Skip to main content
SpringerLink
Log in

A Real-Time Cardiac Arrhythmia Classification Using Hybrid Combination of Delta Modulation, 1D-CNN and Blended LSTM

  • Published:
Neural Processing Letters Aims and scope Submit manuscript

Abstract

The Real-time wearable Electrocardiogram (ECG) monitoring device is a perfect choice for assisting in detecting cardiovascular disease. A novel ECG beat classification algorithm is presented for continuous heart monitoring on low-processing-capacity wearable devices. We discuss the different wearable wristwatch monitoring system, which allows for continuous 24-h heart rate monitoring. This paper introduces a novel method for classifying arrhythmias based on deep learning. The method relies on QRS/PT detection, Sigma-Delta Modulation (SDM), One-Dimensional Convolution Neural Networks (1D-CNN) algorithms. The QRS/PT wave detection system is based on the 1D-CNN and SDM framework with local minimum and local maximum point algorithms. We proposed a Long Short-Term Memory (LSTM) recurrent neural network with a blend classifier. The classifier combines two small LSTM networks’ predictions using two different features directly extracted from 1D-CNN and SDM bitstreams. The proposed model is evaluated by detecting QRS/PT waves and classifying arrhythmias using the MIT-BIH Arrhythmia Database. Five different classifications are performed and evaluated by the AAMI standard: N, F, Q, S, and V. The values for accuracy, positive predictivity, sensitivity, and F1-score are 99.56%, 96.5%, 93.87%, and 95.18%, respectively. The proposed algorithm detects QRS/PT in approximately 2050 ms and classifies each heartbeat in approximately 40–60 ms with wearable devices, and it consumes \(1.5\,\upmu \mathrm{w}\) total power. The results indicate that our proposed system outperforms previous research in accuracy and meets both computation time and low power consumption requirements.

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
Fig. 11
Fig. 12

Similar content being viewed by others

Data Availablity

Not Applicable.

Code Availability

Not Applicable.

References

  1. WHO (2019) New initiative launched to tackle cardiovascular disease, the world’s number one killer [Internet]

  2. Benjamin EJ, Virani SS, Callaway CW, Chamberlain AM, Chang AR, Cheng S, Chiuve SE, Cushman M, Delling FN, Deo R et al (2018) Heart disease and stroke statistics2018 update: a report from the American Heart Association. Circulation 137(12):67–492

    Google Scholar 

  3. Yıldırım Ö, 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

    Google Scholar 

  4. Yu S-N, Lee M-Y (2012) Bispectral analysis and genetic algorithm for congestive heart failure recognition based on heart rate variability. Comput Biol Med 42(8):816–825

    Google Scholar 

  5. Ionescu CM, Copot D (2017) Monitoring respiratory impedance by wearable sensor device: Protocol and methodology. Biomed Signal Process Control 36:57–62

    Google Scholar 

  6. Arunkumar K, Bhaskar M (2019) Heart rate estimation from photoplethysmography signal for wearable health monitoring devices. Biomed Signal Process Control 50:1–9

    Google Scholar 

  7. Girčys R, Kazanavičius E, Maskeliūnas R, Damaševičius R, Woźniak M (2020) Wearable system for real-time monitoring of hemodynamic parameters: implementation and evaluation. Biomed Signal Process Control 59:101873

    Google Scholar 

  8. Tang X, Hu Q, Tang W (2018) A real-time QRS detection system with PR/RT interval and ST segment measurements for wearable ECG sensors using parallel delta modulators. IEEE Trans Biomed Circuits Syst 12(4):751–761

    Google Scholar 

  9. Tang X, Ma Z, Hu Q, Tang W (2019) A real-time arrhythmia heartbeats classification algorithm using parallel delta modulations and rotated linear-kernel support vector machines. IEEE Trans Biomed Eng 67(4):978–986

    Google Scholar 

  10. Pan J, Tompkins WJ (1985) A real-time QRS detection algorithm. IEEE Trans Biomed Eng 3:230–236

    Google Scholar 

  11. Mazomenos EB, Biswas D, Acharyya A, Chen T, Maharatna K, Rosengarten J, Morgan J, Curzen N (2013) A low-complexity ECG feature extraction algorithm for mobile healthcare applications. IEEE J Biomed Health Inform 17(2):459–469

    Google Scholar 

  12. Merah M, Abdelmalik T, Larbi B (2015) R-peaks detection based on stationary wavelet transform. Comput Methods Programs Biomed 121(3):149–160

