Model-based seizure detection for intracranial EEG recordings

This paper presents a novel model-based patient-specific method for automatic detection of seizures in the intracranial EEG recordings. The proposed method overcomes the complexities in the practical implementation of the patient-specific approach of seizure detection. The method builds a seizure model (set of basis functions) for a priori known seizure (the template seizure pattern), and uses the statistically optimal null filters as a building block for the detection of similar seizures. The process of modeling the template seizure is fully automatic. Overall, the detection method involves the segmentation of the template seizure pattern, rejection of the redundant and noisy segments, extraction of features from the segments to generate a set of models, selection of the best seizure model, and training of the classifier. The trained classifier is used to detect similar seizures in the remaining data. The resulting seizure detection method was evaluated on a total of 304 h of single-channel depth EEG recordings from 14 patients. The system performance is further compared to the Qu-Gotman patient-specific system using the same data. A significant improvement in the proposed system, in terms of specificity, is observed over the compared method.     

Electrocardiogram based neonatal seizure detection

A method for the detection of seizures in the newborn using the electrocardiogram (ECG) signal is presented. Using a database of eight recordings, a method was developed for automatically annotating each 1-min epoch as "nonseizure" or "seizure". The system uses a linear discriminant classifier to process 41 heartbeat timing interval features. Performance assessment of the method showed that on a patient-specific basis an average accuracy of 70.5% was achieved in detecting seizures with associated sensitivity of 62.2% and specificity of 71.8%. On a patient-independent basis the average accuracy was 68.3% with sensitivity of 54.6% and specificity of 77.3%. Shifting the decision threshold for the patient-independent classifier allowed an increase in sensitivity to 78.4% at the expense of decreased specificity (51.6%), leading to increased false detections. The results of our ECG-based method are comparable with those reported for EEG-based neonatal seizure detection systems and offer the benefit of an easier acquisition methodology for seizure detection.     

Gaussian process modeling of EEG for the detection of neonatal seizures

Gaussian process (GP) probabilistic models have attractive advantages over parametric and neural network modeling approaches. They have a small number of tuneable parameters, can be trained on relatively small training sets, and provide a measure of prediction certainty. In this paper, these properties are exploited to develop two methods of highlighting the presence of neonatal seizures from electroencephalograph (EEG) signals. In the first method, the certainty of the GP model prediction is used to indicate the presence of seizures. In the second approach, the hyperparameters of the GP model are used. Tests are carried out with a feature set of ten EEG measures developed from various signal processing techniques. Features are evaluated using a neural network classifier on 51 h of real neonatal EEG. The GP measures, in particular, the prediction certainty approach, produce a high level of performance compared to other modeling methods and methods currently in clinical use for EEG analysis, indicating that they are an important and useful tool for the real-time detection of neonatal seizures.     

EEG信号瞬时同步性分析

同步是大脑不同区域之间交换信息时存在的重要特征.对EEG信号在不同频段的同步性研究是认识大脑的一种重要手段.研究信号同步性的方法分为参数化方法与非参数化方法.这里介绍一种基于ARMA模型的算法,该方法属于参数化方法,并且以从雄性老鼠获得的实验数据为例,探讨EEG信号的瞬时同步性,以便更好地了解大脑功能.在此基础上,讨论了该方法中Cs参数对计算结果稳定性的影响.另外,该方法相对于非参数方法具有更好的频率分辨率.     

Morphology-based automatic seizure detector for intracerebral EEG recordings

In this paper, a new seizure detection system aimed at assisting in a rapid review of prolonged intracerebral EEG recordings is described. It is based on quantifying the sharpness of the waveform, one of the most important electrographic EEG features utilized by experts for an accurate and reliable identification of a seizure. The waveform morphology is characterized by a measure of sharpness as defined by the slope of the half-waves. A train of abnormally sharp waves resulting from subsequent filtering are used to identify seizures. The method was optimized using 145 h of single-channel depth EEG from seven patients, and tested on another 158 h of single-channel depth EEG from another seven patients. Additionally, 725 h of depth EEG from 21 patients was utilized to assess the system performance in a multichannel configuration. Single-channel test data resulted in a sensitivity of 87% and a specificity of 71%. The multichannel test data reported a sensitivity of 81% and a specificity of 58.9%. The new system detected a wide range of seizure patterns that included rhythmic and nonrhythmic seizures of varying length, including those missed by the experts. We also compare the proposed system with a popular commercial system.     

