Skip to main content
SpringerLink
Log in

Heart disease detection system based on ECG and PCG signals with the aid of GKVDLNN classifier

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

One among the major causes of death in the country is Heart disease (HD). 17.3 million deaths per year are caused by cardiac diseases, which is the primary reason for death in the globe as per the World Health Organization (WHO). Hence, the early detection of heart disease increases the survival rate. For an efficient detection of HD, a wide variety of techniques are used. But, the techniques rely on limited data; this degrades the accuracy and the performance of the model. So, to deal with these shortcomings, this paper has proposed Gaussian Kaiming Variance-based Deep Learning Neural Network (GKVDLNN) classifier for HD. In this study, the problem of heart disease detection using ECG and PCG signals is investigated, which provides valuable information about heart function. Thereby, the possible abnormalities of the heart are identified. Moreover, large data are used in this work to ensure the model’s accuracy. Firstly, from the publically accessible dataset, the input ECG along with PCG signals is gathered. Next, pre-processing is undergone by both signals. Utilizing Improved Empirical Mode Decomposition (IEMD), the signals are decomposed into bands after pre-processing. Then, for extracting the signal features, the decomposed bands are utilized. Then, for further process, the most suitable features are chosen as of the extracted features of both the ECG and PCG signals. After that, both the features are concatenated, and then classification is performed. In the classification process, the HD’s type is identified and classified by GKVDLNN. Experimental results stated the proposed model’s superiority and the proposed system withstands with 96.103% of accuracy with reduced costs. Overall, the method highlights the importance of early heart disease detection [32, 33] and provides a novel approach for achieving this goal through the integration of ECG and PCG signals with advanced signal processing and machine learning techniques [31].

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  1. Khan RU, Hussain T, Quddus H, Haider A, Adnan, ZM (2019) An intelligent real-time heart diseases diagnosis algorithm, 2019 international conference on computing mathematics and engineering technologies, 30-31, Sukkur, Pakistan, 2019. https://doi.org/10.1109/ICOMET.2019.8673506

  2. Abadi AM, Sumarna (2019) Construction of fuzzy system for classification of heart disease based on phonocardiogram signal, 2019 1st international conference on artificial intelligence and data Sciences,19-19, Ipoh, Malaysia, 2019. https://doi.org/10.1109/AiDAS47888.2019.8970975

  3. Verma V, Dwivedi B, Singh S, Kumar G (2019) A novel approach to study electrical, mechanical and hydraulic activities of heart and their coordination based on ECG and PCG. Int J Appl Eng Res 14(2):227–231 https://www.ripublication.com/ijaerspl2019/ijaerv14n2spl_42.pdf

    Google Scholar 

  4. Yu L, Ye L, Zhuang W (2018) ECG signal classification with deep learning for heart disease identification, 2018 international conference on big data and artificial intelligence (BDAI), 22-24 June 2018, Beijing, China, https://doi.org/10.1109/BDAI.2018.8546681

  5. Javeed A, Zhou S, Yongjian L, Qasim I, Noor A, Nour R, Basit SWA (2016) An intelligent learning system based on random search algorithm and optimized random forest model for improved heart disease detection. IEEE Access 4:1–11. https://doi.org/10.1109/ACCESS.2019.2952107

    Article  Google Scholar 

  6. Low JX, Choo KW (2018) IoT-enabled heart monitoring device with signal de-noising and segmentation using discrete wavelet transform, 2018 15th International Conference on Control, Automation, Robotics and Vision, November 18–21, Singapore, https://doi.org/10.1109/ICARCV.2018.8581315

  7. Low JX, Choo KW (2018) Automatic classification of periodic heart sounds using convolutional neural network, Int J Electric Comput Energ Electron Commun Eng. https://doi.org/10.5281/ZENODO.1315910

  8. Vijayavanan M, Rathikarani V, Dhanalakshmi P (2014) Automatic classification of ECG signal for heart disease diagnosis using morphological features. Int J Comput Sci Eng Technol 5(4):449–455 http://www.ijcset.com/docs/IJCSET14-05-04-168.pdf

