An automatic analysis method for detecting and eliminating ECG artifacts in EEG

Jiang, Joe‐Air; Chao, Chia‐Ter; Chiu, Ming‐Jang; Lee, Ren-Guey; Tseng, Chwan-Lu; Lin, Robert

doi:10.1016/j.compbiomed.2007.03.007

Cited by 79 publications

(38 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Samar et al (1999) and Quian Quiroga et al (2001) have presented evidence that wavelets may improve the extraction and analysis of ERP waveforms. The applications of wavelets to ERPs are broad ranging, including joint time-frequency analysis of ERPs (Samar et al, 1992), artifact removal (Jiang et al, 2007) and event detection (Demiralp et al, 1999;Samar et al, 1995). Furthermore, features derived from wavelet coefficients (Merzagora et al, 2006;Trejo and Shensa, 1999) perform well in preprocessing (Kalayci et al, 1994) stages of classification problems using statistical learning algorithms (Abootalebi et al, 2006;Browne and Cutmore, 2002).…”

Section: Introductionmentioning

confidence: 99%

A wavelet based algorithm for the identification of oscillatory event-related potential components

Aniyan¹,

Philip²,

Samar

et al. 2014

Journal of Neuroscience Methods

View full text Add to dashboard Cite

Event Related Potentials (ERPs) are very feeble alterations in the ongoing Electroencephalogram (EEG) and their detection is a challenging problem. Based on the unique time-based parameters derived from wavelet coefficients and the asymmetry property of wavelets a novel algorithm to separate ERP components in single-trial EEG data is described. Though illustrated as a specific application to N170 ERP detection, the algorithm is a generalized approach that can be easily adapted to isolate different kinds of ERP components. The algorithm detected the N170 ERP component with a high level of accuracy. We demonstrate that the asymmetry method is more accurate than the matching wavelet algorithm and t-CWT method by 48.67 and 8.03 percent respectively. This paper provides an off-line demonstration of the algorithm and considers issues related to the extension of the algorithm to real-time applications.

show abstract

Section: Introductionmentioning

confidence: 99%

A wavelet based algorithm for the identification of oscillatory event-related potential components

Aniyan¹,

Philip²,

Samar

et al. 2014

Journal of Neuroscience Methods

View full text Add to dashboard Cite

show abstract

“…A potential solution to this problem was proposed by Jiang et al (2007) who used the wavelet transform in addition to adaptive thresholding to remove ECG artefacts from 30 EEG, without use of an ECG signal.…”

Section: Filtering and Regressionmentioning

confidence: 99%

Artefact Detection and Removal Algorithms for EEG Diagnostic Systems

O’Regan¹

2018

Preprint

View full text Add to dashboard Cite

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...

show abstract

“…sensor. Wavelet transform can be used to extract temporal features such as heart rate, standard deviation of heart rate and low frequency top high frequency ration (Section IV-A) from EEG without using ECG as the reference signal [14].…”

Section: Feasibility Of Model-based Attackmentioning

confidence: 99%

“…Research in ECG artifacts reduction from electroencephalogram (EEG) measurements show that when two individuals are in close proximity (less than four feet), the ECG of one person gets reflected or coupled with the EEG of the other person [12], [13]. However, exact re-construction of the ECG from other person's EEG may not be successfully achieved unless actual physiological data for ECG is available [14]. We use ECG artifact reduction algorithms proposed in recent literature and modify them to extract diagnostic parameters such as R-R intervals, and R peak locations.…”

Section: Introductionmentioning

confidence: 99%

Protect your BSN: No Handshakes, just Namaste!

Bagade

Banerjee

Milazzo

et al. 2013

2013 IEEE International Conference on Body Sensor Networks

View full text Add to dashboard Cite

Abstract-Privacy of physiological data collected by a network of embedded sensors on human body is an important issue to be considered. Physiological signal-based security is a light weight solution which eliminates the need for security key storage and complex exponentiation computation in sensors. An important concern is whether such security measures are vulnerable to attacks, where the attacker is in close proximity to a Body Sensor Network (BSN) and senses physiological signals through non-contact processes such as electromagnetic coupling. Recent studies show that when two individuals are in close proximity, the electrocardiogram (ECG) of one person gets coupled to the electroencephalogram (EEG) of the other, thus indicating a possibility of proximity-based security attacks. This paper proposes a model-driven approach to proximity-based attacks on security using physiological signals and evaluates its feasibility. Results show that a proximity-based attack can be successful even without the exact reconstruction of the physiological data sensed by the attacked BSN. Our results show that with a 30 second handshake we can break PSKA with an average probability of 0.3 (0.24 minimum and 0.5 maximum).

show abstract

An automatic analysis method for detecting and eliminating ECG artifacts in EEG

Cited by 79 publications

References 15 publications

A wavelet based algorithm for the identification of oscillatory event-related potential components

A wavelet based algorithm for the identification of oscillatory event-related potential components

Artefact Detection and Removal Algorithms for EEG Diagnostic Systems

Protect your BSN: No Handshakes, just Namaste!

Contact Info

Product

Resources

About