Electrocardiogram signal analysis for R-peak detection and denoising with hybrid linearization and principal component analysis

In the areas of biomedical and healthcare, electrocardiogram (ECG) signal analysis is one of the major aspects of research. The accuracy in the detection of subtle characteristic features in ECG is of great significance. This paper deals with an algorithm based on hybrid linearization and principal component analysis for ECG signal denoising and R-peak detection. The ECG data have been taken from the MIT-BIH Arrhythmia Database for performance evaluation. The signal is denoised by applying the hybrid linearization method, which is an arrangement of the extended Kalman filter along with discrete wavelet transform, and then principal component analysis is employed to detect R waves and the QRS complex. The reported work has been implemented in the MATLAB environment for 25 different ECG records yielding 99.90% sensitivity, 99.97% positive predictivity, and a detection error rate of 0.120%. The achieved performance outperforms the recent research done in the area of interest.

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[1] Acharya UR. Advances in Cardiac Signal Processing. 1st ed. Berlin, Germany: Springer, 2007.
[2] Poungponsri S, Yu XH. An adaptive filtering approach for electrocardiogram (ECG) signal noise reduction using neural networks. Neurocomputing 2013; 117: 206-213.
[3] Ku CT, Hung KC, Wu TC, Wang HS. Wavelet based ECG data compression system with linear quality control scheme. IEEE T Bio-Med Eng 2010; 57: 1399-1409.
[4] Sufi F, Khalil I. Diagnosis of cardiovascular abnormalities from compressed ECG: a data mining based approach. IEEE T Inf Technol B 2011; 15: 33-39.
[5] Banerjee S, Gupta R, Mitra M. Delineation of ECG characteristic features using multiresolution wavelet analysis method. Measurement 2012; 45: 474-487.
[6] Javadi M, Arani SAAA, Sajedin A, Ebrahimpour R. Classification of ECG arrhythmia by a modular neural network based on mixture of experts and negatively correlated learning. Biomed Signal Proces 2013; 8: 289-296.
[7] Reddy DC. Biomedical Signal Processing: Principles and Techniques. 1st ed. New Delhi, India: Tata McGraw-Hill, 2005.
[8] Rodriguez R, Mexicano A, Bila J, Cervantes S, Ponce R. Feature extraction of electrocardiogram signals by applying adaptive threshold and principal component analysis. J Appl Res Technol 2015; 13: 261-269.
[9] Vullings R, Veries BD, Bergmans JWM. An adaptive Kalman filter for ECG signal enhancement. IEEE T Bio-Med Eng 2011; 58: 1094-1103.
[10] Smital L, Vitek M, Kozumplik J, Provaznik I. Adaptive wavelet Wiener filtering of ECG signals. IEEE T Bio-Med Eng 2013; 60: 437-445.
[11] Wu Y, Rangayyan RM, Zhou Y, Ng SC. Filtering electrocardiographic signals using an unbiased and normalized adaptive noise reduction system. Med Eng Phys 2009; 31: 17-26.
[12] Pan J, Tompkins WJ. A real time QRS complex detection algorithm. IEEE T Bio-Med Eng 1985; 32: 230-236.
[13] Slimane ZEH, Nait-Ali A. QRS complex detection using empirical mode decomposition. Digit Signal Process 2010; 20: 1221-1228.
[14] Lin HY, Liang SY, Ho YL, Lin YH, Ma HP. Discrete wavelet transform based noise removal and feature extraction for ECG signals. IRBM 2014; 35: 351-361.
[15] Madeiro JPV, Cortez PC, Oliveira FI, Siqueira RS. A new approach to QRS segmentation based on wavelet bases and adaptive threshold technique. Med Eng Phys 2007; 29: 26-37.
[16] Kohler BU, Hennig C, Orglmeister R. QRS detection using zero crossing counts. Progress in Biomedical Research 2003; 8: 138-145.
[17] Arzeno N, Deng ZD, Poon CS. Analysis of first derivative based QRS detection algorithms. IEEE T Bio-Med Eng 2008; 55: 478-484.
[18] Xing H, Huang M. A new approach for QRS complex detection algorithm based on empirical mode decomposition. In: 2nd International Conference on Bioinformatics and Biomedical Engineering; 1618 May 2008, Shanghai, China. New York, NY, USA: IEEE. pp. 693-696.
