Purpose: Medical practitioners specially cardiologist utilize their expertise to assess the diagnosis of heart’s electrical activity in human body. Hence, in order to accurately represent these vital signals, a physiological monitoring technique known as vector-cardiography (VCG) is used. This work focuses on denoising the VCG signal during the acquisition process as well as compressing the signal for subsequent storage. Here, the modeled noise is powerline noise. Methods: The proposed method uses variational mode decomposition (VMD) & notch filter, and facilitates the utilization of empirical mode decomposition (EMD), tunable Q-factor wavelet transform (TQWT) techniques for compression. Here, compression is achieved by using different mode functions of the denoised signals by employing TQWT, quantization and run-length encoding. Results: To evaluate the performance, various metrics such as mean-square-error, peak-signal-to-noise-ratio, percent-root-difference, compression-ratio, and signal-to-noise-ratio have been employed for both denoising and compression purposes. The denoising performance at SNRINPUT of 10dB, shows an MSE of 1.11*10-4, a PSNR of 40.85, SNROUT of 26.61, a SNRIMP of 16.61, at a PRD of 5.12 respectively. The compression performance of the proposed method highlights a CR of 71.38 at a PRD of 4.44, a PSNR of 10.41, along with an SNR of 19.96, with a QS of 16.05, and obtaining a MSE of 2.00*10-4. Conclusion: The proposed method successfully removes the powerline noise in the signal. The observations and results obtained from these metrics reinforce the effectiveness of the proposed method as a denoising and compression technique to set a precedent for future work in the field of VCG signals.

The VCG denoising and compression technique proposed here adheres to the powerline noise suppression and amalgamation of different techniques for compression of the signal. The involvement of VMD and notch filter enhances the denoising performance followed by the mode-wise implementation of TQWT on EMD’s IMFs to produce coefficient matrix for the deployment of deadzone quantization and RLE for compressing the signal. The quantitative and qualitative analysis of the algorithm obtained from utilization of the performance matrix solidifies the proposition discussed in the work to consolidate the performance of the proposed method to suppress the noise and have the characteristics of a compressed signal. This method achieves an average MSE value in the order of 10-5, a good PSNR, SNROUT, and SNRIMP value, with a lower PRD, when inquired about the denoising performance. It yields a greater CR, SNR scores, associated with a lesser MSE and PRD score, with respect to compressing the signal. The superiority of the algorithm is verified and validated over the whole dataset to conclude that this algorithm sets a benchmark for further enhancement in the field of VCG signal processing.