An improved channel detection method for IEEE 802.11N MIMO system

Coexistence and interoperability between 20 MHz and 40 MHz device and modes of operations are stressed in standard IEEE 802.11n system. It is mandate to report the both sub-channels states to Medium Access Control (MAC) at receiver, since for 40 MHz device, it should serve not only 20 MHz but also 40 MHz signals receiving. Both energy detection and carrier sense are employed to detect channel state. In the case of 20/40 M mode, the power difference between the two sub-channels is also detected in order to report the channel state accurately. The simulation results demonstrate that the performance of the proposed methods are much better than the methods which just employ energy detection. Besides, the simulation results show that the proposed methods ensure that the channel sensing is not a roadblock of IEEE 802.11n system design.     

一种基于小波变换的脑电信号处理的新方法

文章提出一种利用小波变换对脑电信号瞬态提取的新方法。实验表明基于小波变换的脑电信号瞬态检测法能方便而有效地完成瞬态波形的检测与参数提取。     

A patient-specific algorithm for the detection of seizure onset inlong-term EEG monitoring: possible use as a warning device

During long-term electroencephalogram (EEG) monitoring of epileptic patients, a seizure warning system would allow patients and observers to take appropriate precautions. It would also allow observers to interact with patients early during the seizure, thus revealing clinically useful information. We designed patient-specific classifiers to detect seizure onsets. After a seizure and some nonseizure data are recorded in a patient, they are used to train a classifier. In subsequent monitoring sessions, EEG patterns have to pass this classifier to determine if a seizure onset occurs. If it does, an alarm is triggered. Extreme care has been taken to ensure a low false-alarm rate, since a high false-alarm rate would render the system ineffective. Features were extracted from the time and frequency domains and a modified nearest-neighbor (NN) classifier was used. The system reached an onset detection rate of 100% with an average delay of 9.35 s after onset. The average false-alarm rate was only 0.02/h. The method was evaluated in 12 patients with a total of 47 seizures. Results indicate that the system is effective and reasonably reliable. Computation load has been kept to a minimum so that real-time processing is possible     

Birnbaum's measure of component importance for noncoherent systems

Importance analysis of noncoherent systems is limited, and is generally inaccurate because all measures of importance that have been developed are strictly for coherent analysis. This paper considers the probabilistic measure of component importance developed by Birnbaum (1969). An extension of this measure is proposed which enables noncoherent importance analysis. As a result of the proposed extension the average number of system failures in a given interval for noncoherent systems can be calculated more efficiently. Furthermore, because Birnbaum's measure of component importance is central to many other measures of importance; its extension should make the derivation of other measures possible.     

Detection of pseudosinusoidal epileptic seizure segments in the neonatal EEG by cascading a rule-based algorithm with a neural network

This paper presents an approach to detect epileptic seizure segments in the neonatal electroencephalogram (EEG) by characterizing the spectral features of the EEG waveform using a rule-based algorithm cascaded with a neural network. A rule-based algorithm screens out short segments of pseudosinusoidal EEG patterns as epileptic based on features in the power spectrum. The output of the rule-based algorithm is used to train and compare the performance of conventional feedforward neural networks and quantum neural networks. The results indicate that the trained neural networks, cascaded with the rule-based algorithm, improved the performance of the rule-based algorithm acting by itself. The evaluation of the proposed cascaded scheme for the detection of pseudosinusoidal seizure segments reveals its potential as a building block of the automated seizure detection system under development.     

