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A Parametric Lossy Compression Techniques for Biosignals: A Review

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Abstract

Rapid growth in wireless communication and sensor based technology has contributed to the integration of wearable device with internet technology which supports wireless body area network (WBAN) enabled tele-health monitoring applications. Wearable technology promotes long-term monitoring of biosignals for individuals suffering from chronic conditions like cardiovascular, sleep disorder, mood disorder, epileptic seizure etc. The hardware used in these wearables are miniaturized in nature and resource constrained. This resource constrained wearable devices have to collect, analyze and transmit large amount of data with limited power consumption. Hence, the wearable device must have faster computational speed and least communication cost. In order to address these issues, various light weight lossy compression schemes based on parametric method are advocated so far to reduce the size of the data. The acquired data has been compressed at once where they are acquired (on node processing) to support the battery life for long term monitoring. This article reviews parametric method based two major paradigms Compressive Sensing and Autoencoder techniques for biosignal compression. The biosignals that are acquired from surface-mounted on body communicating wearable IoT devices are considered for this review. And this study presents a complete investigation of compression techniques of these Biosignals.

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References

  1. Seneviratne, S., Yining, Hu., Nguyen, T., et al. (2017). A survey of wearable devices and challenges. IEEE Communications Surveys & Tutorials, 19(4), 2573–2620.

    Article  Google Scholar 

  2. Haghi, M., Stoll, R., & Thurow, K. (2019). Pervasive and personalized ambient parameters monitoring: a wearable, modular, and configurable watch. IEEE Access, 7, 20126–20143.

    Article  Google Scholar 

  3. Yang, G., Deng, J., Pang, G., et al. (2018). An IoT-enabled stroke rehabilitation system based on smart wearable armband and machine learning. IEEE Journal of Translational Engineering in Health and Medicine, 6, 1–10.

    Article  Google Scholar 

  4. Umair, M., Chalabianloo, N., Sas, C., & Ersoy, C. (2021). HRV and stress: A mixed-methods approach for comparison of wearable heart rate sensors for biofeedback. IEEE Access., 9, 14005–14024.

    Article  Google Scholar 

  5. Youngsu Cha , Senior Member, IEEE, Jaehoon Chung, and Sung-Moon Hur (2020) Torsion sensing on a cylinder using a flexible piezoelectric wrist band. IEEE/ASME Transactions on Mechatronics, 25(1),460–467.

  6. Bhuiyan, M. N., Rahman, M. M., Billah, M. M., & Saha, D. (2021). Internet of things (IoT): a review of its enabling technologies in healthcare applications. Standards Protocols, Security, and Market Opportunities, IEEE Internet of Things Journal, 8(13), 10474–10498.

  7. Oguntala, A. G., Abd-Alhameed, A. R., Nazarali, T., et al. (2019). SmartWall: novel rfid-enabled ambient human activity recognition using machine learning for unobtrusive health monitoring. IEEE Access, 7, 68022–68033.

    Article  Google Scholar 

  8. Di Lin, Yu., Tang, Y. Y., et al. (2017). User-priority based power control over the D2D assisted Internet of vehicles for mobile health. IEEE Internet of Things Journal, 4(3), 824–831.

    Article  Google Scholar 

  9. Soumyendu, B., Graduate & Singh, G. K. (2021). Monte Carlo filter-based motion artifact removal from electrocardiogram signal for real-time telecardiology system. IEEE Transactions on Instrumentation and Measurement, 70.

  10. Kharel, J., Reda, HT., & Shin SY (2017) Fog computing-based smart health monitoring system deploying LoRa wireless communication, IETE Technical Review.

  11. Xie, C., Yang, Po., & Yang, Y. (2018). Open knowledge accessing method in IoT-based hospital information system for medical record enrichment. IEEE Access, 6, 15202–15211.

    Article  Google Scholar 

  12. Yang, Xu., Yi, X., Nepal, S., Khalil, I., Huang, X., & Shen, J. (2019). Efficient and anonymous authentication for healthcare service with cloud based WBANs. IEEE Transactions On Services Computing, 00(00), 000.

