A secure ECG signal transmission for heart disease diagnosis

Document Type : Research Paper


1 College of Computer Science & Information Technology, University of Anbar, Iraq

2 Department of Computer Science, College of Science, AL Mustansiriyah University, Iraq.

3 Faculty of Computer Science and Mathematics, University of Kufa, Iraq.


Due to the high sampling rate, the recorded Electrocardiograms (ECG) data are huge. For storing and transmitting ECG data, wide spaces and more bandwidth are therefore needed. The ECG data are also very important to preprocessing and compress so that it is distributed and processed with less bandwidth and less space effectively. This manuscript is aimed at creating an effective ECG compression method. The reported ECG data are processed first in the pre-processing unit (ProUnit) in this method. In this unit, ECG data have been standardized and segmented. The resulting ECG data would then be sent to the Compression Unit (CompUnit). The unit consists of an algorithm for lossy compression (LosyComp), with a lossless algorithm for compression (LossComp). The randomness ECG data is transformed into high randomness data by the failure compression algorithm. The data's high redundancy is then used with the LosyComp algorithm to reach a high compression ratio (CR) with no degradation. The LossComp algorithms recommended in this manuscript are the Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT). LossComp algorithms such as Arithmetic Encoding (Arithm) and Run Length Encoding (RLE) are also suggested. To evaluate the proposed method, we measure the Compression Time (CompTime), and Reconstruction Time (RecTime) (T), RMSE and CR. Simulation results suggest the highest output in compression ratio and in complexity by adding RLE after the DCT algorithm. The simulation findings indicate that the inclusion of RLE following the DCT algorithm increases performance in terms of CR and complexity. With CR = 55% with RMSE = 0:14 and above 94% with RMSE = 0:2, DCT as a LossComp algorithm was utilized initially, followed by RLE as a LossComp algorithm.