[1] U.S National Cancer Institute, Body Functions & Life Process – SEER Training, 2019 (n.d.). [2] S.S. Barold, Willem Einthoven and the birth of clinical electrocardiography a hundred years ago, Card. Electrophysiol. Rev. 7 (2003) 99–104. [3] P. Gloor, Hans Berger on Electroencephalography, Am. J. EEG Technol. 9 (1969) 1–8. [4] G.E. Burch, The history of vectorcardiography, Med. Hist. 29 (1985) 103–131. [5] C.L. Levkov, Orthogonal electrocardiogram derived from the limb and chest electrodes of the conventional 12-lead system, Med. Biol. Eng. Comput. 25 (1987) 155–164. [6] J. Huang, C. Wang, W. Zhao, A. Grau, X. Xue, F. Zhang, LTDNet-EEG: A Lightweight Network of Portable/Wearable Devices for Real-Time EEG Signal Denoising, IEEE Trans. Consum. Electron. (2024). [7] M. Geng, X. Meng, J. Yu, L. Zhu, L. Jin, Z. Jiang, B. Qiu, H. Li, H. Kong, J. Yuan, K. Yang, H. Shan, H. Han, Z. Yang, Q. Ren, Y. Lu, Content-Noise Complementary Learning for Medical Image Denoising, IEEE Trans. Med. Imaging 41 (2022) 407–419. [8] Q. Sun, J. Li, C. Liang, R. Liu, J. Pang, Y. Chen, C. Wang, Multiscale Joint Recurrence Quantification Analysis Integrating ECG Spatiotemporal and Dynamic Information for Cardiopathy Detection, IEEE Trans. Instrum. Meas. 73 (2024) 1–13. [9] T.M. Chen, Y.H. Tsai, H.H. Tseng, K.C. Liu, J.Y. Chen, C.H. Huang, G.Y. Li, C.Y. Shen, Y. Tsao, SRECG: ECG Signal Super-Resolution Framework for Portable/Wearable Devices in Cardiac Arrhythmias Classification, IEEE Trans. Consum. Electron. 69 (2023) 250–260. [10] B. Jiang, Y. Lu, X. Chen, X. Lu, G. Lu, Graph Attention in Attention Network for Image Denoising, IEEE Trans. Syst. Man, Cybern. Syst. 53 (2023) 7077–7088. [11] M.E. Womble, J.S. Halliday, S.K. Mitter, M.C. Lancaster, J.H. Triebwasser, Data Compression for Storing and Transmitting ECG’s/VCG’s, Proc. IEEE 65 (1977) 702–706. [12] S. Banerjee, G.K. Singh, Quality Guaranteed ECG Signal Compression Using Tunable-Q Wavelet Transform and Möbius Transform-Based AFD, IEEE Trans. Instrum. Meas. 70 (2021) 1–11. [13] K. Hayashi, R. Kohno, N. Akamatsu, D.G. Benditt, H. Abe, Abnormal repolarization: A common electrocardiographic finding in patients with epilepsy, J. Cardiovasc. Electrophysiol. 30 (2019) 109–115. [14] G.D. Clifford, ECG Statistics, Noise, Artifacts, and Missing Data, (n.d.). [15] J.A. Van Alsté, T.S. Schilder, Removal of Base-Line Wander and Power-Line Interference from the ECG by an Efficient FIR Filter with a Reduced Number of Taps, IEEE Trans. Biomed. Eng. BME-32 (1985) 1052–1060. [16] L. Frølich, I. Dowding, Removal of muscular artifacts in EEG signals: a comparison of linear decomposition methods, Brain Informatics 5 (2018) 13–22. [17] S. Chatterjee, R.S. Thakur, R.N. Yadav, L. Gupta, D.K. Raghuvanshi, Review of noise removal techniques in ECG signals, IET Signal Process. 14 (2020) 569–590. [18] S. Jain, V. Bajaj, A. Kumar, Effective de-noising of ECG by optimised adaptive thresholding on noisy modes, IET Sci. Meas. Technol. 12 (2018) 640–644. [19] H.Y. Mir, O. Singh, Powerline interference reduction in ECG signals using variable notch filter designed via variational mode decomposition, Analog Integr. Circuits Signal Process. 118 (2024) 317–328. [20] B.H. Tracey, E.L. Miller, Nonlocal means denoising of ECG signals, IEEE Trans. Biomed. Eng. 59 (2012) 2383–2386. [21] R.N. Vargas, A.C.P. Veiga, Electrocardiogram signal denoising by a new noise variation estimate, Res. Biomed. Eng. 36 (2020) 13–20. [22] R. Vimal, A. Kumar, A. Tiwari, A new VCG signal compression technique based on discrete Karhunen-Loeve expansion and tunable quality wavelet transform, J. Electrocardiol. 89 (2025) 153894. [23] S.M.S. Jalaleddine, C.G. Hutchens, R.D. Strattan, W.A. Coberly, ECG Data Compression Techniques—A Unified Approach, IEEE Trans. Biomed. Eng. 37 (1990) 329–343. [24] A.A. Shinde, P. Kanjalkar, The comparison of different transform based methods for ECG data compression, in: 2011 – Int. Conf. Signal Process. Commun. Comput. Netw. Technol. ICSCCN-2011, 2011: pp. 332–335. [25] A.M. Kamal, T. Sakorikar, U.M. Pal, H.J. Pandya, Engineering Approaches for Breast Cancer Diagnosis: A Review, IEEE Rev. Biomed. Eng. 16 (2023) 687–705. [26] W.C. Mueller, Arrhythmia detection program for an ambulatory ECG monitor, Biomed. Sci. Instrum. 14 (1978) 81–85. [27] C.K. Jha, M.H. Kolekar, Empirical Mode Decomposition and Wavelet Transform Based ECG Data Compression Scheme, IRBM 42 (2021) 65–72. [28] K. Akazawa, T. Uchiyama, S. Tanaka, A. Sasamori, E. Harasawa, Adaptive data compression of ambulatory ECG using multi templates, in: Comput. Cardiol., Publ by IEEE, 1993: pp. 495–498. [29] S.J. Simske, D.R. Blakley, Using the vectorcardiogram to remove ECG noise, in: Proc. – Int. Conf. Image Process. ICIP, 2012: pp. 2301–2304. [30] Nutzung der EKG-Signaldatenbank CARDIODAT der PTB über das Internet, Biomed. Tech. / Biomed. Eng. 40 (n.d.) 317–318. [31] W. Xu, A. Li, B. Shi, J. Zhao, A Novel Design of Sparse FIR Multiple Notch Filters with Tunable Notch Frequencies, Math. Probl. Eng. 2018 (2018). [32] K. Dragomiretskiy, D. Zosso, Variational Mode Decomposition, IEEE Trans. Signal Process. 62 (2014) 531–544. [33] I.W. Selesnick, Wavelet transform with tunable Q-factor, IEEE Trans. Signal Process. 59 (2011) 3560–3575. [34] C.K. Jha, M.H. Kolekar, Tunable Q-wavelet based ECG data compression with validation using cardiac arrhythmia patterns, Biomed. Signal Process. Control 66 (2021) 102464. [35] S. Akhter, M.A. Haque, ECG comptression using run length encoding, in: 2010 18th Eur. Signal Process. Conf., 2010: pp. 1645–1649. [36] A. Tiwari, R. Vimal, A. Kumar, Compression and Reconstruction of VCG Signal Based on DCT and Nearest Neighbour Interpolation, in: 2024 IEEE 8th Int. Conf. Inf. Commun. Technol., 2024: pp. 1–6. [37] D. Mishra, A. Kumar, VCG signal compression based on singular value decomposition, Int. J. Electron. Lett. 13 (2025) 280–290. [38] S.A. Dyer, N. Ahmed, D.R. Hummels, Vectorcardiographic Data Compression via Walsh And Cosine Transforms, IEEE Trans. Electromagn. Compat. EMC-27 (1985) 24–34. [39] P. Augustyniak, The Use of Predictive Coding for Effective Compression of Vectocardiograms, IFMBE Vol.1 (2001) 364–367. [40] D. Mishra, A. Kumar, Vectorcardiogram signal compression: A hybrid approach using discrete wavelet transform and singular vector sparse reconstruction, J. Electrocardiol. 91 (2025) 154042.

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