An association rule mining-based methodology for automated detection of ischemic ECG beats

Currently, an automated methodology based on association rules is presented for the detection of ischemic beats in long duration electrocardiographic (ECG) recordings. The proposed approach consists of three stages. 1) Preprocessing: Noise is removed and all the necessary ECG features are extracted. 2) Discretization: The continuous valued features are transformed to categorical. 3) Classification: An association rule extraction algorithm is utilized and a rule-based classification model is created. According to the proposed methodology, electrocardiogram (ECG) features extracted from the ST segment and the T-wave, as well as the patient's age, were used as inputs. The output was the classification of the beat as ischemic or not. Various algorithms were tested both for discretization and for classification using association rules. To evaluate the methodology, a cardiac beat dataset was constructed using several recordings of the European Society of Cardiology ST-T database. The obtained sensitivity (Se) and specificity (Sp) was 87% and 93%, respectively. The proposed methodology combines high accuracy with the ability to provide interpretation for the decisions made, since it is based on a set of association rules.     

Mining reproducible activation patterns in epileptic intracerebral EEG signals: application to interictal activity

The study of interictal transient events may substantially complement the analysis of seizures in the presurgical evaluation of intractable epilepsy. A comprehensive methodology of quantifying reproducibility of activation patterns in intracerebral electroencephalography signals is presented. It may be applied to various forms of transient epileptic events under the assumption that a time of occurrence may be assigned to them. In this paper, the method is used on two different forms of interictal events (interictal spikes or sharpwaves and transient bursts of fast activity). The methodology is based on signal processing and data mining algorithms and proceeds in three steps: (1) detection of transient paroxysmal events (monochannel event); (2) identification of quasisynchronous transient paroxysmal events (multichannel events); and (3) automatic extraction of similar activation patterns. Results show that the methodology allows reproducible sequential activation sets to be identified from signals recorded in four patients. Potential advantages of the method are discussed with respect to other approaches.     

模糊FP-growth在入侵检测中的应用

关联规则挖掘技术目前被广泛应用于入侵检测系统中。关联规则挖掘算法之一的FP-growth算法在处理数值量的输入时需要二值化,使得准确率不高;而Fuzzy Apriori算法需要重复扫描数据库,效率较低。针对此问题,改进现有的FP-growth算法,提出模糊化FP-growth算法,从而提取模糊关联规则,用于N类异常数据的分类入侵检测。在KDDCup＇99数据集上评估,结果表明对于数值量的输入,该方法应用于入侵检测准确率高于FP-growth算法,学习效率高于Fuzzy Apriori算法。     

Seizure onset detection based on frequency domain metric of empirical mode decomposition

This paper explores the data-driven properties of the empirical mode decomposition (EMD) for detection of epileptic seizures. A new method in frequency domain is presented to analyze intrinsic mode functions (IMFs) decomposed by EMD. They are used to determine whether the electroencephalogram (EEG) recordings contain seizure or not. Energy levels of the IMFs are extracted as threshold level to detect the changes caused by seizure activity. A scalar value energy resulting from the energy levels is individually used as an indicator of the epileptic EEG without the requirements of multidimensional feature vector and complex machine learning algorithms. The proposed methods are tested on different EEG recordings to evaluate the effectiveness of the proposed method and yield accuracy rate up to 97.89%.     

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%.     

Approximate entropy-based epileptic EEG detection using artificial neural networks.

The electroencephalogram (EEG) signal plays an important role in the diagnosis of epilepsy. The EEG recordings of the ambulatory recording systems generate very lengthy data and the detection of the epileptic activity requires a time-consuming analysis of the entire length of the EEG data by an expert. The traditional methods of analysis being tedious, many automated diagnostic systems for epilepsy have emerged in recent years. This paper proposes a neural-network-based automated epileptic EEG detection system that uses approximate entropy (ApEn) as the input feature. ApEn is a statistical parameter that measures the predictability of the current amplitude values of a physiological signal based on its previous amplitude values. It is known that the value of the ApEn drops sharply during an epileptic seizure and this fact is used in the proposed system. Two different types of neural networks, namely, Elman and probabilistic neural networks, are considered in this paper. ApEn is used for the first time in the proposed system for the detection of epilepsy using neural networks. It is shown that the overall accuracy values as high as 100% can be achieved by using the proposed system.     

