International Journal of Image, Graphics and Signal Processing(IJIGSP)
ISSN: 2074-9074 (Print), ISSN: 2074-9082 (Online)
Published By: MECS Press
IJIGSP Vol.9, No.2, Feb. 2017
Analysis of Abdominal ECG Signal for Fetal Heart Rate Estimation Using Adaptive Filtering Technique
Full Text (PDF, 785KB), PP.19-26
This paper presents a method for fetal heart rate estimation from an abdominal electrocardiogram (ECG) signal based on adaptive filter analysis using least mean square (LMS) adaptive filtering algorithm in order to determine the health status of a baby in its mother's womb. The fetal ECG signal is extracted from abdominal ECG containing other sources of interference using the maternal ECG signal obtained from mother's chest cavity as the reference signal. Interference/noise model used for this work include the power-line noise, the white noise and the unwanted propagating maternal ECG signal. Thereafter, the heart rate is estimated using an automated peak voltage measurement algorithm at 75 percent threshold voltage. It is found that irrespective of the estimated heart rate of the baby, 100 percent estimation is achieved at signal-to-noise ratio (SNR) greater than or equal to -31dB.
Cite This Paper
Ashraf Adamu Ahmad, Aminu Inuwa Kuta, Abdulmumini Zubairu Loko,"Analysis of Abdominal ECG Signal for Fetal Heart Rate Estimation Using Adaptive Filtering Technique", International Journal of Image, Graphics and Signal Processing(IJIGSP), Vol.9, No.2, pp.19-26, 2017.DOI: 10.5815/ijigsp.2017.02.03
Rajesh, P., Umamaheswari, K. and Kumar, V.N., 2014. A Novel Approach of Fetal ECG Extraction Using Adaptive Filtering." International Journal of Information Science and Intelligent System, 3(2), pp.55-70.
Nasiri, M., Faez, K., Nasrabadi, A.M., 2012. Fetal Electrocardiogram Signal Extraction by ANFIS Trained with PSO Method. International Journal of Electrical and Computer Engineering, 2(2), p.247.
Ghodsi, M., Hassani, H. and Sanei, S., 2010. Extracting fetal heart signal from noisy maternal ECG by singular spectrum analysis. Journal of Statistics and its Interface, Special Issue on the Application of SSA, 3(3), pp.399-411.
Lweesy, K., Fraiwan, L., Maier, C. and Dickhaus, H., 2009. Extraction of fetal heart rate and fetal heart rate variability from mother's ECG signal. World Academy of Science Engineering and Technology, 3, pp.599-604.
Inan, O.T., Giovangrandi, L. and Kovacs, G.T., 2006. Robust neural-network-based classification of premature ventricular contractions using wavelet transform and timing interval features. Biomedical Engineering, IEEE Transactions on, 53(12), pp.2507-2515.
Nilanjan Dey, Sayantan Mukhopadhyay,Achintya Das,Sheli Sinha Chaudhuri, "Analysis of P-QRS-T Components Modified by Blind Watermarking Technique Within the Electrocardiogram Signal for Authentication in Wireless Telecardiology Using DWT", International Journal of Image, Graphics and Signal Processing (IJIGSP), Vol.4, No.7, pp.33-46, 2012.
Mehta, S.S. and Lingayat, N.S., 2007, May. Support Vector Machine for Cardiac Beat Detection in Single Lead Electrocardiogram. In IMECS (pp. 1630-1635).
Akhbari, M., Niknazar, M., Jutten, C., Shamsollahi, M.B. and Rivet, B., 2013, September. Fetal electrocardiogram R-peak detection using robust tensor decomposition and extended kalman filtering. In Computing in Cardiology Conference (CinC), 2013 (pp. 189-192). IEEE.
Xu-Wilson, M., Carlson, E., Cheng, L. and Vairavan, S., 2013, September. Spatial filtering and adaptive rule based fetal heart rate extraction from abdominal fetal ECG recordings. In Computing in Cardiology Conference (CinC), 2013 (pp. 197-200). IEEE.
Petrolis, R. and Krisciukaitis, A., 2013, September. Multi stage principal component analysis based method for detection of fetal heart beats in abdominal ECGs. In Computing in Cardiology Conference (CinC), 2013 (pp. 301-304). IEEE.
Shinwari, M.F., Ahmed, N., Humayun, H., ulHaq, I., Haider, S. and ulAnam, A., 2012. Classification algorithm for feature extraction using Linear Discriminant Analysis and Cross-Correlation on ECG signals. International Journal of Advanced Science and Technology, 48, pp.149-162.