    Google Scholar 

  13. Yaqoob, I., Ahmed, E., Hashem, I. A. T., et al. (2017). Internet of things architecture: recent advances, taxonomy, requirements, and open challenges. IEEE Wireless Communications, 24(3), 10–16.

    Article  Google Scholar 

  14. Hooshmand, M., Zordan, D., Del Testa, D., et al. (2017). Boosting the battery life of wearables for health monitoring through the compression of biosignals. IEEE Internet of Things Journal, 4(5), 1647–1662.

    Article  Google Scholar 

  15. Mordor Intelligence Industry. (2022). Smart wearable market - growth, trends, COVID-19 impact, and forecasts (2022–2027), [Online]. Available: https://www.mordorintelligence.com/industry-reports/smart-wearables-market

  16. Gartner, Inc. (2018). Gartner says worldwide wearable device sales to grow 26 percent in 2019, STAMFORD, Conn., November 29, [Online]. Available: https://www.gartner.com/en/newsroom/press-releases/2018-11-29-gartner-says-worldwide-wearable-device-sales-to-grow-

  17. Faust, O., Hagiwara, Y., Hong, T. J., et al. (2018). Deep learning for healthcare applications based on physiological signals: A review. Computer Methods and Programs in Biomedicine, 161, 1–13.

    Article  Google Scholar 

  18. Chambon, S., Galtier, M. N., Arnal, P. J., Wainrib, G., & Gramfort, A. (2018). A deep learning architecture for temporal sleep stage classification using multivariate and multimodal time series. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 26(4), 758–769.

    Article  Google Scholar 

  19. Singh, J., & Sharma, R. K. (2018). Making sleep study instrumentation more unobtrusive. IEEE Instrumentation & Measurement Magazine, 21(1), 50–53.

    Article  Google Scholar 

  20. Bian, X., Wenbo, Xu., Wang, Y., Liyang, Lu., & Wang, S. (2021). Direct feature extraction and diagnosis of ECG signal in the compressed domain. IEEE Sensors Journal, 21(15), 17096–17106.

    Article  Google Scholar 

  21. Xiong, T., Zhang, J., Martinez-Rubio, C., Thakur, C. S., Eskandar, E. N., Chin, S. P., Etienne-Cummings, R., & Tran, T. D. (2018). An unsupervised compressed sensing algorithm for multi-channel neural recording and spike sorting. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 26(6), 1121–1130.

    Article  Google Scholar 

  22. Sarangi, P., & Pal, P. (2022). Measurement Matrix Design for Sample-efficient Binary Compressed Sensing. IEEE Signal Processing Letters, (Early Access).

  23. Natarajan, V., & Vyas, A. (2017) Power efficient compressive sensing for continuous monitoring of ECG and PPG in a wearable system. In IEEE 3rd World Forum on Internet of Things (WF-IoT) Accession No. 16666875

  24. Jain, S., Oswal, U., Xu, K. S., et al. (2017). A compressed sensing based decomposition of electrodermal activity signals. IEEE Transactions on Biomedical Engineering, 64(9), 2142–2151.

    Article  Google Scholar 

  25. Shrestha, A., & Mahmood, A. (2019). Review of deep learning algorithms and architectures. IEEE Access, 7, 53040–53065.

    Article  Google Scholar 

  26. Amini, S., & Ghaemmaghami, S. (2019). A new framework to train autoencoders through non-smooth regularization. IEEE Transactions on Signal Processing, 67(7), 1860–1874.

    Article  MathSciNet  MATH  Google Scholar 

  27. Chen, J., Li, T., Wang, J., & de Silva, C. W. (2020). WSN sampling optimization for signal reconstruction using spatiotemporal autoencoder. IEEE Sensors Journal, 20(23), 14290–14301.

    Article  Google Scholar 

  28. Zhu, Q., Tian, X., Wong, C.-W., & Min, Wu. (2021). Learning your heart actions from pulse: ECG waveform reconstruction from PPG. IEEE Internet of Things Journal, 8(23), 16734–16748.