    Google Scholar 

  13. Thiamchoo N, Phukpattaranont P (2016) R peak detection algorithm based on continuous wavelet transform and Shannon energy. ECTI Trans Comput Inf Technol 10(2):167–175

    Google Scholar 

  14. Manikandan MS, Soman K (2012) A novel method for detecting r-peaks in electrocardiogram (ECG) signal. Biomed Signal Process Control 7(2):118–128

    Google Scholar 

  15. Saadi DB, Tanev G, Flintrup M, Osmanagic A, Egstrup K, Hoppe K, Jennum P, Jeppesen JL, Iversen HK, Sorensen HB (2015) Automatic real-time embedded QRS complex detection for a novel patch-type electrocardiogram recorder. IEEE J Transl Eng Health Med 3:1–12

    Google Scholar 

  16. 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 Sens J 18(9):3694–3702

    Google Scholar 

  17. Zalabarria U, Irigoyen E, Martinez R, Lowe A (2020) Online robust r-peaks detection in noisy electrocardiograms using a novel iterative smart processing algorithm. Appl Math Comput 369:124839

    MathSciNet  MATH  Google Scholar 

  18. Elgendi M (2013) Fast QRS detection with an optimized knowledge-based method: Evaluation on 11 standard ECG databases. PLoS ONE 8(9):73557

    Google Scholar 

  19. De Chazal P, O’Dwyer M, Reilly RB (2004) Automatic classification of heartbeats using ECG morphology and heartbeat interval features. IEEE Trans Biomed Eng 51(7):1196–1206

    Google Scholar 

  20. Jun TJ, Park HJ, Minh NH, Kim D, Kim Y-H (2016) Premature ventricular contraction beat detection with deep neural networks. In: 2016 15th IEEE international conference on machine learning and applications (ICMLA). IEEE, pp 859–864

  21. Osowski S, Hoai LT, Markiewicz T (2004) Support vector machine-based expert system for reliable heartbeat recognition. IEEE Trans Biomed Eng 51(4):582–589

    Google Scholar 

  22. Ince T, Kiranyaz S, Gabbouj M (2009) A generic and robust system for automated patient-specific classification of ECG signals. IEEE Trans Biomed Eng 56(5):1415–1426

    Google Scholar 

  23. Ye C, Kumar BV, Coimbra MT (2012) Heartbeat classification using morphological and dynamic features of ECG signals. IEEE Trans Biomed Eng 59(10):2930–2941

    Google Scholar 

  24. Raj S, Ray KC (2018) Sparse representation of ECG signals for automated recognition of cardiac arrhythmias. Expert Syst Appl 105:49–64

    Google Scholar 

  25. De Chazal P, Reilly RB (2006) A patient-adapting heartbeat classifier using ECG morphology and heartbeat interval features. IEEE Trans Biomed Eng 53(12):2535–2543

    Google Scholar 

  26. Hu YH, Palreddy S, Tompkins WJ (1997) A patient-adaptable ECG beat classifier using a mixture of experts approach. IEEE Trans Biomed Eng 44(9):891–900

    Google Scholar 

  27. Xu G (2020) IoT-assisted ECG monitoring framework with secure data transmission for health care applications. IEEE Access 8:74586–74594

    Google Scholar 

  28. Koya AM, Deepthi P (2019) Plug and play self-configurable IoT gateway node for telemonitoring of ECG. Comput Biol Med 112:103359

    Google Scholar 

  29. Tsipouras MG, Fotiadis DI, Sideris D (2005) An arrhythmia classification system based on the RR-interval signal. Artif Intell Med 33(3):237–250

    Google Scholar 

  30. Haseena HH, Mathew AT, Paul JK (2011) Fuzzy clustered probabilistic and multi layered feed forward neural networks for electrocardiogram arrhythmia classification. J Med Syst 35(2):179–188

    Google Scholar 

  31. Elhaj FA, Salim N, Harris AR, Swee TT, Ahmed T (2016) Arrhythmia recognition and classification using combined linear and nonlinear features of ECG signals. Comput Methods Programs Biomed 127:52–63

    Google Scholar 

  32. Özbay Y (2009) A new approach to detection of ECG arrhythmias: complex discrete wavelet transform based complex valued artificial neural network. J Med Syst 33(6):435–445

