Performance Comparison of Different Digital and Analog Filters Used for Biomedical Signal and Image Processing

Document Type : Research Paper


1 Department of Computer Science and Engineering, S.A. Engineering College (Autonomous), Chennai, Tamil Nadu, India.

2 Global Institute of Engineering and Technology, Tamilnadu, Ranipet, India.

3 Department of Computer Science and Engineering, SRKR Engineering College Bhimavaram, Chinamiram Rural, Andhra Pradesh, India.

4 Associate Professor, Research Supervisor/School of EEE at Sathyabama Institute of Science & Technology, Chennai, Tamilnadu, India.

5 Principal, C. Abdul Hakeem College of Engineering & Technology, Melvisharam-632509, Ranipet District, Tamil Nadu, India.



Getting highly accurate output in biomedical data processing concerning biomedical signals and images is impossible because biomedical data are generated from various electronic and electrical resources that can deliver the data with noise. Filtering is widely used for signal and image processing applications in medical, multimedia, communications, biomedical electronics, and computer vision. The biggest problem in biomedical signal and image processing is developing a perfect filter for the system. Digital filters are more advanced in precision and stability than analog filters. Digital filters are getting more attention due to the increasing advancements in digital technologies. Hence, most medical image and signal processing techniques use digital filters for preprocessing tasks. This paper briefly explains various filters used in medical image and signal processing. Matlab is a famous mathematical, analytical software with a platform and built-in tools to design filters and experiment with different inputs. Even though this paper implements filters like, Mean, Median, Weighted Average, Guassian, and Bilateral in Python to verify their performance, a suitable filter can be selected for biomedical applications by comparing their performance.


Ahmed, S. T. (2017, June). A study on multi objective optimal clustering techniques for medical datasets. In 2017 international conference on intelligent computing and control systems (ICICCS) (pp. 174-177). IEEE.
Ahmed, S. T., Sandhya, M., & Sankar, S. (2020). TelMED: dynamic user clustering resource allocation technique for MooM datasets under optimizing telemedicine network. Wireless Personal Communications112(2), 1061-1077.
Arsene, C. T., Hankins, R., & Yin, H. (2019, September). Deep learning models for denoising ECG signals. In 2019 27th European Signal Processing Conference (EUSIPCO) (pp. 1-5). IEEE.
Chen, J., Li, X., Mohamed, M. A., & Jin, T. (2020). An adaptive matrix pencil algorithm based-wavelet soft-threshold denoising for analysis of low frequency oscillation in power systems. IEEE access8, 7244-7255.
Eminaga, Y., Coskun, A., & Kale, I. (2018, October). Hybrid IIR/FIR wavelet filter banks for ECG signal denoising. In 2018 IEEE Biomedical Circuits and Systems Conference (BioCAS) (pp. 1-4). IEEE.
Ferdous, H., Jahan, S., Tabassum, F., & Islam, M. I. (2023). The Performance Analysis of Digital Filters and ANN in Denoising of Speech and Biomedical Signal. International Journal of Image, Graphics and Signal Processing13(1), 63.
Jung, S., Im, C., Eom, C., & Lee, C. (2019, June). Noise Reduction after RIR removal for Speech De-reverberation and Denoising. In 2019 34th International Technical Conference on Circuits/Systems, Computers and Communications (ITC-CSCC) (pp. 1-3). IEEE.
Khosla, A., Khandnor, P., & Chand, T. (2020). A comparative analysis of signal processing and classification methods for different applications based on EEG signals. Biocybernetics and Biomedical Engineering40(2), 649-690.
Kilicarslan, A., & Contreras-Vidal, J. L. (2019, July). Towards a unified framework for de-noising neural signals. In 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 620-623). IEEE.
Kumar, A., Satheesha, T. Y., Salvador, B. B. L., Mithileysh, S., & Ahmed, S. T. (2023). Augmented Intelligence enabled Deep Neural Networking (AuDNN) framework for skin cancer classification and prediction using multi-dimensional datasets on industrial IoT standards. Microprocessors and Microsystems97, 104755.
Kumar, A., Tomar, H., Mehla, V. K., Komaragiri, R., & Kumar, M. (2021). Stationary wavelet transform based ECG signal denoising method. ISA transactions114, 251-262.
Pise, A. W., & Rege, P. P. (2021, April). Comparative analysis of various filtering techniques for denoising EEG signals. In 2021 6th International Conference for Convergence in Technology (I2CT) (pp. 1-4). IEEE.
Rajeev, R., Samath, J. A., & Karthikeyan, N. K. (2019). An intelligent recurrent neural network with long short-term memory (LSTM) BASED batch normalization for medical image denoising. Journal of medical systems43, 1-10.
S Celin and K. Vasanth, "ECG Signal Classification using Various Machine Learning Techniques," Journal of Medical Systems, pp. 241-251, 18 Oct. 2018
Tay, D. B. (2021). Sensor network data denoising via recursive graph median filters. Signal Processing189, 108302.
Zhao, Z., Liu, C., Li, Y., Li, Y., Wang, J., Lin, B. S., & Li, J. (2019). Noise rejection for wearable ECGs using modified frequency slice wavelet transform and convolutional neural networks. IEEE Access7, 34060-34067.