    Article  Google Scholar 

  29. Luo, C. H., Ma, W. J., Juang, W. H., Kuo, S. H., Chen, C. Y., Tai, P. C., & Lai, S. C. (2016). An ECG acquisition system prototype design with flexible PDMS dry electrodes and variable transform length DCT-IV based compression algorithm. IEEE Sensors Journal, 16(23), 8244–8254.

    Google Scholar 

  30. Gupta, V., & Pachori, R. B. (2022). FB Dictionary Based SSBL-EM and Its Application for Multi-Class SSVEP Classification Using Eight-Channel EEG Signals. IEEE Transactions on Instrumentation and Measurement.

  31. Ghosh, S. K., Ray, A., Tripathy, R. K., & Ponnalagu, R. N. (2021). A Transform domain approach for the compression of fetal phonocardiogram signal”. IEEE Sensors Letters, 5(5), 1–4.

    Article  Google Scholar 

  32. Lai, D., Fan, X., Zhang, Y., & Chen, W. (2021). Intelligent and efficient detection of life-threatening ventricular arrhythmias in short segments of surface ECG signals. IEEE Sensors Journal, 21(13), 14110–14120.

    Article  Google Scholar 

  33. Tan, C., Zhang, L., & Hau-tieng, Wu. (2019). A novel blaschke unwinding adaptive-fourier-decomposition-based signal compression algorithm with application on ECG signals. IEEE Journal of Biomedical and Health Informatics, 23(2), 672–682.

    Article  Google Scholar 

  34. López, A., Ferrero, F. J., & Villar, J. R. (2021). EOG signal compression using turning point algorithm. In 2021 IEEE International Instrumentation and Measurement Technology Conference (I2MTC).

  35. Tripathi, R. P., & Mishra, G. R. (2017). Study of various data compression techniques used in lossless compression of ECG signals. In 2017 International Conference on Computing, Communication and Automation (ICCCA).

  36. Middya, R., Chakravarty, N., & Naskar, M. K. (2016). Compressive Sensing in Wireless Sensor Networks – a Survey. IETE Technical Review.

  37. Foucart, S., & Rauhut, H. (2013).A Mathematical Introduction to Compressive Sensing. Applied and Numerical Harmonic Analysis, Springer, New York Heidelberg Dordrecht London, ISBN 978–0–8176–4948–7, DOI: https://doi.org/10.1007/978-0-8176-4948-7.

  38. Zhou, J., Zhou, S., & Fan, Q. (2013). Mathematics approaches in compressed sensing. TELKOMNIKA, 11(9), 5435–5440.

    Article  Google Scholar 

  39. Da Poian, G., Rozell, C. J., Bernardini, R., Rinaldo, R., & Clifford, G. D. (2018). Matched filtering for heart rate estimation on compressive sensing ECG measurements. IEEE Transactions on Biomedical Engineering, 65(6), 1349–1358.

    Article  Google Scholar 

  40. Zhang, J., Yu, Z. L., Gu, Z., Li, Y., & Lin, Z. (2018). Multichannel electrocardiogram reconstruction in wireless body sensor networks through weighted ℓ1,2 minimization. IEEE Transactions on Instrumentation and Measurement, 67(9), 2024–2034.

    Article  Google Scholar 

  41. OisDeterme, J. F., OmeLouveaux, J., Jacques, L., & Horlin, F. (2017). On the noise robustness of simultaneous orthogonal matching pursuit. IEEE Transactions on Signal Processing, 65(4), 864–875.

    Article  MathSciNet  Google Scholar 

  42. Tao, T.,Compressed sensing, University of California, Los Angeles, Mahler Lecture Series.

  43. Yang, J.,(2013). A machine learning paradigm based on sparse signal representation. Doctor of Philosophy Thesis, Computer and Telecommunication Engineering, University of Wollongong.

  44. Ravelomanantsoa, A., Rabah, H., & Rouane, A. (2014). Simple and efficient compressed sensing encoder for wireless body area network. IEEE Transactions on Instrumentation and Measurement, 63(12), 2973–2982.

    Article  Google Scholar 

  45. Chatterjee, A., & Yuen, P. W. T. (2019). Sample selection with SOMP for robust basis recovery in sparse coding dictionary learning. IEEE Letters of the Computer Society, 2(3), 28–31.

