Skip to main content

Advertisement

Log in

Vector-to-Vector Mapping with Stacked Gated Recurrent Units for Biosignal Enhancement

  • Published:
Circuits, Systems, and Signal Processing Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

Data Availability

Manuscript has no associated data.

References

  1. 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)

    Article  Google Scholar 

  2. 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)

    Article  Google Scholar 

  3. 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)

    Article  Google Scholar 

  4. 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)

  5. 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)

  6. 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

    Article  Google Scholar 

  7. 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

    Article  Google Scholar 

  8. 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

    Article  Google Scholar 

  9. 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)

    Article  Google Scholar 

  10. 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)

    Article  Google Scholar 

  11. 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)

    Article  Google Scholar 

  12. 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)

    Article  Google Scholar 

  13. 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

    Article  Google Scholar 

  14. 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)

    Article  Google Scholar 

  15. 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)

    Article  Google Scholar 

  16. 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)

    Article  Google Scholar 

  17. 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)

    Google Scholar 

  18. 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)

    Article  Google Scholar 

  19. 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

    Article  Google Scholar 

  20. 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)

    Article  Google Scholar 

  21. 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)

    Article  Google Scholar 

  22. 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)

    Article  Google Scholar 

  23. 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

    Article  Google Scholar 

  24. 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)

    Google Scholar 

  25. 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

    Article  Google Scholar 

  26. 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

    Article  Google Scholar 

  27. 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)

    Article  Google Scholar 

  28. 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)

    Article  Google Scholar 

  29. 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)

    Article  Google Scholar 

  30. 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)

    Article  Google Scholar 

  31. 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)

    Article  Google Scholar 

  32. 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)

    Article  Google Scholar 

  33. 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)

    Article  Google Scholar 

  34. 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)

    Article  Google Scholar 

  35. 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

    Article  Google Scholar 

  36. 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)

    Article  Google Scholar 

  37. 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)

    Article  Google Scholar 

  38. 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)

    Article  Google Scholar 

  39. 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

    Article  Google Scholar 

  40. 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)

    Article  Google Scholar 

  41. 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

    Article  Google Scholar 

  42. 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)

    Article  Google Scholar 

  43. 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)

    Article  Google Scholar 

  44. 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)

    Google Scholar 

Download references

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

Authors

Corresponding author

Correspondence to Evangelin Dasan.

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.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

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

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00034-024-02658-6

Keywords

Navigation