基于改进混合高斯建模和短时稳定度的遗留物检测算法

传统遗留物检测算法存在算法过于复杂和环境适应性差的局限。本文将改进的混合高斯建模方法应用于遗留物检测,利用背景匹配失败时生成的前景模型进行前景匹配并引入短时稳定度指标,在深入挖掘前景模型中包含的遗留物信息和像素点级目标状态信息的基础上对遗留物进行综合判断。文中详细分析了传统方法的性能局限并阐述了新方法中前景模型和短时稳定度的作用原理同时给出了具体的算法流程。多场景下的实验分析表明,增加对前景模型的考察使算法在保留传统方法优点的同时具备了良好的遗留物检测能力,而短时稳定度的引入则能够进一步降低传统方法中前景模型向背景模型转换的风险。对比实验结果中本文方法在表现出良好环境适应性的同时误检团块数明显低于其他方法,算法在复杂背景条件下达到了良好的检测性能。      

改进暗通道方法的水下强散射介质目标探测

针对水下场景目标探测图像质量退化问题,提出了一种自适应计算水体衰减系数暗通道融合多尺度Retinex(Multi-scale Retinex,MSR)的复原算法,有效实现了水下目标的复原。通过搭建的水下成像测量装置,借助成像系统获取水下模拟环境的探测图像,对水下探测图像按照算法流程图逐步处理,得到了有效复原水下目标辐射信息的图像。为客观评价算法的效果,采用对比度、平均梯度与信息熵作为定量评价指标因子,对该算法与常规三种算法进行了定量对比研究,结果表明,该算法处理结果各项定量评价指标因子均优于选取的对比算法。研究结果为水下目标探测提供了基础理论探索方法,对水下目标探测实施开展具有一定的指导意义。     

EEG complexity as a measure of depth of anesthesia for patients

A new approach for quantifying the relationship between brain activity patterns and depth of anesthesia (DOA) is presented by analyzing the spatio-temporal patterns in the electroencephalogram (EEG) using Lempel-Ziv complexity analysis. Twenty-seven patients undergoing vascular surgery were studied under general anesthesia with sevoflurane, isoflurane, propofol, or desflurane. The EEG was recorded continuously during the procedure and patients' anesthesia states were assessed according to the responsiveness component of the observer's assessment of alertness/sedation (OAA/S) score. An OAA/S score of zero or one was considered asleep and two or greater was considered awake. Complexity of the EEG was quantitatively estimated by the measure C(n), whose performance in discriminating awake and asleep states was analyzed by statistics for different anesthetic techniques and different patient populations. Compared with other measures, such as approximate entropy, spectral entropy, and median frequency, C(n) not only demonstrates better performance (93% accuracy) across all of the patients, but also is an easier algorithm to implement for real-time use. The study shows that C(n) is a very useful and promising EEG-derived parameter for characterizing the (DOA) under clinical situations.     

Regional head tissue conductivity estimation for improved EEG analysis

We develop a method for estimating regional head tissue conductivities in vivo, by injecting small (1-10 microA) electric currents into the scalp, and measuring the potentials at the remaining electrodes of a dense-array electroencephalography net. We first derive analytic expressions for the potentials generated by scalp current injection in a four-sphere model of the human head. We then use a multistart downhill simplex algorithm to find regional tissue conductivities which minimize the error between measured and computed scalp potentials. Two error functions are studied, with similar results. The results show that, despite the low skull conductivity and expected shunting by the scalp, all four regional conductivities can be determined to within a few percent error. The method is robust to the noise levels expected in practice. To obtain accurate results the cerebrospinal fluid must be included in the forward solution, but may be treated as a known parameter in the inverse solution.     

On the tracking of rapid dynamic changes in seizure EEG

Estimation of autospectra and coherence and phase spectra of the seizure electroencephalograph (EEG), using the fast Fourier transform (FFT) technique, will cause smearing of the rapid dynamic changes which occur during the seizure. This is inherent to FFT spectral estimation, due to the averaging process which is necessary in order to get consistent spectral estimates. A different approach suggested in the present study is to carry out multivariate autoregressive modeling of the multichannel seizure EEG, combined with adaptive segmentation. In order to obtain good estimates in cases of short record length, the vectorial autoregressive (AR) modeling was based on residual energy ratios. The method has been tested on multichannel seizure EEG recordings from rats with focal epilepsy, caused by intracerebral administration of Kainic acid, and in-depth EEG recordings in patients with temporal lobe epilepsy     

