Nothing Special   »   [go: up one dir, main page]

Skip to main content
Log in

An improved cardiac arrhythmia classification using stationary wavelet transform decomposed short duration QRS segment and Bi-LSTM network

  • Published:
Multidimensional Systems and Signal Processing Aims and scope Submit manuscript

Abstract

Arrhythmia is a kind of cardiac conduction disorder those result in irregular heartbeats. The electrocardiograph (ECG) signal may identify conduction system abnormalities. However, its visual analysis is challenging and time-consuming. An automated system for cardiac disorder detection may help in early and prompt diagnosis of diseases. In this paper, stationary wavelet transform (SWT) was used for pre-processing of the raw ECG signal before the segmentation and normalization process. Thereafter, recurrent neural network (RNN), gated recurrent units (GRU), bi-directional long short-term memory (Bi-LSTM) have been implemented for classification of normal, left bundle branch block (L-BBB), right bundle branch block l(R-BBB), premature atrial contraction (PAC), and premature ventricular contraction (PVC) beats. Bi-LSTM networks have shown best accuracy of 99.72% among all three implemented models. This demonstrates that this model is appropriate for computer-aided diagnosis of heartbeats.

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
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

Data availability

MIT-BIH arrhythmia data that support the findings of this study are available in https://physionet.org.

References

  • Acharya, U. R., Hagiwara, Y., Koh, J. E. W., Oh, S. L., Tan, J. H., Adam, M., & San Tan, R. (2018). Entropies for automated detection of coronary artery disease using ECG signals: A review. Biocybernetics and Biomedical Engineering, 38(2), 373–384.

    Google Scholar 

  • Albaba, A., Simões-Capela, N., Wang, Y., Hendriks, R. C., De Raedt, W., & Van Hoof, C. (2021). Assessing the signal quality of electrocardiograms from varied acquisition sources: A generic machine learning pipeline for model generation. Computers in Biology and Medicine, 130, 104164.

    Google Scholar 

  • Arif, M., et al. (2008). Robust electrocardiogram (ECG) beat classification using discrete wavelet transform. Physiological Measurement, 29(5), 555.

    Google Scholar 

  • Asgharzadeh-Bonab, A., Amirani, M. C., & Mehri, A. (2020). Spectral entropy and deep convolutional neural network for ECG beat classification. Biocybernetics and Biomedical Engineering, 40(2), 691–700.

    Google Scholar 

  • Coviello, J. S. (2020). ECG interpretation made incredibly easy! Philadelphia: Lippincott Williams & Wilkins.

    Google Scholar 

  • Edla, S., Kovvali, N., & Papandreou-Suppappola, A. (2014). Electrocardiogram signal modeling with adaptive parameter estimation using sequential Bayesian methods. IEEE Transactions on Signal Processing, 62(10), 2667–2680.

    MathSciNet  MATH  Google Scholar 

  • Elhaj, F. A., Salim, N., Harris, A. R., Swee, T. T., & Ahmed, T. (2016). Arrhythmia recognition and classification using combined linear and nonlinear features of ECG signals. Computer Methods and Programs in Biomedicine, 127, 52–63.

    Google Scholar 

  • Engin, M. (2004). ECG beat classification using neuro-fuzzy network. Pattern Recognition Letters, 25(15), 1715–1722.

    Google Scholar 

  • Gautam, M. K., & Giri, V. K. (2016). An approach of neural network for electrocardiogram classification. APTIKOM Journal on Computer Science and Information Technologies, 1(3), 119–127.

    Google Scholar 

  • Goovaerts, G., Padhy, S., Vandenberk, B., Varon, C., Willems, R., & Van Huffel, S. (2018). A machine learning approach for detection and quantification of QRS fragmentation. IEEE Journal of Biomedical and Health Informatics, 23, 1980–1989.

    Google Scholar 

  • Gupta, P., Sharma, K. K., & Joshi, S. D. (2015). Baseline wander removal of electrocardiogram signals using multivariate empirical mode decomposition. Healthcare Technology Letters, 2(6), 164–166.

    Google Scholar 

  • Habib, A., Karmakar, C., & Yearwood, J. (2019). Impact of ECG dataset diversity on generalization of CNN model for detecting QRS complex. IEEE Access, 7, 93275–93285.

    Google Scholar 

  • Hamdi, S., Abdallah, A. B., & Bedoui, M. H. (2018). A robust QRS complex detection using regular grammar and deterministic automata. Biomedical Signal Processing and Control, 40, 263–274.