Zeng, X., Zhou, Y., Tao, J., Yang, J. and Li, S., 2015. M-estimation Methods for Fetal Heart Rate Estimation in Impulse Noises. International Journal of Signal Processing, Image Processing and Pattern Recognition, 8(2), pp.175-184.
Prasanth, K., Paul, B. and Balakrishnan, A.A., 2013. Fetal ECG Extraction Using Adaptive Filters. International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, 2(4), pp.1483-1487.
Sameni, R. and Clifford, G.D., 2010. A review of fetal ECG signal processing; issues and promising directions. The open pacing, electrophysiology & therapy journal, 3, p.4.
KrishnaSamalla, G.Mallikarjuna Rao, Ch.Stayanarayana, "Survey of Sparse Adaptive Filters for Acoustic Echo Cancellation", International Journal of Image, Graphics and Signal Processing (IJIGSP), Vol.5, No.1, pp.16-24, 2013.DOI: 10.5815/ijigsp.2013.01.03.
Swathi, N., Dutt, V.I. and Rao, G.S., 2016. An Adaptive Filter Approach for GPS Multipath Error Estimation and Mitigation. In Microelectronics, Electromagnetics and Telecommunications (pp. 539-546). Springer India.
Lopes, W.B., Al-Nuaimi, A. and Lopes, C.G., 2016. Geometric-Algebra LMS Adaptive Filter and its Application to Rotation Estimation. Signal Processing Letters, IEEE, 23(6), pp.858-862
Raj, J. and Sahu, A.K., 2014. Fetal electrocardiogram extraction and analysis (B.Tech dissertation). National Institute of Technology, Rourkela, India.
Narayana, K.V.L. and Rao, A.B., 2011. Noise removal using adaptive noise canceling, analysis of ECG using Matlab. International Journal of Engineering Science and Technology, 3(4).
Antoniou, A., 2006. Digital signal processing. Toronto, Canada:: McGraw-Hill.
Widrow, B., Glover Jr, J.R., McCool, J.M., Kaunitz, J., Williams, C.S., Hearn, R.H., Zeidler, J.R., Dong Jr, E. and Goodlin, R.C., 1975. Adaptive noise cancelling: Principles and applications. Proceedings of the IEEE,63(12), pp.1692-1716.
Haykin, S.S., 2008. Adaptive filter theory. Pearson Education India.
Widrow, B. and Stearns, S.D., 1985. Adaptive signal processing. Englewood Cliffs, NJ, Prentice-Hall, Inc., 1985, 491 p., 1.
Diniz, P.S.R., 2008. Adaptive Filtering: Algorithms and Practical Implementation. Springer. New York, NY, USA.
Adam, A.A., Adegboye, B.A. and Ademoh, I.A., 2016. Inter-Pulse Analysis of Airborne Radar Signals using Smoothed Instantaneous Energy. International Journal of Signal Processing Systems (IJSPS). 4(2), pp.139-143.
Ahmad, A.A., Daniyan, A. and Gabriel, D.O., 2015. Selection of Window for Inter-Pulse Analysis of Simple Pulsed Radar Signal using the Short Time Fourier Transform. International Journal of Engineering & Technology, 4(4), pp.531-537.
Ahmad, A.A. and Sha'ameri, A.Z., 2015. Classification of airborne radar signals based on pulse feature estimation using time-frequency analysis. Science & technology research institute for defence, 8(2), p.103.
Ahmad, A.A., Ayeni, J.B. and Kamal, S.M., 2015, September. Determination of the pulse repetition interval (PRI) agility of an incoming radar emitter signal using instantaneous power analysis. In AFRICON, 2015 (pp. 1-4). IEEE.
Sharma, S. and Narwaria, R.P., 2014. Performance Evaluation of Various Window Techniques for Noise Cancellation from ECG Signal. International Journal of Computer Applications, 93(19).
Kholdi, E., Bigdeli, N. and Afshar, K., 2011. A New GA-Based Adaptive filter for fetal ECG extraction. World Academy of Science, Engineering and Technology, 54.
Üstündağ, M., Gökbulut, M., Şengür, A. and Ata, F., 2012. Denoising of weak ECG signals by using wavelet analysis and fuzzy thresholding.Network Modeling Analysis in Health Informatics and Bioinformatics, 1(4), pp.135-140.