[19] Zhu J, He L, Gao Z. Feature extraction from a novel ECG model for arrhythmia diagnosis. Bio-Med Mater Eng 2014; 24: 2883-2891.
[20] Min YJ, Kim HK, Kang YR, Kim GS, Park J, Kim SW. Design of wavelet-based ECG detector for implantable cardiac pacemakers. IEEE T Biomed Circ S 2013; 7: 426-436.
[21] Martis RJ, Acharya UR, Min LC. ECG beat classification using, PCA, LDA, ICA and discrete wavelet transform. Biomed Signal Proces 2013; 8: 437-448.
[22] Goldberger AL, Amaral LAN, Glass L, Hausdroff JM, Ivanov PC, Mark RG, Mietus JE, Moody GB, Peng CK, Stanely EH. PhysioBank, PhysioToolkit and PhysioNet: components of a new research resource for complex physiologic signals. Circulation 2000; 101: E215-20.
[23] Martis RJ, Acharya UR, Lim CM, Suri JS. Characterization of ECG beats from cardiac arrhythmia using discrete cosine transform in PCA framework. Knowl-Based Syst 2013; 45: 76-82.
[24] Islam MK, Haque ANMM, Tangim G, Ahammad T, Khandokar MRH. Study and analysis of ECG signal using MATLAB & LABVIEW as effective tools. Int J Comput Electr Eng 2012; 4: 404-408.
[25] Sameni R, Shamsollahi MB, Jutten C, Clifford GD. A nonlinear Bayesian filtering framework for ECG denoising. IEEE T Bio-Med Eng 2007; 54: 2172-2185.
[26] Sayadi O, Shamsollahi MB. ECG denoising and compression using a modified extended Kalman filter structure. IEEE T Bio-Med Eng 2008; 55: 2240-2248.
[27] Roonizi EK, Sassi R. A signal decomposition model-based Bayesian framework for ECG components separation. IEEE T Signal Proces 2016; 64: 665-674.
[28] Quali MA, Chafaa K, Ghanai M, Lorente LM, Rojas DB. ECG denoising using extended Kalman filter. In: 2013 International Conference on Computer Applications Technology; 2022 January 2013; Sousse, Tunisia. New York, NY, USA: IEEE. pp. 1-6.
[29] Lakshmi PS, Raju VL. ECG denoising using hybrid linearization method. TELKOMNIKA Indonesian Journal of Electrical Engineering 2015; 15: 504-508.
[30] Akansu AN, Serdijn WA, Selesnick IW. Emerging applications of wavelets: a review. Physical Communication 2010; 3: 1-18.
[31] Daamounche A, Harmami L, Alajlan N, Melgani F. A wavelet optimization approach for ECG signal classification. Biomed Signal Proces 2012; 7: 342-349.
[32] Khorrami H, Moavenian M. A comparative study of DWT, CWT and DCT transformations in ECG arrhythmias classification. Expert Syst Appl 2010; 37: 5751-5757.
[33] Rai HM, Trivedi A, Shukla S. ECG signal processing for abnormalities detection using multiresolution wavelet transform and artificial neural network classifier. Measurement 2013; 46: 3238-3246.
[34] Kaur I, Rajni, Sikri G. Denoising of ECG signal with different wavelets. International Journal of Engineering Trends and Technology 2014; 9: 658-661.
[35] Martis RJ, Acharya UR, Mandana KM, Ray AK, Chakraborty C. Application of principal component analysis to ECG signals for automated diagnosis of cardiac health. Expert Syst Appl 2012; 39: 11792-11800.
[36] Castells F, Laguna P, Sornmo L, Bollnann A, Riog JM. Principal component analysis in ECG signal processing. EURASIP J Adv Sig Pr 2007; 2007: 074580.
[37] Chawla MPS. Detection of indeterminacies in corrected ECG signals using parameterized multidimensional independent component analysis. Comput Math Method M 2009; 10: 85-115.
[38] Kaur I, Rajni, Marwaha A. ECG signal analysis and arrhythmia detection using wavelet transform. Journal of the Institution Engineers (India) Series B 2016; 97: 499.