A review of developments of EEG-based automatic medical support systems for epilepsy diagnosis and seizure detection

Song, Yuedong

doi:10.4236/jbise.2011.412097

Cited by 27 publications

(11 citation statements)

References 44 publications

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“…Seizure detection and therapy systems generally consist of five processes: (1) signal acquisition, (2) signal processing, (3) feature extraction, (4) classification, and (5) therapy (5,6,24). The processes mentioned above are illustrated in Figure 1.…”

Section: Analyzing Physiology Signals Of Epileptic Seizurementioning

confidence: 99%

“…Second, after capturing the physiology signals, it is important to accurately detect and classify the type of detected seizures (5,6,24). Existing seizure classification methods primarily include classical machine learning approaches [e.g., support vector machine (SVM)] and novel deep-learning solutions [e.g., artificial neural network (ANN) (7)].…”

Section: Introductionmentioning

confidence: 99%

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Epileptic Seizure Detection and Experimental Treatment: A Review

et al. 2020

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One-fourths of the patients have medication-resistant seizures and require seizure detection and treatment continuously to cope with sudden seizures. Seizures can be detected by monitoring the brain and muscle activities, heart rate, oxygen level, artificial sounds, or visual signatures through EEG, EMG, ECG, motion, or audio/video recording on the human head and body. In this article, we first discuss recent advances in seizure sensing, signal processing, time-or frequency-domain analysis, and classification algorithms to detect and classify seizure stages. Then, we show a strong potential of applying recent advancements in non-invasive brain stimulation technology to treat seizures. In particular, we explain the fundamentals of brain stimulation approaches, including (1) transcranial magnetic stimulation (TMS), (2) transcranial direct current stimulation (tDCS), (3) transcranial focused ultrasound stimulation (tFUS), and how to use them to treat seizures. Through this review, we intend to provide a broad view of both recent seizure diagnoses and treatments. Such knowledge would help fresh and experienced researchers to capture the advancements in sensing, detection, classification, and treatment seizures. Last but not least, we provide potential research directions that would attract seizure researchers/engineers in the field.

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Section: Analyzing Physiology Signals Of Epileptic Seizurementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Epileptic Seizure Detection and Experimental Treatment: A Review

et al. 2020

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“…After measuring impedance, we next performed a recording with the electrode array in PBS and a reference screw in the same solution roughly 5 cm away. Median root mean square noise across 400 µm 2…”

Section: Benchtop Noise Testing Of Electrodesmentioning

confidence: 99%

Sputtered porous Pt for wafer-scale manufacture of low-impedance flexible microelectrodes

Fan

Rodriguez

Vercosa

et al. 2020

Preprint

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Recording electrical activity from individual cells in vivo is a key technology for basic neuroscience and has growing clinical applications. To maximize the number of independent recording channels as well as the longevity, and quality of these recordings, researchers often turn to small and flexible electrodes that minimize tissue damage and can isolate signals from individual neurons. One challenge when creating these small electrodes, however, is to maintain a low interfacial impedance by applying a surface coating that is stable in tissue and does not significantly complicate the fabrication process. Here we use a highpressure Pt sputtering process to create low-impedance electrodes at the wafer scale using standard microfabrication equipment. Direct-sputtered Pt provides a reliable and well-controlled porous coating that reduces the electrode impedance by 5-9 fold compared to flat Pt and is compatible with the microfabrication technologies used to create flexible electrodes. These porous Pt electrodes show reduced thermal noise that matches theoretical predictions. In addition, we show that these electrodes can be implanted into rat cortex, record single unit activity, and be removed all without disrupting the integrity of the coating. We also demonstrate that the shape of the electrode (in addition to the surface area) has a significant effect on the electrode impedance when the feature sizes are on the order of tens of microns.Overall, porous Pt represents a promising method for manufacturing low-impedance electrodes that can be seamlessly integrated into existing processes for producing flexible neural probes.

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“…Different bands carry information of different brain activities. EEG signals are extensively studied by numerous researches to classify different mental or brain activities [6]- [9]. Few studies have been proposed on hand movement classifications using support vector machine (SVM), linear discrimination analysis, adaptive Gaussian coefficients, C-SVM and combination of EEG and MEG.…”

Section: Introductionmentioning

confidence: 99%

Left and Right Hand Movements EEG Signals Classification Using Wavelet Transform and Probabilistic Neural Network

Hossain

Rahman

Riheen

2015

IJECE

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Electroencephalogram (EEG) signals have great importance in the area of brain-computer interface (BCI) which has diverse applications ranging from medicine to entertainment. BCI acquires brain signals, extracts informative features and generates control signals from the knowledge of these features for functioning of external devices. The objective of this work is twofold. Firstly, to extract suitable features related to hand movements and secondly, to discriminate the left and right hand movements signals finding effective classifier. This work is a continuation of our previous study where beta band was found compatible for hand movement analysis. The discrete wavelet transform (DWT) has been used to separate beta band of the EEG signal in order to extract features. The performance of a probabilistic neural network (PNN) is investigated to find better classifier of left and right hand movements EEG signals and compared with classical back propagation based neural network. The obtained results shows that PNN (99.1%) has better classification rate than the BP (88.9%). The results of this study are expected to be helpful in brain computer interfacing for hand movements related bio-rehabilitation applications.

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A review of developments of EEG-based automatic medical support systems for epilepsy diagnosis and seizure detection

Cited by 27 publications

References 44 publications

Epileptic Seizure Detection and Experimental Treatment: A Review

Epileptic Seizure Detection and Experimental Treatment: A Review

Sputtered porous Pt for wafer-scale manufacture of low-impedance flexible microelectrodes

Left and Right Hand Movements EEG Signals Classification Using Wavelet Transform and Probabilistic Neural Network

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