Abstract
Through wearable technology, several chronic diseases are diagnosed by long-term monitoring of vital signs specifically ECG, EMG, EEG biosignals. Such prolonged monitoring and transmitting these multiple recordings may decline the battery power of wireless wearable device. This work aims at preserving the battery power of wireless wearables by jointly compressing ECG–EMG–EEG signals before sending to the receiver. This work proposes multimodal deep denoising convolutional autoencoder architecture for joint compression (encoding) and reconstruction (decoding) of ECG–EMG–EEG biosignals. In addition, the system may encounter new data stream in future with varying range of statistics in this real-time scenario; hence, it is required to remodel the system. But these wearables are memory constrained, so the model’s learned optimized parameters should not increase in size when it is remodeled or updated. The incremental learning addresses this issue by reusing the previously learned weights as initial weight for retraining the model for new dataset and avoids random weight initialization thereby maintaining the space and time complexity. The experimental result shows that the proposed model achieves better compression efficiency of 99.8% with highest reconstruction Quality Score of 156, 254 & 149.4 for ECG, EMG & EEG signals, respectively, than state-of-the-art methods, and it is observed that the computation time is low for joint compression than compressing each signal individually.
Similar content being viewed by others
Data availability
Manuscript has no associated data.
References
Z. Abeer, A. M. Al-Marridi, A. Erbad, Convolutional autoencoder approach for EEG compression and reconstruction in m-health systems. In: IEEE 14th International Wireless Communications and Mobile Computing Conference (IWCMC) (2018). https://doi.org/10.1109/IWCMC.2018.8450511
M. M. Andrade, J. C. Carmo, F. A. O. Nascimento, J. F. Camapum, I. dos Santos, L. R. Mochizuki, A. F. Rocha, Evaluation of techniques for the study of electromyographic signals. In: Proc. 28th IEEE Int. Conf. Eng. Med. Biol. Soc. (EMBS) 1335–1338 (2006)
R. G. Andrzejak, K. Lehnertz, F. Mormann, C. Rieke, P. David, C. E. Elger, Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: dependence on recording region and brain state. Phys. Rev. E Stat. Nonlinear Soft Matter Phys. 64, 1–8 (2001). https://doi.org/10.1103/physreve.64.061907
S. Banerjee, G.K. Singh, Quality guaranteed ECG signal compression using tunable-Q wavelet transform and Möbius transform-based AFD. IEEE Trans. Instrum. Measur. 70, 4008211–4008221 (2021). https://doi.org/10.1109/TIM.2021.3122119
P. Bera, R. Gupta, J. Saha, Preserving abnormal beat morphology in long-term ECG recording: an efficient hybrid compression approach. IEEE Trans Instrum Measur 69, 2084–2092 (2019). https://doi.org/10.1109/TIM.2019.2922054
B. Blankertz, G. Dornhege, M. Krauledat, K.-R. Müller, G. Curio, The non-invasive Berlin brain-computer interface: fast acquisition of effective performance in untrained subjects. Neuroimage 37, 539–550 (2007)
L. Brechet, L. Marie-Françoise, C. Doncarli, D. Farina, Compression of biomedical signals with mother wavelet optimization and best-basis wavelet packet selection. IEEE Trans. Biomed. Eng. 54, 2186–2192 (2007). https://doi.org/10.1109/TBME.2007.896596
A. Burguera, Fast QRS detection and ECG compression based on signal structural analysis. IEEE J. Biomed. Health Inform. 23, 123–131 (2019). https://doi.org/10.1109/JBHI.2018.2792404
Y. Cao, H. Zhang, C. Yong-Bae, H. Wang, S. Xiao, Hybrid deep learning model assisted data compression and classification for efficient data delivery in mobile health applications. IEEE Access 8, 94757–94766 (2020). https://doi.org/10.1109/ACCESS.2020.2995442
C. Chou, C. En-Jui, L. Huai-Ting, W. An-Yeu, Low-complexity privacy—preserving compressive analysis using subspace-based dictionary for ECG telemonitoring system. IEEE Trans. Biomed. Circ. Syst. 12, 801–811 (2018). https://doi.org/10.1109/TBCAS.2018.2828031
