Papers by Keyword: Epilepsy

Abstract: Epilepsy is type of neurological disorder characterized by recurrent seizures that may cause injury to self and others. The ability to predict seizure before its occurrence, so that counter measures are considered, would improve the quality of life of epileptic patients. This research work proposes an adaptive seizure prediction approach based on electroencephalography (EEG) signals analysis. We use cross-correlation to estimate synchronization between EEG channels. Abnormal synchronization between brain regions may reveal brain condition and functionality. Two EEG synchronization baselines, normal and pre-seizure, are used to continuously monitor sliding windows of EEG recording to predict the upcoming seizure. The two baselines are continuously updated using distance-based method based on the most recent prediction outcome. Up to 570 hours continuous EEG recording taken from CHB-MIT dataset is used for validating the proposed method. An overall of 84% sensitivity (46 out of 55 seizures are correctly predicted) and 63% specificity are achieved with one hour prediction horizon. The proposed method is suitable to be implemented in mobile or embedded device which has limited processing resources due to its simplicity.

Abstract: Pentose phosphate (PP) pathway, which is ubiquitously present in all living organisms, is one of the major metabolic pathways associated with glucose metabolism. The most important functions of this pathway includes the generation of reducing equivalents in the form of NADPH for reductive biosynthesis, and production of ribose sugars for the biosynthesis of nucleotides, amino acids, and other macromolecules required by all living cells. Under normal conditions of growth, PP pathway is important for cell cycle progression, myelin formation, and the maintenance of the structure and function of brain, liver, cortex and other organs. Under diseased conditions, such as in cases of many metabolic, neurological or malignant diseases, pathological mechanisms augment due to defects in the PP pathway genes. Adoption of alternative metabolic pathways by cells that are metabolically abnormal, or malignant cells that are resistant to chemotherapeutic drugs often plays important roles in disease progression and severity. Accordingly, the PP pathway has been suggested to play critical roles in protecting cancer or abnormal cells by providing reduced environment, to protect cells from oxidative damage and generating structural components for nucleic acids biosynthesis. Novel drugs that targets one or more components of the PP pathway could potentially serve to overcome challenges associated with currently available therapeutic options for many metabolic and non-metabolic diseases. However, careful designing of drugs is critical that takes into the accounts of cell’s broader genomic, proteomic and metabolic contexts under consideration, in order to avoid undesirable side-effects. In this review, we discuss the role of PP pathway under normal and abnormal physiological conditions and the potential of the PP pathway as a target for new drug development to treat metabolic and non-metabolic diseases.

Abstract: The cross-correlation performance between epilepsy electroencephalogram (EEG) signals reflects the status of epilepsy patients which has importance for analyzing long-range correlation of non-stationary signals. For the first time, detrended cross-correlation analysis (DCCA) was applied to analyze different physiological and pathological states of epilepsy EEG signals. It were compared the difference of DCCA values between epilepsy patients EEG signals and normal subjects EEG signals. It was found that the DCCA values of epilepsy patients EEG signals increased compared the normal subjects EEG signals which can be helpful for medical diagnosis and treatment.

Abstract: In this paper, symbolic relative entropy was used to analyze the average energy dissipation of epilepsy electroencephalagram (EEG) signals and normal electroencephalagram signals. Hypothesis testing showed that the average energy dissipation of epilepsy electroencephalagram signals was distinctly higher than that of normal electroencephalagram signals. It discoved that symbolic relative entropy can be used to analyze the irreversibility of time series and to assess the health state of human brain. It can be used to assisted clinical diagnosis.

Abstract: Epilepsy is one of the most common neurological disorders that greatly disturb patients’ daily lives. Traditional epileptic diagnosis relies on tedious visual screening by neurologists from lengthy EEG recording that requires the presence of seizure (ictal) activities. We proposed to study automated epileptic diagnosis using interictal EEG data that was much easier to collect than ictal data. The research aims to develop an automated diagnostic system that can use interictal EEG data to diagnose whether the person is epileptic. This system could also test epileptic seizures in order to provide doctors with further tests and potential monitor of patients. To test such a system, we extract power spectral feature, Petrosian fractal dimension, Higuchi fractal dimension and Hjorth parameters for analysis, from which we find our system can be used in patient monitoring(seizure detection) and seizure focus localization, with 98.333% and75.5% accuracy respectively.

