Separation of Fetal ECG from Composite Abdominal ECG

Transcription

1 Volume 116 No , ISSN: (printed version); ISSN: (on-line version) url: ijpam.eu Separation of Fetal ECG from Composite Abdominal ECG 1 M. Anisha, 2 S.S. Kumar and 3 M. Benisha 1 Department of Biomedical Engineering, Noorul Islam Centre for Higher Education, Kumaracoil, Kanyakumari District, Tamilnadu, India. 2 EIE, Noorul Islam Centre for Higher Education, Kumaracoil, India. 3 ECE, Jeppiaar institute of Technology, Sriperumbudur, Chennai, India. Abstract This paper proffers a methodology for fetal electrocardiogram extraction. Non-invasive abdominal electrocardiogram is a complex mixture, in which fetal electrocardiogram is hidden entirely. In order to extricate the clinically essential information from the FECG and diagnosis the presence of cardiac anomaly, it must be disintegrated with high signal to noise ratio from the complex abdominal electrocardiogram mixture. Hence FastICA is applied to separate the fetal electrocardiogram with the help of fixed point iterative algorithm and compared with other techniques. FastICA is experimentally evaluated on the data available in Fetal ECG Synthetic Database and obtained results are promising. Key Words:FastICA, abdominal electrocardiogram, fetal electrocardiogram, cardiac anomaly. 327

2 1. Introduction Congenital Heart Diseases,(CHDs) are common reason of infant death due to birth defects, approximately each year there are infant deaths because of unrecognized heart disease. 25% of children born with a CHD will need heart surgery or other interventions to survive. People with CHD face a life-long risk of health problems such as issues with growth and eating, developmental delays, difficulties with exercise, heart rhythm problems, heart failure, sudden cardiac arrest or stroke [1]. CHD is highly associated with birth defects related death [2]. Sudden Intrauterine Fetal death is a Chief clinical hitch. In 2009 there were 2.64 million stillbirths approximated in worldwide [3]. Hence regular observation during pregnancy help in the early diagnosis of congenital heart diseases may assist obstetrics to provide required treatment during pregnancy and paediatric cardiologists to prescribe or suggest appropriate treatment to plan and manage health of the fetus after birth. So there is a need of routine analysis of Fetal Heart Rate (FHR) and Fetal Electrocardiogram (FECG) which holds highly accurate essential particulars, to diagnosis the presence of cardiac defects very early during pregnancy also make instant decision if necessary. There are many FECG observation techniques applied to have precise fetal ECG, but unfavourably some invasive methods have certain boundaries. Though external observation that is AECG has lot of recompenses over internal observation, it has a snag that is intricate noise concoction. Maternal ECG is twenty times stronger than fetal ECG, also extremely and intimately overlaps FECG. Hence chief foremost overlapped maternal interference must be estranged to have precise FECG. Once the low frequency FECG is estranged, from which clinically crucial information can be extracted by scrutinizing its waveform morphology. So there is a need of a proficient performance to isolate the FECG. Once the low frequency FECG is estranged, from which clinically crucial information can be extracted by scrutinizing its waveform morphology which assist in the early cardiac anomaly diagnosis. There are loads of researches have been carried out on fetal ECG extraction and fetal heart rate detection including, frequency tracking method[4], kurtosis analysis[5], temporal structure analysis[6], matched filtering [7], combination of biorthogonal quadratic spline wavelet and modulus maxima theory [8], combination of Singular Value Decomposition (SVD) and neuro-fuzzy inference [9], independent component analysis [10, 11,12], fuzzy wavelet neural networks method and genetic algorithm[15], extended state kalman filter and hilbert huang transform [13], and smoothed pseudo wigner-ville distribution time-frequency analysis[14] etc. In this paper FastICA is proffered for Fetal ECG extraction. It is a four stage approach. At the initial stage the existing interferences are eliminated. Initially impulsive noises are eradicated with the aid of median filter. Baseline wander is eliminated using low-pass butter worth filter Hz Power-line Interferences are eliminated by obtaining power spectral density using Welch transform. If 328