[1] A. S. Abdulbaqi, Recruitment internet of things for medical condition assessment: Electrocardiogram signal
surveillance, AUS Journal, Institute of Architecture and Urbanism, University of Austral de Chile, Special Issue
(2019) 434-440.
[2] A. S. Abdulbaqi and I. Y. Panessai, Designing and implementation of a biomedical module for vital signals
measurements based on embedded system, Int. J. Adv. Sci. Tech. 29(3) (2020) 3866–3877.
[3] A. S. Abdulbaqi, S. A. M. Najim and R. H. Mahdi, Robust multichannel EEG signals compression model based
on hybridization technique, Int. J. Engin. Tech. 7(4) (2018) 3402–3405.
[4] Z. Arbabi et al, Application of neural networks in evaluation of key factors of knowledge management system,
case study: Iranian companies based in Alborz province, J. Phys. Conf. Ser. 1530 (2020) 012111.
[5] M. K. Adimulam, M. B. Srinivas, A 1.0 V, 9.84 fJ/c-s FOM reconfigurable hybrid SAR-sigma delta ADC for
signal processing applications, Analog Integ. Circ. Sig. Proc. 99(2) (2019) 261-–276.
[6] N. Alajlan, Y. Bazi, F. Melgani, S. Malek, M. A. Bencherif, Detection of premature ventricular contraction
arrhythmias in electrocardiogram signals with kernel methods, Signal Image Video Proc. 8(5) (2014) 931—942.
[7] A. Burguera, Fast QRS detection and ECG compression based on signal structural analysis, IEEE J. Biomed.
Health Inf. 23(1) (2019) 123-–131.
[8] S. L. Chen, M. C. Tuan, H. Y. Lee and T. L. Lin, VLSI implementation of a cost-efficient micro control unit with
an asymmetric encryption for wireless body sensor networks, IEEE Access 5 (2017) 4077-–4086.
[9] C. C. Chiu, T. H. Lin, and B. Y. Liau, Using correlation coefficient in ECG waceform for arrhythmia detection,
Biomed. Engin. Appl. Basis Commun. 17 (2005) 147–152.
[10] C. J. Deepu, C. H. Heng and Y. Lian, A hybrid data compression scheme for power reduction in wireless sensors
for IoT, IEEE Trans. Biomed. Circuits Syst. 11(2) (2017) 245—254.
[11] A. Dhiman, A. Singh, S. Dubey and S. Jain, Design of lead II ECG waveform and classification Performance for
Morphological features using Different Classifiers on Lead II, Res. J. Pharm. Biological Chemical Sci. 7(4) (2016)
[12] A. Diker, D. Avci, E. Avci and M. Gedikpinar, A new technique for ECG signal classification genetic algorithm
Wavelet Kernel extreme learning machine, Optik 180 (2019) 46–55.
[13] J. Dogra, M. Sood, S. Jain and N. Prashar, Segmentation of magnetic resonance images of brain using thresholding
techniques, 4th IEEE Int. Conf. Signal Proc. Control, Jaypee University of Information technology, Waknaghat,
Solan, H. P, India, 311-315,- (2017) 21–23.
[14] S. Eftekharifar, T.Y. Rezaii, S. Beheshti and S. Daneshvar, Block sparse multi-lead ECG compression exploiting
between-lead collaboration, IET Sig. Proc. 13(1) (2019) 46–55.
[15] M. Elgendi, Less is more in biosignal analysis: compressed data could open the door to faster and better diagnosis,
Diseases 6(18) (2018) 1–3.
[16] M. Elgendi, A. Mohamed and R. Ward, Efficient ECG compression and QRS detection for e-Health applications,
Sci. Rep. 7(1) (2017) 1—16.
[17] R. Gupta, S. Singh, K. Garg and S. Jain, Indigenous design of electronic circuit for electrocardiograph, Int. J.
Innov. Res. Sci. Engin. Tech. 3(5) (2014) 12138–12145.
[18] A. E. Hassanien, M. Kilany and E. H. Houssein, Combining support vector machine and elephant herding optimization for cardiac arrhythmias, ArXiv:1806.08242v1[eee.SP], June 20, (2018).[19] Y. Hirai, T. Matsuoka, S. Tani, S. Isami and K. Tatsumi, A biomedical sensor system with stochastic A/D
conversion and error correction by machine learning, IEEE Access 7 (2019) 21990—22001.
[20] Y. Hou, J. Qu, Z. Tian, M. Atef, K. Yousef, Y. Lian and G. Wang, A 61-nW level-crossing ADC with adaptive
sampling for biomedical applications, IEEE Trans. Circuits Syst. II Express Briefs 66(1) (2019) 56-–60.
[21] H. Huang, S. Hu and Y. Sun, ECG signal compression for low-power sensor nodes using sparse frequency spectrum
features, IEEE Biomed. Circuits Syst. Conf. 2018.
[22] C. I. Ieong, M. Li, M. K. Law, P. I. Mak, M. I. Vai and R. P. Martins, A 0.45 V 147–375 nW ECG compression
processor with wavelet shrinkage and adaptive temporal decimation architectures, IEEE Trans. VLSI Syst. 25(4)
(2017) 1307-–1319.
[23] S. Jain, Classification of protein kinase B using discrete wavelet transform, Int. J. Inf. Tech. 10(2) (2018) 211–216.
[24] R. Javaid, R. Besar and F. S. Abas, Performance evaluation of percent root mean square difference for ECG
