Abstract
In recent years, vector-to-vector mapping-based raw waveform biosignal enhancement methods have gained significant attention in remote health monitoring system. In this paper, a novel end-to-end convolutional encoder–decoder (CED) model with stacked gated recurrent unit (SGRU) is proposed to learn sequential information of biosignal for signal enhancement. The proposed model CED-SGRU employs convolutional neural network to capture the spatial features and SGRU to capture temporal distributions of the biosignal layer by layer which increases the robustness of the proposed model. This work applies mean absolute error as a loss function for CED-SGRU-based vector-to-vector model. The proposed method has been evaluated on three foremost required biosignals, namely electrocardiogram, photoplethysmography and heart rate signals for diagnosing cardiovascular diseases. Experimental result shows outstanding denoising capability of the proposed CED-SGRU model on three biosignals which yields significantly higher reconstruction signal-to-noise ratio value and lower average absolute error, root mean square error and percent root mean square difference values when compared with state-of-the-art-methods. Moreover, the simple architecture of SGRU lowers the complexity of the model; thereby, reducing the inference time for denoising and restoring compressed biosignal tasks is fairly compared with baseline models, namely recurrent neural network model and long short-term memory model.
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs00034-024-02658-6/MediaObjects/34_2024_2658_Fig1_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs00034-024-02658-6/MediaObjects/34_2024_2658_Fig2_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs00034-024-02658-6/MediaObjects/34_2024_2658_Fig3_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs00034-024-02658-6/MediaObjects/34_2024_2658_Fig4_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs00034-024-02658-6/MediaObjects/34_2024_2658_Fig5_HTML.png)
Similar content being viewed by others
Data Availability
Manuscript has no associated data.
References
P. Bera, R. Gupta, J. Saha, Preserving abnormal beat morphology in long-term ECG recording: an efficient hybrid compression approach. IEEE Trans. Instrum. Meas. 69, 2084–2092 (2020)
I. Capurro, F. Lecumberry, A. Martin, I. Ramirez, E. Rovira, G. Seroussi, Efficient sequential compression of multi-channel biomedical signals. IEEE J. Biomed. Health Inform. 21, 904–916 (2017)
T. Chaudhuri, M. Wu, Y. Zhang, P. Liu, X. Li, An attention-based deep sequential GRU model for sensor drift compensation. IEEE Sens. J. 21, 7908–7917 (2021)
K. Cho, B. van Merrienboer, D. Bahdanau, Y. Bengio, On the properties of neural machine translation: encoder-decoder approaches. arXiv preprint arXiv:1409.1259 (2014)
J. Chung, C. Gulcehre, K. Cho, A. Bengio, Empirical evaluation of gated recurrent neural networks on sequence modelling, in NIPS 2014 Workshop on Deep Learning (2014)
E. Dasan, R. Gnanaraj, A parametric Lossy compression techniques for biosignals: a review. Wirel. Pers. Commun. 128, 507–536 (2023). https://doi.org/10.1007/s11277-022-09965-8
E. Dasan, R. Gnanaraj, 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
E. Dasan, I. Panneerselvam, A novel dimensionality reduction approach for ECG signal via convolutional denoising autoencoder with LSTM. Biomed. Signal Process. Control 63, 102225 (2021). https://doi.org/10.1016/j.bspc.2020.102225
Y. Deng, L. Wang, H. Jia, X. Tong, F. Li, A sequence-to-sequence deep learning architecture based on bidirectional GRU for type recognition and time location of combined power quality disturbance. IEEE Trans. Ind. Inf. 15, 4481–4493 (2019)