3 there are any Hz power-line components, they are rejected by applying band reject notch filter. In second stage FastICA is applied to separate the maternal components. Independent components obtained from FastICA are upsampled using Fourier transform to find out the locations of maternal QRS complexes. Identified maternal components are enhanced by applying combfilter. Again the filtered signal is filtered using moving average derivative filter. The obtained derivative signal helped to determine the maternal ECG. In the third stage detected maternal components are eliminated using Singular value decomposition (SVD).Finally in the fourth stage again FastICA is performed to separate the fetal components. Arrangement of the manuscript is as follows: stages of the proffered performance are given in section II. Results and discussion of the proffered algorithms are depicted in section III and conclusion is specified in section IV. 2. Materials and Methods This section explicates the proffered maternal and fetal ECG detachment FastICA approach. Data Collection AECG signals taken from the following database are considered for this study. Fetal ECG Synthetic Database (FECGSYNDB) The FECGSYNDB is a huge simulated non-invasive fetal ECG (NI-FECG or AECG) signal repository which offers 1750 synthetic signals that facilitates reproducible study in the field. Each simulated artificial signal is composed of 5 minutes length, and realized at 1000 Hz, has 16-bit resolution [15]. Pre-Processing In pre-processing stage, the existing interferences are eliminated as illustrated below. Elimination of Impulsive Noises Presence of impulsive noises must be eliminated else it highly affects the further processing steps such as baseline wander elimination, mixed source detachment, and feature extraction. Though median filter is a best way of eliminating the impulsive noises, it generates non-linear phase distortion which highly affects the efficiency of ICA detachment by spoiling the linearity of mixed sources. Hence, here median filter was applied just to get a reference signal. A median filter with window size 60ms was applied on input signals. Each input signal was subtracted from the filtered signal to obtain absolute difference. From the maximum absolute difference threshold value was estimated at each interval wherever the absolute difference goes greater than the threshold, then they are suppressed by taking the average of previous and next position amplitude. Fig: 1 illustrates the output of this step. 329

4 Fig. 1: (a) Subtraction of Input Signal (red) with the Median Filtered Signal (Blue) (b) Threshold Computation From the Maximum Absolute Difference (c) Impulsive Artefact Eliminated Signal Elimination of Baseline Wander Baseline wander is low frequency interference occurs due to the patient movements or breathing at the time of observation. In order to eliminate this interference a baseline signal is estimated by applying low pass butter worth filter with 5 Hz cut off frequency, which was subtracted from the median filtered input signal to get a detrended signal. While tracking the baseline interference, there exist residual interferences due to the filter delay, which was eliminated by applying 0.26ms window sized median filter, only if the residual signal is greater than the threshold. Estimated baseline signal is shown in Fig:2 where the estimated noise is given in blue colour. Fig:3 shows the detrended signal. Fig. 2: Original ECG (Red) and the Estimated Baseline Signal (Blue) Fig. 3: Original ECG (red) and the Detrended ECG (Blue) Elimination of Power-Line Interferences 50 or 60 Hz Power-line Interferences occurs due to the electromagnetic force, which was eliminated by obtaining power spectral density using Welch 330

5 transform. Presence of power line components were identified by analysing the amplitudes of existing peaks. If there were Hz power-line components, then band reject notch filter was applied at that frequency and its next three harmonics to remove these interferences. Fig:4 depicts the original ECG Welch spectrum and Fig:5 shows the detrended Welch spectrum. Fig: 6 shows the power-line interference eliminated signal Fig. 4: The Original ECG Welch Spectrum Fig. 5: The Detrended Welch Spectrum Fig. 6: Power-Line Interference Eliminated Signal Application of FastICA on Pre-processed Signal After the successful elimination of existing artefacts, ICA was used to disintegrate the major dominant interference called maternal ECG from other signal components. There is a contradiction with our dataset, continuously moving waves of ECG generates time variant mixing matrix but best and accurate ICA application needs invariant mixing matrix, other ICA requirements such as presence of statistically independent sources, non- Gaussian sources were satisfied. For an efficient maternal ECG disintegration FastICA was applied. If it fails to produce better estimates, ICA was performed using the kurtosis. For all input signals computed mean is eliminated and normalized to unitary standard deviation after which subspace analysis often known as whitening, which was performed in order to estimate the maternal ECG subspace, so that maternal ECG could be projected out of the dataset. 331

