A Comparison of Rule-Based and Machine Learning Methods for Classification of Spikes in EEG

Ganglberger, Wolfgang; Gritsch, G.; Hartmann, M.; Fürbass, Franz; Perko, Hannes; Skupch, A.M.; Kluge, Tilmann

doi:10.12720/jcm.12.10.589-595

Cited by 10 publications

(4 citation statements)

References 11 publications

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“…On a cluster level, the sensitivity of automated spike detection ranged from 57% in the most prevalent cluster to 23% in the fourth most prevalent cluster, with an average of 38%. For comparison, on a single spike level and in a different study cohort, the same spike detector as used here had a sensitivity of 44% [26], while other algorithms had similar sensitivities of 44-47% [10,32]. Experienced human EEG readers were found to have pairwise sensitivities of 40-52% for each other's spike markings [26].…”

Section: Spike Detectionmentioning

confidence: 96%

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Automated interictal source localisation based on high-density EEG

Vorderwülbecke

Baroumand

Spinelli

et al. 2021

Seizure

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To study the accuracy of automated interictal EEG source localisation based on high-density EEG, and to compare it to low-density EEG. Methods: Thirty patients operated for pharmacoresistant focal epilepsy were retrospectively examined. Twelve months after resective brain surgery, 18 were seizure-free or had 'auras' only, while 12 had persistence of disabling seizures. Presurgical 257-channel EEG lasting 3-20 h was down-sampled to 25, 40, and 204 channels for separate analyses. For each electrode setup, interictal spikes were detected, clustered, and averaged automatically before validation by an expert reviewer. An individual 6-layer finite difference head model and the standardised low-resolution electromagnetic tomography were used to localise the maximum source activity of the most prevalent spike. Sublobar concordance with the resected brain area was visually assessed and related to favourable vs. unfavourable postsurgical outcome. Results: Depending on the EEG setup, epileptic spikes were detected in 21-24 patients (70-80%). The median number of single spikes per average was 470 (range 17-15,066). Diagnostic sensitivity of EEG source localisation was 58-75%, specificity was 50-67%, and overall accuracy was 55-71%. There were no significant differences between low-and high-density EEG setups with 25 to 257 electrodes. Conclusion: Automated high-density EEG source localisation provides meaningful information in the majority of cases. With hundreds of single spikes averaged, diagnostic accuracy is similar in high-and low-density EEG. Therefore, low-density EEG may be sufficient for interictal EEG source localisation if high numbers of spikes are available.

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Section: Spike Detectionmentioning

confidence: 96%

“…5A, together with their 95%-CIs. Across all setups, the average sensitivity was 68%, the average specificity was 61%, and the average diagnostic 15 [6][7][8][9][10][11][12][13][14][15][16][17] 15 [12][13][14][15][16] accuracy was 65%. There were no statistically significant differences between the electrode setups (0.69 < p < 0.86).…”

Section: Source Localisationmentioning

confidence: 99%

Automated interictal source localisation based on high-density EEG

Vorderwülbecke

Baroumand

Spinelli

et al. 2021

Seizure

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“…Cronin et al developed an automated patient portal message classifiers with the rule-based approach using natural language processing (NLP) and the bag of words [12]. Ganglberger et al discuss different automatic spike detection methods in order to improve detection performance and establish a user-adjustable sensitivity parameter mainly by examining the functioning of a rule-based system, artificial neural networks (ANNs) and random forests [13]. Accordingly, the rule-based system needed a feature selection to classify text documents.…”

Section: Related Workmentioning

confidence: 99%

A Rule-Based Approach to Embedding Techniques for Text Document Classification

Aubaid

Mishra

2020

Applied Sciences

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With the growth of online information and sudden expansion in the number of electronic documents provided on websites and in electronic libraries, there is difficulty in categorizing text documents. Therefore, a rule-based approach is a solution to this problem; the purpose of this study is to classify documents by using a rule-based. This paper deals with the rule-based approach with the embedding technique for a document to vector (doc2vec) files. An experiment was performed on two data sets Reuters-21578 and the 20 Newsgroups to classify the top ten categories of these data sets by using a document to vector rule-based (D2vecRule). Finally, this method provided us a good classification result according to the F-measures and implementation time metrics. In conclusion, it was observed that our algorithm document to vector rule-based (D2vecRule) was good when compared with other algorithms such as JRip, One R, and ZeroR applied to the same Reuters-21578 dataset.

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“…In these methods, similarities between the waveform of interest and the IED template are measured and then is determined whether the waveform is IED or not. Some researchers have also established feature engineering methods according to the time domain, frequency domain, or nonlinear features of scalp EEG, and detection of IEDs via classifiers with one or more features, such as decision trees [15], artificial neural network [16], AdaBoost [17], and clustering [18]. Although the aforementioned automated IED detection methods have achieved some good results, several obstacles remain to their clinical application.…”

Section: Introductionmentioning

confidence: 99%

A deep learning framework with multi-perspective fusion for interictal epileptiform discharges detection in scalp electroencephalogram

et al. 2021

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Objective. Interictal epileptiform discharges (IEDs) are an important and widely accepted biomarker used in the diagnosis of epilepsy based on scalp electroencephalography (EEG). Because the visual detection of IEDs has various limitations, including high time consumption and high subjectivity, a faster, more robust, and automated IED detector is strongly in demand. Approach. Based on deep learning, we proposed an end-to-end framework with multi-scale morphologic features in the time domain and correlation in sensor space to recognize IEDs from raw scalp EEG. Main Results. Based on a balanced dataset of 30 patients with epilepsy, the results of the five-fold (leave-6-patients-out) cross-validation shows that our model achieved state-of-the-art detection performance (accuracy: 0.951, precision: 0.973, sensitivity: 0.938, specificity: 0.968, F1 score: 0.954, AUC: 0.973). Furthermore, our model maintained excellent IED detection rates in an independent test on three datasets. Significance. The proposed model could be used to assist neurologists in clinical EEG interpretation of patients with epilepsy. Additionally, this approach combines multi-level output and correlation among EEG sensors and provides new ideas for epileptic biomarker detection in scalp EEG.

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A Comparison of Rule-Based and Machine Learning Methods for Classification of Spikes in EEG

Cited by 10 publications

References 11 publications

Automated interictal source localisation based on high-density EEG

Automated interictal source localisation based on high-density EEG

A Rule-Based Approach to Embedding Techniques for Text Document Classification

A deep learning framework with multi-perspective fusion for interictal epileptiform discharges detection in scalp electroencephalogram

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