    Article  Google Scholar 

  46. Determe, J.-F., Louveaux, J., Jacques, L., et al. (2017). On the noise robustness of simultaneous orthogonal matching pursuit. IEEE Transactions on Signal Processing, 65(4), 864–875.

    Article  MathSciNet  MATH  Google Scholar 

  47. Hsieh, S.-H., Liang, W.-J., Chun-Shien, Lu., & Pei, S.-C. (2020). Distributed compressive sensing: performance analysis with diverse signal ensembles. IEEE TRANSACTIONS ON SIGNAL PROCESSING, 68, 3500–3515.

    Article  MathSciNet  MATH  Google Scholar 

  48. Djelouat, H., Zhai, X., Al Disi, M., Amira, A., & Bensaali, F. (2018). System-on-chip solution for patients biometric: A compressive sensing-based approach. IEEE Sensors Journal, 18(23), 9629–9639.

    Article  Google Scholar 

  49. Srivastava, S., Mishra, A., Rajoriya, A., Jagannatham, A. K., & Ascheid, G. (2019). Quasi-static and time-selective channel estimation for block-sparse millimeter wave hybrid mimo systems: sparse bayesian learning (SBL) based approaches. IEEE Transactions on Signal Processing, 67(5), 1251–1266.

    Article  MathSciNet  MATH  Google Scholar 

  50. Singh, A., & Dandapat, S. (2017). Block sparsity-based joint compressed sensing recovery of multi-channel ECG signals. IET Healthcare Technology Letters, 4(2), 50–56.

    Article  Google Scholar 

  51. Zhang, Z., Liu, X., Wei, S., Gan, H., Liu, F., Li, Y., Liu, C., & Liu, F. (2019). Electrocardiogram reconstruction based on compressed sensing. IEEE Access, 7, 37228–37237.

    Article  Google Scholar 

  52. Muduli, P. R., & Mukherjee, A. A. (2017). Subspace projection-based joint sparse recovery method for structured biomedical signals. IEEE Transactions on Instrumentation and Measurement, 66(2), 234–242.

    Article  Google Scholar 

  53. Pareschi, F., Mangia, M., Bortolotti, D., et al. (2017). Energy analysis of decoders for rakeness-based compressed sensing of ECG signals. IEEE Transactions on Biomedical Circuits and Systems, 11(6), 1278–1289.

    Article  Google Scholar 

  54. Yang Qiu; Weidong Zhou; Nana Yu; Peidong Du. (2018). Denoising sparse autoencoder-based Ictal EEG classification. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 26(9), 1717–1726.

    Article  Google Scholar 

  55. Pei, D., Burns, M., Chandramouli, R., et al. (2018). Decoding asynchronous reaching in electroencephalography using stacked autoencoders. IEEE Access, 6, 52889–52898.

    Article  Google Scholar 

  56. Min, Wu., Qin, H., Wan, X., Yuxiao, D., Fellow, IEEE. (2021). , HFO detection in epilepsy: A stacked denoising autoencoder and sample weight adjusting factors-based method. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 29, 1965–1975.

    Article  Google Scholar 

  57. Del Testa, D., & Rossi, M. (2015). Lightweight lossy compression of biometric patterns via denoising autoencoders. IEEE Signal Processing Letters, 22(12), 2304–2308.

    Article  Google Scholar 

  58. Lee, W. H., Ozger, M., Challita, U., & Sung, K. W. (2021). Noise learning-based denoising autoencoder. IEEE Communications Letters, 25(9), 2983–2987.

    Article  Google Scholar 

  59. Dinashi, K., Ameri, A., Akhaee, M. A., Englehart, K., & Scheme, E. (2022).Compression of EMG signals using deep convolutional autoencoders. IEEE Journal of Biomedical and Health Informatics (Early Access).

  60. Dasan, E., & Panneerselvam, I. (2021). A novel dimensionality reduction approach for ECG signal via convolutional denoising autoencoder with LSTM. Biomedical Signal Processing and Control. https://doi.org/10.1016/j.bspc.2020.102225

    Article  Google Scholar 

  61. Yildirim, O., San Tan, R., & Acharya, U. R. (2018). An efficient compression of ecg signals using deep convolutional autoencoders, Article in Cognitive Systems Research.