    Google Scholar 

  • Henzel, N. (2017). QRS complex detection based on ensemble empirical mode decomposition. In: Innovations in biomedical engineering (pp. 286–293). Springer.

  • Hossain, M. B., Bashar, S. K., Walkey, A. J., McManus, D. D., & Chon, K. H. (2019). An accurate QRS complex and P wave detection in ECG signals using complete ensemble empirical mode decomposition with adaptive noise approach. IEEE Access, 7, 128869–128880.

    Google Scholar 

  • Hou, Z., Dong, Y., Xiang, J., Li, X., & Yang, B. (2018). A real-time QRS detection method based on phase portraits and box-scoring calculation. IEEE Sensors Journal, 18(9), 3694–3702.

    Google Scholar 

  • Huang, J., Chen, B., Yao, B., & He, W. (2019). ECG arrhythmia classification using STFT-based spectrogram and convolutional neural network. IEEE Access, 7, 92871–92880.

    Google Scholar 

  • Jung, W.-H., & Lee, S.-G. (2017). An arrhythmia classification method in utilizing the weighted KNN and the fitness rule. IRBM, 38(3), 138–148.

    Google Scholar 

  • Keselbrener, L., Keselbrener, M., & Akselrod, S. (1997). Nonlinear high pass filter for R-wave detection in ECG signal. Medical Engineering & Physics, 19(5), 481–484.

    Google Scholar 

  • Kim, J., Shin, H. S., Shin, K., & Lee, M. (2009). Robust algorithm for arrhythmia classification in ECG using extreme learning machine. Biomedical Engineering Online, 8(1), 1–12.

    Google Scholar 

  • Korürek, M., & Doğan, B. (2010). ECG beat classification using particle swarm optimization and radial basis function neural network. Expert Systems with Applications, 37(12), 7563–7569.

    Google Scholar 

  • Kropf, M., Hayn, D., & Schreier, G. (2017). ECG classification based on time and frequency domain features using random forests. In: 2017 Computing in cardiology (CinC) organization (pp. 1–4). IEEE.

  • Kumar, A., Ranganatham, R., Komaragiri, R., & Kumar, M. (2019). Efficient QRS complex detection algorithm based on Fast Fourier Transform. Biomedical Engineering Letters, 9(1), 145–151.

    Google Scholar 

  • Ledezma, C. A., & Altuve, M. (2019). Optimal data fusion for the improvement of QRS complex detection in multi-channel ECG recordings. Medical & Biological Engineering & Computing, 57(8), 1673–1681.

    Google Scholar 

  • Lee, J. M., & Hauskrecht, M. (2021). Modeling multivariate clinical event time-series with recurrent temporal mechanisms. Artificial Intelligence in Medicine, 112, 102021.

    Google Scholar 

  • Lee, J. S., Lee, S. J., Choi, M., Seo, M., & Kim, S. W. (2019). QRS detection method based on fully convolutional networks for capacitive electrocardiogram. Expert Systems with Applications, 134, 66–78.

    Google Scholar 

  • Lesyuk, W., Kriza, C., & Kolominsky-Rabas, P. (2018). Cost-of-illness studies in heart failure: A systematic review 2004–2016. BMC Cardiovascular Disorders, 18(1), 74.

    Google Scholar 

  • Lih, O. S., Jahmunah, V., San, T. R., Ciaccio, E. J., Yamakawa, T., Tanabe, M., Kobayashi, M., Faust, O., & Acharya, U. R. (2020). Comprehensive electrocardiographic diagnosis based on deep learning. Artificial Intelligence in Medicine, 103, 101789.

    Google Scholar 

  • Li, T., & Zhou, M. (2016). ECG classification using wavelet packet entropy and random forests. Entropy, 18(8), 285.

    Google Scholar 

  • Madeiro, J. P., Marques, J. A. L., Han, T., & Pedrosa, R. C. (2020). Evaluation of mathematical models for QRS feature extraction and QRS morphology classification in ECG signals. Measurement, 156, 107580.

    Google Scholar 

  • Martis, R. J., Acharya, U. R., Lim, C. M., Mandana, K., Ray, A. K., & Chakraborty, C. (2013). Application of higher order cumulant features for cardiac health diagnosis using ECG signals. International Journal of Neural Systems, 23(04), 1350014.