Abstract: Nowadays, diagnosis for epilepsy depends on many systems helping the neurologists to quickly find interesting segments from the lengthy signal by automatic seizure detection. However, we notice that it is very difficult, to obtain long-term EEG data with seizure activities for epilepsy patients in areas lack of medical resources and trained neurologists. Therefore, we propose to study automated epileptic diagnosis using interictal EEG data that is much easier to collect than ictal data. The research, therefore, aims to develop an automated diagnostic system that can use interictal EEG data to diagnose whether the person is epileptic. To develop such a system, we extract from the EEG data three classes of features which respectively are Petrosian fractal dimension, Higuchi fractal dimension and Hjorth parameters and build a Probabilistic Neural Network (PNN) fed with these features. Meanwhile, we also broach demand for data standardization by analysis with EEG of epileptic patients.

Abstract: In this paper, we propose a new biological signal-responsive implantable device that triggers direct an anticonvulsive drug into the epileptogenic zone at electrographic seizure onset. We describe the high-performance seizure-onset detection algorithm, low-power circuit technique and focal drug delivery system. The implantable device is composed of a preamplifier, a signal processor, a seizure detector and a micropump. The device records high quality intracerebral electroencephalographic (icEEG) signals using high conductive electrodes and a low noise preamplifier. The recorded signal is processed continuously using low-power technique to detect onset of seizures accurately. The low-power miniaturized micropump is able to deliver sufficient amount of anticonvulsive drug in a short duration (50µL/sec) to epileptogenic zone. The detection algorithm was validated with Matlab tools and a prototype device was assembled with discrete components in a circular (Ø 40 mm) printed circuit board. The device was validated offline using the icEEG recordings obtained from 3 drug-resistant epilepsy patients. The average seizure detection delay was 10 sec from electrographic seizure onset, well before seizure progression to adjacent functional cortex.

Abstract: The automated detection of seizures in EEG is significant for epilepsy monitoring, diagnosis and rehabilitation. In this work, we evaluated the differences between epileptic EEG, interictal EEG and normal EEG by computing their Higuchi Fractal Dimension (HFD) and Approximate Entropy (ApEn). The calculated results show that there are significant differences between epileptic EEG and normal EEG in the variations of HFD and ApEn. HFD and ApEn have been shown to be useful to characterize normal and epileptic brain electrical activities, and the degree of complexity of epileptic EEG is lower than that of normal EEG even during interictal time. Our results could be helpful for interpreting the epileptic brain electrical activity and the normal brain electrical activity, and their neurodynamics.

Abstract: The Hilbert-Huang transform (HHT) is a popular time-frequency analysis methods employed to decompose electric signals into intrinsic mode functions (IMFs). In this paper, we use HHT analysis to discuss the time-frequency characteristics of a spike wave for epilepsy symptoms. The differences between the IMFs and IMFs-energy distributions for the spike and normal waves are discussed. The ratios of the energy of the spike wave, IMF1 and the residual function to the total energy are 11.27% and 75.84%, respectively. In contrast, the ratios of the energy of the normal wave, IMF3, IMF4, and the residual function to the total energy are 10.99%, 43.31%, and 37.69%, respectively.

Abstract: We propose a subdural electrode array guided by a 0.3mm-diamter shape memory alloy guidewire for a minimally-invasive method of electrocorticogram recording. The measured electric characteristics showed that the proposed electrodes are compatible with the application of electrocorticogram recording. Somatosensory evoked potential was measured by the proposed method in the animal test in vivo. The results confirmed that the proposed electrode array is available for the electrocorticogram recording under a minimally-invasive surgery.