Seizure detection

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Various epileptic seizure detection techniques using biomedical signals: a review

Various epileptic seizure detection techniques using biomedical signals: a review

Runarsson and Sigurdsson [14] The idea behind this paper is: first, find the half-wave form of the EEG epoch at hand and then find the consecutive peaks and minima in that half-wave signal segment. The histograms are estimated for two variables: the amplitude difference (Δ, Y-axis) and time separation (τ, X-axis) between two con- secutive peak values as well as minima. Here we have two histograms one for minima and other for maxima. The features used for classification of an epoch as a seizure or non-seizure are these estimated values like Δ and τ from local minima and maxima. Actual features used are the frequencies of co- occurrences of τ and Δ and each feature is generated from 8 s long signal with 2 s overlap 12 h self-recorded data using 10 EEG channels with 256 sampling. Support vector machines (SVMs) with chunk- ing method are used as classifiers. An average sensitivity of about 90% is achieved. Proposed algorithm in [14] may not perform better than [13] on CHB-MIT because CHB- MIT has very long duration data and requires investiga- tion. On the other hand most of the values in the feature vector extracted by half-wave method will be zero and it gives the sparse representation of the data and hence less data to be processed as compared to the methods in [2, 13]. Problem with this method is that actual pro- cessing of the signal like extraction of the features starts only after finding the half-wave representation of the original signal. Once the half-wave is in hand this could be very fast algorithm in time domain applications with large amount of dataset and can be used as online seizure detection method. In [15] the researchers designed the seizure detector (hardware) and implemented the algo- rithm (software) in the designed processor. In [16] they have developed a improved network of seizure detection devices.
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VLSI Based Efficient Epeleptic Seizure Detection System

VLSI Based Efficient Epeleptic Seizure Detection System

However, training classical SVM is solving a quadratic programming (QP) problem which is computationally complex and energy-consuming, so integrating an efficient SVM training algorithm is very important. Evolving applications require processing of high quality data. One obvious way to accommodate this demand is to increase the bandwidth available to hardware. Of course, this "solution" is not without technological and economical difficulties. Another way is to reduce the volume of the data. There has been a tremendous amount of progress in the field of seizure detection using SVM as the base. In order to make further progress in this area we have proposed to use DTWT(dual tree wavelet transforms) to provide data translation and normalization.
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Combination of EEG Complexity and Spectral Analysis for Epilepsy Diagnosis and Seizure Detection

Combination of EEG Complexity and Spectral Analysis for Epilepsy Diagnosis and Seizure Detection

subjects (Set E). The data length of each nonoverlapping window for ApEn calculation was 512 points, and the other ApEn parameters were r = 0 and m = 1 [12]. Based on these data, ApEn was a good index for discriminating between normal (Set A) and ictal EEGs (Set E). However, the ApEn values of the interictal EEGs overlapped with those of the normal and the ictal EEGs. Figure 2(b) shows the results from the sample entropy analysis method [36] that was proposed to eliminate bias caused by self-matching. The values from sample entropy analysis corresponding to the interictal EEGs overlap with those of the normal EEGs, and ApEn analysis performs better than the sample entropy method for discriminating between normal and ictal EEGs. Thus, to improve the performance of epileptic seizure detection, it is required to combine additional, complementary features to ApEn analysis. Spectral features and the autoregressive model are compared for this purpose.
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Automatic Seizure Detection Based on Nonlinear Dynamical Analysis of EEG Signals and Mutual Information

Automatic Seizure Detection Based on Nonlinear Dynamical Analysis of EEG Signals and Mutual Information

Sudden and recurrent seizures can have significant ef- fect on the life of the epileptic patient. Obviously reli- able real-time detection of seizures could significantly improve the therapeutic potentials like “closed-loop” therapies. In closed-loop therapies, electrical stimula- tion, drug infusion, cooling, or biofeedback may be de- livered in response to seizure detection (Ramgopal et al., 2014). Patients with epilepsy are usually treated with Antiepileptic Drugs (AEDs) to control their seizures (Wlodarczyk, Palacios, George, & Finnell, 2012); accu- rate real-time detection of seizures is critical to reduce the side effects by on demand delivering of AEDs during the preictal phase with short-acting drugs.
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An Efficient Method for Epileptic Seizure Detection in Long Term EEG Recordings

An Efficient Method for Epileptic Seizure Detection in Long Term EEG Recordings

In [8], R. Yadav, et al., proposed a new algorithm for seizure detection using frequency-weighted energy. The method performance was evaluated using 100 hours of a single channel stereo encephalograms (SEEG) obtained from five different patients, resulting 96.6% and 0.21/h of sensitivity and false detection rate respectively. An overall improvement has been achieved in terms of sensitivity, specificity and FDR in [9], by using a dual-stage classifier applied for 300 hours of SEEG recordings obtained from fifteen patients. In [10], a patient-specific model for seizure detection method has been proposed using statistically optimal null filters. This method was relied on a priori known seizure (template patterns) for subsequent detection of similar seizures in EEG data for seven patients. In [11], a novel morphology-based classifier (with simple computations appropriate to real-time applications) to identify sharp waves in intracranial EEG was presented. The method detects various types of seizures (rhythmic, non-rhythmic, short and long-seizures) with a sensitivity of 100%, a false detection rate of 0.1/h and an average onset delay of 9.1 s. In terms of spike detection and sorting, a spike classification algorithm combining template matching and principal component analysis (PCA) was proposed in [12]. Using intracranial EEG from 5 patients, the method resulted in 82.1% of the detected spikes in non-overlapping and disjoint clus- ters. In the literature review, Oikonomou, et al., in [13] and Alexandros T. Tzallas, et al., in [14] summarized the most techniques used to detect spikes in EEG signal. The EEG signal is characterized by rapid dynamics that in- stantly change through distinctive stages before (preictal), during (ictal), and after (post ictal) a seizure [15]. This nonlinearity nature and fast transitions between non-seizure, pre-seizure, and seizure states, guide re- searchers to a promising solution by combining two or more techniques to capture the EEG features in multiple domains, where epileptic spikes could be recognized by its characteristics in wide feature spaces. Therefore, two techniques that analyze the EEG signal in temporal and spatial or temporal and frequency domains were com- bined in previous detection methods [16]. Our method presented in this work, is extensive so that; it recognizes three types of epileptic spikes in a broad framework including more than one domain. Our algorithm was de- signed to proceed sequentially in short windowing epochs of the tested data, to suspend its rapid dynamics and capture any morphological fluctuation through distinctive states before, during, and after seizures.
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Automatic Seizure Detection using Inter Quartile Range

Automatic Seizure Detection using Inter Quartile Range

A new combination of statistical features and linear classifier for seizure detection is presented in this paper, which shows 100% accuracy. The use of features those quantify the dispersion characteristics (Inter Quartile Range and variance) and rythmicity (entropy) help to achieve this. The inclusion of a median based measure of dispersion like inter quartile range, which is highly resilient to outliers, in the feature list is found to be a good discriminating feature between seizure and non seizure signals. As per the authors’ knowledge, the inter quartile range has not been previously used for epileptic seizure detection. Estimation of four statistical parameters in each frame significantly reduces the dimension of feature vector. This reduces the complexity of classification and thereby increases the speed. The use of linear classifier also accelerates the classification process.
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A Multi Channel Fusion Based Newborn Seizure Detection

A Multi Channel Fusion Based Newborn Seizure Detection

This paper is organized as follows. Section 2 addresses each step involved in development of both the pro- posed multi-channel fusion approaches. These steps include data acquisition, preprocessing of the EEG to re- move unwanted noise, extracting time, frequency and TF/TS domain features from the multi-channel EEG, se- lecting non-redundant and discriminative EEG features from the larger extracted set and finally fusing the se- lected EEG features at two different levels as mentioned above. In section 3, the performances of both proposed fusion approaches are evaluated and discussed. Performance comparison between the proposed newborn mul- ti-channel EEG seizure detection with two of the most widely cited newborn EEG seizure detection algorithms is also included in the section. Finally, a conclusion of our work is presented. Method
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FPGA Implementation of EEG Feature Extraction and Seizure Detection

FPGA Implementation of EEG Feature Extraction and Seizure Detection

Carlos Guerrero et al.,[4] describes a new approach in features extraction using time-frequency distributions (TFDs) for detecting epileptic seizures to identify abnormalities in electroencephalogram (EEG). Particularly, the method extracts features using the Smoothed Pseudo Wigner-Ville distribution combined with the McAulay-Quatieri sinusoidal model and identifies abnormal neural discharges. Rajeev Yadav et al .,[5] present a novel, simple, computationally efficient seizure detection system to enhance the sensitivity with a low FDR by proposing a dual-stage classifier. An overall improvement has been observed in terms of sensitivity, specificity and FDR. Naveen Verma,[6] presents a low-power SoC that performs EEG acquisition and feature extraction required for continuous detection of seizure onset in epilepsy patients. The SoC corresponds to one EEG channel, and, depending on the patient, up to 18 channels may be worn to detect seizures as part of a chronic treatment system. R. Yadav et al.,[7] 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.
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Sensitivity of Amplitude-Integrated Electroencephalography for Neonatal Seizure Detection

Sensitivity of Amplitude-Integrated Electroencephalography for Neonatal Seizure Detection

neonatologists. The seizures in those records were rela- tively long ( ⬎ 1 minute) or of unspecified “high ampli- tude.” The authors speculated that the poor sensitivity for neonatal seizure detection by aEEG was largely at- tributable to their interpreters’ lack of experience. How- ever, despite including both internationally recognized experts and less experienced clinicians in our study, we found similarly poor sensitivity. Only 19 of our 125 aEEG records were identified by all of the neonatologists as having seizures, and in only 1 of 125 did all of the interpreters recognize every seizure in the tracing. This implies that there are features inherent both to neonatal seizures and to the aEEG technique that limit the sensi- tivity of aEEG for neonatal seizure detection.
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Time-Frequency Analysis of Heart Rate Variability for Neonatal Seizure Detection

Time-Frequency Analysis of Heart Rate Variability for Neonatal Seizure Detection

There are a number of automatic techniques available for detecting epileptic seizures using solely electroencephalogram (EEG), which has been the primary diagnosis tool in newborns. The electrocardiogram (ECG) has been much neglected in automatic seizure detection. Changes in heart rate and ECG rhythm were previously linked to seizure in case of adult humans and animals. However, little is known about heart rate variability (HRV) changes in human neonate during seizure. In this paper, we assess the suitability of HRV as a tool for seizure detection in newborns. The features of HRV in the low-frequency band (LF: 0.03–0.07 Hz), mid-frequency band (MF: 0.07–0.15 Hz), and high-frequency band (HF: 0.15–0.6 Hz) have been obtained by means of the time- frequency distribution (TFD). Results of ongoing time-frequency (TF) research are presented. Based on our preliminary results, the first conditional moment of HRV which is the mean/central frequency in the LF band and the variance in the HF band can be used as a good feature to discriminate the newborn seizure from the nonseizure.
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Informed Under Sampling for Enhancing Patient Specific Epileptic Seizure Detection

Informed Under Sampling for Enhancing Patient Specific Epileptic Seizure Detection

Seizure detection algorithms are divided into multiple categories which includes the classification between epileptic and non-epileptic patients, seizure episode counting and onset seizure detection. In case of the latter, the algorithm focuses on detecting the seizure with the least possible delay unlike the seizure episode counting algorithms which are more focused on getting the number of seizure episodes encountered by the patient rather than its early detection. Seizure detection approaches described in other papers included the use of frequency based analysis [1] for feature extraction; using methods such as Fourier transformation, wavelet transformation or filter banks. Other approaches includes the use of nonlinear analysis [2] such as largest Lyapunov exponent, Kolmogorov entropy or approximate
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Electroencephalography (EEG) based automatic Seizure Detection and Prediction UsingDWT

Electroencephalography (EEG) based automatic Seizure Detection and Prediction UsingDWT

Till now recently, seizures be situated identified only visually by an expert neurologist. Nevertheless, this method constitutes a laborious task mainly in the case of long term EEG recordings. So, automatic computer aided algorithms have advanced in order to reduce and automate this methodology and many seizure detection methods are reported in the international literature [4,5]. Figure 1 displays a diagrammatic representation of a seizure detection systems

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Electrocardiogram based neonatal seizure detection

Electrocardiogram based neonatal seizure detection

A variety of time and frequency domain heartbeat timing fea- tures were tested and measures of their ability to discriminate between classes are supplied here (ROC area and Wilcoxon rank-sum test -value, Table II). As there were 32 RR PSD features used for classification of each epoch, there was po- tentially a large amount of redundant information contained in these features, considering the relatively small impact they have on the classification performance. The inclusion of the electro- cardiogram derived respiration (EDR) features did not impact the performance of the classifier. As a result these features were removed from further analysis. The EDR signal is highly sus- ceptible to artifact and noise, and so improved calculation of this quantity may yield improved classification performance. Possible future research directions include the use of other non- linear features to better describe the dynamical changes in the signal during seizure and the use of feature selection. Such ad- vances may allow the deployment of an ECG-based neonatal seizure detection system in a clinical environment.
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EEG seizure detection and prediction algorithms: a survey