    Google Scholar 

  • Mayer, T., Marro, S., Cabrio, E., & Villata, S. (2021). Enhancing evidence-based medicine with natural language argumentative analysis of clinical trials. Artificial Intelligence in Medicine, 118, 102098.

    Google Scholar 

  • Melin, P., Amezcua, J., Valdez, F., & Castillo, O. (2014). A new neural network model based on the LVQ algorithm for multi-class classification of arrhythmias. Information Sciences, 279, 483–497.

    MathSciNet  Google Scholar 

  • Merino, M., Gómez, I. M., & Molina, A. J. (2015). Envelopment filter and K-means for the detection of QRS waveforms in electrocardiogram. Medical Engineering & Physics, 37(6), 605–609.

    Google Scholar 

  • Mihandoost, S., & Amirani, M. C. (2017). Cyclic spectral analysis of electrocardiogram signals based on GARCH model. Biomedical Signal Processing and Control, 31, 79–88.

    Google Scholar 

  • Moody, G. B., & Mark, R. G. (2001). The impact of the MIT-BIH arrhythmia database. Engineering in Medicine and Biology Magazine, IEEE, 20(3), 45–50.

    Google Scholar 

  • Nayak, C., Saha, S. K., Kar, R., & Mandal, D. (2019). An Efficient and Robust Digital Fractional Order Differentiator Based ECG Pre-processor Design for QRS Detection. IEEE Transactions on Biomedical Circuits and Systems, 13, 682–696.

    Google Scholar 

  • Oh, S. L., Ng, E. Y., San Tan, R., & Acharya, U. R. (2018). Automated diagnosis of arrhythmia using combination of CNN and LSTM techniques with variable length heart beats. Computers in Biology and Medicine, 102, 278–287.

    Google Scholar 

  • Pellicer-Valero, O. J., Cattinelli, I., Neri, L., Mari, F., Martín-Guerrero, J. D., & Barbieri, C. (2020). Enhanced prediction of hemoglobin concentration in a very large cohort of hemodialysis patients by means of deep recurrent neural networks. Artificial Intelligence in Medicine, 107, 101898.

    Google Scholar 

  • Rahul, J., & Sharma, L. D. (2022a). Automatic cardiac arrhythmia classification based on hybrid 1-D CNN and bi-LSTM model. Biocybernetics and Biomedical Engineering,42(1), 312–324.

  • Rahul, J., & Sharma, L. D. (2022b). Artificial intelligence-based approach for atrial fibrillation detection using normalised and short-duration time-frequency ECG. Biomedical Signal Processing and Control,71, 103270.

  • Rahul, J., & Sora, M. (2020). A novel adaptive window based technique for T wave detection and delineation in the ECG. Bio-Algorithms and Med-Systems, 16(1), 20190064.

  • Rahul, J., Sharma, L. D., & Bohat, V. K. (2021d). Short duration vector cardiogram based inferior myocardial infarction detection: Class and subject-oriented approach. Biomedical Engineering/Biomedizinische Technik,66(5), 489–501.

  • Rahul, J., Sora, M., & Sharma, L. D. (2021a). Dynamic thresholding based efficient QRS complex detection with low computational overhead. Biomedical Signal Processing and Control,67, 102519.

  • Rahul, J., Sora, M., & Sharma, L. D. (2021b). A novel and lightweight P, QRS, and T peaks detector using adaptive thresholding and template waveform. Computers in Biology and Medicine,132, 104307.

  • Rahul, J., Sora, M., Sharma, L. D., & Bohat, V. K. (2021c). An improved cardiac arrhythmia classification using an RR interval-based approach. Biocybernetics and Biomedical Engineering. https://doi.org/10.1016/j.bbe.2021.04.004. ISSN 0208-5216.

  • Rahul, J., Sora, M., & Sharma, L. D. (2020). Exploratory data analysis based efficient QRS-complex detection technique with minimal computational load. Physical and Engineering Sciences in Medicine, 43(3), 1049–1067.

    Google Scholar 

  • Rangayyan, R. M. (2015). Biomedical signal analysis. New York: Wiley.

    Google Scholar 

  • Sangaiah, A. K., Arumugam, M., & Bian, G.-B. (2020). An intelligent learning approach for improving ECG signal classification and arrhythmia analysis. Artificial Intelligence in Medicine, 103, 101788.

