Skip to main content
SpringerLink
Log in

ECG Signal Denoising Techniques for Cardiac Pacemaker Systems

  • Chapter
  • First Online:
High Performance and Power Efficient Electrocardiogram Detectors

Part of the book series: Energy Systems in Electrical Engineering ((ESIEE))

Abstract

Electrocardiogram (ECG) signals are used to diagnose cardiovascular diseases. Various noises like power line interference, baseline wandering, motion artifacts, and electromyogram noise corrupt the ECG signal during ECG signal acquisition. As ECG signal is non-stationary, removing these noises from the recorded ECG signal is tricky. This chapter studies various ECG signal denoising techniques to denoise an ECG signal corrupted with noise.

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

Access this chapter

Log in via an institution
eBook
USD 16.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  • (Online) Available: https://www.who.int/nmh/publications/ncd-profiles-2018/en/. Last accessed on 14th Jan 2022

  • (Online). Available: https://www.datasci.com/solutions/cardiovascular/ecg-research. Last accessed on 14th Jan 2022

  • Banerjee S, Mitra M (2014) Application of cross wavelet transform for ECG pattern analysis and classification. IEEE Trans Instrum Meas 63(2):326–333

    Article  Google Scholar 

  • Bhavtosh, Berwal D, Kumar Y (2015) High performance QRS complex detector for wearable ECG systems using multi scaled product with booth multiplier and soft threshold algorithm. In: 2015 international conference on signal processing and communication (ICSC). IEEE, pp 204–209

    Google Scholar 

  • Chang KM (2010) Arrhythmia ECG noise reduction by ensemble empirical mode decomposition. Sensors 10(6):6063–6080

    Article  Google Scholar 

  • Chang CH, Gu J, Zhang M (2005) A review of 0.18-/spl mu/m full adder performances for tree structured arithmetic circuits. IEEE Trans Very Large Scale Integr (VLSI) Syst 13(6):686–695

    Google Scholar 

  • Chouhan VS, Mehta SS (2008) Detection of QRS complexes in 12-lead ECG using adaptive quantized threshold. Int J Comput Sci Netw Secur 8(1):155–163

    Google Scholar 

  • Dilaveris PE, Gialafos EJ, Sideris SK, Theopistou AM, Andrikopoulos GK, Kyriakidis M, Gialafos JE, Toutouzas PK (1998) Simple electrocardiographic markers for the prediction of paroxysmal idiopathic atrial fibrillation. Am Heart J 135(5):733–738

    Article  Google Scholar 

  • Dinh HAN, Kumar DK, Pah ND, Burton P (2001) Wavelets for QRS detection. Australas Phys Eng Sci Med 24(4):207

    Article  Google Scholar 

  • Elgendi M (2013) Fast QRS detection with an optimized knowledge-based method: evaluation on 11 standard ECG databases. PLoS ONE 8(9):e73557

    Article  Google Scholar 

  • Elgendi M, Jonkman M, DeBoer F (2010) Frequency bands effects on QRS detection. Pan 5, p.15Hz

    Google Scholar 

  • Friesen GM, Jannett TC, Jadallah MA, Yates SL, Quint SR, Nagle HT (1990) A comparison of the noise sensitivity of nine QRS detection algorithms. IEEE Trans Biomed Eng 37(1):85–98

    Article  Google Scholar 

  • Jansen M (2012) Noise reduction by wavelet thresholding, vol 161. Springer Science & Business Media

    Google Scholar 

  • Johnston J (1980) A filter family designed for use in quadrature mirror filter banks. In: IEEE international conference on ICASSP'80 acoustics, speech, and signal processing, vol 5. IEEE, pp 291–294

    Google Scholar 

  • Kim KH, Kim SJ (2003) A wavelet-based method for action potential detection from extracellular neural signal recording with low signal-to-noise ratio. IEEE Trans Biomed Eng 50(8):999–1011

    Article  Google Scholar 

  • Komaty A, Boudraa AO, Augier B, Dare-Emzivat D (2014) EMD-based filtering using similarity measure between probability density functions of IMFs. IEEE Trans Instrum Meas 63(1):27–34

    Article  Google Scholar 

  • Kumar A, Komaragiri R, Kumar M (2018a) From pacemaker to wearable: techniques for ECG detection systems. J Med Syst 42(2):34

    Article  Google Scholar 

  • Kumar A, Komaragiri R, Kumar M (2018b). Heartrate monitoring and therapeutic devices: a wavelet transform based approach for the modeling and classification of congestive heart failure. ISA Trans