EEG seizure detection and prediction algorithms: a survey

along n-dimensional spaces to generate n-dimensional envelopes, which are then averaged to get the local mean. The projection direction vectors are based on spherical and polar coordinates. To sample the n-sphere, quasi- Monte Carlo low-discrepancy sequences are exploited for sampling enhancement. This style of sampling is non- uniform but not random. Its objective is to get the most useful samples over a certain space. The other steps of the algorithm to estimate the MEMD are similar to the stand- ard EMD algorithm. The use of the MEMD enables the multi-channel EEG signal decomposition into narrow fre- quency bands that can be analyzed separately for better detectability of seizures. In addition, the mean frequency of the signal segments can be used as a discriminative fea- ture for seizure detection. This mean frequency can be calculated by applying the Hilbert transform on the result- ing IMFs. Rehman et al. investigated the use of the MEMD with the mean frequency as possible features for EEG seizure detection [70], but this proposal needs fur- ther studies.
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Multiresolution Analysis in EEG Signal Feature Engineering for Epileptic Seizure Detection

Multiresolution Analysis in EEG Signal Feature Engineering for Epileptic Seizure Detection

Despite the fact that the wavelet domain based feature engineering is an ideal method of feature extraction and selection in EEG signal processing, it is also an effective tool for preprocessing the EEG signals which will ease the feature selection process even without reducing the dimensionality of features at least in some of the classifiers. In many EEG classification problems, wavelet based statistical features are extracted without preprocessing the signal data. Discrete Wavelet Transform (DWT) based EEG signal preprocessing is a more effective through MRA analysis. This paper reveals the effectiveness of DWT by processing the EEG signals for identifying distinctive features for classification problem of epileptic seizure detection.
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A Survey On Approaches For Epileptic Seizure Detection And Prediction

A Survey On Approaches For Epileptic Seizure Detection And Prediction

Machine learning methods cannot accommodate multichannel EEG data effectively [2]. Seizure detection can be performed effectively using deep learning approaches. Convolutional neural network (CNN) can extract the robust features from EEG signal to provide more accurate classification results. Using CNN, manual feature extraction is not required, since automatic features extraction is performed. CHB MIT dataset has been used in the proposed approach by [1] for performing binary classification using the multichannel EEG signals into seizure and non seizure class by making use of CNN. The deep learning model proposed by them was inspired by the winning architecture in computer vision [12]. They have used a CNN model that extracts temporal, spatial and spectral features of an EEG signal. The system was tested for both cross patient and patient specific EEG data. Correlation maps were generated that can relate to the output in the form of images. Also, brain mapping images were produced that can be used by the clinicians for the diagnosis purpose. Overall accuracy achieved for cross patient and patient specific data was obtained as 98.05% and 99.65% respectively. It was observed that CNN needs huge training and is not easy to interpret. Moreover, due to the availability of small dataset, cropped training dataset was used to increase the size of dataset. It is very difficult to use multichannel EEG data in a machine learning approach. 3D CNN can be used effectively given a multichannel EEG signal input. In the methodology proposed by [2] they have claimed it as first approach to use 3D kernel CNN for the detection of epileptic seizures using EEG signals. Automatic features extraction was performed using CNN. For each channel, a 2D image was constructed and then using these 2D images, 3D images were generated. 3D kernel was responsible to characterize different stages of epilepsy namely: inter-ictal, pre-ictal, ictal. The conversion of EEG signal into 3D array was performed such that it keeps most of the information of EEG signal. Dropout and 10 fold cross validation strategy were used to get better accuracy. It was observed that the overall accuracy obtained using 3D CNN model was better than the accuracy obtained by 2D CNN model. Fig. 6 shows accuracy, false negative rate, false positive rate result obtained by [2]. It was observed that multichannel 3D CNN provide better accuracy results. Fig. 5 shows the accuracy, false negative rate and false positive rate for approach proposed by [2].
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Efficient feature selection using a hybrid algorithm for the task of epileptic seizure detection