    Google Scholar 

  33. Zhang Z, Dong J, Luo X, Choi K-S, Wu X (2014) Heartbeat classification using disease-specific feature selection. Comput Biol Med 46:79–89

    Google Scholar 

  34. Dilmac S, Korurek M (2015) ECG heart beat classification method based on modified ABC algorithm. Appl Soft Comput 36:641–655

    Google Scholar 

  35. Javadi M, Arani SAAA, Sajedin A, Ebrahimpour R (2013) Classification of ECG arrhythmia by a modular neural network based on mixture of experts and negatively correlated learning. Biomed Signal Process Control 8(3):289–296

    Google Scholar 

  36. Martis RJ, Acharya UR, Min LC (2013) ECG beat classification using PCA, LDA, ICA and discrete wavelet transform. Biomed Signal Process Control 8(5):437–448

    Google Scholar 

  37. Martis RJ, Acharya UR, Prasad H, Chua CK, Lim CM (2013) Automated detection of atrial fibrillation using Bayesian paradigm. Knowl-Based Syst 54:269–275

    Google Scholar 

  38. Wang J-S, Chiang W-C, Hsu Y-L, Yang Y-TC (2013) Ecg arrhythmia classification using a probabilistic neural network with a feature reduction method. Neurocomputing 116:38–45

    Google Scholar 

  39. Martis RJ, Acharya UR, Lim CM, Mandana K, Ray AK, Chakraborty C (2013) Application of higher order cumulant features for cardiac health diagnosis using ECG signals. Int J Neural Syst 23(04):1350014

    Google Scholar 

  40. Mar T, Zaunseder S, Martínez JP, Llamedo M, Poll R (2011) Optimization of ECG classification by means of feature selection. IEEE Trans Biomed Eng 58(8):2168–2177

    Google Scholar 

  41. Jiang W, Kong SG (2007) Block-based neural networks for personalized ECG signal classification. IEEE Trans Neural Netw 18(6):1750–1761

    Google Scholar 

  42. Moon S-W, Kong S-G (2001) Block-based neural networks. IEEE Trans Neural Netw 12(2):307–317

    Google Scholar 

  43. Kiranyaz S, Ince T, Gabbouj M (2015) Real-time patient-specific ECG classification by 1-D convolutional neural networks. IEEE Trans Biomed Eng 63(3):664–675

    Google Scholar 

  44. Ye C, Kumar BV, Coimbra MT (2015) An automatic subject-adaptable heartbeat classifier based on multiview learning. IEEE J Biomed Health Inform 20(6):1485–1492

    Google Scholar 

  45. Arzeno NM, Deng Z-D, Poon C-S (2008) Analysis of first-derivative based QRS detection algorithms. IEEE Trans Biomed Eng 55(2):478–484

    Google Scholar 

  46. Keselbrener L, Keselbrener M, Akselrod S (1997) Nonlinear high pass filter for R-wave detection in ECG signal. Med Eng Phys 19(5):481–484

    Google Scholar 

  47. Fard PJM, Moradi M, Tajvidi M (2008) A novel approach in R peak detection using hybrid complex wavelet (HCW). Int J Cardiol 124(2):250–253

    Google Scholar 

  48. Li C, Zheng C, Tai C (1995) Detection of ECG characteristic points using wavelet transforms. IEEE Trans Biomed Eng 42(1):21–28

    Google Scholar 

  49. Pandit D, Zhang L, Liu C, Chattopadhyay S, Aslam N, Lim CP (2017) A lightweight QRS detector for single lead ECG signals using a max-min difference algorithm. Comput Methods Programs Biomed 144:61–75

    Google Scholar 

  50. Adnane M, Jiang Z, Choi S (2009) Development of QRS detection algorithm designed for wearable cardiorespiratory system. Comput Methods Programs Biomed 93(1):20–31

    Google Scholar 

  51. Chen S-W, Chen H-C, Chan H-L (2006) A real-time QRS detection method based on moving-averaging incorporating with wavelet denoising. Comput Methods Programs Biomed 82(3):187–195

    Google Scholar 

  52. Deepu CJ, Zhang X, Heng CH, Lian Y (2016) A 3-lead ECG-on-chip with QRS detection and lossless compression for wireless sensors. IEEE Trans Circuits Syst II Express Briefs 63(12):1151–1155