G. Cisotto, A.V. Guglielmi, L. Badia, A. Zanella, Joint compression of EEG and EMG signals for wireless biometrics. IEEE Glob. Commun. Conf. (GLOBECOM) (2018). https://doi.org/10.1109/GLOCOM.2018.8647543
D.A. Clevert, T. Unterthiner, S. Hochreiter, Fast accurate deep network learning by exponential linear units (ELUs). https://arxiv.org/abs/1511.07289 (2015)
D. Cogan, J. Birjandtalab, M. Nourani, Multi-biosignal analysis for epileptic seizure monitoring. Int. J. Neural Syst. 26, 1–18 (2016). https://doi.org/10.1142/s0129065716500313
D. Craven, B. McGinley, L. Kilmartin, M. Glavin, E. Jones, Compressed sensing for bioelectric signals: a review. IEEE J. Biomed. Health Inform. 19, 529–540 (2015). https://doi.org/10.1109/JBHI.2014.2327194
E. Dasan, I. Panneerselvam, A novel dimensionality reduction approach for ECG signal via convolutional denoising autoencoder with LSTM. Elsevier Biomed. Signal Process. Control 63, 1–11 (2021). https://doi.org/10.1016/j.bspc.2020.102225
B. Deepa, K. Ramesh, Preprocessed CHB-MIT scalp EEG database. IEEE Dataport (2021). https://doi.org/10.21227/awcw-mn88
S. Devuyst, The dreams subjects database. http://www.tcts.fpms.ac.be/devuyst/Databases/DatabaseSubjects/ (2004)
K. Dinashi, A. Ameri, M.A. Akhaee, K. Englehart, E. Scheme, Compression of EMG signals using deep convolutional autoencoders. IEEE J. Biomed. Health Inform. (2022). https://doi.org/10.1109/JBHI.2022.3142034
G. Dornhege, B. Blankertz, G. Curio, K.R. Müller, Boosting bitrates in noninvasive EEG single-trial classications by feature combination and multiclass paradigms. IEEE Trans. Biomed. Eng. 51, 993–1002 (2004). https://doi.org/10.1109/TBME.2004.827088
E.B.L. Filho, E.A.B. da Silva, M.B. de Carvalho, On EMG signal compression with recurrent patterns. IEEE Trans. Biomed. Eng 55, 1920–1923 (2008). https://doi.org/10.1109/TBME.2008.919729
M. Fira, L. Goras, On compressed sensing for EEG signals—validation with P300 speller paradigm. Int. Conf. Commun. (COMM) (2016). https://doi.org/10.1109/ICComm.2016.7528296
A. Gogna, A. Majumdar, R. Ward, Semi-supervised stacked label consistent autoencoder for reconstruction and analysis of biomedical signals. IEEE Trans. Biomed. Eng. 64, 2196–2205 (2017). https://doi.org/10.1109/TBME.2016.2631620
A. Goldberger, L. Amaral, L. Glass, J. Hausdorff, P.C. Ivanov, R. Mark, J.E. Mietus, G.B. Moody, C.K. Peng, H.E. Stanley, PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation [Online]. 101(23), e215–e220. https://doi.org/10.13026/C24S3D
M. Hooshmand, D. Zordan, T. Melodia, M. Rossi, SURF: subject-adaptive unsupervised ECG signal compression for wearable fitness monitors. IEEE Access 5, 19517–19535 (2017). https://doi.org/10.1109/ACCESS.2017.2749758
F. Hu, Y. Xiao, Q. Hao, Congestion-aware loss-resilient bio-monitoring sensor networking for mobile health applications. IEEE J. Select. Areas Commun. 27, 450–465 (2009). https://doi.org/10.1109/JSAC.2009.090509
N. Iyengar, C.-K. Peng, R. Morin, A.L. Goldberger, L.A. Lipsitz, Age-related alterations in the fractal scaling of cardiac interbeat interval dynamics. Am. J. Physiol. 271, 1078–1084 (1996). https://doi.org/10.13026/C2RG61
L. Jin, Y. Zhang, X.-L. Wang, W.-J. Zhang, Y.-H. Liu, Z. Jiang, Postictal apnea as an important mechanism for SUDEP: a near-SUDEP with continuous EEG-ECG-EMG recording. ELSEVIER J. Clin. Neurosci. 43, 130–132 (2017). https://doi.org/10.1016/j.jocn.2017.04.035
B. Khalid, M. Majid, I.F. Nizami, S.M. Anwar, M. Alnowami, EEG compression using motion compensated temporal filtering and wavelet Based subband coding. IEEE Access 8, 102502–102511 (2020). https://doi.org/10.1109/ACCESS.2020.2999091
G. Klösch, B. Kemp, T. Penzel, A. Schlögl, P. Rappelsberger, E. Trenker, G. Gruber, J. Zeitlhofer, B. Saletu, W.M. Herrmann, S.L. Himanen, D. Kunz, M.J. Barbanoj, J. Röschke, A. Värri, G. Dorffner, The SIESTA project polygraphic and clinical database. IEEE Eng. Med. Biol. Mag. 20, 51–57 (2001). https://doi.org/10.1109/51.932725