Efficient feature selection using a hybrid algorithm for the task of epileptic seizure detection

In general, the use of mathematical models in the discipline of epilepsy seizure falls into two broad categories, namely seizure detection [2] and seizure prediction [3]. In seizure detection, an expert system which is able to differentiate between interictal (normal) and ictal (epileptic) EEG signals is highly desirable. Visual inspection of the long-term EEG recordings is a very tedious and time consuming process. Therefore, an automated classifier will not only save time, but also medical expenditure. On the other hand, seizure prediction can also be viewed as a binary classification problem, where a trained mathematical model will be used to differentiate between interictal and pre-ictal EEG signals. A therapeutic intervention, based on seizure warning algorithms, can be developed to predict the onset of impending seizures.
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Epileptic seizure detection from EEG signals using logistic model trees

Epileptic seizure detection from EEG signals using logistic model trees

(1) First, divide the whole EEG signal of a class (e.g. healthy, seizure-free, and seizure) into several seg- ments based on specific time interval and then select representative samples by using OAT from each and every segment of the entire signal data of that category. The reason of segmentation is to properly account for possible stationeries as signal processing methods require stationary of signals, while EEG signals are non-stationary, aperiodic, and the mag- nitudes of the signals are changed over time. The time period is determined viewing the signals periodic patterns in each class within a time casement. As can be seen in Fig. 1, in this study, the EEG signals of each class is divided into k non- overlapping segments denoted as Seg 1 , Seg 2 ,…,Seg k
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Evolutionary coherence on EEG signals for epileptic seizure detection

Evolutionary coherence on EEG signals for epileptic seizure detection

The analysis of epileptic seizures can be viewed from different ways. First of them is the domain in which the signals are looked into, which are time domain, frequency domain and also phase. Further inquisition involves techniques such as correlation and coherence which finds the relations between each channel. Secondly, the temporal structure of epileptic event is analyzed with reference to the onset of seizure. Ictal defines the duration where seizure occurs, while preictal is before the onset of seizure and postictal is after the onset of seizure. This is particularly important in the analysis since the characteristics of the signal (including amplitude, frequency and phase) changes dramatically in these different stages. Thirdly,
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A computer aided analysis scheme for detecting epileptic seizure from EEG data

A computer aided analysis scheme for detecting epileptic seizure from EEG data

Over the past few years, numerous epileptic seizure detection algorithms have developed from several countries throughout the world. More recently, Supriya et al. [3] introduced a methodology to detect epilepsy from EEG signals considering an edge weight in the visibility graph with the complex network. After transforming the EEG signals into the complex network, they extracted average weighted degree of complex network as a feature. They used Support Vector Machine (SVM) and linear discriminant analysis (LDA) classifier to evaluate the obtained feature set. Kabir et al. [4] reported an analysis system based on logistic model trees (LMT) for detecting epileptic seizures from EEG signals. Siuly et al. [5] developed principal component analysis aided optimum allocation scheme for extracting discriminating information from epileptic EEG signals. They used an optimum allocation (OA) scheme to select representative samples from a large number of EEG data and then used principal component analysis (PCA) to construct uncorrelated components and also to reduce the dimensionality of the sample set. ALÇİN et al. [6] proposed a time-frequency (T-F) image representation approach based on Grey Level Co- occurrence Matrix (GLCM) descriptors and Fisher Vector (FV) encoding for automatic classification of epileptic EEG signals. Zhu et al. [7] introduced a weighted horizontal visibility graph in the complex network to detect epileptic seizure from EEG. But they did not clearly mention on which criteria they used an edge weight function and how it helps to detect the sudden fluctuation in epileptic EEG signals. Pachori and Patidar [8] designed a method for the classification of ictal and seizure-free EEG signals based on the EMD and the second-order difference plot (SODP). The EMD method decomposed an EEG signal into a set of symmetric and band-limited signals (the IMFs). The SODP of the IMFs provided an elliptical structure. Li et al. [9] developed a methodology based on empirical model decomposition (EMD) and SVM for detection of epileptic seizure. Firstly they decomposed EEG signals into intrinsic mode functions (IMFs) using the EMD, and then the coefficients of the variation and the fluctuation index of the IMFs were extracted as features. Shen et al. [10] developed a method based on a cascade of wavelet-approximate entropy for feature extraction in the epileptic EEG signal classification and tested the obtained feature set by SVM, k-nearest neighbour
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