  62. Wang, F., Ma, Q., Liu, W., et al. (2019). A novel ECG signal compression method using spindle convolutional auto-encoder. Computer Methods and Programs in Biomedicine, 175, 1–14.

    Article  Google Scholar 

  63. Chiang, H.-T., Hsieh, Y.-Y., Szu-Wei, Fu., et al. (2019). Noise reduction in ECG signals using fully convolutional denoising autoencoders. IEEE Access, 4, 1–13.

    Google Scholar 

  64. Sun, B., & Feng, H. (2017). Efficient compressed sensing for wireless neural recording: A deep learning approach. IEEE Signal Processing Letters, 24(6), 863–867.

    Article  Google Scholar 

  65. Liu, Y., Song, T., & Zhuang, Y. (2020). A high-throughput subspace pursuit processor for ecg recovery in compressed sensing using square-root-free MGS QR decompositio. IEEE Transactions on Very Large-Scale Integration VLSI Systems, 28(1), 174–187.

    Article  Google Scholar 

  66. Zanddizari, H., Rajan, S., Zarrabi, H., & Rabah, H. (2022). Privacy assured recovery of compressively sensed ECG signals. IEEE Access.

  67. Sameni, R. OSET: The open-source electrophysiological toolbox. January 2012. [Online]. Available: http://www.oset.ir/

  68. Dornhege, G., Blankertz, B., Curio, G., & Müller, K.-R. (2004). Boosting bitrates in noninvasive EEG single-trial classications by feature combination and multiclass paradigms. IEEE Transactions on Biomedical Engineering, 51, 993–1002.

    Article  Google Scholar 

  69. Qian, J., Tiwari, P., Gochhayat, S. P., & Pandey, H. M. (2020). A noble double-dictionary-based ECG compression technique for IoTH. IEEE Internet of Things Journal, 7(10), 10160–10170.

    Article  Google Scholar 

  70. Chen, J., Zhou, F., Guo, Z., & Wan, J. (2020). Compressed data collection method for wireless sensor networks based on optimized dictionary updating learning. IEEE Access, 8, 205124–205135.

    Article  Google Scholar 

  71. Alam, S., Gupta, R., & Sharma, K. D. (2021). On-board signal quality assessment guided compression of photoplethysmogram for personal health monitoring. IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, 70, 1–9.

    Google Scholar 

  72. Niu, B., Cao, X., Wei, Z., & He, Y. (2021). Entropy optimized deep feature compression. IEEE Signal Processing Letters, 28, 324–328.

    Article  Google Scholar 

  73. Leinonen, M., & Codreanu, M. (2020). Low-complexity vector quantized compressed sensing via deep neural networks. IEEE Open Journal of the Communications Society, 1, 1278–1294.

    Article  Google Scholar 

  74. Leinonen, M., Codreanu, M., & Juntti, M. (2018). Distributed distortion-rate optimized compressed sensing in wireless sensor networks. IEEE Transactions on Communications, 66(4), 1609–1623.

    Article  Google Scholar 

  75. Hooshmand, M., Zordan, D., Melodia, T., et al. (2017). SURF: Subject-adaptive unsupervised ECG signal compression for wearable fitness monitors. IEEE Access, 5, 19517–19535.

    Article  Google Scholar 

  76. Gowgi, P., Machireddy, A., & Garani, S. S. (2021).spatiotemporal memories for missing samples reconstruction. IEEE Transactions on Neural Networks and Learning Systems ( Early Access ), 1–15.

  77. Kim, J., & Mazumder, P. (2017). Energy-efficient hardware architecture of self-organizing map for ECG clustering in 65-nm CMOS”. IEEE Transactions on Circuits and Systems II: Express Briefs, 64(9), 1097–1101.

    Google Scholar 

  78. Jovanović, S., & Hikawa, H. (2022). A Survey of Hardware Self-Organizing Maps. IEEE Transactions on Neural Networks and Learning Systems ( Early Access ), 1–20.