Z. Du, X. Zhang, J. Han, A joint framework of denoising autoencoder and generative vocoder for monaural speech enhancement. IEEE/ACM Trans. Audio Speech Lang. Process. 28, 1493–1505 (2020)
Y. Fujita, M. Hiromoto, T. Sato, PARHELIA: particle filter-based heart rate estimation from photoplethysmographic signals during physical exercise. IEEE Trans. Biomed. Eng. 65, 189–198 (2018)
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 101(23), e215–e220 (2000)
S. Hochreiter, J. Schmidhuber, Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997). https://doi.org/10.1162/neco.1997.9.8.1735
M. Hooshmand, D. Zordan, D. Del Testa, E. Grisan, M. Rossi, Boosting the battery life of wearables for health monitoring through the compression of biosignals. IEEE Internet Things J. 4, 1647–1662 (2017)
T. Hsieh, H.-M. Wang, X. Lu, Y. Tsao, WaveCRN: an efficient convolutional recurrent neural network for end-to-end speech enhancement. IEEE Signal Process. Lett. 27, 2149–2153 (2020)
D.Y. Hwang, B. Taha, D.S. Lee, D. Hatzinakos, Evaluation of the time stability and uniqueness in PPG based biometric system. IEEE Trans. Inf. Forensics Secur. 16, 116–130 (2020)
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)
S. Jain, V. Bajaj, A. Kumar, Riemann Liouvelle fractional integral based empirical mode decomposition for ECG denoising. IEEE J. Biomed. Health Inf. 22, 1133–1139 (2018)
Y.S. Jhang, S.-T. Wang, M.-H. Sheu, S.-H. Wang, S.-C. Lai, Integration design of portable ECG signal acquisition with deep-learning based electrode motion artifact removal on an embedded system. IEEE Access 10, 57555–57564 (2022). https://doi.org/10.1109/ACCESS.2022.3178847
A. Koneshloo, D. Du, A novel motion artifact removal method via joint basis pursuit linear program to accurately monitor heart rate. IEEE Sens. J. 19, 9945–9952 (2019)
A. Kumar, R. Ranganatham, M. Kumar, R. Komaragiri, Hardware emulation of a biorthogonal wavelet transform-based heart rate monitoring device. IEEE Sens. J. 21(4), 5271–5281 (2021)
J. Lee, S. Sun, S.M. Yang, J.J. Sohn, J. Park, S. Lee, H.C. Kim, Bidirectional recurrent auto-encoder for photoplethysmogram denoising. IEEE J. Biomed. Health Inf. 23, 2375–2385 (2018)
L. Lu, C. Zhang, K. Cao, T. Deng, Q. Yang, A Multichannel CNN-GRU model for human activity recognition. IEEE Access 10, 66797–66810 (2022). https://doi.org/10.1109/ACCESS.2022.3185112
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. Circuits Syst. 14(3), 545–557 (2020)
G.B. Moody, R.G. Mark, A.L. Goldberger, Physionet: a web-based resource for the study of physiologic signals. IEEE Eng. Med. Biol. Mag. 25, 70–75 (2001). https://doi.org/10.1109/51.932728
S.K. Mukhopadhyay, M.O. Ahmad, M.N.S. Swamy, SVD and ASCII character encoding-based compression of multiple biosignals for remote healthcare systems. IEEE Trans. Biomed. Circuits Syst. 12(1), 137–150 (2018). https://doi.org/10.1109/TBCAS.2017.2760298
T. Pokaprakarn, R.R. Kitzmiller, R. Moorman, D.E. Lake, A.K. Krishnamurthy, M. Kosorok, Sequence to sequence ECG cardiac rhythm classification using convolutional recurrent neural networks. IEEE J. Biomed. Health Inf. (Early Access) 26, 572–580 (2021)
H. Purwins, B. Li, T. Virtanen, J. Schlüter, S. Chang, T. Sainath, Deep learning for audio signal processing. IEEE J. Sel. Top. Signal Process. 14(8), 206–219 (2019)
J. Qi, J. Du, S.M. Siniscalchi, C.-H. Lee, A theory on deep neural network based vector-to-vector regression with an illustration of its expressive power in speech enhancement. IEEE/ACM Trans. Audio Speech Lang. Process. 27(12), 1932–1943 (2019)