Principal component analysis-enhanced cosine radial basis function neural network for robust epilepsy and seizure detection.

A novel principal component analysis (PCA)-enhanced cosine radial basis function neural network classifier is presented. The two-stage classifier is integrated with the mixed-band wavelet-chaos methodology, developed earlier by the authors, for accurate and robust classification of electroencephalogram (EEGs) into healthy, ictal, and interictal EEGs. A nine-parameter mixed-band feature space discovered in previous research for effective EEG representation is used as input to the two-stage classifier. In the first stage, PCA is employed for feature enhancement. The rearrangement of the input space along the principal components of the data improves the classification accuracy of the cosine radial basis function neural network (RBFNN) employed in the second stage significantly. The classification accuracy and robustness of the classifier are validated by extensive parametric and sensitivity analysis. The new wavelet-chaos-neural network methodology yields high EEG classification accuracy (96.6%) and is quite robust to changes in training data with a low standard deviation of 1.4%. For epilepsy diagnosis, when only normal and interictal EEGs are considered, the classification accuracy of the proposed model is 99.3%. This statistic is especially remarkable because even the most highly trained neurologists do not appear to be able to detect interictal EEGs more than 80% of the times.     

Detection and classification of sensory information from acute spinal cord recordings

One avenue of research for partial restoration of function following spinal cord injury is the use of neural prostheses, an example of which is functional electrical stimulation (FES) devices for motor functions. Neural prostheses may also be useful for the extraction of sensory information directly from the nervous system. We suggest the spinal cord as a possible site for the detection of peripheral sensory information from neural activity alone. Acute multichannel extracellular recordings were used to extract neural spike activity elicited from peripheral sensations from the spinal cords of rats. To test the recording method and classification potential, eight classes of sensory events were recorded consisting of electrical stimulation of seven locations on rat forepaws, and another class of data during which no stimulus was present. A dual-stage classification scheme using principal component analysis and k-Means clustering was devised to classify the sensory events during single trials. The eight tasks were correctly identified at a mean accuracy of 96%. Thus, we have shown the methodology to detect and classify peripheral sensory information from multichannel recordings of the spinal cord. These methods may be useful, for example, in a closed-loop FES for restoration of hand grasp.     

基于高效用神经网络的文本分类方法

现有的基于深度学习的文本分类方法没有考虑文本特征的重要性和特征之间的关联关系,影响了分类的准确率.针对此问题,本文提出一种基于高效用神经网络（High Utility Neural Networks,HUNN）的文本分类模型,可以有效地表示文本特征的重要性及其关联关系.利用高效用项集挖掘（Mining High Utility Itemsets,MHUI）算法获取数据集中各个特征的重要性以及共现频率.其中,共现频率在一定程度上反映了特征之间的关联关系.将MHUI作为HUNN的挖掘层,用于挖掘每个类别数据中重要性和关联性强的文本特征.然后将这些特征作为神经网络的输入,再经过卷积层进一步提炼类别表达能力更强的高层次文本特征,从而提高模型分类的准确率.通过在6个公开的基准数据集上进行实验分析,提出的算法优于卷积神经网络（Convolutional Neural Networks,CNN）,循环神经网络（Recurrent Neural Networks,RNN）,循环卷积神经网络（Recurrent Convolutional Neural Networks,RCNN）,快速文本分类（Fast Text Classifier,FAST）,分层注意力网络（Hierarchical Attention Networks,HAN）等5个基准算法.     

Multiple channel detection of steady-state visual evoked potentials for brain-computer interfaces

In this paper, novel methods for detecting steady-state visual evoked potentials using multiple electroencephalogram (EEG) signals are presented. The methods are tailored for brain-computer interfacing, where fast and accurate detection is of vital importance for achieving high information transfer rates. High detection accuracy using short time segments is obtained by finding combinations of electrode signals that cancel strong interference signals in the EEG data. Data from a test group consisting of 10 subjects are used to evaluate the new methods and to compare them to standard techniques. Using 1-s signal segments, six different visual stimulation frequencies could be discriminated with an average classification accuracy of 84%. An additional advantage of the presented methodology is that it is fully online, i.e., no calibration data for noise estimation, feature extraction, or electrode selection is needed.     