6 Performed subspace analysis is defined in Equation (1). yy = 1/2 WW TT xx(1) Where xx is the normalized data vector, is the Eigen values diagonal matrix, WW is the Eigen vector matrix ofee[xx. xx TT ]. The performed FastICA algorithm can be described as follows: 1. Initialization of weight vector v 2. Iteration on each channel (a)updating weights EE[yy. gg(vv TT yy)] EE[gg (vv TT yy)]vv vv Where gg(yy) and gg (yy)are the first and second order derivatives of the contrast function. Hyperbolic cosine choice leads to EE[yy. tttttth(vv TT yy)] EE 1 tttttth 2 (vv TT yy) vv vv Here if Kurtosis is chosen then we get EE[yy. (vv TT yy) 3 ] 3vv vv (b) Deflationary Orthogonalization At this step the weight vector v is projected to the space that is orthogonal to the space spanned by the already found vectors[vv1, vv2,. vvvv 1]. Deflationary orthogonalization is achieved when we define the matrix VV = [vv1, vv2,. vvvv 1] as follows: vv VVVV TT vv vv (c) Normalization Normalization is done by vv vv vv (d) Iteration is stopped when vv becomes lesser than the prefixed tolerance. 3. New column matrix is generated with the estimated weight vector vv, that is VV = [VV, vv] 4. If the total number of components goes less than four then back to the initialization step (i).identified independent components are defined as in Equation (2) xx = VV TT yy (2) Fig:7 shows the separated maternal components after the application of FastICA. Fig. 7: Separated Maternal ECG. Detection of Maternal QRS Selected independent components were upsampled at 4 Hz by applying Fourier Transform time-frequency analysis method, in order to obtain an accurate time 332

7 location of the maternal QRS. Then by considering QRS derivative s priori information a best precise maternal ECG component was determined and enhanced by applying comb-filter with 16 ms delay, output of which is filtered using moving average derivative filter of 9ms. Priori knowledge of pseudoperiodicity also taken into account to differentiate maternal ECG from other existing interferences. Factually for every 2 seconds window there must be one maternal ECG, and extreme rarely it occurs in 0.2 seconds window. Similarly interferences are likely to occur in 8 seconds window and rarely in 2seconds window. These inferences along with the absolute values of the derivative signal were highly used in the best maternal ECG determination. The chosen maternal signal was again filtered by Butterworth filter in both forward and backward directions. Each maternal QRS was identified by following two approaches: one is based on adaptive threshold and the other is based on temporal distance taken from the detected previous maternal QRS. After the detection of maternal QRS, corresponding onset, offset points were detected based on the maximum and minimum values of the derivative signal. Fig. 8 shows the derivative signal. Fig. 9 shows the detected maternal components. Fig. 8: Obtained Derivative Signal Fig. 9: Detected Maternal QRS Complexes Elimination of Maternal ECG SVD was applied for optimal maternal ECG elimination by approximating each maternal PQRST waves. Here a window was considered whose length was highly depended on mean RR interval of the entire signal called trapezoidal window used to synchronise and weight the signal that surrounds the identified maternal QRS complexes. The chosen weighted maternal PQRST components formed a column matrix MM with ml mn dimension where ml represents the entire length of the weighted maternal PQRST components and mn represents the total number of computed maternal QRS complexes.svd was applied on matrix MM hence 333

8 Where the matrix UU of dimension ml mn is the unitary matrix of left singular values similarly WW(mn mn) is the unitary matrix of right singular values and the matrix DD(mn mn) is the singular values diagonal matrix. Though the matrix MM is (composed of maternal PQRST components) responsible for the covariance contribution, in which the first column eign vectors UU (unitary matrix of left singular values) takes a major role in the contribution of covariance that means they highly have only maternal components rather than others. Hence the matrix MM was rearranged by having the first left singular vectors. MM rr = UU rr DD rr WW rr TT MM rr is the rearranged matrix. is the diagonal matrix of first left singular eigen values of dimension (me me), (ml me)is the matrix of first left singular values, similarly (mn me) is the matrix of first right singular values. When we have given me=4, there was the good detection of maternal PQRST components. For few records instead of me=4, me=3 was given to have efficient estimation. Then the detected maternal components were unweighted and replaced with a baseline. Post-Processing At this stage the obtained output after the maternal ECG elimination is enhanced intended to raise the strength of the desired signal. FIR filter is applied which is composed of two cascaded high-pass and low-pass filters. Here high-pass filter allows the frequency components those are greater than 0.4Hz and the low-pass filter allows the frequency components those are less than 40Hz. This cascaded FIR filter gives the band pass output signal with the range Hz. The normal frequency range of the fetal ECG lies in between Hz hence these frequencies were chosen for enhancing the extricated FECG.Fig: 10 depicts the separated FECG for 5 seconds. Fig. 10: The Separated FECG for 5 Seconds 3. Results and Discussion The FastICA Algorithm for fetal ECG extraction is experimentally evaluated on the AECG signals obtained from the publicly available Fetal ECG Synthetic Database (FECGSYNDB).The extracted FECG may still have some maternal and noise components in addition to FECG signals. Hence the obtained output is presumed to be the aggregate of maternal, fetal and noise components, can be expressed as follows in Equation (3) (3) 334

