Abstract
The use of Internet of Things (IoT) that integrate smart bio sensor devices to the internet and shows individual health in real time. Healthcare organizations gain measurable insights into their most demanding problems like chronic diseases which demands long term monitoring. Various types of nano-sensors like Ingestible embedded in pills, Blood sampling sensor and tissue sensors are used in Healthcare IoT. Such implantable device collects, process and sends the vital signs called biosignals from particular organ of the human body where it has been implanted or fixed throughout the day to remote clinician. Such prolonged monitoring may weaken the battery power of nano-sensors. Since nano-sensors are miniaturized in nature and completely relies on its battery, energy awareness is incorporated in this paradigm that can help to avoid unnecessary energy consumption. This is achieved by data compression scheme. As the nano-sensors are light weight devices the designed algorithm should be low complex as well as efficient. As well, the signal acquired through this wireless sensor device are prone to be contaminated with noises because of the wearer’s movements. In this study, regularized denoising autoencoder (DAE) has been employed to compress and recover the signal from its noisy version. Lasso (Least Absolute Shrinkage and Selection Operator) and Ridge regularization concepts are used and contrasted in this article. The cost function now includes these penalty clauses to address the overfitting problem. The experimental findings demonstrate that LASSO Norm has outperformed over RIDGE in 18% for ECG, 57% for EMG & 31% for EEG signal with respect to Quality Score. The datasets used in this investigation were taken from a database that was open to the public for testing.
Similar content being viewed by others
Data Availability
Manuscript has no associated data.
References
Wong, D. L. T., Yu, J., Li, Y., Deepu, C. J., Ngo, D. H., Zhou, C., Shashi, R. S., Koh, A. S. L., et al. (2019). An integrated wearable wireless vital signs biosensor for continuous inpatient monitoring. IEEE Sensors Journal (Early Access), 20, 448–462.
Habibzadeh, H., Dinesh, K., Shishvan, O. R., Boggio-Dandry, A., Sharma, G., & Soyata, T. (2019). A survey of healthcare Internet of Things (HIoT): A clinical perspective. IEEE Internet of Things Journal (Early Access), 7(1), 53–71.
Shih, H. Y., Chang, Y. C., Yang, C. W., & Chen, C. C. (2019). A low-power and small chip-area multi-rate human body communication DPFSK transceiver for wearable devices. IEEE Transactions on Circuits and Systems II: Express Briefs (Early Access), 67, 1–5.
Shimly, S. M., Smith, D. B., & Movassaghi, S. (2019). Experimental analysis of cross-layer optimization for distributed wireless body-to-body networks. IEEE Sensors Journal (Early Access), 19, 1–16.
Li, M., Song, Y., Hou, Y., Li, N., Jiang, Y., Sulaman, M., & Hao, Q. (2019). Comparable investigation of characteristics for implant intra-body communication based on galvanic and capacitive coupling. IEEE Transactions on Biomedical Circuits and Systems (Early Access), 13, 1747–1758.
Peng, Y., & Peng, L. (2016). A cooperative transmission strategy for body-area networks in healthcare systems. IEEE Access, 4, 9155–9162.
Yao, X., Liao, W., Du, X., Cheng, X., & Guizani, M. (2019). Using bloom filter to generate a physiological signal-based key for wireless body area networks. IEEE Internet of Things Journal, 6, 1–12.
Kim, D. H., Lee, E., Kim, J., Park, P., & Cho, S. (2019). A sleep apnea monitoring IC for respiration, heart-rate, SpO2 and pulse-transit time measurement using thermistor, PPG and body-channel communication. IEEE Sensors Journal (Early Access), 20(4), 1997–2007.
Ongenae, De Turck, F., Braem, B., Famaey, J., & Latré, S. (2019). Continuous athlete monitoring in challenging cycling environments using IoT technologies. IEEE Internet of Things Journal (Early Access), 6, 10875–10887.
Majumder, S., Chen, L., Marinov, O., Chen, C., Mondal, T., & Jamal Deen, M. (2014). Non-contact wearable wireless ECG systems for long term monitoring. In: 2013 international conference on communications, circuits and systems (ICCCAS), pp.1–18.
Lin, C.-T., Chang, K.-C., Lin, C.-L., Chiang, C.-C., Shao-Wei, Lu., Chang, S.-S., Lin, B.-S., Liang, H.-Y., & Chen, R.-J. (2010). An intelligent telecardiology system using a wearable and wireless ECG to detect atrial fibrillation. IEEE Transactions on Information Technology In Biomedicine, 14(3), 726–733.
Giannakakis, G., Grigoriadis, D., Giannakaki, K., Simantiraki, O., Roniotis, A., & Tsiknakis, M. (2019). Review on psychological stress detection using biosignals. IEEE Transactions on Affective Computing, 13(1), 440–460.
Zhao, Z., Liu, C., Li, Y., Li, Y., Wang, J., Lin, B.-S., & Li, J. (2019). Noise rejection for wearable ECGs using modified frequency slice wavelet transform andconvolutional neural networks. IEEE Access, 7, 34060–34067.
De Faria, M. L. L., Cugnasca, C. E., & Amazonas, J. R. A. (2017). Insights into IoT data and an innovative DWT-based technique to denoise sensor signals. IEEE Sensors Journal, 18(1), 237–247.
Nagai, S., Anzai, D., & Wang, J. (2017). Motion artefact removals for wearable ECG using stationary wavelet transform. Healthcare Technology Letters, 4(4), 138–141.
Orphanidou, C., & Drobnjak, I. (2017). Quality assessment of ambulatory ECG using wavelet entropy of the HRV signal. IEEE Journal of Biomedical and Health Informatics, 21(5), 1216–1223.
Chiang, H. T., Hsieh, Y. Y., Fu, S. W., Hung, K. H., Tsao, Y., & Chien, S. Y. (2019). Noise reduction in ECG signals using fully convolutional denoising autoencoders. IEEE Access, 7, 60806–60813.
Leicht, L., Eilebrecht, B., Weyer, S., Leonhardt, S., Member, S., & Teichmann, D. (2016). Closed-loop control of humidification for artifact reduction in capacitive ECG measurements. IEEE Transactions on Biomedical Circuits and Systems 1–18.
Noro, M., Anzai, D., & Wang, J. (2017). Common-mode noise cancellation circuit for wearable ECG. Healthcare Technology Letters, 4(2), 64–67.
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, 10160–10170.
Niu, B., Cao, X., Wei, Z., & He, Y. (2021). Entropy optimized deep feature compression. IEEE Signal Processing Letters, 28, 324–328.
Gowgi, P., Machireddy, A., & Garani, S. S. (2021). Spatiotemporal memories for missing samples reconstruction. IEEE Transactions on Neural Networks and Learning Systems, 33, 1–15.
Mukhopadhyay, S. K., Omair Ahmad, M., & Swamy, M. N. S. (2019). Compression of steganographed PPG signal with guaranteed reconstruction quality based on optimum truncation of singular values and ASCII character encoding. IEEE Transactions on Biomedical Engineering, 66(7), 2081–2090.
Feuillen, T., Davies, M. E., Vandendorpe, L., & Jacques, L. (2020). (ℓ1, ℓ2)-RIP and projected back-projection reconstruction for phase-only measurements. IEEE Signal Processing Letters, 27, 396–400.
Zhang, S., Xia, Y., Xia, Y., & Wang, J. (2021). Matrix-form neural networks for complex-variable basis pursuit problem with application to sparse signal reconstruction. IEEE Transactions on Cybernetics, 52, 1–11.
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 decomposition. IEEE Transactions on Very Large Scale Integration (VLSI) Systems, 28(1), 174–187.
Zhao, R., Wang, Q., Jun, Fu., & Ren, L. (2019). Exploiting block-sparsity for hyperspectral kronecker compressive sensing: A tensor-based bayesian method. IEEE Transactions on Image Processing, 29, 1654–1668.
Wilmot, C., Baldassarre, G., & Triesch, J. (2021). Learning abstract representations through lossy compression of multi-modal signals. IEEE Transactions on Cognitive and Developmental Systems, 15, 1–1.
Zamani, H., Bahrami, H. R., Garris, P. A., & Mohseni, P. (2020). Compressed principal component regression (C–PCR) algorithm and FPGA validation. IEEE Transactions on Circuits and Systems II: Express Briefs, 67(12), 3512–3516.
Lu, X., Yi, Q., & Tian, G. Y. (2020). A comparison of feature extraction techniques for delamination of CFRP using eddy current pulse-compression thermography. IEEE Sensors Journal, 20(20), 12415–12422.
Chang, C. Y., Hsu, S. H., Pion-Tonachini, L., & Jung, T. P. (2019). Evaluation of artifact subspace reconstruction for automatic artifact components removal in multi-channel EEG recordings. IEEE Transactions on Biomedical Engineering, 67(4), 1114–1121.
Yu, T., Guo, C., Wang, L., Xiang, S., & Pan, C. (2018). Self-paced AutoEncoder. IEEE Signal Processing Letters, 25(7), 1054–1058.
Li, G., Peng, S., Wang, C., Niu, J., & Yuan, Y. (2018). An energy-efficient data collection scheme using denoising autoencoder in wireless sensor networks. Tsinghua Science and Technology, 24(1), 86–96.
Lee, W. H., Ozger, M., Challita, U., & Sung, K. W. (2021). Noise learning-based denoising autoencoder. IEEE Communications Letters, 25(9), 2983–2987.
Lu, X., Tsao, Y., Matsuda, S., & Hori, C. (2013). Speech enhancement based on deep denoising autoencoder. In Proceedings of Interspeech, pp. 436–440.
Lai, Y. H., Chen, F., Wang, S.-S., Lu, X., Tsao, Y., & Lee, C.-H. (2017). A deep denoising autoencoder approach to improving the intelligibility of vocoded speech in cochlear implant simulation. IEEE Transactions on Biomedical Engineering, 64(7), 1568–1578.
Gondara, L. (2016). Medical image denoising using convolutional denoising autoencoders. In 2016 IEEE 16th international conference on data mining workshops (ICDMW), pp. 241–246.
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, 70–75.
Andrzejak, R. G., Lehnertz, K., Mormann, F., Rieke, C., David, P., & Elger, C. E. (2001). Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: dependence on recording region and brain state. Physical Review E, Statistical, Nonlinear, and Soft Matter Physics, 64, 1–8.
Goldberger, A. L., et al. (2000). PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals. Circulation, 101, 1–6.
Tan, C., Zhang, L., & Wu, H. T. (2018). A novel Blaschke unwinding adaptive-Fourier-decomposition-based signal compression algorithm with application on ECG signals. IEEE Journal of Biomedical and Health Informatics, 23, 672–682.
Liu, T. Y., Lin, K. J., & Wu, H. C. (2017). ECG data encryption then compression using singular value decomposition. IEEE Journal of Biomedical and Health Informatics, 22, 707–713.
Banerjee, S., & Singh, G. K. (2021). Quality guaranteed ECG signal compression using tunable-Q wavelet transform and Möbius transform-based AFD. IEEE Transactions on Instrumentation and Measurement, 70, 4008211–4008221.
Mitra, D., Zanddizari, H., & Rajan, S. (2019). Investigation of Kronecker-based recovery of compressed ECG signal. IEEE Transactions on Instrumentation and Measurement, 69, 3642–3653.
Burguera, A. (2019). Fast QRS detection and ECG compression based on signal structural analysis. IEEE Journal of Biomedical and Health Informatics, 23, 123–131.
Trabuco, M. H., Costa, M. V., Macchiavello, B., & Nascimento, F. A. D. O. (2017). S-EMG signal compression in one-dimensional and two-dimensional approaches. IEEE Journal of Biomedical and Health Informatics, 22(4), 1104–1113.
Wu, F. Y., 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.
Okassa, A. J. O., Ngono, J. M., & Ele, P. (2019). Compression of the EMG signals by Walsh-Hadamard transform associated with the predictive coding DPCM. International Journal of Signal System Control and Engineering Application, 12, 1–7.
Oyobé-Okassa, A. J., Assoumou, D. A., & Elé, P. (2016). Compression of EMG signals by super imposing methods: Case of WPT and DCT. International Journal of Engineering and Technology, 8, 1335–1343.
Brechet, L., Lucas, M. F., Doncarli, C., & Farina, D. (2007). Compression of biomedical signals with mother wavelet optimization and best-basis wavelet packet selection. IEEE Transactions on Biomedical Engineering, 54, 2186–2192.
Filho, E. B. L., da Silva, E. A., & de Carvalho, M. B. (2008). On EMG signal compression with recurrent patterns. IEEE Transactions on Biomedical Engineering, 55, 1920–1923.
Khalid, B., Majid, M., Nizami, I. F., Anwar, S. M., & Alnowami, M. (2020). EEG compression using motion compensated temporal filtering and wavelet based subband coding. IEEE Access, 8, 102502–102511.
Li, C., Tao, W., Cheng, J., Liu, Y., & Chen, X. (2019). Robust multichannel EEG compressed sensing in the presence of mixed noise. IEEE Sensors Journal, 19, 10574–10583.
Liu, D. H., & Imtiaz, S. A. (2020). Studying the effects of compression in EEG-based wearable sleep monitoring systems. IEEE Access, 8, 168486–168501.
Shaw, L., & Rahman, D. (2018). Aurobinda routray, highly efficient compression algorithms for multichannel EEG. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 26, 957–968.
Dasan, E., & Gnanaraj, R. (2023). A parametric lossy compression techniques for biosignals: A review. Wireless Personal Communication, 128, 507–536. https://doi.org/10.1007/s11277-022-09965-8
Funding
No funding source available.
Author information
Authors and Affiliations
Contributions
ED: Conceptualization, Methodology, Software, Validation, Data Curation, Writing–original draft. NSJ: Investigation, Review & Editing.
Corresponding author
Ethics declarations
Conflict of interest
No known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Dasan, E., Jeyabalan, N.S.J. Towards the Analysis of Regularized Denoising Autoencoder for Biosignal Processing: Lasso Versus Ridge Norms. Wireless Pers Commun 134, 319–338 (2024). https://doi.org/10.1007/s11277-024-10912-y
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11277-024-10912-y