Automated Removal of EKG Artifact From EEG Data Using Independent Component Analysis and Continuous Wavelet Transformation

Hamaneh, Mehdi Bagheri; Chitravas, Numthip; Kaiboriboon, Kitti; Lhatoo, Samden D.; Loparo, Kenneth A.

doi:10.1109/tbme.2013.2295173

Cited by 94 publications

(53 citation statements)

References 36 publications

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“…LetM represent the whitened form of M (i,j,n) , where we drop the subscript notation temporarily for simplicity. Independent Component Analysis (ICA) then attempts to separate source signals from unwanted interference and noise [29]. The method assumes that a single measurement is a linear mixture of non-Gaussian sources and independent components are obtained by searching for a linear combination of the signal data which maximises this non-Gaussianity.…”

Section: A Skin Calibration Techniquementioning

confidence: 99%

“…, w T pm must be de-correlated after every iteration using a deflation scheme based on a Gram-Schmidtlike method, described in detail in [30]. The number of independent components p is determined by eliminating principal components whose corresponding eigenvalues are below a specified threshold [28], [29]. The resulting columns of S are unordered.…”

Section: A Skin Calibration Techniquementioning

confidence: 99%

See 1 more Smart Citation

Compound Radar Approach for Breast Imaging

Byrne

Sarafianou²,

Craddock³

2017

IEEE Trans. Biomed. Eng.

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Section: A Skin Calibration Techniquementioning

confidence: 99%

Section: A Skin Calibration Techniquementioning

confidence: 99%

Compound Radar Approach for Breast Imaging

Byrne

Sarafianou²,

Craddock³

2017

IEEE Trans. Biomed. Eng.

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“…Mateenuddin, Patil, and Paradesi Rao (2008) have experimented on ICA for eliminating an extensive variety of artefacts from the EEG records. Hamaneh, Chitravas, Kaiboriboon, Lhatoo, and Loparo (2014) employed ICA along with continuous wavelet transform to remove ECG artefact from EEG signals. However, the success of ICA depended on certain conditions such as the sources must be statistically independent, the sources should have non-Gaussian distribution, the number of mixtures should be the same as the number of independent sources and so on, as discussed by Djuwari, Kant Kumar, and Palaniswami (2005).…”

Section: Related Workmentioning

confidence: 99%

A novel approach for the elimination of artefacts from EEG signals employing an improved Artificial Immune System algorithm

Suja

Rajan²,

Sheniha

2015

Journal of Experimental & Theoretical Artificial Intelligen

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Electroencephalogram (EEG) is the recording of electrical activities of the brain. It is contaminated by other biological signals, such as cardiac signal (electrocardiogram), signals generated by eye movement/eye blinks (electrooculogram) and muscular artefact signal (electromyogram), called artefacts. Optimisation is an important tool for solving many real-world problems. In the proposed work, artefact removal, based on the adaptive neuro-fuzzy inference system (ANFIS) is employed, by optimising the parameters of ANFIS. Artificial Immune System (AIS) algorithm is used to optimise the parameters of ANFIS (ANFIS-AIS). Implementation results depict that ANFIS-AIS is effective in removing artefacts from EEG signal than ANFIS. Furthermore, in the proposed work, improved AIS (IAIS) is developed by including suitable selection processes in the AIS algorithm. The performance of the proposed method IAIS is compared with AIS and with genetic algorithm (GA). Measures such as signal-to-noise ratio, mean square error (MSE) value, correlation coefficient, power spectrum density plot and convergence time are used for analysing the performance of the proposed method. From the results, it is found that the IAIS algorithm converges faster than the AIS and performs better than the AIS and GA. Hence, IAIS tuned ANFIS (ANFIS-IAIS) is effective in removing artefacts from EEG signals.

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“…Moreover, template matching was applied in [8]. The morphological nature and periodicity of QRS waveforms of the ECG were exploited for instance in [9,10]. A combination of both approaches, primarily sorting out undesired BSS components and further selecting the (best) ECG component among the residual channels, each utilizing frequency characteristics, was proposed by our group earlier in [3].…”

Section: Introductionmentioning

confidence: 99%

Sparse Coding of Cardiac Signals for Automated Component Selection after Blind Source Separation

Wedekind

Kleyko

Osipov

et al. 2016

2016 Computing in Cardiology Conference (CinC)

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Wearable sensor technology like textile electrodes provides novel ambulatory health monitoring solutions but most often goes along with low signal quality. Blind Source Separation (BSS) is capable of extracting the Electrocardiogram (ECG) out of heavily distorted multichannel recordings. However, permutation indeterminacy has to be solved, i.e. the automated selection of the desired BSS output. To that end we propose to exploit the sparsity of the ECG modeled as a spike train of successive heartbeats. A binary code derived from a two-item dictionary {peak, no peak} and physiological a-priori information temporally represents every BSS output component. The (best) ECG component is automatically selected based on a modified Hamming distance comparing the components' code with the expected code behavior.Non-standard ECG recordings from ten healthy subjects performing common motions while wearing a sensor garment were subsequently processed in 10 s segments with spatio-temporal BSS. Our sparsity-based selection RCODE achieved 98.1% heart beat detection accuracy (ACC) by selecting a single component each after BSS. Traditional component selection based on higherorder statistics (e.g. skewness) achieved only 67.6% ACC. IntroductionAmbulatory vital sign recording supplements the standard clinical data acquisition by long-term measurements for early diagnosis of diseases or health and stress monitoring of people performing potentially dangerous tasks. Using measurement techniques like textile electrodes for wearable sensing, the recorded electrocardiogram (ECG) is of non-standard nature compared to its clinical counterpart. Moreover, the minimal-conductive measurement principle which allows for a flexible health monitoring is also strongly affected by movement artifacts [1].Blind Source Separation (BSS) is a signal processing technique capable of separating signal mixtures (e.g. mixtures of ECG and distortions) into its constituting components [2]. Spatio-temporal Independent Component Analysis (ICA) based on the FastICA algorithm [2] is one realization of BSS which has shown a superior performance on wearable data compared to the standard ICA [1]. Despite its ability to separate ECG from distortions, ICA is typically only solved up to a permutation (i.e. separated components are available but the output is mostly unordered). Accordingly, a desired output component (e.g. the one best representing the ECG) has to be automatically selected. This selection gains special interest while processing a large number of channels. This is typical for ambulatory multi-channel health recordings and particularly spatio-temporal ICA which adds extra channels during the processing [3].Two principles for handling permutation indeterminacy have been proposed in the context of ECG processing. The first principle identifies and discards the undesired components (i.e. artifacts) thus indirectly obtaining the ECG component. A combination of second-order and higherorder statistics was used for that purpose in [4] whereas auto-correlat...

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Automated Removal of EKG Artifact From EEG Data Using Independent Component Analysis and Continuous Wavelet Transformation

Cited by 94 publications

References 36 publications

Compound Radar Approach for Breast Imaging

Compound Radar Approach for Breast Imaging

A novel approach for the elimination of artefacts from EEG signals employing an improved Artificial Immune System algorithm

Sparse Coding of Cardiac Signals for Automated Component Selection after Blind Source Separation

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