9 Where represents maternal ECG, represents fetal ECG, η denotes noise are the coefficients have to be computed. To compute these coefficients, and η are considered as orthogonal or extremely decorrelated. Based on the orthogonality principle the estimator attains minimum mean square error when it is satisfied with which directs to (4) (5) (6) then Equation 4,5,6 are used to compute the coefficients. For an efficient evaluation the components of the desired fetal ECG must be plenty than the components of unnecessary maternal interferences and other artefacts. When the total power of the is higher than the total power of the then the undesired signal components are extremely less in the estimated fetal ECG. Just to quantize the presence of the desired fetal components in the obtained output, Signal to Noise Ratio (SNR) of the output is defined as in Equation (7) Where - constitutes the SNR of the estimated fetal ECG, PPaa ff -means the total power of the fetal ECG, - constitutes the total power of the noise. - means the total power of the maternal ECG SIR signal to interference ratio of the estimated FECG is defined as; Equation 6 and 7 is used to compute the SNR and SIR of the estimated FECG at various noise levels and amplitude levels. Signal to Error Ratio (SER) is computed using the following formula defined in Equation (9). Where L is the length of the fetal signal - is the real reference signal (8) (7) (9) 335

10 -is the estimated fetal signal using FastICA -means the estimated SNR error Actually SIR rates were swifted in the range of -5dB to -30dB.SNR rates also swifted in the range of 0 db to 30dB. FastICA is evaluated for all aforementioned SNR and SIR ranges. Signals extracted using FastICA is depicted in Fig:10. Each input signal is considered for 5 seconds and the corresponding sampling frequency was 1000Hz. The SER values computed for the proffered algorithm is given in Table I. The obtained SER values are compared with the other ICA algorithms namely Joint Approximation Diagonalization of Eigen-matrices (JADE), Weights-Adjusted Second Order Blind Identification (WASOBI). SER values of JADE and WASOBI is obtained from [16], and compared with the proffered FastICA approach. SER comparison is shown in table II. When analysing and comparing the obtained SER values with JADE and WASOBI algorithms, FastICA algorithm has given promising results. The reason is the mixed AECG signal is having statistically independent mixed sources and they are not having higher order Gaussian distribution, which is determined by its second-order statistics or lesser than that. Fig. 11: Graphical Representation of SER Values Obtained by FastICA Fig. 12: Graphical Representation of SER Values Obtained by JADE Algorithm Fig. 13: Graphical Representation of SER Values Obtained by WASOBI Algorithm 336

11 Table I: SER Values of FastICA Algorithm Algorithm FastICA SIR db SNR db Table II: Comparison of SER Values with Existing Algorithms Algorithm JADE WASOBI FastICA 4. Conclusion SIR db SNR db This approach proffered a FastICA based FECG extraction which efficiently done the separation of independently mixed fetal and maternal sources. The performance evaluation is done by computing SNR, SIR and SER of the estimated FECG signal. The efficiency of the proffered approach is compared with the already existing ICA algorithms namely JADE and WASOBI, which shows that the SER values of the proffered approach are higher than the other algorithms. Here only the FECG is extracted from the composite AECG mixture with good SNR and less SER. Hence the waveform morphology can be well analysed to know the clinical features which help in disease diagnosis. Factually for an efficient diagnosis purpose efficient FECG separation is necessary with less or zero signal-to-noise ratio which is accomplished. Acknowledgment We express our sincere thanks to UGC, New Delhi for the financial support under MANF fellowship scheme for the above research work. References [1]. Congenital Heart Defects in Children Fact Sheet, American Heart Association (2017). 337

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