EEG-based fatigue driving detection using correlation dimension

Driver fatigue is an important cause of traffic accidents and the detection of fatigue driving has been a hot issue in automobile active safety during the past decades. The purpose of this study is to develop a novel method to detect fatigue driving based on electroencephalogram (EEG). The volunteer is asked to perform simulated driving tasks under different mental state while EEG signals are acquired simultaneously from six electrodes at central, parietal and occipital lobe, including C3, C4, P3, P4, O1 and O2. Due to the non-linearity of human brain responses, correlation dimension is estimated with G-P algorithm to quantify the collected EEGs. Statistical analysis reveals significant decreases from awake to fatigue state of the correlation dimension for all the channels across 5 subjects (awake state: 3.87±0.13; fatigue state: 2.76±0.34; p< 0.05, paired t-test), which indicates that the correlation dimension is a promising parameter in detecting fatigue driving with EEGs

Automatic detection of epileptic slow-waves in EEG based on empirical mode decomposition and wavelet transform

Slow-wave is one of the most typical epileptic activities in EEGs and plays an important role in the diagnosis of disorders related to epilepsy in clinic. However artifacts such as blinking resemble slow-waves in shape and confuse slow-wave detection. Thus, differentiating and removing these artifacts are of great importance in slow-wave detection. In this paper, we propose an improved slow-wave detection algorithm based on discrete wavelet transform (DWT) that specially concerns on removal of blinking artifact (BA). EMD that can break down a complicated signal without a basis function such as sine or wavelet functions is used to decompose EEG. Two intrinsic mode functions (IMFs) which have BA’s characteristic are extracted. Then, we compute the similarity between original EEG and the combination of IMFs for identifying BA. Regression method is used to remove influence of BA from all channels. Finally, improved DWT is employed to detect slow-waves. We employ this method to clinical data and results show that the average false detection rate of the improved method is much lower than that of the traditional DWT method

Extraction of operation characteristics in mechanical systems using genetic morphological filter

Operation characteristics such as rotating speed are of great importance in condition monitoring and fault diagnosis of rotating machineries. Since different components in the mechanical system are often correlated and interacted, the acquired signals are highly coupled and contaminated by lots of high-frequency noises. As a result, the frequency and phase of the observed signal cannot reflect actual condition of the mechanical component. In this paper, we propose a genetic morphological filter to purify the operation characteristics of the mechanical system in the time domain. Firstly, an average weighted combination of open-closing and close-opening morphological operator, which eliminates statistical deflection of amplitude, is utilized to remove stochastic noises from the original signal. Then, according to the geometric characteristic of the noises, the structure elements are constructed with two parabolas and four parameters of the structure elements are synchronously optimized with genetic algorithm. The combination of Hurst exponent and Kurtosis is selected as the fitness function of the genetic algorithm and the optimal parameters of the structure elements correspond to the maximum of fitness function. The proposed method is evaluated by simulated signals with different frequencies, vibration signals measured on condensate pump and sound signals acquired from motor engine, respectively. Results show that with genetic morphological filter, the operation characteristics such as rotating speed and phase can be extracted in the time domain efficiently

Extraction of operation characteristics in mechanical systems using genetic morphological filter

Operation characteristics such as rotating speed are of great importance in condition monitoring and fault diagnosis of rotating machineries. Since different components in the mechanical system are often correlated and interacted, the acquired signals are highly coupled and contaminated by lots of high-frequency noises. As a result, the frequency and phase of the observed signal cannot reflect actual condition of the mechanical component. In this paper, we propose a genetic morphological filter to purify the operation characteristics of the mechanical system in the time domain. Firstly, an average weighted combination of open-closing and close-opening morphological operator, which eliminates statistical deflection of amplitude, is utilized to remove stochastic noises from the original signal. Then, according to the geometric characteristic of the noises, the structure elements are constructed with two parabolas and four parameters of the structure elements are synchronously optimized with genetic algorithm. The combination of Hurst exponent and Kurtosis is selected as the fitness function of the genetic algorithm and the optimal parameters of the structure elements correspond to the maximum of fitness function. The proposed method is evaluated by simulated signals with different frequencies, vibration signals measured on condensate pump and sound signals acquired from motor engine, respectively. Results show that with genetic morphological filter, the operation characteristics such as rotating speed and phase can be extracted in the time domain efficiently