J. Qi, J. Du, S.M. Siniscalchi, X. Ma, C.-H. Lee, On mean absolute error for deep neural network based vector-to-vector regression. IEEE Signal Process. Lett. 27, 1485–1489 (2020)
L.G. Rocha, D. Biswas, B.-E. Verhoef, S. Bampi, C. Van Hoof, M. Konijnenburg, M. Verhelst, N. Van Helleputte, Binary CorNET: Accelerator for HR estimation from wrist-PPG. IEEE Trans. Biomed. Circuits Syst. 14, 715–726 (2020)
M.S. Roy, B. Roy, R. Gupta, K. Das Sharma, On-device reliability assessment and prediction of missing photoplethysmographic data using deep neural networks. IEEE Trans. Biomed. Circuits Syst. 14, 1323–1332 (2020)
M.S. Roy, R. Gupta, J.K. Chandra, K. Das Sharma, A. Talukdar, Improving photoplethysmographic measurements under motion artifacts using artificial neural network for personal healthcare. IEEE Trans. Instrum. Meas. 67, 2820–2829 (2018)
S. Saadatnejad, M. Oveisi, M. Hashemi, LSTM-based ECG classification for continuous monitoring on personal wearable devices. IEEE J. Biomed. Health Inf. 24, 515–523 (2018)
G. Sharma, A.M. Joshi, R. Gupta, L.R. Cenkeramaddi, DepCap: a smart healthcare framework for EEG based depression detection using time-frequency response and deep neural network. IEEE Access 11, 52327–52338 (2023). https://doi.org/10.1109/ACCESS.2023.3275024
S.B. Song, J.W. Nam, J.H. Kim, NAS-PPG: PPG based heart rate estimation using Neural Architecture Search. IEEE Sens. J. 21, 14941–14949 (2021)
C. Tan, L. Zhang, H. Wu, A novel Blaschke unwinding adaptive Fourier decomposition based signal compression algorithm with application on ECG signals. J. Biomed. Health Inf. 23, 672–682 (2017)
Q. Wang, Y. Zhang, G. Chen, Z. Chen, H.I. Hee, Assessment of heart rate and respiratory rate for perioperative infants based on ELC model. IEEE Sens. J. 21, 13685–13694 (2021)
T. Wilaiprasitporn, A. Ditthapron, K. Matchaparn, T. Tongbuasirilai, N. Banluesombatkul, E. Chuangsuwanich, Affective EEG-based person identification using the deep learning approach. IEEE Trans. Cogn. Dev. Syst. 12(3), 486–496 (2020). https://doi.org/10.1109/TCDS.2019.2924648
M. Xia, H. Shao, X. Ma, C.W. de Silva, A stacked GRU-RNN-based approach for predicting renewable energy and electricity load for smart grid operation. IEEE Trans. Ind. Inf. 17, 7050–7059 (2021)
K. Yamamoto, R. Hiromatsu, T. Ohtsuki, ECG signal reconstruction via Doppler sensor by hybrid deep learning model with CNN and LSTM. IEEE Access 8, 130551–130560 (2020). https://doi.org/10.1109/ACCESS.2020.3009266
S. Yang, J. Sohn, S. Lee, J. Lee, H.C. Kim, Estimation and validation of arterial blood pressure using photoplethysmogram morphology features in conjunction with pulse arrival time in large open databases. IEEE J. Biomed. Health Inf. 25, 1018–1030 (2021)
C. Ye, T. Ohtsuki, Spectral viterbi algorithm for contactless wide-range heart rate estimation with deep clustering. IEEE Trans. Microw. Theory Tech. 69(5), 2629–2641 (2021)
L. Zhu, C. Kan, Y. Du, D. Du, Heart rate monitoring during physical exercise from photoplethysmography using neural network. IEEE Sens. Lett. 3, 1–4 (2019)
Acknowledgements
We sincerely thank the editor and the anonymous reviewers for their insightful suggestions, which enabled us to raise the overall quality of our article.
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
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., Gnanaraj, R. & Jeyabalan, N.S.J. Vector-to-Vector Mapping with Stacked Gated Recurrent Units for Biosignal Enhancement. Circuits Syst Signal Process 43, 4412–4438 (2024). https://doi.org/10.1007/s00034-024-02658-6
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00034-024-02658-6