Maximally Informative Feature and Sensor Selection in Pattern Recognition Using Local and Global Independent Component Analysis

In pattern recognition, a suitable criterion for feature selection is the mutual information (MI) between feature vectors and class labels. Estimating MI in high dimensional feature spaces is problematic in terms of computation load and accuracy. We propose an independent component analysis based MI estimation (ICA-MI) methodology for feature selection. This simplifies the high dimensional MI estimation problem into multiple one-dimensional MI estimation problems. Nonlinear ICA transformation is achieved using piecewise local linear approximation on partitions in the feature space, which allows the exploitation of the additivity property of entropy and the simplicity of linear ICA algorithms. Number of partitions controls the tradeoff between more accurate approximation of the nonlinear data topology and small-sample statistical variations in estimation. We test the ICA-MI feature selection framework on synthetic, UCI repository, and EEG activity classification problems. Experiments demonstrate, as expected, that the selection of the number of partitions for local linear ICA is highly problem dependent and must be carried out properly through cross validation. When this is done properly, the proposed ICA-MI feature selection framework yields feature ranking results that are comparable to the optimal probability of error based feature ranking and selection strategy at a much lower computational load.     

小波滤波与AR模型在脑电信号处理的应用

针对脑-计算机接口技术中的脑电信号处理、事件相关同步和事件相关去同步的特点,提出了一种基于离散小波滤波和AR模型来提取脑电信号特征向量的方法。利用Daubechies类小波函数对脑电信号进行4层分解,然后使用Burg算法提取脑电信号8阶AR模型系数,最后用BP神经网络进行分类和比较。得到最优的正确率为71.64%,小波滤波的效果要优于FIR滤波器。     

一种基于WRCE-CSP的运动想象电位识别方法

针对脑一机接口系统中运动想象脑电信号（Electroencephalography,EEG）的模式识别问题,提出了加权节律成分提取（WeightedRhythmicComponentExtraction,WRCE）与共空间模式（CommonSpacePattern,CSP）相结合的特征提取方法,并使用Fisher线性判别分析进行分类。采用2003年的BCI竞赛数据Datasetm对该方法进行评估,测试数据的分类正确率达到86．13％,比使用传统CSP方法进行特征提取时的分类正确率提高了5．71％,表明该方法可有效地应用于运动想象EEG的模式识别中。     

Mining statistically significant associations for exploratory analysis of human sleep data.

We introduce a specialized association rule mining technique that can extract patterns from complex sleep data comprising polysomnographic recordings, clinical summaries, and sleep questionnaire responses. The rules mined can describe associations among temporally annotated events and questionnaire or summary data; e.g., the likelihood that an occurrence of a rapid eye movement (REM) sleep stage during the second 100 sleep epochs of the night is associated with moderate caffeine intake. We use chi2 analysis to ensure statistical significance of the mined rules at the level P < 0.05. Our results, obtained by mining sleep-related data from 242 human subjects, reveal clinically interesting associations among the polysomnographic and summary variables. Our experience suggests that association mining may also be useful for selection of variables prior to using logistic regression.     

用核学习算法的意识任务特征提取与分类

介绍了核学习算法中核主分量分析(KPCA)和支持向量机(SVM)的基本原理,给出一种推广误差上界估计判据,实现了SVM核参数及惩罚因子的优化选取.根据多变量自回归模型理论对4个受试对象、三种不同意识任务的脑电信号进行特征提取,并利用KPCA方法进行降维预处理,对SVM进行训练和分类测试.结果表明,KPCA算法在高维特征空间具有较强的特征选择能力,优化核参数的SVM的分类正确率明显高于径向基函数网络,三种意识任务的平均分类正确率达78.6％.     

Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms

In this paper, we propose a method for the analysis and classification of electroencephalogram (EEG) signals using EEG rhythms. The EEG rhythms capture the nonlinear complex dynamic behavior of the brain system and the nonstationary nature of the EEG signals. This method analyzes common frequency components in multichannel EEG recordings, using the filter bank signal processing. The mean frequency (MF) and RMS bandwidth of the signal are estimated by applying Fourier-transform-based filter bank processing on the EEG rhythms, which we refer intrinsic band functions, inherently present in the EEG signals. The MF and RMS bandwidth estimates, for the different classes (e.g., ictal and seizure-free, open eyes and closed eyes, inter-ictal and ictal, healthy volunteers and epileptic patients, inter-ictal epileptogenic and opposite to epileptogenic zone) of EEG recordings, are statistically different and hence used to distinguish and classify the two classes of signals using a least-squares support vector machine classifier. Experimental results, with 100 % classification accuracy, on a real-world EEG signals database analysis illustrate the effectiveness of the proposed method for EEG signal classification.     

