A Novel Wavelet Based Algorithm for Spike and Wave Detection in Absence Epilepsy

Xanthopoulos, Petros; Rebennack, Steffen; Liu, Chang-Chia; Zhang, Jicong; Holmes, Gregory L.; Uthman, Basim M.; Pardalos, Pãnos M.

doi:10.1109/bibe.2010.12

Cited by 27 publications

(42 citation statements)

References 12 publications

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“…The algorithm was trained using a dataset with 2500 putative events and labels (SWD or nonSWD) from 4 expert human scorers, using fivefold internal cross validation, and tested against the performance of 2 human scorers (S1 and S4) on multiple, unannotated (no computer information), and out-of-training data records. Although other groups have developed algorithms for the detection of SWDs, [16][17][18][19] to the best of our knowledge, no other group has designed an algorithm to mirror human confidence in scoring. Instead, it drew fuzzy, tightly interleaved boundaries similar to those of human scorers.…”

Section: Discussionmentioning

confidence: 99%

“…Although algorithms have been developed for detection of SWDs, [16][17][18][19] there has been little development of algorithms for the detection or quantification of SWDs in mice. Although algorithms have been developed for detection of SWDs, [16][17][18][19] there has been little development of algorithms for the detection or quantification of SWDs in mice.…”

Section: Introductionmentioning

confidence: 99%

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An automated, machine learning–based detection algorithm for spike‐wave discharges (SWDs) in a mouse model of absence epilepsy

2019

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Summary Objective Manual detection of spike‐wave discharges (SWDs) from electroencephalography (EEG) records is time intensive, costly, and subject to inconsistencies/biases. In addition, manual scoring often omits information on SWD confidence/intensity, which may be important for the investigation of mechanistic‐based research questions. Our objective is to develop an automated method for the detection of SWDs in a mouse model of absence epilepsy that is focused on the characteristics of human scoring of preselected events to establish a confidence‐based, continuous‐valued scoring. Methods We develop a support vector machine (SVM)–based algorithm for the automated detection of SWDs in the γ2R43Q mouse model of absence epilepsy. The algorithm first identifies putative SWD events using frequency‐ and amplitude‐based peak detection. Four humans scored a set of 2500 putative events identified by the algorithm. Then, using predictors calculated from the wavelet transform of each event and the labels from human scoring, we trained an SVM to classify (SWD/nonSWD) and assign confidence scores to each event identified from 60, 24‐hour EEG records. We provide a detailed assessment of intra‐ and interrater scoring that demonstrates advantages of automated scoring. Results The algorithm scored SWDs along a continuum that is highly correlated with human confidence and that allows us to more effectively characterize ambiguous events. We demonstrate that events along our scoring continuum are temporally and proportionately correlated with abrupt changes in spectral power bands relevant to normal behavioral states including sleep. Significance Although there are automated and semi‐automated methods for the detection of SWDs in humans and rats, we contribute to the need for continued development of SWD detection in mice. Our results demonstrate the value of viewing detection of SWDs as a continuous classification problem to better understand “ground truth” in SWD detection (ie, the most reliable features agreed upon by humans that also correlate with objective physiologic measures).

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An automated, machine learning–based detection algorithm for spike‐wave discharges (SWDs) in a mouse model of absence epilepsy

2019

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“…In this work Chang et al showed that consideration of multiple channels in group improve the accuracy of your system. Xanthopoulos et al [10] used sliding variance on Continuous Wavelet Transformed (CWT) epochs to detect the clinically important epileptic patterns up to 98.625% accuracy. 60 &5 "-?4 [11] work advocates the importance of feature reduction techniques like Principal Component Analysis (PCA).…”

Section: Existing Workmentioning

confidence: 99%

Computer Assisted Analysis System of Electroencephalogram for Diagnosing Epilepsy

