Cited by 4ECG Monitoring and AnalysisAdvanced Computational Techniques in Science and EngineeringNon-Invasive Vital Sign Monitoring

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Studies on biomedical signal processing: A review

Jalagam, Mahesh Kumar, Mittal, Vinay Kumar·2024·AIP Conference Proceedings

Abstract

The main aim of this manuscript is to write a brief a study on various biomedical signal processing.This paper reviews the bases of signal processing in digital domain, a prominent path for biomedical signal processing.Abundant approaches have been performed on biomedical signal processing concerned with ECG, EEG, EMG and PCG exclusively over biomedical, Neurological, Cardio logical signal processing's and other biomedical Equipment's.Various databases, having signal processing methods, features, classifiers, and performance results have been put forward, which are worthwhile for researchers, biomedical engineers, and clinicians.It contents the appropriate filters used for biomedical Applications.Major classifications such as CNN, RNN, LSTM, ANN and Fuzzy neural networks (FNN) linked with AI have been analyzed in this article.The recent research of the AI based biomedical signal processing was also embedded here.Comparisons have been made between different parameters of various methods, techniques, and Classifiers utilized in biomedical signal processing.The performance results have been extracted from various databases and kept before for a better implementation of biomedical Signal Processing.

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Research works printed but not linked: 67