P. Kovács, S. Fridli, F. Schipp, Generalized rational variable projection with application in ECG compression. IEEE Trans. Signal Process. 68, 478–492 (2020). https://doi.org/10.1109/TSP.2019.2961234
C. Li, W. Tao, J. Cheng, Y. Liu, X. Chen, Robust multichannel EEG compressed sensing in the presence of mixed noise. IEEE Sens. J. 19, 10574–10583 (2019). https://doi.org/10.1109/JSEN.2019.2930546
D.H. Liu, S.A. Imtiaz, Studying the effects of compression in EEG-based wearable sleep monitoring systems. IEEE Access 8, 168486–168501 (2020). https://doi.org/10.1109/ACCESS.2020.3023915
H. Lin, S. Yuan-Yao, P. Ai-Chun, C. Chun-Ting, Virtual local-hub: a service platform on the edge of networks for wearable devices. IEEE Network 32, 114–121 (2018). https://doi.org/10.1109/MNET.2018.1700367
T.Y. Liu, K.J. Lin, H.C. Wu, ECG data encryption then compression using singular value decomposition. IEEE J. Biomed. Health Inform. 22, 707–713 (2017). https://doi.org/10.1109/JBHI.2017.2698498
M. Ma, C. Sun, X. Chen, Deep coupling autoencoder for fault diagnosis with multisensory data. IEEE Trans. Ind. Inform. 14, 1137–1145 (2018). https://doi.org/10.1109/TII.2018.2793246
M. Mangia, L. Prono, A. Marchioni, F. Pareschi, R. Rovatti, G. Setti, Deep neural oracles for short-window optimized compressed sensing of biosignals. IEEE Trans. Biomed. Circ. Syst. 14, 545–557 (2020). https://doi.org/10.1109/TBCAS.2020.2982824
R. Martínez, Mónica, S. Higuita, L. Yanet, Jordanic, Mislav, Marateb, H. Reza, Merletti, Roberto, Villanueva, M.A. Mañanas, A high-density surface EMG dataset of upper-limb muscles during isometric contractions in healthy humans: Part1/3. figshare. Dataset (2020). https://doi.org/10.6084/m9.figshare.11860572.v1
D. Mitra, H. Zanddizari, S. Rajan, Investigation of Kronecker-based recovery of compressed ECG signal. IEEE Trans. Instrum. Measur. 69, 3642–3653 (2020). https://doi.org/10.1109/TIM.2019.2936776
A. Goldberger, L. Amaral, L. Glass, J. Hausdorff, P.C. Ivanov, R. Mark, J.E. Mietus, G.B. Moody, C.K. Peng, H.E. Stanley, PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation [Online]. 101(23), e215–e220. https://doi.org/10.13026/C2F305
S.K. Mukhopadhyay, M. Omair Ahmad, M.N.S. Swamy, SVD and ASCII character encoding-based compression of multiple biosignals for remote healthcare systems. IEEE Trans. Biomed. Circ. Syst. 12, 137–150 (2018). https://doi.org/10.1109/TBCAS.2017.2760298
A.J. Oyobé-Okassa, D.A. Assoumou, P. Elé, Compression of EMG signals by super imposing methods: case of WPT and DCT. Int. J. Eng. Technol. 8, 1335–1343 (2016)
A.J. Oyob-Okassa, J.M. Ngono, P. Ele, Compression of the EMG signals by Walsh-Hadamard transform associated with the predictive coding DPCM. Int. J. Syst. Signal Control Eng. Appl. 12, 1–7 (2019). https://doi.org/10.36478/ijssceapp.2019.1.7
R. Paradiso, G. Loriga, N. Taccini, A wearable health care system based on knitted integrated sensors. IEEE Trans. Inf. Technol. Biomed. 9, 337–344 (2005). https://doi.org/10.1109/TITB.2005.854512
T. Penzel, G.B. Moody, R.G. Mark, A.L. Goldberger, J.H. Peter, The Apnea-ECG database. Comput. Cardiol. 27, 255–258 (2000)
J. Qian, P. Tiwari, S.P. Gochhayat, H.M. Pandey, A noble double dictionary based ECG compression technique for IoTH. IEEE Internet Things J. 7, 10160–10170 (2020). https://doi.org/10.1109/JIOT.2020.2974678
A. Ravelomanantsoa, A. Rouane, H. Rabah, N. Ferveur, L. Collet, Design and implementation of a compressed sensing encoder: application to EMG and ECG wireless biosensors. Circ. Syst. Signal Process. 36, 2875–2892 (2017). https://doi.org/10.1007/s00034-016-0444-y
A.B. Said, A. Mohamed, T. Elfouly, K. Harras, Z. Jane-Wang, Multimodal deep learning approach for joint EEG-EMG data compression and classification. IEEE Wireless Commun. Networking Conf. (WCNC) (2017). https://doi.org/10.1109/WCNC.2017.7925709
