Event-synchronous cancellation of the heart interference in biomedical signals

Strobach, P.; Abraham-Fuchs, K.; Härer, Wolfgang

doi:10.1109/10.284962

Cited by 70 publications

(45 citation statements)

References 14 publications

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“…The data were preprocessed as follows. First, the cardiac artifact was removed by the method of event synchronous subtraction (15). A representative heartbeat from the data was used as a template.…”

Section: Methodsmentioning

confidence: 99%

Differential brain activity states during the perception and nonperception of illusory motion as revealed by magnetoencephalography

Crowe¹,

Leuthold²,

Georgopoulos³

2010

Proc. Natl. Acad. Sci. U.S.A.

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We studied visual perception using an annular random-dot motion stimulus called the racetrack. We recorded neural activity using magnetoencephalography while subjects viewed variants of this stimulus that contained no inherent motion or various degrees of embedded motion. Subjects reported seeing rotary motion during viewing of all stimuli. We found that, in the absence of any motion signals, patterns of brain activity differed between states of motion perception and nonperception. Furthermore, when subjects perceived motion, activity states within the brain did not differ across stimuli of different amounts of embedded motion. In contrast, we found that during periods of nonperception brainactivity states varied with the amount of motion signal embedded in the stimulus. Taken together, these results suggest that during perception the brain may lock into a stable state in which lowerlevel signals are suppressed.racetrack illusion | visual motion perception

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Section: Methodsmentioning

confidence: 99%

Differential brain activity states during the perception and nonperception of illusory motion as revealed by magnetoencephalography

Crowe¹,

Leuthold²,

Georgopoulos³

2010

Proc. Natl. Acad. Sci. U.S.A.

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“…Removal of cardiac artefacts poses a different problem, in that the artefacts often appear morphologically different to the recorded ECG signal. To deal with this, Strobach et al (1994) suggested using the ECG reference signal as a trigger to an artificial ECG artefact reference model signal to remove ECG artefact from the EEG using linear regression. When an ECG spike was detected, an ECG artefact signal was generated from a reference model, scaled to the detected ECG signal and was then subtracted from the EEG signal .…”

Section: Filtering and Regressionmentioning

confidence: 99%

“…The sources corresponding to respiratory artefact must then be identified; to accomplish this, each source is compared with the simultaneously recorded polygraphy signal. However, this comparison is confounded by the fact that, like ECG artefacts, respiratory artefacts can be remarkably different in morphology to those of the signal in the polygraphy measurement (Strobach et al, 1994). To overcome this problem, De Vos et al (2011) propose an intermediary step whereby both EEG and polygraphy sources are transformed in order to enhance the similarity between them.…”

Section: Removing Respiratory Artefact Componentsmentioning

confidence: 99%

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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“…The first technique is proposed by Widrow et al [7] and extended by Strobach et al [8] and is based on adaptive noise cancellation using an artificial reference signal. This artificial reference signal is built from mECG templates that are generated by averaging several consecutive mECG complexes, synchronized on the QRScomplex.…”

Section: B Reference Techniquesmentioning

confidence: 99%

“…This artificial reference signal is built from mECG templates that are generated by averaging several consecutive mECG complexes, synchronized on the QRScomplex. This technique is referred to as event-synchronous adaptive interference cancelling (ESAIC) [8].…”

Section: B Reference Techniquesmentioning

confidence: 99%

Artifact reduction in maternal abdominal ECG recordings for fetal ECG estimation

Vullings

Peters

Mischi

et al. 2007

2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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Abstract-Monitoring the fetal electrocardiogram (fECG) is currently one of the most promising methods to assess fetal health. However, the main problem associated with this method is that the signals recorded from the maternal abdomen are affected by noise and interferences: the maternal electrocardiogram (mECG) being the dominant interference. In this paper a mECG removal technique is described, which is based on dynamic segmentation of the mECG and subsequent linear prediction of the mECG segments. Moreover, as the linear prediction is significantly affected by artifacts, a signal validation technique is presented to suppress the effect of artifacts on the mECG prediction. The performance of the presented technique is evaluated by comparison to the performance of three other mECG removal techniques: the presented technique outperforms the other techniques for all recordings.

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Event-synchronous cancellation of the heart interference in biomedical signals

Cited by 70 publications

References 14 publications

Differential brain activity states during the perception and nonperception of illusory motion as revealed by magnetoencephalography

Differential brain activity states during the perception and nonperception of illusory motion as revealed by magnetoencephalography

Artefact Detection and Removal Algorithms for EEG Diagnostic Systems

Artifact reduction in maternal abdominal ECG recordings for fetal ECG estimation

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