    Google Scholar 

  • Sharma, L. D., & Sunkaria, R. K. (2018a). Stationary wavelet transform based technique for automated external defibrillator using optimally selected classifiers. Measurement,125, 29–36.

  • Sharma, L. D., & Sunkaria, R. K. (2018b). Inferior myocardial infarction detection using stationary wavelet transform and machine learning approach. Signal, Image and Video Processing,12(2), 199–206.

  • Sharma, A., Garg, N., Patidar, S., San Tan, R., & Acharya, U. R. (2020). Automated pre-screening of arrhythmia using hybrid combination of Fourier–Bessel expansion and LSTM. Computers in Biology and Medicine, 120, 103753.

    Google Scholar 

  • Sharma, A., Patidar, S., Upadhyay, A., & Acharya, U. R. (2019). Accurate tunable-Q wavelet transform based method for QRS complex detection. Computers & Electrical Engineering, 75, 101–111.

    Google Scholar 

  • Sharma, H., & Sharma, K. (2018). ECG-derived respiration based on iterated Hilbert transform and Hilbert vibration decomposition. Australasian Physical & Engineering Sciences in Medicine, 41(2), 429–443.

    MathSciNet  Google Scholar 

  • Tereshchenko, L. G., & Josephson, M. E. (2015). Frequency content and characteristics of ventricular conduction. Journal of Electrocardiology, 48(6), 933–937.

    Google Scholar 

  • Tsipouras, M. G., Fotiadis, D. I., & Sideris, D. (2005). An arrhythmia classification system based on the RR-interval signal. Artificial Intelligence in Medicine, 33(3), 237–250.

    Google Scholar 

  • Van Steenkiste, T., Ruyssinck, J., De Baets, L., Decruyenaere, J., De Turck, F., Ongenae, F., & Dhaene, T. (2019). Accurate prediction of blood culture outcome in the intensive care unit using long short-term memory neural networks. Artificial Intelligence in Medicine, 97, 38–43.

    Google Scholar 

  • Wang, Z., Wan, F., Wong, C. M., & Zhang, L. (2016). Adaptive Fourier decomposition based ECG denoising. Computers in Biology and Medicine, 77, 195–205.

    Google Scholar 

  • Yang, H., & Wei, Z. (2020). Arrhythmia recognition and classification using combined parametric and visual pattern features of ECG morphology. IEEE Access, 8, 47103–47117.

    Google Scholar 

  • Yildirim, Ö. (2018). A novel wavelet sequence based on deep bidirectional LSTM network model for ECG signal classification. Computers in Biology and Medicine, 96, 189–202.

    Google Scholar 

  • Yıldırım, Ö., Pławiak, P., Tan, R.-S., & Acharya, U. R. (2018). Arrhythmia detection using deep convolutional neural network with long duration ECG signals. Computers in Biology and Medicine, 102, 411–420.

    Google Scholar 

  • Yu, S.-N., & Chou, K.-T. (2008). Integration of independent component analysis and neural networks for ECG beat classification. Expert Systems with Applications, 34(4), 2841–2846.

    Google Scholar 

  • Yuen, B., Dong, X., & Lu, T. (2019). Inter-patient CNN-LSTM for QRS complex detection in noisy ECG signals. IEEE Access, 7, 169359–169370.

    Google Scholar 

  • Zhang, J., Liu, A., Gao, M., Chen, X., Zhang, X., & Chen, X. (2020). ECG-based multi-class arrhythmia detection using spatio-temporal attention-based convolutional recurrent neural network. Artificial Intelligence in Medicine, 106, 101856.

    Google Scholar 

  • Zhong, W., Liao, L., Guo, X., & Wang, G. (2018). A deep learning approach for fetal QRS complex detection. Physiological Measurement, 39(4), 045004.

    Google Scholar 

  • Zhou, F.-Y., Jin, L.-P., & Dong, J. (2017). Premature ventricular contraction detection combining deep neural networks and rules inference. Artificial Intelligence in Medicine, 79, 42–51.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Jagdeep Rahul.

Ethics declarations

Conflict of interest

There are no conflict of interest to disclose.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sharma, L.D., Rahul, J., Aggarwal, A. et al. An improved cardiac arrhythmia classification using stationary wavelet transform decomposed short duration QRS segment and Bi-LSTM network. Multidim Syst Sign Process 34, 503–520 (2023). https://doi.org/10.1007/s11045-023-00875-x

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11045-023-00875-x

Keywords

Navigation