signals compression, Int. J. Signal Proc. 48 (2017) 1–9.
[25] C. K. Jha, and M. H. Kolekar, ECG data compression algorithm for telemonitoring of cardiac patients, Int. J.
Telemed. Clinical Pract. 2(1) (2017) 31-–41.
[26] S. Kalaivani and C. Tharini, Analysis and modification of rice golomb coding lossless com-pression algorithm for
wireless sensor networks, J. Theo. Appl. Inf. Tech. 96(12) (2018) 3802-–3814.
[27] S. Kalaivani, I. Shahnaz, S. R. Shirin and C. Tharini, Real-time ECG acquisition and detection of anomalies,
Artificial Intelligence and Evolutionary Computations in Engineering Systems, Springer, 2016.
[28] S. Kumar, B. Deka and S. Datta, Block-sparsity based compressed sensing for multichannel ecg reconstruction,
Lecture Notes in Computer Science, Springer, 2019.
[29] H. Mamaghanian, G. Ansaloni, D. Atienza and P. Vandergheynst, Power-efficient joint compressed sensing of
multi-lead ECG signals, IEEE Int. Conf. Acoustics, Speech and Signal Proc. (2014) 4409-–4412.
[30] S. Mitra and D. Das, A critical study on the applications of run-length encoding techniques in combined encoding
schemes, Int. J. Adv. Res. Comput. Sci. 8(5) (2017) 2556-–2561.
[31] B. Pandey and R. B. Mishra, An integrated intelligent computing method for the detection and interpretation of
ECG based cardiac diseases, Int. J. Knowledge Engin. SoftData Parad., 2 (2010) 182–203.
[32] L. F. Polania, R. E. Carrillo, M. Blanco-Velasco and K. E. Barner, Exploiting prior knowledge in compressed
sensing wireless ECG systems, IEEE J. Biomed. Health Inf. 19(2) (2015) 508-–519.
[33] N. Prashar, S. Jain, M. Sood and J. Dogra, Review of biomedical system for high performance applications, 4th
IEEE Int. Conf. Signal Proc. Control (ISPCC 2017), Jaypee University of Information technology, Waknaghat,
Solan, H.P, India, 300-304 (2017) 21–23.
[34] U. Rasool, S. Mairaj, T. Nazeer and S. Ahmed, Wavelet-based image compression techniques: comparative
analysis and performance evaluation, Int. J. Emerg. Tech. Engin. Res. 5(9) (2017) 9—13.
[35] A. Sharma, A. Polley, S. B. Lee, S. Narayanan, W. Li, T. Sculley and S. Ramaswamy, A Sub-60-µ A multimodal
smart biosensing SoCwith¿80-dB SNR, 35Aphotoplethysmography signal chain, IEEE J. Solid State Circ. 52(4)
(2017) 1021-–1033.
[36] A. Singh and S. Dandapat, Block sparsity-based joint compressed sensing recovery of multi-channel ECG signals,
Healthcare Tech. Lett. 4(2) (2017) 50-–56.
[37] A. Singh and S. Dandapat, Exploiting multi-scale signal information in joint compressed sensing recovery of
multi-channel ECG signals, Biomed. Signal Proc. Cont. 29 (2016) 53-–66.
[38] C. Tan, L. Zhang and H. T. Wu, A novel blaschke unwinding adaptive-fourier-decomposition- based signal
compression algorithm with application on ECG signals, IEEE J. Biomed. Health Inf. 23(2) (2019) 672-–682.
[39] R. V. Tornekar and S. S. Gajre, Comparative study of lossless ECG signal compression tech-niques for wireless
networks, IEEE Comput. Card. 2017.
[40] T. H. Tsai and W. T. Kuo, An efficient ECG lossless compression system for embedded plat- forms with
telemedicine applications, IEEE. 6 (2018) 42207–42215.
[41] J. Uthayakumar, T. Venkattaraman and P. Dhayachelvan, A survey on data compression techniques: From the
perspective of data quality, coding schemes, data types and applications, J. King Saud Univ. Comput. Inf. Sci.
32(2) (2018) 119–140.
[42] L. Yan, P. Harpe, V. R. Pamula, M. Osawa, Y. Harada, K. Tamiya, C. Van Hoof and R. F. Yazicioglu, ECG
acquisition IC for leadless pacemaker applications, IEEE Trans. Biomed. Circuits Syst. 8(6) (2014) 779—786.
[43] O. Yildirim, ¨ A novel wavelet sequence based on deep bidirectional LSTM network model for ECG signal classification, Comput. Biology Med. 96 (2018) 189-202.
[44] J. Zhang, Z. Gu, Z.L. Yu and Y. Li, Energy-efficient ECG compression on wireless biosensors via minimal
coherence sensing and weighted l1 minimization reconstruction, IEEE J. Biomed. Health Inf. 19(2) (2015) 520—
528.[45] Z. Zhang, J. Li, Q. Zhang, K. Wu, N. Ning and Q. Yu, A dynamic tracking algorithm based SAR ADC in
bio-related applications, IEEE Access, 6 (2018) 62166—62173.
[46] X. Zhang, Y. Lian, A 300-mV 220-nW Event-driven ADC with real-time QRS detection for wearable ECG sensors.
IEEE Trans, Biomed. Circuits Syst. 8(6) (2014) 834—843.
Volume 12, Issue 2
November 2021
Pages 1353-1370
  • Receive Date: 21 March 2021
  • Revise Date: 13 May 2021
  • Accept Date: 02 June 2021