A channel differential EZW coding scheme for EEG data compression

In this paper, a method is proposed to compress multichannel electroencephalographic (EEG) signals in a scalable fashion. Correlation between EEG channels is exploited through clustering using a k-means method. Representative channels for each of the clusters are encoded individually while other channels are encoded differentially, i.e., with respect to their respective cluster representatives. The compression is performed using the embedded zero-tree wavelet encoding adapted to 1-D signals. Simulations show that the scalable features of the scheme lead to a flexible quality/rate tradeoff, without requiring detailed EEG signal modeling.     

An improved channel coding algorithm for low-frequency spectralsuppression

An improved low-frequency spectral suppression (LOFS) code is introduced and analyzed as a method to suppress low-frequency energy in a digital baseband signal with significantly lower redundancy than required methods. Alternatively, the LOFS code will suppress more energy near DC with a given redundancy when compared to current coding methods, allowing insertion of a pilot tone which leads to a simple unambiguous carrier recovery subsystem that can track system-induced noise. The LOFS code reduces redundancy by adding control bits in word format for multilevel signals, that is, control bits for multiple frames are inserted in one symbol duration. Use of this LOFS code could reduce the redundancy of currently used digital transmission systems from 4% to 1%. Analysis of computer simulations shows that a premodulation high-pass filter, while significantly degrading the uncoded PAM signal, causes little degradation of the coded data and keeps data to pilot interference to a minimum. Experimental hardware results are included to verify simulation results     

Mixed-band wavelet-chaos-neural network methodology for epilepsy and epileptic seizure detection

A novel wavelet-chaos-neural network methodology is presented for classification of electroencephalograms (EEGs) into healthy, ictal, and interictal EEGs. Wavelet analysis is used to decompose the EEG into delta, theta, alpha, beta, and gamma sub-bands. Three parameters are employed for EEG representation: standard deviation (quantifying the signal variance), correlation dimension, and largest Lyapunov exponent (quantifying the non-linear chaotic dynamics of the signal). The classification accuracies of the following techniques are compared: (1) unsupervised k-means clustering; (2) linear and quadratic discriminant analysis; (3) radial basis function neural network; (4) Levenberg-Marquardt backpropagation neural network (LMBPNN). To reduce the computing time and output analysis, the research was performed in two phases: band-specific analysis and mixed-band analysis. In phase two, over 500 different combinations of mixed-band feature spaces consisting of promising parameters from phase one of the research were investigated. It is concluded that all three key components of the wavelet-chaos-neural network methodology are important for improving the EEG classification accuracy. Judicious combinations of parameters and classifiers are needed to accurately discriminate between the three types of EEGs. It was discovered that a particular mixed-band feature space consisting of nine parameters and LMBPNN result in the highest classification accuracy, a high value of 96.7%.     

A wavelet-chaos methodology for analysis of EEGs and EEG subbands to detect seizure and epilepsy

A wavelet-chaos methodology is presented for analysis of EEGs and delta, theta, alpha, beta, and gamma subbands of EEGs for detection of seizure and epilepsy. The nonlinear dynamics of the original EEGs are quantified in the form of the correlation dimension (CD, representing system complexity) and the largest Lyapunov exponent (LLE, representing system chaoticity). The new wavelet-based methodology isolates the changes in CD and LLE in specific subbands of the EEG. The methodology is applied to three different groups of EEG signals: 1) healthy subjects; 2) epileptic subjects during a seizure-free interval (interictal EEG); 3) epileptic subjects during a seizure (ictal EEG). The effectiveness of CD and LLE in differentiating between the three groups is investigated based on statistical significance of the differences. It is observed that while there may not be significant differences in the values of the parameters obtained from the original EEG, differences may be identified when the parameters are employed in conjunction with specific EEG subbands. Moreover, it is concluded that for the higher frequency beta and gamma subbands, the CD differentiates between the three groups, whereas for the lower frequency alpha subband, the LLE differentiates between the three groups.