EEG Signal Description with Spectral-Envelope-Based Speech Recognition Features for Detection of Neonatal Seizures

Temko, Andriy; Nadeu, Climent; Marnane, William P.; Boylan, Geraldine B.; Lightbody, Gordon

doi:10.1109/titb.2011.2159805

Cited by 47 publications

(36 citation statements)

References 24 publications

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“…Perhaps more realistically, there exists ample scope for the discovery of features in both the EEG and gyroscope signals that would provide incremental improvement in classification performance. Recently, Temko et al (2011a) verified the applicability of automated speech recognition (ASR) features in neonatal seizure detection.…”

Section: Eeg Head-movement Artefact Detectionmentioning

confidence: 97%

“…Feature selection was performed by Temko et al (2011a) and showed that this feature set provides separation between neonatal seizure activity and normal EEG, and also between adult epileptic seizure activity and normal EEG (Faul, 2007;Faul et al, 2009). In Chapter 3 this feature set, supplemented with additional artefact specific features, was demonstrated to effectively separate between normal EEG and artefacts generated by head-movements (O'Regan et al, 2010a(O'Regan et al, , 2013a …”

Section: Eeg Feature Generationmentioning

confidence: 99%

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Artefact Detection and Removal Algorithms for EEG Diagnostic Systems

O’Regan¹

2018

Preprint

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Type of publicationDoctoral thesis Rights AbstractThe electroencephalogram (EEG) is a medical technology that is used in the monitoring of the brain and in the diagnosis of many neurological illnesses. Although coarse in its precision, the EEG is a non-invasive tool that requires minimal set-up times, and is suitably unobtrusive and mobile to allow continuous monitoring of the patient, either in clinical or domestic environments. Consequently, the EEG is the current tool-of-choice with which to continuously monitor the brain where temporal resolution, ease-of-use and mobility are important.Traditionally, EEG data are examined by a trained clinician who identifies neurological events of interest. However, recent advances in signal processing and machine learning techniques have allowed the automated detection of neurological events for many medical applications. In doing so, the burden of work on the clinician has been significantly reduced, improving the response time to illness, and allowing the relevant medical treatment to be administered within minutes rather than hours.However, as typical EEG signals are of the order of microvolts (µV ), contamination by signals arising from sources other than the brain is frequent. These extra-cerebral sources, known as artefacts, can significantly distort the EEG signal, making its interpretation difficult, and can dramatically disimprove automatic neurological event detection classification performance.This thesis therefore, contributes to the further improvement of automated neurological event detection systems, by identifying some of the major obstacles in deploying these EEG systems in ambulatory and clinical environments so that the EEG technologies can emerge from the laboratory towards real-world settings, where they can have a real-impact on the lives of patients. In this context, the thesis tackles three major problems in EEG monitoring, namely: (i) the problem of head-movement artefacts in ambulatory EEG, (ii) the high numbers of false detections in state-of-the-art, automated, epileptiform activity detection systems and (iii) false detections in state-of-the-art, automated neonatal seizure detection systems. To accomplish this, the thesis employs a wide range of statistical, signal processing and machine learning techniques drawn from mathematics, engineering and computer science.The first body of work outlined in this thesis proposes a system to automatically detect head-movement artefacts in ambulatory EEG and utilises supervised machine learning i classifiers to do so. The resulting head-movement artefact detection system is the first of its kind and offers accurate detection of head-movement artefacts in ambulatory EEG.Subsequently, additional physiological signals, in the form of gyroscopes, are used to detect head-movements and in doing so, bring additional information to the head-movement artefact detection task. A framework for combining EEG and gyroscope signals is then developed, offering improved head-movement artefact detection.The artefact detecti...

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Section: Eeg Head-movement Artefact Detectionmentioning

confidence: 97%

Section: Eeg Feature Generationmentioning

confidence: 99%

Artefact Detection and Removal Algorithms for EEG Diagnostic Systems

O’Regan¹

2018

Preprint

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“…In [6], features which are usually employed in automatic speech recognition (ASR) are used for the detection of seizures in newborn EEG. In particular, spectral envelope-based features, composed of spectral powers and their spectral derivatives are compared to the established feature set which has been previously developed for EEG analysis.…”

Section: Related Workmentioning

confidence: 99%

Epileptic Seizure Classification of EEG Image Using SVM

Pazhanirajan¹

2014

IJIRSET

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ABSTRACT:In recent years humans suffer from various neurological disorders such as headache, dementia, traumatic brain injuries, strokes and epilepsy. Nearly 50 million people of the world population in all ages suffer from epilepsy. To diagnose epilepsy an automatic seizure detection system is an important tool. In this paper we present a new approach for classification of Electroencephalogram (EEG) signals into two categories namely epilepsy and non epilepsy. The features of the EEG images are extracted using Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT). The extracted features are used in the model generation. The pattern classification model of SVM observes the distribution of the EEG features of classes. The experiment on various EEG image illustrate that the results of SVM are significant and effective.

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“…EEG signals are important indicators for several pathological conditions [1,2]. Recently there has been an interest in telemonitoring of EEG signals over wireless body area networks (WBAN).…”

Section: Introductionmentioning

confidence: 99%

“…Here it is assumed that the sparsifying transform ( ) is either orthogonal or tight-frame. 1 Incorporating the transform into (1), we get:…”

Section: Introductionmentioning

confidence: 99%