    Google Scholar 

  53. Abo-Zahhad M, Ahmed SM, Zakaria A (2012) An efficient technique for compressing ECG signals using QRS detection, estimation, and 2D DWT coefficients thresholding. Modelling and simulation in engineering, vol 2012

  54. Goldberger AL, Amaral LA, Glass L, Hausdorff JM, Ivanov PC, Mark RG, Mietus JE, Moody GB, Peng C-K, Stanley HE (2000) Physiobank, physiotoolkit, and physionet: components of a new research resource for complex physiologic signals. Circulation 101(23):215–220

    Google Scholar 

  55. Silva I, Moody GB (2014) An open-source toolbox for analysing and processing physionet databases in matlab and octave. J Open Res Softw 2(1):e27

    Google Scholar 

  56. Zhou P, Schwerin B, Lauder B, So S (2020) Deep learning for real-time ECG R-peak prediction. In: 2020 14th international conference on signal processing and communication systems (ICSPCS). IEEE, pp 1–7

  57. Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735–1780

    Google Scholar 

  58. Sill J, Takács G, Mackey L, Lin D (2009) Feature-weighted linear stacking. arXiv preprint arXiv:0911.0460

  59. Ji Y, Zhang S, Xiao W (2019) Electrocardiogram classification based on faster regions with convolutional neural network. Sensors 19(11):2558

    Google Scholar 

  60. 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 

  61. Yildirim O, Baloglu UB, Tan R-S, Ciaccio EJ, Acharya UR (2019) A new approach for arrhythmia classification using deep coded features and LSTM networks. Comput Methods Programs Biomed 176:121–133

    Google Scholar 

  62. Acharya UR, Fujita H, Lih OS, Adam M, Tan JH, Chua CK (2017) Automated detection of coronary artery disease using different durations of ECG segments with convolutional neural network. Knowl-Based Syst 132:62–71

    Google Scholar 

  63. Acharya UR, Fujita H, Oh SL, Hagiwara Y, Tan JH, Adam M (2017) Application of deep convolutional neural network for automated detection of myocardial infarction using ECG signals. Inf Sci 415:190–198

    Google Scholar 

  64. Yildirim Ö (2018) A novel wavelet sequence based on deep bidirectional LSTM network model for ECG signal classification. Comput Biol Med 96:189–202

    Google Scholar 

  65. Oh SL, Ng EY, San Tan R, Acharya UR (2018) Automated diagnosis of arrhythmia using combination of CNN and LSTM techniques with variable length heart beats. Comput Biol Med 102:278–287

    Google Scholar 

  66. Nurmaini S, Darmawahyuni A, Sakti Mukti AN, Rachmatullah MN, Firdaus F, Tutuko B (2020) Deep learning-based stacked denoising and autoencoder for ECG heartbeat classification. Electronics 9(1):135

    Google Scholar 

  67. Saadatnejad S, Oveisi M, Hashemi M (2019) LSTM-based ECG classification for continuous monitoring on personal wearable devices. IEEE J Biomed Health Inform 24(2):515–523

    Google Scholar 

Download references

Funding

There is no funding for this manuscript.

Author information

Author notes

  1. Chandra Sekhara Rao Annavarapu and Kishore Kumar Pedapenki have contributed equally to this work.

Authors and Affiliations

  1. Computer Science and Engineering, Indian Institute of Technology (ISM), Dhanbad, 826004, Jharkhand, India

    Meghana Karri & Chandra Sekhara Rao Annavarapu

  2. Electrical and Electronic Engineering, Jain University, Bangalore, 562112, India

    Kishore Kumar Pedapenki

Authors
  1. Meghana Karri
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chandra Sekhara Rao Annavarapu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Kishore Kumar Pedapenki
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

MK: Conceptualization, Methodology, Formal analysis, Validation, Resources, Investigation, Data curation, Writing—original draft, Writing—review & editing, Visualization. CSA: Supervision, Investigation, Project administration, Writing—review & editing.

Corresponding author

Correspondence to Meghana Karri.

Ethics declarations

Conflict of interest

The authors declare that they have 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

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Karri, M., Annavarapu, C.S.R. & Pedapenki, K.K. A Real-Time Cardiac Arrhythmia Classification Using Hybrid Combination of Delta Modulation, 1D-CNN and Blended LSTM. Neural Process Lett 55, 1499–1526 (2023). https://doi.org/10.1007/s11063-022-10949-9

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11063-022-10949-9

Keywords

Search

Navigation