Detección de puntas epilépticas en señales electroencefalográficas para pacientes con epilepsia del lóbulo temporal utilizando wavelets

This paper describes a method for detecting epileptic spikes in a record electroencephalographic (EEG) surface by taking a single channel. We identified a pattern using multiresolution analysis with a biorthogonal wavelet after processing and analyzing the Wavelet Toolbox of Matlab, 207 records and 132 records of tips tricks previously classified by Neurophysiologist. This pattern enabled an algorithm for detecting spikes in patients with refractory temporal lobe epilepsy, based on the maximum voltage in each of the six levels of reconstruction using biorthogonal 3.7 wavelet. The algorithm was applied on records of patients with epilepsy, getting a sensitivity of 92% and a specificity of 80% in the diagnosis of epileptic spikes.PACS: 87.57.-sMSC: 65T60, 42C40En este trabajo se describe un método para la detección de puntas epilépticasen un registro electroencefalográfico(EEG) de superficie tomando un solocanal. Se identificó un patrón al utilizar el análisis multirresolución con unawavelet biortogonal después de procesar y analizar con el Toolbox Waveletde Matlab, 207 registros de puntas y 132 registros de artificios previamenteclasificadas por el neurofisiólogo. Este patrón permitió diseñar un algoritmopara la detección de puntas en pacientes con epilepsia refractaria del lóbulotemporal, a partir de los máximos voltajes en cada uno de los seis niveles de reconstrucción usando la wavelet biortogonal 3.7. El algoritmo se aplicó sobreregistros de pacientes con epilepsia, obteniéndose una sensibilidad del 92% yuna especificidad del 80% en el diagnóstico de las puntas epilépticas.PACS: 87.57.-sMSC: 65T60, 42C4

On the Dynamics of Epileptic Spikes and Focus Localization in Temporal Lobe Epilepsy

abstract: Interictal spikes, together with seizures, have been recognized as the two hallmarks of epilepsy, a brain disorder that 1% of the world's population suffers from. Even though the presence of spikes in brain's electromagnetic activity has diagnostic value, their dynamics are still elusive. It was an objective of this dissertation to formulate a mathematical framework within which the dynamics of interictal spikes could be thoroughly investigated. A new epileptic spike detection algorithm was developed by employing data adaptive morphological filters. The performance of the spike detection algorithm was favorably compared with others in the literature. A novel spike spatial synchronization measure was developed and tested on coupled spiking neuron models. Application of this measure to individual epileptic spikes in EEG from patients with temporal lobe epilepsy revealed long-term trends of increase in synchronization between pairs of brain sites before seizures and desynchronization after seizures, in the same patient as well as across patients, thus supporting the hypothesis that seizures may occur to break (reset) the abnormal spike synchronization in the brain network. Furthermore, based on these results, a separate spatial analysis of spike rates was conducted that shed light onto conflicting results in the literature about variability of spike rate before and after seizure. The ability to automatically classify seizures into clinical and subclinical was a result of the above findings. A novel method for epileptogenic focus localization from interictal periods based on spike occurrences was also devised, combining concepts from graph theory, like eigenvector centrality, and the developed spike synchronization measure, and tested very favorably against the utilized gold rule in clinical practice for focus localization from seizures onset. Finally, in another application of resetting of brain dynamics at seizures, it was shown that it is possible to differentiate with a high accuracy between patients with epileptic seizures (ES) and patients with psychogenic nonepileptic seizures (PNES). The above studies of spike dynamics have elucidated many unknown aspects of ictogenesis and it is expected to significantly contribute to further understanding of the basic mechanisms that lead to seizures, the diagnosis and treatment of epilepsy.Dissertation/ThesisPh.D. Electrical Engineering 201