This research work proposes an improved algorithm to extract maternal and fetal heart rate from an ECG measured of the mother’s abdomen. Recently various research efforts have been devoted to this field. The most recent ones include filtering and threshold methods, wavelet methods, neural networks and others. Each of these methods has different effectiveness and weaknesses. In spite of the fact that their performance is quite apt, the main weakness is that these methods are threshold dependent. Recent developments resulted in threshold free detection of heart rate that involves fixed length RR moving interval, calculated on the basis of normal maximum and minimum heart rate. In the proposed algorithm, we update the length of RR moving interval every time a peak is detected, based on the average maternal heart rate. The effectiveness of this algorithm lies in the fact that it uses optimized RR interval length, which is more capable of detecting a peak towards the end of the ECG which was left undetected using fixed length RR interval algorithm. This algorithm is implemented using MATLAB. The results showed that our proposed approach performs better compared to the fixed length RR interval approach.

1. Khamene, A., and Negahdaripour, S. “A New Method for the Extraction of Fetal ECG from the Composite Abdominal Signal.” IEEE Trans. Biomed. Engineering 47 (2000): 507–516.
2. B. U. K'ohler, C. Henning and R. Orgelmeister, "The principles of software QRS detection”, IEEE Eng. Med. Bioi. Mag. vol. 21, pp. 42-57, Jan/Feb 2002.
3. Pan and W. I. Tompkins, "A Real-Time QRS Detection Algorithm”, IEEE Trans. Biomed. Eng. vol 32, pp. 230-236, 1985.
4. E.C. Karvounis, C. Papaloukas, D.I. Fotiadis and L.K. Michalis, “Fetal Heart Rate Extraction from Composite Maternal ECG Using Complex Continuous Wavelet Transform”, IEEE computer in cardiology. 31:737-740, 2004.
5. C.W. Li, C.X.Zheng, C.F.Tai, “Detection of ECG Characteristic Points Using Wavelet Transforms”, IEEE Transaction on Biomedical Eng.,42.No.1:22-28, January 1995.
6. J.S.Sahambi, S.N.Tandon, and R.K.P.Bhatt, “Using wavelet transforms for ECG characterization.” IEEE Engineering in Medicine and Biology, 1997, 77-83
7. Qing Chen, Jicheng Liu, Guoliang Li, “QRS wave group detection based on B-Spline wavelet and adaptive threshold”, International Conference on Computer, Mechatronics, Control and Electronic Engineering (CMCE), 2010
8. Faezipour, M. Tiwari, T.M. Saeed, A. Nourani, M. Tamil, L.S, "wavelet based denoising and beat detection of ECG signal," Life Science Systems and Applications Workshop. LiSSA 2009. IEEE/NIH, 100-103,2009
9. Khaled Daqrouq, Ibrahim N. Abu-Isbeiht and Abdel-Rahman AIQawasmi, "QRS Complex Detection Based on Symmlets Wavelet Function", IEEE 5th International Multi-Conference on Systems. Signals and Devices, 1-5, 2008
10. M.G. Strintzis, G. Stalidis, X. Magnisalis, and N. Maglaveras, "Use of neural networks for electrocardiogram (ECG) feature extraction, recognition and classification”, Neural Net. World, vol. 3, no. 6, pp. 477-484, 1992
11. Q. Xue, Y.H. Hu, and W.J. Tompkins, "Neural-network-based Adaptive filtering for QRS detection", IEEE Trans. Biomed Eng.voI.39, pp. 317-329, 1992
12. Ziimray Doh, Tamer Olmez, Erturn1 Yazgan, “ECG waveform classification using the neural network and wavelet transform”, The First Joint BMEW/EMBS Conference Serving Humanity, Advancing Technology Od 1316, ‘99. Atlanta, GA, USA, 1999
13. M. Sheikh M. Algunaidi, M.A. Mohd Ali, “Threshold-Free Detection of Maternal Heart Rate from Abdominal ECG”, IEEE International Conference on Signal and Image Processing Applications, 2009
14. M. M Sheikh Algunaidi, M. A. Mohd Ali and Md. Fokhrul Islam. “Evaluation of an improved algorithm for fetal QRS detection”, International Journal of the Physical Sciences Vol. 6(2), pp. 213-220, 18 January, 2011.

Maternal ECG fetal ECG heart rate RR interval