  1. D.C. Reddy (2005). “Biomedical Signal Processing-Principles and Techniques”. Tata McGraw-Hill Publishing Company Limited, New Delhi. ISBN-13:978-0-07-058388-7. pp-1–411.
  2. Tudor, M.; Tudor, L.; Tudor, K. I. (2005). "Hans Berger (1873-1941)--the history of electroencephalography". Acta Medica Croatica : Casopis Hravatske Akademije Medicinskih Znanosti. 59 (4): 307–13. PMID 16334737. Referred on 20th, October, 2020.
  3. 10.1113/jphysiol.1887.sp000257 · DOI
  4. 10.1109/MSPEC.1981.6369478 · DOI
  5. 10.1007/s12652-020-01852-z · DOI
  6. 10.1251/bpo115 · DOI
  7. 10.1109/ISMCR47492.2019.8955725 · DOI
  8. 10.1109/RBME.2019.2938061 · DOI
  9. 10.1016/j.bspc.2019.101784.pp-1-7 · DOI
  10. 10.1016/j.bspc.2018.12.028 · DOI
  11. 10.1016/j.bspc.2018.08.010 · DOI
  12. 10.1109/ACCESS.2020.2985280 · DOI
  13. Chih-Hung Lin1, Robert Chen-Hao Chang1,2, Tz-Han Pang1, and Kuang-Hao Lin3, “A Low-Complexity Bio-Medical Signal Receiver for Wireless Body Area Network”, 978-1-4673-2990-3/12/$31.00 ©2012 IEEE, ISOCC 2012, 443–446. pp-443-446.
  14. 10.1109/ISVLSI.2018.00088 · DOI
  15. Veerayya Javvaji, Sarada Musala, “0.4 V, Low Power GmC Integrator and Band pass Filter Design For EMG Bio Medical Signal Processing”, 978-1-7281-2414-8/19/$31.00 c 2019 IEEE, 2nd International Conference on Power and Embedded Drive Control (ICPEDC), pp-441–444. · DOI
  16. 10.1109/TBCAS.2020.2974154 · DOI
  17. D. Callaerts. “Signal separation methods based on singular value decomposition and their application to the real-time extraction of the fetal electrocardiogram from cutaneous recordings.“ Doctoral Dissertation, Katholieke Universiteit Leuven, 1989.
  18. 10.1109/10.841326 · DOI
  19. 10.1109/10.650369 · DOI
  20. D.B. Springer, L. Tarassenko, G.D. Clifford, Logistic regression-HSMM-based heart sound segmentation, IEEE Trans. Biomed. Eng. 63 (2016) 822–832. · DOI
  21. 10.1109/JBHI.2013.2294399 · DOI
  22. 10.1109/TBME.2016.2559800 · DOI
  23. 10.1109/TBME.2009.2028693 · DOI
  24. 10.1109/TBME.2014.2377695 · DOI
  25. 10.1016/j.bspc.2008.07.003 · DOI
  26. 10.1016/j.bspc.2016.10.004 · DOI
  27. 10.1007/BF02446718 · DOI
  28. 10.1109/TBME.1985.325532 · DOI · Paper page
  29. 10.1109/TMECH.2007.897262 · DOI
  30. 10.1109/83.918566 · DOI
  31. 10.1016/j.jneumeth.2005.05.015 · DOI
  32. 10.1016/S0950-3552(96)80032-0 · DOI
  33. 10.1111/aogs.12387 · DOI
  34. 10.1016/j.ajog.2012.10.873 · DOI
  35. 10.1016/j.jbi.2014.04.010 · DOI
  36. 10.1111/jep.12368 · DOI
  37. 10.1111/j.1471-0528.2009.02204.x · DOI
  38. 10.1515/jpm.2010.025 · DOI
  39. 10.1111/aogs.12286 · DOI
  40. 10.1109/TBME.2015.2422698 · DOI
  41. 10.1109/JBHI.2014.2349156 · DOI
  42. 10.1016/j.patcog.2004.12.013 · DOI
  43. 10.1007/s11063-014-9358-5 · DOI
  44. 10.1016/j.patcog.2004.08.009 · DOI
  45. 10.1016/j.compbiomed.2014.04.018 · DOI
  46. 10.1109/34.824819 · DOI
  47. 10.1109/TVLSI.2008.2000594 · DOI
  48. G. Camps, M. Martinez, and E. Soria, “Fetal ECG extraction using an FIR neural network,” In Comput. Cardiology 2001, vol. 28, pp. 249–252), Rotterdam, Netherlands, 23-26 September 2001. · DOI
  49. G. Camps, M. Martinez, E. Soria, L. Gomez, J. Calpe, J. Guerrero, and J. Munoz, “ECG Fetal Recovery Using Dynamic Neural Networks with FIR Synapses,” AI Med., vol. 31, pp. 197–209, 2004. · DOI
  50. 10.1016/j.bspc.2015.09.002 · DOI
  51. J.-Y. Yu, C.-Y. Yu, S.-B. Huang, T.-W. Chen, J.-T. Chen, K.-L. Kuo, and C.-Y. Lee, “A 0.5V 4.85Mbps dual-mode baseband transceiver with extended frequency calibration for biotelemetry applications,” in Proc. IEEE Asian Solid-State Circuits Conference, 2008, pp. 293–296. · DOI
  52. 10.1109/TVLSI.2008.2000594 · DOI
  53. Suraj Kiran Raman, Jayadev Kumar Jayaram, Sidhaarth Murugan, Arnab Saha and R.K. Kavitha, “Design of a robust method to acquire EOG signals using Bio-medical signal processing”, 2016 Online International Conference on Green Engineering and Technology(IC-GET), IEEE, pp-3/16.
  54. Xiaolan Zhao, Zuguo Wu, Jiren Xu, Keren Wang, and Jihai Niu, “Speech Signal Feature Extraction Based on Wavelet Transform”, 2011 International Conference on Intelligent Computation and Bio-Medical Instrumentation”, IEEE computer society, pp-179–182. · DOI
  55. Wei Zhang, Xu Wang, Linlin Ge, and Zhuo Zhang, “Noise Reduction in ECG Signal Based on Adaptive Wavelet Transform.” Proceedings of the 2005 IEEE, EM&B 27th Annual Conference, Shangai, China, September1-4, 2005, IEEE, pp-2699–2702. · DOI
  56. Zeyu You, Raviv Raich, XiaoliZ, Fern and Jinsub Kim, “Discriminative recurring signal detection and Localization”, ICASSP 2017, IEEE, pp-2377–2381. · DOI
  57. Heyoung Lee, Zeungnam Bien “Variable bandwidth filter for reconstruction of bio-medical signals with time-varying instantaneous bandwidth”, Proceedings of the second Joint EMBS/BMES Conference, Houston, TX, USA, October 23-26, 2002, pp-141–142. · DOI
  58. B. John Phillip, S. S. S. Kalyan and V. K. Mittal, “Discriminating High Arousal and Low Arousal Emotional Speech Using Mahalanobis Distance Among Acoustic Features”, in Proc. 26th National Conference on Communications (NCC 2020), IIT Kharagpur, India, 21-23 Feb 2020.
  59. Abhijit Mohanta, Prerana Mukherjee and Vinay Kumar Mittal, “Prosody Features Characterization of Autism Speech for Automated Detection and Classification”, in Proc. 26th National Conference on Communications (NCC 2020), IIT Kharagpur, India, 21-23 Feb 2020. · DOI
  60. Abhijit Mohanta and Vinay Kumar Mittal, “Classifying Speech of ASD Affected and Normal Children Using Acoustic Features”, in Proc. 26th National Conference on Communications (NCC 2020), IIT Kharagpur, India, 21-23 Feb 2020. · DOI · Paper page
  61. Abhijit Mohanta and Vinay Kumar Mittal, “Autism Speech Analysis using Acoustic Features”, in Proc. 16th International Conference on Natural Language Processing (ICON 2019), IIIT Hyderabad, India, 18-21 Dec 2019.
  62. Shubham Mittal and V. K. Mittal, “Biomedical Requirements for Human Machine Interface towards building a Humanoid: A Review”, in Proc. 16th International IEEE India Conference (INDICON 2019), Marwadi University, Rajkot, Gujarat, India, 13–15 Dec 2019. · DOI
  63. Abhijit Mohanta and Vinay Kumar Mittal, “Acoustic Features for Characterizing Speech of Children Affected with ASD”, in Proc. 16th International IEEE India Conference (INDICON 2019), Marwadi University, Rajkot, Gujarat, India, 13-15 Dec 2019. · DOI
  64. G. Radha Krishna, R. Krishnan and V. K. Mittal, “An Automated System for Regional Nativity Identification of Indian speakers from English Speech”, in Proc. 16th International IEEE India Conference (INDICON 2019), Marwadi University, Rajkot, Gujarat, India, 13-15 Dec 2019. · DOI
  65. G. Radha Krishna, R. Krishnan and V. K. Mittal, “Non-native Accent Partitioning for Speakers of Indian Regional Languages”, in Proc. 16th International Conference on Natural Language Processing (ICON 2019), IIIT Hyderabad, India, 18-21 Dec 2019.
  66. K. Devi Bhavani and V. K. Mittal, "Studies on Paralinguistic Speech Sounds", in Proc. 17th International IEEE India Conference (INDICON 2020), IEEE, 2020, pp. 1–6. · DOI
  67. D. B. Kadali and V. K. Mittal, Studies on paralinguistic sounds, emotional speech and expressive voices, in Proc. SMM20, Workshop on Speech, Music and Mind, 2020, ISCA, pp. 11–15. · DOI

