Abstract
Capacitive ECG (cECG), as a contactless solution for measuring ECG, has been extensively explored in existing works. However, the signal quality obtained by cECG can abruptly degrade due to body movement. Hence, it substantially increases the challenge in signal quality assessment of cECG. In this paper, a novel multi-classifier fusion approach is proposed to assess the cECG signal quality. It combines three commonly used classifiers namely, support vector machine (SVM), K-nearest neighbor (KNN) model, and decision tree (DT) and fuse these classifiers with a voting mechanism to provide a robust decision. With the proposed approach, the overall accuracy of 98.32% can be achieved in distinguishing the cECG signal quality into three categories, namely clear ECG signal, blurry ECG signal with clear R peaks, and noisy ECG signal. Experimental results exhibit that the proposed method outperforms existing works. The classification accuracy and F1-Score of this method are better than traditional methods. Meanwhile, the proposed method is expected to be integrated with cECG device for practical long-term heart monitoring.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Zhu, H. (ed.): Sudden Death: Advances in Diagnosis and Treatment. Springer, Heidelberg (2020)
Mensah, G.A., Sampson, U.K., Roth, G.A., et al.: Mortality from cardiovascular diseases in sub-Saharan Africa, 1990–2013: a systematic analysis of data from the Global Burden of Disease Study 2013. Cardiovasc. J. Afr. 26(2 Suppl 1), S6 (2015)
Benjamin: Heart Disease and stroke statistics-2018 update: a report from the american heart association, vol. 137, p. e67 (2018)
Peng, S., Bao, S., Chen, W.: Capacitive coupled electrodes based non-contact ECG measurement system with real-time wavelet denoising algorithm. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). IEEE (2019). Author, F., Author, S.: Title of a proceedings paper. In: Editor, F., Editor, S. (eds.) CONFERENCE 2016, LNCS, vol. 9999, pp. 1–13. Springer, Heidelberg (2016)
Sharma, M., Ritchie, P., Ghirmai, T., Cao, H., Lau, M.P.H.: Unobtrusive acquisition and extraction of fetal and maternal ECG in the home setting. IEEE Sens. 2017, 1–3 (2017). https://doi.org/10.1109/ICSENS.2017.8234188
Satija, U., Ramkumar, B., Manikandan, M.S.: Automated ECG noise detection and classification system for unsupervised healthcare monitoring. IEEE J. Bio-Med. Health Inform. 22(3), 722–732 (2018). https://doi.org/10.1109/JBHI.2017.2686436
Ge, Z., Zhu, Z., Feng, P., Zhang, S., Wang, J., Zhou, B.: ECG-signal classification using SVM with multi-feature. In: 2019 8th International Symposium on Next Generation Electronics (ISNE), pp. 1–3 (2019). https://doi.org/10.1109/ISNE.2019.8896430
Prabhakararao, E., Dandapat, S.: Automatic quality estimation of 12-lead ECG for remote healthcare monitoring systems. In: 2018 IEEE-EMBS Conference on Biomedical Engineering and Sciences (IECBES), pp. 554–559 (2018). https://doi.org/10.1109/IECBES.2018.8626686
Demirel, B.U., Serinağaoğlu, Y.: Quality assessment of ECG signals based on support vector machines and binary decision trees. In: 2020 28th Signal Processing and Communications Applications Conference (SIU), pp. 1–4 (2020). https://doi.org/10.1109/SIU49456.2020.9302262.
Kropf, M., Hayn, D., Schreier, G.: ECG classification based on time and frequency do-main features using random forests. In: 2017 Computing in Cardiology (CinC), pp. 1–4 (2017). https://doi.org/10.22489/CinC.2017.168-168
Yao, W., Wu, M., Wang, J.: RobustICA, Kurtosis- and negentropy-based FastICA in maternal-Fetal ECG separation. In: 2018 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), 2018, pp. 1–5. https://doi.org/10.1109/CISP-BMEI.2018.8633123.
Thakor, N.V., Webster, J.G., Tompkins, W.J.: Estimation of QRS complex power spectra for design of a QRS filter. IEEE Trans. Biomed. Eng. BME-31(11), 702–706 (1984). https://doi.org/10.1109/TBME.1984.325393
Balachandran, A., Ganesan, M., Sumesh, E.P.: Daubechies algorithm for highly accurate ECG feature extraction. In: 2014 International Conference on Green Computing Communication and Electrical Engineering (ICGCCEE), pp. 1–5 (2014). https://doi.org/10.1109/ICGCCEE.2014.6922266
Xia, Y., Jia, H.: ECG quality assessment based on multi-feature fusion. In: 2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD), pp. 672–676 (2017). https://doi.org/10.1109/FSKD.2017.8393352.
Medapati, B., Rajani Kumari, L.V.: KNN based sleep Apnea detection using ECG signals. In: 2021 2nd International Conference for Emerging Technology (INCET), pp. 1–5 (2021). https://doi.org/10.1109/INCET51464.2021.9456404
Healey, J., Logan, B.: Wearable wellness monitoring using ECG and accelerometer data. In: Ninth IEEE International Symposium on Wearable Computers (ISWC 2005), pp. 220–221 (2005). https://doi.org/10.1109/ISWC.2005.59
Özaltın, Ö., Yeniay, Ö.: ECG classification performing feature extraction automatically using a hybrid CNN-SVM algorithm. In: 2021 3rd International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA), pp. 1–5 (2021). https://doi.org/10.1109/HORA52670.2021.9461295
Infant Lincy, P.M., Santhi, D., Geetha, A.: A proficient evalution with the pre-term birth classification in ECG signal using KNN. In: 2020 International Conference on Inventive Computation Technologies (ICICT), pp. 276–282 (2020). https://doi.org/10.1109/ICICT48043.2020.9112448
Xiaolin, L., Cardiff, B., John, D.: A 1D convolutional neural network for heartbeat classification from single lead ECG. In: 2020 27th IEEE International Conference on Electronics, Circuits and Systems (ICECS), pp. 1–2 (2020). https://doi.org/10.1109/ICECS49266.2020.9294838
Acknowledgment
This work was supported in part by Shanghai Municipal Science and Technology International R&D Collaboration Project (Grant No. 20510710500) in part by the National Natural Science Foundation of China under Grant No. 62001118, and in part by the Shanghai Committee of Science and Technology under Grant No. 20S31903900.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Lie, Z., Wu, Y., Zhu, G., Li, Y., Chen, C., Chen, W. (2022). A Multi-classifier Fusion Approach for Capacitive ECG Signal Quality Assessment. In: Gao, X., Jamalipour, A., Guo, L. (eds) Wireless Mobile Communication and Healthcare. MobiHealth 2021. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 440. Springer, Cham. https://doi.org/10.1007/978-3-031-06368-8_17
Download citation
DOI: https://doi.org/10.1007/978-3-031-06368-8_17
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-06367-1
Online ISBN: 978-3-031-06368-8
eBook Packages: Computer ScienceComputer Science (R0)