Abstract
Deep learning (DL) has emerged as a critical technology in the advancement of non-invasive cardiac monitoring by analyzing electrocardiogram (ECG) data. Although traditional approaches utilizing up to 12-lead ECGs have been effective, their practicality for real-time monitoring during daily activities is constrained. In contrast, single-lead ECG data lacks the comprehensive diagnostic information necessary for precise assessment. To overcome these challenges, this research introduces a novel deep neural network architecture, CardiacRT-NN. This model integrates a self-attention-based convolutional neural network (CNN) with long short-term memory (LSTM) components to accurately classify various heart diseases from the mean of customized ECG patterns. The experiment for real-time diagnosis has been validated on Raspberry Pi 4 to ensure the balance of accuracy and efficiency. This research lays a foundational step towards interpretable AI in healthcare, with the aim of facilitating proactive detection and management of potential cardiac issues, ultimately striving to mitigate the risk of sudden cardiac death.
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
Oster, J., Pietquin, O., Abächerli, R., Kraemer, M., Felblinger, J.: Independent component analysis-based artefact reduction: application to the electrocardiogram for improved magnetic resonance imaging triggering. Physiol. Meas. 30(12), 1381 (2009)
Gao, H., Duan, X., Guo, X., Huang, A., Jiao, B.: Design and tests of a smartphones-based multi-lead ECG monitoring system. In: 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 2267–2270. IEEE (2013)
Herry, C.L., Frasch, M.G., Seely, A.J.E., Wu, H.T.: Heart beat classification from single-lead ECG using the synchrosqueezing transform. Physiol. Meas. 38(2), 171 (2015)
Mathews, S.M., Kambhamettu, C., Barner, K.: A novel application of deep learning for single-lead ECG classification. Comput. Biol. Med. 99, 53–62 (2018)
Yıldırım, Ö., Plawiak, P., Tan, R.S., Acharya, U.R.: Arrhythmia detection using deep convolutional neural network with long duration ECG signals. Comput. Biol. Med. 102, 411–420 (2018)
Huang, K., Zhang, L.: Cardiology knowledge free ECG feature extraction using generalized tensor rank one discriminant analysis. EURASIP J. Adv. Signal Process. 2014(1), 1–15 (2014)
Kumar, A., Komaragiri, R., Kumar, M.: Heart rate monitoring and therapeutic devices: a wavelet transform based approach for the modeling and classification of congestive heart failure. ISA Trans. 79, 239–250 (2018)
Somani, S., et al.: Deep learning and the electrocardiogram: review of the current state-of-the-art. Europace 23, 1179–1191 (2021)
Johnson, A., et al.: MIMIC-III, a freely accessible critical care database. Sci. Data 3, 160035 (2016)
Smith, G., Nguyen, H.D., Clifton, D.A., Tarassenko, L.: Noise and robustness in deep learning-based electrocardiogram analysis. ArXiv arxiv:2007.11779 (2020)
Lee, J., Ng, A.Y., Rajpurkar, P.: Diversity in clinical electrocardiogram datasets for machine learning: a review. J. Electrocardiol. 65, 108–114 (2021)
Ouda, H., Hassanein, H.S., Elgazzar, K.: Adaptive ECG leads selection for low-power ECG monitoring systems using multi-class classification. In: 2022 5th International Conference on Communications, Signal Processing, and their Applications (ICCSPA), pp. 1–6 (2022)
Majumder, A.J.A., ElSaadany, M., Izaguirre, J.A., Ucci, D.: A real-time cardiac monitoring using a multisensory smart IoT system. In: 2019 IEEE 43rd Annual Computer Software and Applications Conference (COMPSAC), vol. 2, pp. 281–287 (2019)
Lee, S.Y., Hong, J.H., Hsieh, C.H., Liang, M.C., Chien, S.Y.C., Lin, K.H.: Low-power wireless ECG acquisition and classification system for body sensor networks. IEEE J. Biomed. Health Inform. 19, 236–246 (2015)
Reyna, M.A., et al.: Will two do? varying dimensions in electrocardiography: the PhysioNet/computing in cardiology challenge 2021. In: Computing in Cardiology 2021, vol. 48, pp. 1–4 (2021)
Reyna, M.A., et al.: Issues in the automated classification of multilead ECGs using heterogeneous labels and populations. Physiol. Meas. (2022)
Daubechies, I.: The wavelet transform, time-frequency localization and signal analysis. IEEE Trans. Inf. Theory 36(5), 961–1005 (1990)
Sharma, A., Sharma, R., Toshniwal, S.: Efficient use of bi-orthogonal wavelet transform for cardiac signals. Int. J. Comput. Appl. 89, 19–23 (2014)
Su, L., Zhao, G.: De-noising of ECG signal using translation-invariant wavelet de-noising method with improved thresholding. In: 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference, pp. 5946–5949 (2005)
Engelse, W., Zeelenberg, C.: A single scan algorithm for QRS detection and feature extraction. IEEE Comput. Cardiol. 6, 37–42 (1979)
Christov, I.I.: Real time electrocardiogram QRS detection using combined adaptive threshold. Biomed. Eng. Online 3, 28 (2004)
Hamilton, P.: Open source ECG analysis. In: Computers in Cardiology, pp. 101–104 (2002)
Chen, T., Guestrin, C.: XGBoost: a scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2016, pp. 785–794 (2016)
Acknowledgement
The work is partially supported by NSF FRR2246672 and NC TraCS CTS 0302 grants.
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Li, Y., Sui, N., Gehi, A., Guo, C., Guo, Z. (2024). CardiacRT-NN: Real-Time Detection of Cardiovascular Disease Using Self-attention CNN-LSTM for Embedded Systems. In: Le, X., Zhang, Z. (eds) Advances in Neural Networks – ISNN 2024. ISNN 2024. Lecture Notes in Computer Science, vol 14827. Springer, Singapore. https://doi.org/10.1007/978-981-97-4399-5_58
Download citation
DOI: https://doi.org/10.1007/978-981-97-4399-5_58
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-97-4398-8
Online ISBN: 978-981-97-4399-5
eBook Packages: Computer ScienceComputer Science (R0)