed to diagnose epileptic seizures automatically using EEG and MRI modalities are described. In addition, rehabilitation systems developed for epileptic seizures using DL have been analyzed, and a summary is provided. The rehabilitation tools include cloud computing techniques and hardware required for implementation of DL algorithms. The important challenges in accurate detection of automated epileptic seizures using DL with EEG and MRI modalities are discussed. The advantages and limitations in employing DL-based techniques for epileptic seizures diagnosis are presented. Finally, the most promising DL models proposed and possible future works on automated epileptic seizure detection are delineated.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Epileptic Seizures Detection Using Deep Learning Techniques: A Review

Shoeibi

Khodatars

Ghassemi

et al. 2021

IJERPH

254

109

View full text Add to dashboard Cite

show abstract

“…One essential feature of X. Wang machine learning and in particular neural networks (NNs) is their flexibility that makes them suitable for a wide range of applications, including computer vision [2], natural language processing [3], biomedical [4], and several others [5]- [7]. Today, machine learning on IoT devices is applied with the traditional cloud computing paradigm where the whole data processing is performed in the cloud, and the IoT devices stream the data out in raw form or possibly after simple filtering and/or compression [8], [9].…”

Section: Introductionmentioning

confidence: 99%

FANN-on-MCU: An Open-Source Toolkit for Energy-Efficient Neural Network Inference at the Edge of the Internet of Things

Wang

Magno

Cavigelli

et al. 2020

IEEE Internet Things J.

125

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The growing number of low-power smart devices in the Internet of Things is coupled with the concept of "Edge Computing", that is moving some of the intelligence, especially machine learning, towards the edge of the network. Enabling machine learning algorithms to run on resource-constrained hardware, typically on low-power smart devices, is challenging in terms of hardware (optimized and energy-efficient integrated circuits), algorithmic and firmware implementations. This paper presents FANN-on-MCU, an open-source toolkit built upon the Fast Artificial Neural Network (FANN) library to run lightweight and energy-efficient neural networks on microcontrollers based on both the ARM Cortex-M series and the novel RISC-Vbased Parallel Ultra-Low-Power (PULP) platform. The toolkit takes multi-layer perceptrons trained with FANN and generates code targeted to low-power microcontrollers. This paper also presents detailed analyses of energy efficiency across the different cores, and the optimizations to handle different network sizes. Moreover, it provides a detailed analysis of parallel speedups and degradations due to parallelization overhead and memory transfers. Further evaluations include experimental results for three different applications using a self-sustainable wearable multi-sensor bracelet. Experimental results show a measured latency in the order of only a few microseconds and power consumption of a few milliwatts while keeping the memory requirements below the limitations of the targeted microcontrollers. In particular, the parallel implementation on the octa-core RISC-V platform reaches a speedup of 22x and a 69% reduction in energy consumption with respect to a single-core implementation on Cortex-M4 for continuous real-time classification.

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“…Zazzaro et al have proposed a new technique to identify automatically EEG signals as a comprehensive tool for extraction of characteristic from time series data. This tool includes signal processing, window-placement, extraction and selection of characteristics and SVM [13].…”

Section: Introductionmentioning

confidence: 99%

“…Length of these signals lasts for 86.8s. These data were collected using method of placement of standard electrode (10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20).…”

Section: Introductionmentioning

confidence: 99%