    Google Scholar 

  9. Somwanshi D, Tiwari R, Saini H, Gupta S (2018) ECG feature extraction and detection of first degree atrioventricular block, 3rd international conference and workshops on recent advances and innovations in Engineering, 22–25 November, https://doi.org/10.1109/ICRAIE.2018.8710343

  10. Isina A, Ozdalili S (2017) Cardiac arrhythmia detection using deep learning. Proced Comput Sci 120:268–275. https://doi.org/10.1016/j.procs.2017.11.238

    Article  Google Scholar 

  11. Zarrabi M, Parsaei H, Boostani R, Zare A, Dorfeshan Z, Zarrabi K, Kojuri J (2017) A system for accurately predicting the risk of myocardial infarction using pcg, ecg and clinical features. Biomed Eng Appl Basis Commun 29(3):1–10. https://doi.org/10.4015/S1016237217500235

    Article  Google Scholar 

  12. Nedoma J, Fajkus M, Martinek R, Kepak S, Cubik J, Zabka S, Vasinek V (2017) Comparison of BCG, PCG and ECG signals in application of heart rate monitoring of the human body, 2017 40th International Conference on Telecommunications and Signal Processing , 5-7 July 2017, Barcelona, Spain https://doi.org/10.1109/TSP.2017.8076019

  13. Latif S, Usman M, Rana R, Qadir J (2018) Phonocardiographicsensing using deep learning for abnormal heartbeat detection. IEEE Sens J 18(22):1–8. https://doi.org/10.48550/arXiv.1801.08322

    Article  Google Scholar 

  14. Gjoreski M, Simjanoska M, Gradisek A, Peterlin A, Gams M, Poglajen G (2017) Chronic heart failure detection from heart sounds using a stack of machine-learning classifiers, 2017 13th international conference on intelligent environments, 21-25 Aug. 2017, Seoul, Korea (South), https://doi.org/10.1109/IE.2017.19

  15. Gharehbaghi A, Linden M, Babic A (2019) An artificial intelligent-based model for detecting systolic pathological patterns of phonocardiogram based on time-growing neural network. Appl Soft Comput J 83:1–12. https://doi.org/10.1016/j.asoc.2019.105615

    Article  Google Scholar 

  16. Amiri AM, Abtahi M, Constant N, Mankodiya K (2017) Mobile phonocardiogram diagnosis in newborns using support vector machine. Healthcare 5:1–10. https://doi.org/10.3390/healthcare5010016

    Article  CAS  Google Scholar 

  17. Bao X, Deng Y, Gall N, Kamavuako EN (2020) Analysis of ECG and PCG time delay around auscultation sites, 13th international conference on bio-inspired systems and signal processing, January 2020, Valletta, Malta. https://www.scitepress.org/Papers/2020/89426/89426.pdf. Accessed 2021

  18. Queyam AB, Pahuja SK, Singh D (2018) Doppler ultrasound based non-invasive heart rate telemonitoring system for wellbeing assessment. Int J Intell Syst Appl 12:69–79. https://doi.org/10.5815/ijisa.2018.12.07

    Article  Google Scholar 

  19. Li H, Wang X, Liu C, Wang Y, Li P, Tang H, Yao L, Zhang H (2019) Dual-input neural network integrating feature extraction and deep learning for coronary artery disease detection using electrocardiogram and phonocardiogram. IEEE Access 7:146457–146469. https://doi.org/10.1109/ACCESS.2019.2943197

    Article  Google Scholar 

  20. Khan MA (2020) An IoTframework for heart disease prediction based on MDCNN classifier. IEEE Access 8:34717–34727. https://doi.org/10.1109/ACCESS.2020.2974687

    Article  Google Scholar 

  21. Yıldırım O, Pławiak P, Tan R-S, Acharya UR (2018) Arrhythmia detection using deep convolutional neural network with long duration ECG signals. Comput Biol Med 102:411–420. https://doi.org/10.1016/j.compbiomed.2018.09.009

    Article  PubMed  Google Scholar 

  22. Huang J, Chen B, Yao B, He W (2019) ECG arrhythmia classification using stft-based spectrogram and convolutional neural network. IEEE Access. https://doi.org/10.1109/ACCESS.2019.2928017