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Metadata

Authors
Jalagam, M. K., & Mittal, V. K.
Affiliations
Koneru Lakshmaiah Education Foundation (IN)
Corresponding author
Jalagam, Mahesh Kumar
Venue
AIP Conference Proceedings
Published
2024
Volume
2512
Pages
020005
ISSN
0094-243X (ISSN-L)
OpenAlex
W4391098241
Cited by
4
References
61
Citations by year
2026: 1 · 2025: 3
FWCI
5.79
Type
conference paper
Language
en
Open access
closed
Locations
AIP conference proceedings (publishedVersion)
Topics
  • Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › ECG Monitoring and Analysis
  • Physical Sciences › Engineering › Biomedical Engineering › Advanced Computational Techniques in Science and Engineering
  • Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
Keywords
biomedical signal processing, digital signal processing, ECG, EEG, electromyography, biomedical signal classification, artificial neural networks, convolutional neural networks, recurrent neural networks, LSTM, fuzzy neural network

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CoChat. (2026). Citation check: Studies on biomedical signal processing: A review. https://app.cochat.ai/papers/10.1063/5.0159010
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Jalagam, M. K., & Mittal, V. K. (2024). Studies on biomedical signal processing: A review. AIP Conference Proceedings. https://doi.org/10.1063/5.0159010