Automated artifact removal as preprocessing refines neonatal seizure detection

Vos, Maarten De; Deburchgraeve, W.; Cherian, Perumpillichira J.; Matić, Vladimir; Swarte, Renate; Govaert, Paul; Visser, Gerhard H.; Huffel, Sabine Van

doi:10.1016/j.clinph.2011.04.026

Cited by 79 publications

(45 citation statements)

References 25 publications

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“…In the respiratory artefact removal method outlined by De Vos et al (2011), the EEG is first decomposed into its prominent underlying sources using an appropriate blind source separation method. It is then necessary to automatically identify which (if any) of these sources correspond to respiratory artefact.…”

Section: De Vos Method: Correlating Sobi With Respiratory Tracementioning

confidence: 99%

“…In this multimodal technique, the independent components were compared to filtered polygraphy signals and removed if the correlation exceeded a pre-defined threshold. These ICA-based multimodal correlation methods proved effective in the system of De Vos et al (2011); however, the reliance on the presence of a respiration trace is problematic, as they are not available in many NICUs.…”

Section: Eeg Artefacts In Neonatal Seizure Detectionmentioning

confidence: 99%

“…Similarly, Devuyst et al (2008) used a modified version of ICA to implement an automatic ensemble average subtraction of the ECG signal from independent components to remove ECG artefacts. De Vos et al (2011) proposed a combination of filtering and correlation with the relevant polygraphy signal to remove cardiac and respiration artefacts from neonatal EEG.…”

Section: Blind Source Separationmentioning

confidence: 99%

“…Karayiannis et al (2006) andMitra et al (2009) used similar approaches by including a post-processing stage where seizure decisions were rejected if the corresponding section of EEG was highly correlated to any of the ECG, EMG or respiration signals. De Vos et al (2011) proposed an automated ICA method to remove ECG, pulse and respiration artefacts. In this multimodal technique, the independent components were compared to filtered polygraphy signals and removed if the correlation exceeded a pre-defined threshold.…”

Section: Eeg Artefacts In Neonatal Seizure Detectionmentioning

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: De Vos Method: Correlating Sobi With Respiratory Tracementioning

confidence: 99%

Section: Eeg Artefacts In Neonatal Seizure Detectionmentioning

confidence: 99%

Section: Blind Source Separationmentioning

confidence: 99%

Section: Eeg Artefacts In Neonatal Seizure Detectionmentioning

confidence: 99%

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Artefact Detection and Removal Algorithms for EEG Diagnostic Systems

O’Regan¹

2018

Preprint

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“…The evaluation of algorithms that use ICA for the automatic removal of artifacts in neonatal EEG before processing the EEG with the purpose of detecting seizures was discussed by De Vos et al [12]. Bartels et al [13] introduced an algorithm that removes artifacts from EEG, based on BSS and support vector machine (SVM).…”

Section: Introductionmentioning

confidence: 99%

Enhanced Automatic Wavelet Independent Component Analysis for Electroencephalographic Artifact Removal

Mammone

Morabito

2014

Entropy

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Electroencephalography (EEG) is a fundamental diagnostic instrument for many neurological disorders, and it is the main tool for the investigation of the cognitive or pathological activity of the brain through the bioelectromagnetic fields that it generates. The correct interpretation of the EEG is misleading, both for clinicians' visual evaluation and for automated procedures, because of artifacts. As a consequence, artifact rejection in EEG is a key preprocessing step, and the quest for reliable automatic processors has been quickly growing in the last few years. Recently, a promising automatic methodology, known as automatic wavelet-independent component analysis (AWICA), has been proposed. In this paper, a more efficient and sensitive version, called enhanced-AWICA (EAWICA), is proposed, and an extensive performance comparison is carried out by a step of tuning the different parameters that are involved in artifact detection. EAWICA is shown to minimize information loss and to outperform AWICA in artifact removal, both on simulated and real experimental EEG recordings.

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Relationship of EEG sources of neonatal seizures to acute perinatal brain lesions seen on MRI: A pilot study

et al. 2012

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Even though it is known that neonatal seizures are associated with acute brain lesions, the relationship of electroencephalographic (EEG) seizures to acute perinatal brain lesions visible on magnetic resonance imaging (MRI) has not been objectively studied. EEG source localization is successfully used for this purpose in adults, but it has not been sufficiently explored in neonates. Therefore, we developed an integrated method for ictal EEG dipole source localization based on a realistic head model to investigate the utility of EEG source imaging in neonates with postasphyxial seizures. We describe here our method and compare the dipole seizure localization results with acute perinatal lesions seen on brain MRI in 10 full-term infants with neonatal encephalopathy. Through experimental studies, we also explore the sensitivity of our method to the electrode positioning errors and the variations in neonatal skull geometry and conductivity. The localization results of 45 focal seizures from 10 neonates are compared with the visual analysis of EEG and MRI data, scored by expert physicians. In 9 of 10 neonates, dipole locations showed good relationship with MRI lesions and clinical data. Our experimental results also suggest that the variations in the used values for skull conductivity or thickness have little effect on the dipole localization, whereas inaccurate electrode positioning can reduce the accuracy of source estimates. The performance of our fused method indicates that ictal EEG source imaging is feasible in neonates and with further validation studies, this technique can become a useful diagnostic tool.

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Automated artifact removal as preprocessing refines neonatal seizure detection

Cited by 79 publications

References 25 publications

Artefact Detection and Removal Algorithms for EEG Diagnostic Systems

Artefact Detection and Removal Algorithms for EEG Diagnostic Systems

Enhanced Automatic Wavelet Independent Component Analysis for Electroencephalographic Artifact Removal

Relationship of EEG sources of neonatal seizures to acute perinatal brain lesions seen on MRI: A pilot study

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