  79. Kumar, R., Kumar, A., Singh, G. K., et al. (2017). Efficient compression technique based on temporal modelling of ECG signal using principle component analysis. IET Science, Measurement & Technology, 11(3), 346–353.

    Article  Google Scholar 

  80. Burrello, A., Marchioni, A., Brunelli, D., Benatti, S., Mangia, M., & Benini, L. (2021). Embedded streaming principal components analysis for network load reduction in structural health monitoring. IEEE Internet of Things Journal, 8(6), 4433–4447.

    Article  Google Scholar 

  81. Wilmot, C., Baldassarre, G., & Triesch, J. (2021). Learning abstract representations through lossy compression of multi-modal signals. IEEE Transactions on Cognitive and Developmental Systems ( Early Access ).

  82. Bera, P., Gupta, R., & Saha, J. (2020). Preserving abnormal beat morphology in long-term ECG recording: An efficient hybrid compression approach. IEEE Transactions on Instrumentation and Measurement, 69(5), 2084–2092.

    Article  Google Scholar 

  83. Wei, X., Shen, H., Li, Y., Tang, X., Wang, F., Kleinsteuber, M., & Murphey, Y. L. (2019). Reconstructible nonlinear dimensionality reduction via joint dictionary learning. IEEE Transactions on Neural Networks and Learning Systems, 30(1), 175–189.

    Article  MathSciNet  Google Scholar 

  84. Song, C., Wang, A., Lin, F., et al. (2018). Selective CS: An energy-efficient sensing architecture for wireless implantable neural decoding. IEEE Journal on Emerging and Selected Topics in Circuits and Systems, 8(2), 201–210.

    Article  Google Scholar 

  85. Singhal, V., Majumdar, A., & Ward, R. K. (2017). Semi-supervised deep blind compressed sensing for analysis and reconstruction of biomedical signals from compressive measurements. IEEE Access, 6, 545–553.

    Article  Google Scholar 

  86. Wickramasinghe, C. S., Amarasinghe, K., & Manic, M. (2019).Deep self-organizing maps for unsupervised image classification. IEEE Transactions on Industrial Informatics, 1–9.

  87. Moody, G. B., Mark, R. G., & Goldberger, A. L. (2001). Physionet: A web based resource for the study of physiologic signals. IEEE Engineering in Medicine and Biology Magazine, 20(3), 70–75.

    Article  Google Scholar 

  88. Craven, D., McGinley, B., Kilmartin, L., et al. (2017). Adaptive dictionary reconstruction for compressed sensing of ECG signals. IEEE Journal of Biomedical and Health Informatics, 21(3), 645–654.

    Article  Google Scholar 

  89. Goldberger, A. L., Amaral, L. A. N., Glass, L., et al. (2000). Physiobank, physiotoolkit, and physionet: components of a new research resource for complex physiologic signals. Circulation, 101(23), e215–e220.

    Article  Google Scholar 

  90. Fei-Yun, W., Yang, K., & Yang, Z. (2017). Compressed acquisition and denoising recovery of EMGdi signal in WSNs and IoT. IEEE Transactions on Industrial Informatics, 14, 2210–2219. https://doi.org/10.1109/TII.2017.2759185

    Article  Google Scholar 

  91. Quiroga, R. Q., Nadasdy, Z., & Ben-Shaul, Y. (2004). Unsupervised spike detection and sorting with wavelets and superparamagnetic clustering. Neural computation, 16(8), 1661–1687.

    Article  MATH  Google Scholar 

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We thank Almighty God and greatly appreciate the commentary assistance of the editor & the anonymous reviewers that has helped us to improve the overall quality of our manuscript.

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  1. Department of Electronics and Communication Engineering, Francis Xavier Engineering College, Tirunelveli, India

    Evangelin Dasan & Rajakumar Gnanaraj

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Dasan, E., Gnanaraj, R. A Parametric Lossy Compression Techniques for Biosignals: A Review. Wireless Pers Commun 128, 507–536 (2023). https://doi.org/10.1007/s11277-022-09965-8

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