  23. Ghosh SK, Ponnalagu RN, Tripathy RK, Rajendra Acharya U (2020) Automated detection of heart valve diseases using chirplet transform and multiclass composite classifier with PCG signals. Comput Biol Med 118:1–7. https://doi.org/10.1016/j.compbiomed.2020.103632

    Article  Google Scholar 

  24. Alloqmani A, Abushark YB, Khan AI. Anomaly detection of breast cancer using deep learning. Arab J Sci Eng, pp 1–26. https://doi.org/10.1007/2Fs13369-023-07945-z

  25. Alloqmani A, Abushark YB, Khan AI, Alsolami F (2021) Deep learning based anomaly detection in images: insights, challenges and recommendations. (IJACSA) Int J Adv Comput Sci Appl 12(4):205–21. https://thesai.org/Downloads/Volume12No4/Paper_28-Deep_Learning_based_Anomaly_Detection_in_Images.pdf

  26. Khan AI, Abushark YB, Alsolami F, Almalawi A, Alam M, Kshirsagar P, Khan RA. Prediction of breast cancer based on computer vision and artificial intelligence techniques. Measurement, vol. 218, https://doi.org/10.1016/j.measurement.2023.113230

  27. Manju BR, Sneha MR (2020) ECG Denoising Using Wiener Filter and Kalman Filter. Proced Comput Sci 171:273–281

    Article  Google Scholar 

  28. Zhang D, Wang S, Li F, Tian S, Wang J, Ding X, Gong R (2020) An efficient ECG denoising method based on empirical mode decomposition, sample entropy and improved threshold function", Wireless Communication and Mobile Computing. pp 1–11, https://downloads.hindawi.com/journals/wcmc/2020/8811962.pdf. Accessed 2021

  29. Niu XD, Lu LR, Wang J, Han XC, Li X, Wang LM (2021) An improved empirical mode decomposition based in local integral mean and its application in signal processing. Mathematical Problems in Engineering, pp 1–30, https://downloads.hindawi.com/journals/mpe/2021/8891217.pdf. Accessed 2021

  30. Sharma V, Rasool A, Hajela G (2020) Prediction of heart disease using DNN, 2020 second international conference on inventive research in computing applications (ICIRCA), 15-17 July 2020, Coimbatore, India. https://doi.org/10.1109/ICIRCA48905.2020.9182991

  31. Abdulsalam G, Meshoul S, Shaiba H (2023) Explainable heart disease prediction using ensemble-quantum machine learning approach. Intell Autom Soft Comput 36(1):761–779. https://doi.org/10.32604/iasc.2023.032262

    Article  Google Scholar 

  32. Raza A, Tran KP, Koehl L, Li S (2022) Designing ECG monitoring healthcare system with federated transfer learning and explainable AI. Knowl-Based Syst 236:1–19. https://doi.org/10.1016/j.knosys.2021.107763

    Article  Google Scholar 

  33. Taniguchi H, Takata T, Takechi M, Furukawa A, Iwasawa J, Kawamura A, Taniguchi T, Tamura Y (2021) Explainable artificial intelligence model for diagnosis of atrial fibrillation using holter electrocardiogram waveforms. Int Heart J 62(3):534–539. https://doi.org/10.1536/ihj.21-094

    Article  PubMed  Google Scholar 

  34. Kavila SD (2021) Explainable artificial intelligence to predict cardiovascular diseases. J Emerg Technol Innov Res 8(6):711–716. https://doi.org/10.6017/ITAL.V41I2.14683

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. CSE Department, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur, AP, India

    P. Jyothi & G. Pradeepini

Authors
  1. P. Jyothi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. G. Pradeepini
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to P. Jyothi.

Ethics declarations

Competing interests

The authors have no relevant financial or non-financial interests to disclose.

The authors have no competing interests to declare that are relevant to the content of this article.

All authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript.

The authors have no financial or proprietary interests in any material discussed in this article.

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

Jyothi, P., Pradeepini, G. Heart disease detection system based on ECG and PCG signals with the aid of GKVDLNN classifier. Multimed Tools Appl 83, 30587–30612 (2024). https://doi.org/10.1007/s11042-023-16562-9

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-023-16562-9

Keywords

Search

Navigation