L. Shaw, D. Rahman, A. Routray, Highly efficient compression algorithms for multichannel EEG. IEEE Trans. Neural Syst. Rehab. Eng. 26, 957–968 (2018). https://doi.org/10.1109/TNSRE.2018.2826559
J. Singh, R.K. Sharma, Making sleep study instrumentation more unobtrusive. IEEE Instrum. Meas. Mag. 21, 50–53 (2018). https://doi.org/10.1109/MIM.2018.8278812
N. Sriraam, C. Eswaran, Performance evaluation of neural network and linear predictors for near-lossless compression of EEG signals. IEEE Trans. Inf Technol. Biomed. 12, 87–93 (2008). https://doi.org/10.1109/TITB.2007.899497
N. Sriraam, Quality-on-demand compression of EEG signals for telemedicine applications using neural network predictors. HINDAWI Int. J. Telemed. Appl. (2011). https://doi.org/10.1155/2011/860549
C. Tan, L. Zhang, W. Hau-tieng, A Novel Blaschke unwinding adaptive Fourier decomposition based signal compression algorithm with application on ECG signals. IEEE J. Biomed. Health Inform. 23, 672–682 (2017). https://doi.org/10.1109/JBHI.2018.2817192
D.D. Testa, M. Rossi, Lightweight lossy compression of biometric patterns via denoising autoencoders. IEEE Signal Process. Lett. 22, 2304–2308 (2015). https://doi.org/10.1109/LSP.2015.2476667
K. Takabayashi, H. Karvonen, T. Pas, H. Tanaka, C. Sugimoto, R. Kohno, Performance evaluation of a QoS-aware error control scheme for multiple-WBAN environment. IEEJ Trans. Electr. Electron. Eng. 12, S146–S157 (2017). https://doi.org/10.1002/tee.22445
M.H. Trabuco, M.V.C. Costa, B. Macchiavello, F.A.O. de Nascimento, S-EMG signal compression in one-dimensional and two-dimensional approaches. IEEE J. Biomed. Health Inform. 22, 1104–1113 (2018). https://doi.org/10.1109/JBHI.2017.2765922
Y. Taigman, M. Yang, M. Ranzato, L. Wolf, DeepFace: closing the gap to human-level performance in face verification. IEEE Conf. Comput. Vis. Pattern Recogn. (2014). https://doi.org/10.1109/CVPR.2014.220
F. Wang, Q. Ma, W. Liu, S. Chang, H. Wang, J. He, Q. Huang, A novel ECG signal compression method using spindle convolutional auto-encoder. Elsevier Comput. Methods Programs Biomed. 175, 139–150 (2019). https://doi.org/10.1016/j.cmpb.2019.03.019
F. Wu, K. Yang, Z. Yang, Compressed acquisition and denoising recovery of EMGdi signal in WSNs and IoT. IEEE Trans. Ind. Inform. 14, 2210–2219 (2017). https://doi.org/10.1109/TII.2017.2759185
N.Y. Wang, S.W. Hsiao-Lan, W. Tao-Wei, F. Szu-Wei, L. Xugan, W. Hsin-Min, Y. Tsao, Improving the intelligibility of speech for simulated electric and acoustic stimulation using fully convolutional neural networks. IEEE Trans. Neural Syst. Rehabilit. Eng. 29, 184–195 (2020). https://doi.org/10.1109/TNSRE.2020.3042655
P. Wagner, N. Strodthoff, R. Bousseljot, W. Samek, T. Schaeffter, PTB-XL, a large publicly available electrocardiography dataset (version 1.0.1). Physionet (2020). https://doi.org/10.13026/x4td-x982
O. Yildirim, U. Rajendra Acharya, R. San Tan, An efficient compression of ECG signals using deep convolutional autoencoders. Article Cogn. Syst. Res. 52, 198–211 (2018). https://doi.org/10.1016/j.cogsys.2018.07.004
H. Zhao, H. Wang, Y. Fu, F. Wu, X. Li, Memory efficient class-incremental learning for image classification. IEEE Trans. Neural Networks Learn. Syst. (Early Access) (2021). https://doi.org/10.1109/TNNLS.2021.3072041
Acknowledgements
We gratefully acknowledge the insightful comments of the editor and the anonymous reviewers that has helped us to improve the overall quality of our manuscript.
Funding
No funding source available.
Author information
Authors and Affiliations
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
About this article
Cite this article
Dasan, E., Gnanaraj, R. Joint ECG–EMG–EEG signal compression and reconstruction with incremental multimodal autoencoder approach. Circuits Syst Signal Process 41, 6152–6181 (2022). https://doi.org/10.1007/s00034-022-02071-x
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00034-022-02071-x