Voice Activity Detection Algorithm Using Spectral-Correlation and Wavelet-Packet Transformation

It is developed the voice activity detection algorithm using noise classification technique. It is proposed the spectral-correlation and wavelet-packet (WP) features of frames for voice activity estimation. There are tested three WP trees for effective representing of audio segments: mel-scaled wavelet packet tree, bark-scaled wavelet packet tree and ERB-scaled (equivalent rectangular bandwidth) wavelet packet tree. Application only two principal components of WP features allows to classify accurately the environment noise. The using wavelet-packet tree design which follows the concept of equivalent rectangular bandwidth for acoustic feature extraction allows to increase the voice/silence segments classification accuracy by at least 4% in compare to other classification based voice activity detection algorithms for different noise.     

A multistage system to detect epileptiform activity in the EEG

A PC-based system has been developed to automatically detect epileptiform activity in 16-channel bipolar EEGs. The system consists of 3 stages: data collection, feature extraction, and event detection. The feature extractor employs a mimetic approach to detect candidate epileptiform transients on individual channels, while an expert system is used to detect focal and nonfocal multichannel epileptiform events. Considerable use of spatial and temporal contextual information present in the EEG aids both in the detection of epileptiform events and in the rejection of artifacts and background activity as events. Classification of events as definite or probable overcomes, to some extent, the problem of maintaining high detection rates while eliminating false detections. So far, the system has only been evaluated on development data but, although this does not provide a true measure of performance, the results are nevertheless impressive. Data from 11 patients, totaling 180 minutes of 16-channel bipolar EEGs, have been analyzed. A total of 45-71% (average 58%) of epileptiform events reported by the human expert in any EEG were detected as definite with no false detections (i.e., 100% selectivity) and 60-100% (average 80%) as either definite or probable but at the expense of up to 9 false detections per hour. Importantly, the highest detection rates were achieved on EEGs containing little epileptiform activity and no false detections were made on normal EEGs     

A comparison of algorithms for detection of spikes in the electroencephalogram

Identification of the short transient waveform, called a spike, in the cortical electroencephalogram (EEG) plays an important role during diagnosis of neurological disorders such as epilepsy. It has been suggested that artificial neural networks (ANN) can be employed for spike detection in the EEG, if suitable features are provided as input to an ANN. In this paper, we explore the performance of neural network-based classifiers using features selected by algorithms suggested by four previous investigators. Of these, three algorithms model the spike by mathematical parameters and use them as features for classification while the fourth algorithm uses raw EEG to train the classifier. The objective of this paper is to examine if there is any inherent advantage to any particular set of features, subject to the condition that the same data are used for all feature selection algorithms. Our results suggest that artificial neural networks trained with features selected using any one of the above three algorithms as well as raw EEG directly fed to the ANN will yield similar results.     

Prediction of Employee Turn Over Using Random Forest Classifier with Intensive Optimized Pca Algorithm

Employee turnover is the important issue in the recent day organizations. In this paper, a data mining based employee turnover predictor is developed in which ORACLE ERP dataset was used for sample training to predict the employee turnover with much higher accuracy. This paper deploys impactful algorithms and methodologies for the accurate prediction employee turnover taking place in any organization. First of all preprocessing is done as a precautionary step as always before proceeding with the core part of the proposed work. New Intensive Optimized PCA-Principal Component Analysis is used for feature selection and RFC-Random Forest Classifier is used for the classification purposes to classify accordingly to make the prediction more feasible. For classifying and predicting accurately, a methodology called Random Forest Classifier (RFC) classifier is deployed. The main objective of this work is to utilize Random Forest Classification methodology to break down fundamental purposes lying behind the worker turnover by making use of the information mining technique refer as Intensive Optimized PCA for feature selection. Comparative study taking the proposed novel work with the existing is made for showing the efficiency of this work. The performance of this proposed method was found to perform better with improved yields of ROC, accuracy, precision, recall, and F1 score when compared to other existing methodologies.