Ahmad

Khan

Majeed

2014

2014 22nd International Conference on Pattern Recognition

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Automation of Electroencephalogram (EEG)analysis can significantly help the neurologist during the diagnosis of epilepsy. During last few years lot of work has been done in the field of computer assisted analysis to detect an epileptic activity in an EEG. Still there is a significant amount of need to make these computer assisted EEG analysis systems more convenient and informative for a neurologist. After briefly discussing some of the existing work we have suggested an approach which can make these systems more helpful, detailed and precise for the neurologist. In our proposed approach we have handled each epoch of each channel for each type of epileptic pattern exclusive to each other. In our approach feature extraction starts with an application of multilevel Discrete Wavelet Transform (DWT) on each 1 sec non-overlapping epochs. Then we apply Principal Component Analysis (PCA) to reduce the effect of redundant and noisy data. Afterwards we apply Support Vector Machine (SVM) to classify these epochs as Epileptic or not. In our system a user can mark any mistakes he encounters. The concept behind the inclusion of the retraining is that, if there is more than one example with same attributes but different labels, the classifier is going to get trained to the one with most population. These corrective marking will be saved as examples. On retraining the classifier will improve its classification, hence it will tries to adapt the user. In the end we have discussed the results we have acquired till now. Due to limitation in the available data we are only able to report the classification performance for generalised absence seizure. The reported accuracy is resulted on very versatile dataset of 21 patients from Punjab Institute of Mental Health (PIMH) and 21 patients from Children Hospital Boston (CHB) which have different number of channel and sampling frequency. This usage of the data proves the robustness of our algorithm.

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“…Let us call it as a function f (t). Let us also take into account a structuring element g (t) which together with f (t) be the subsets of Euclidean space E. Accordingly, the Minkowski addition and subtraction [6] for the function f (t) is given by the relation Addition:…”

Section: Morphological Filtering For Feature Extraction Of Eeg Signalsmentioning

confidence: 99%

“…Epilepsy is characterized by an uncontrolled excessive activity or potential discharge by either a part or all of the central nervous system [5]. The different types of epileptic seizures are characterized by different EEG waveform patterns [6]. With real-time monitoring to detect epileptic seizures gaining widespread recognition, the advent of computers has made it possible to effectively apply a host of methods to quantify the changes occurring based on the EEG signals [4].…”

Section: Introductionmentioning

confidence: 99%

Performance analysis of wavelet transforms and morphological operator-based classification of epilepsy risk levels

Rajaguru¹,

Vijayakumar²

2014

EURASIP J. Adv. Signal Process.

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The objective of this paper is to compare the performance of singular value decomposition (SVD), expectation maximization (EM), and modified expectation maximization (MEM) as the postclassifiers for classifications of the epilepsy risk levels obtained from extracted features through wavelet transforms and morphological filters from electroencephalogram (EEG) signals. The code converter acts as a level one classifier. The seven features such as energy, variance, positive and negative peaks, spike and sharp waves, events, average duration, and covariance are extracted from EEG signals. Out of which four parameters like positive and negative peaksand spike and sharp waves, events and average duration are extracted using Haar, dB2, dB4, and Sym 8 wavelet transforms with hard and soft thresholding methods. The above said four features are also extracted through morphological filters. Then, the performance of the code converter and classifiers are compared based on the parameters such as performance index (PI) and quality value (QV).The performance index and quality value of code converters are at low value of 33.26% and 12.74, respectively. The highest PI of 98.03% and QV of 23.82 are attained at dB2 wavelet with hard thresholding method for SVD classifier. All the postclassifiers are settled at PI value of more than 90% at QV of 20.

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A Novel Wavelet Based Algorithm for Spike and Wave Detection in Absence Epilepsy

Cited by 27 publications

References 12 publications

An automated, machine learning–based detection algorithm for spike‐wave discharges (SWDs) in a mouse model of absence epilepsy

An automated, machine learning–based detection algorithm for spike‐wave discharges (SWDs) in a mouse model of absence epilepsy

Computer Assisted Analysis System of Electroencephalogram for Diagnosing Epilepsy

Performance analysis of wavelet transforms and morphological operator-based classification of epilepsy risk levels

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