    Google Scholar 

  • Lin CH (2008) Frequency-domain features for ECG beat discrimination using grey relational analysis-based classifier. Comput Math Appl 55(4):680–690

    Article  MathSciNet  Google Scholar 

  • Mallat S (2008) A wavelet tour of signal processing: the sparse way. Academic Press

    Google Scholar 

  • Mallat SG (1989) A theory for multiresolution signal decomposition: the wavelet representation. IEEE Trans Pattern Anal Mach Intell 11(7):674–693

    Article  Google Scholar 

  • Min YJ, Kim HK, Kang YR, Kim GS, Park J, Kim SW (2013) Design of wavelet-based ECG detector for implantable cardiac pacemakers. IEEE Trans Biomed Circ Syst 7(4):426–436

    Article  Google Scholar 

  • Nguyen P, Kim JM (2016) Adaptive ECG denoising using genetic algorithm-based thresholding and ensemble empirical mode decomposition. Inf Sci 373:499–511

    Article  Google Scholar 

  • Priya KD, Rao GS, Rao PS (2016) Comparative analysis of wavelet thresholding techniques with wavelet-Wiener filter on ECG signal. Procedia Comput Sci 87:178–183

    Article  Google Scholar 

  • Rodrigues JN, Olsson T, Sornmo L, Owall V (2005) Digital implementation of a wavelet-based event detector for cardiac pacemakers. IEEE Trans Circ Syst I Regul Pap 52(12):2686–2698

    Article  Google Scholar 

  • Sivannarayana N, Reddy DC (1999) Biorthogonal wavelet transforms for ECG parameters estimation. Med Eng Phys 21(3):167–174

    Article  Google Scholar 

  • Soman KP (2010) Insight into wavelets: from theory to practice. PHI Learning Pvt. Ltd.

    Google Scholar 

  • Tan X, Chen X, Hu X, Ren R, Zhou B, Fang Z, Xia S (2014) EMD-based electrocardiogram delineation for a wearable low-power ECG monitoring device. Can J Electr Comput Eng 37(4):212–221

    Article  Google Scholar 

  • Unser MA (1997) Ten good reasons for using spline wavelets. In Wavelet applications in signal and image processing V, vol 3169. International Society for Optics and Photonics, pp 422–432

    Google Scholar 

  • Van LD, Yang CC (2005) Generalized low-error area-efficient fixed-width multipliers. IEEE Trans Circ Syst I Regul Pap 52(8):1608–1619

    Article  Google Scholar 

  • Wang J, Ye Y, Pan X, Gao X, Zhuang C (2014) Fractional zero-phase filtering based on the Riemann-Liouville integral. Sig Process 98:150–157

    Article  Google Scholar 

  • Webster JG (2009) Medical instrumentation application and design. Wiley, New York

    Google Scholar 

  • Weng B, Blanco-Velasco M, Barner KE (2006) ECG denoising based on the empirical mode decomposition. In: 28th annual international conference of the IEEE engineering in medicine and biology Society, 2006. EMBS'06. IEEE, pp 1–4

    Google Scholar 

  • Zhang X, Lian Y (2014) A 300-mV 220-nW event-driven ADC with real-time QRS detection for wearable ECG sensors. IEEE Trans Biomed Circ Syst 8(6):834–843

    Article  Google Scholar 

  • Zorzi M (2014) A new family of high-resolution multivariate spectral estimators. IEEE Trans Autom Control 59(4):892–904

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Electronics Engineering, Vellore Institute of Technology, Chennai, Tamil Nadu, India

    Ashish Kumar

  2. Department of Electronics and Communication, Delhi Technological University, Shahbad Daulatpur, Delhi, India

    Manjeet Kumar

  3. Department of Electronics and Communication, Bennett University, Greater Noida, Uttar Pradesh, India

    Rama S. Komaragiri

Authors
  1. Ashish Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Manjeet Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Rama S. Komaragiri
    View author publications

    You can also search for this author in PubMed Google Scholar

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Kumar, A., Kumar, M., Komaragiri, R.S. (2023). ECG Signal Denoising Techniques for Cardiac Pacemaker Systems. In: High Performance and Power Efficient Electrocardiogram Detectors. Energy Systems in Electrical Engineering. Springer, Singapore. https://doi.org/10.1007/978-981-19-5303-3_3

Download citation

  • DOI: https://doi.org/10.1007/978-981-19-5303-3_3

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-19-5302-6

  • Online ISBN: 978-981-19-5303-3

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation