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JournalAGRAWAL of Scientific et & al: Industrial Research REDUCTION OF ARTIFACTS IN 12-CHANNEL ECG SIGNALS USING FASTICA ALGORITHM Vol. 67, January 2008, pp.43-48
Reduction of artifacts in 12-channel ECG signals using FastICA algorithm Gracee Agrawal1, Manju Singh2*, V R Singh2 and H R Singh2 1
Indian Institute of Technology, New Delhi 110 016 National Physical Laboratory, New Delhi 110 012
2
Received 26 July 2007; revised & accepted 17 October 2007 ECG signals are required to be reduced for accurate and easier diagnosis in clinical resting ECG, ambulatory ECG, exercise ECG and long-term ECG monitoring. In this paper, Fast ICA algorithm has been used for reducing artifacts and noise from 12channel ECG recordings. A Graphical User Interface is also made for the same purpose. Keywords: Artifacts reduction, Blind source separation, ECG, Fast ICA algorithm, Independent component analysis
Introduction 1-3
A number of attempts have been made to reduce artifacts and noise from ECG signals and various types of analog and digital filters, both linear and non-linear, have been implemented to reduce interferences from ECG signals. Several adaptive filtering techniques have also been implemented 4-5. Other methods include averaging6 and neural networks7. However, many of these methods are only partially successful and tend to suppress ECG components along with noise. Artifacts, which are generated independently as compared to ECG signals, can be separated effectively using Independent Component Analysis (ICA). It is superficially related to Principal Component Analysis (PCA) and Factor Analysis (FA). ICA can be used for reduction of artifacts and noise in many biomedical signals like EEG 8-9, EMG 10 etc. In fetal11, ECG is effectively extracted from multi-lead cutaneous potential recordings of a pregnant woman and the concept of application of ICA for ECG noise reduction has been introduced successfully12-13. This paper discusses fast ICA algorithm to carry out ICA of 12-channel ECG recordings and hence reduces artifacts and noise present in the recordings. Graphical User Interface (GUI) has also been developed for bio-signal artifacts reduction, which enables user to operate the application intuitively. All the implementations are done in Matlab on a Windows platform. *Author for correspondence E-mail:
[email protected]
Independent Component Analysis (ICA) ICA is a statistical technique for finding underlying components from multidimensional data14. The most well known application of ICA is blind source separation (BSS). Fast ICA Algorithm
Fast ICA algorithm 15 is an efficient and popular algorithm for ICA, which is based on a Newton-iteration based fixed-point optimization scheme for maximizing non-gaussianity of yi = w iT ~x . This algorithm estimates just one of the ICs. To estimate n ICs, this algorithm has to be run n times. To ensure that a different IC is estimated each time, one must de-correlate the outputs ( w iT ~x ) after every iteration. Based on the type of decorrelation method used, algorithm can be operated in the following two modes: Deflation Mode
In this mode, ICs are estimated one by one. Gram-Schmidtlike de-correlation is used to de-correlate the data. i −1
wi = wi −
∑ w iT w j w j j =1
wi = wi wi
... (1)
... (2) In this, previously estimated ICs are privileged in step 1. This means that only currently estimated weight vector is affected and the order of estimation matters. Thus, estimation error might accumulate into the last components in this mode.
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Fig. 1— Source signals
Fig. 2 — Mixed signals
Symmetric Mode
In certain applications, it may be desired that no vectors be privileged over others. This can be achieved in this mode by estimating first all ICs and then performing orthogonalization. W = (WW T ) −1 2 W
...(3)
Demonstration of ICA
Source signals consisting of 7 typical artificially
generated biological signals16 (Fig. 1) were mixed to generate mixed signals (Fig. 2). It is almost impossible to guess the shape of original signals by just looking at the mixtures. Estimated source signals using ICA (Fig. 3) show that neither the order nor the sign of sources has been preserved. Also, energy of ICs has changed. However, these problems don’t matter much in most of the applications.
AGRAWAL et al: REDUCTION OF ARTIFACTS IN 12-CHANNEL ECG SIGNALS USING FASTICA ALGORITHM
Fig. 3 — Estimated signals
Fig. 4 — GUI – ECG artifact removal
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Fig. 5 — ECG recordings
Fig. 6 — Independent components
Results and Discussion ECG Artifacts Removal
GUI for demonstrating artifact reduction from ECG signals using ICA (Fig. 4) obtained data from Physikalisch-Technische Bundesanstalt (PTB: National Metrology Institute, Germany) Diagnostic ECG Database 17 containing 549 records. PTB compiled digitized ECGs collected from healthy volunteers and patients for research and teaching purposes. Each record included 15 simultaneously measured signals; conventional 12 leads (I, II, III, aVR, aVL, aVF, V1,
V2, V3, V4, V5, V6) together with 3 Frank leads (Vx, Vy, Vz). Each signal was digitized at 1000 Hz with 16-bit resolution over a range of ±16.384 mV. A small 2-second portion of ECG data with 12 conventional leads (Fig. 5) shows that there are some artifacts resembling muscle tremor in leads I, III, aVR, aVL and aVF, and their residues in all the chest leads. It seems that artifact is due to some movement in left arm because it has affected all the leads except the lead II. Results of ICA decomposition show (Fig. 6) that IC 6 contains artifact due to muscle tremor while IC 9 contains
AGRAWAL et al: REDUCTION OF ARTIFACTS IN 12-CHANNEL ECG SIGNALS USING FASTICA ALGORITHM
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Fig. 7 — Corrected ECG signals
some kind of baseline wander artifact. Noise is separated to ICs 4, 8, 10, 11 and 12. Corrected ECG signals (Fig. 7) obtained after removing the artifacts shows no artifact in the signals now. This was tested for many ECG records of the database and proved to be very successful in ECG artifact removal. Conclusions and Further Work ICA has been found a suitable tool for separating artifacts blindly from biomedical signals. ICAs applied to real 12-channel ECG recordings, contaminated with various artifacts and noise, have shown promising artifact rejection. However, one has to rely on visual inspection of ICA components for further processing. Statistical properties of the independent components (like kurtosis and variance13) were explored to identify the artifacts and noise. Similar statistical parameters can be employed in present study to identify artifacts and noise automatically, instead of visual inspection, which is not acceptable in routine clinical ECG monitoring. Kurtosis (one of the method to identify continuous noise) can be estimated by using the fourth moment of sample data. For continuous noise, Kurtosis value is smaller compared with that of normal ECG. However, abrupt changes cannot be differentiated from normal ECG by using kurtosis. Variance difference between segments of any ICA component can be used to identify abrupt changes. It is comparatively larger for the
component containing abrupt changes (artifacts) and this can be used to identify that component. Acknowledgement Authors thank Director, NPL for encouragement and permission to publish the paper. References 1
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