References

  • Akansu AN, Haddad PA, Haddad RA. 2001. Multiresolution signal decomposition: transforms, subbands, and wavelets. Sandiego, USA: Academic press.
  • Turky Alotaiby, Fathi E Abd El-Samie, Saleh A Alshebeili, and Ishtiaq Ahmad. 2015. A review of channel selection algorithms for EEG signal processing. EURASIP J Adv Sig Pr. 20150(1):66.
  • Alzamil Y, Hicks Y, Yang X, Marshall C. 2018. Optimising graphical techniques applied to irreversible tracers. Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: BIOIMAGING. INSTICC, SciTePress. ISBN 978-989-758-278-3. p. 17–26. doi: 10.5220/0006513700170026.
  • Julián L Cárdenas-Barrera, Juan V Lorenzo-Ginori, and Ernesto Rodrguez-Valdivia. 2004. A wavelet-packets based algorithm for EEG signal compression. Med Inform Internet Med. 290(1):15–27.
  • Dean Cvetkovic, Elif Derya Übeyli, and Irena Cosic. 2008. Wavelet transform feature extraction from human ppg, ECG, and EEG signal responses to ELF PEMF exposures: a pilot study. Digital Signal Process. 180(5):861–874.
  • Junior FCFM, Saraiva AA, Sousa JVM, Ferreira NMF, Valente A. 2018. Manipulation of bio-inspired robot with gesture recognition through fractional calculus. 2018 Latin American Robotic Symposium, 2018 Brazilian Symposium on Robotics (SBR) and 2018 Workshop on Robotics in Education (WRE). Joao Pessoa, Paraíba Brazil: IEEE. p. 230–235.
  • Nasir Memon, Xuan Kong, and Judit Cinkler. 1999. Context-based lossless and near-lossless compression of EEG signals. Vol. 3. IEEE. p. 231–238.
  • Gil Nave and Arnon Cohen. 1993. Ecg compression using long-term prediction. IEEE Transactions on Biomedical Engineering, 400 (9): 877–885.
  • Nguyen B, Nguyen D, Ma W, Tran D. 2017. Investigating the possibility of applying EEG lossy compression to EEG-based user authentication. Neural Networks (IJCNN), 2017 International Joint Conference on. Anchorage, AK: IEEE. p. 79–85.
  • Princy R, Thamarai P, Karthik B. 2015. Denoising eeg signal using wavelet transform. Int J Adv Res Comput Eng Tech. 3.
  • Raj S, Ray KC. 2017 Mar. Ecg signal analysis using DCT-based dost and pso optimized SVM. Vol. 66; Anchorage, AK: IEEE Transactions on instrumentation and measurement. p. 470–478. doi: 10.1109/TIM.2016.2642758.
  • Ranjeet K, Kumar A, Pandey RK. 2011. Ecg signal compression using different techniques. International Conference on Advances in Computing, Communication and Control. Mumbai, India: Springer. p. 231–241.
  • Saka K, Aydemir Ö, Mehmet Ö. 2016. Classification of EEG signals recorded during right/left hand movement imagery using fast walsh hadamard transform based features. Telecommunications and Signal Processing (TSP), 2016 39th International Conference on. Vienna, Austria: IEEE. p. 413–416.
  • Saraiva AA, Almeida FM, de Araújo M, Barros P, Brandim ADS. 2015. Mathematical morphology applied in recognition of heart signs. Int J Differ Equations Appl. 140(3).
  • Saraiva AA, Castro FMJ, Costa NJC, Sousa JVM, Ferreira NMF, Soares S. 2019. Comparative study of compression techniques applied in differentbiomedical signals. Proceedings of the 12th International Joint Conference on Biomedical Engineering Systems and Technologies: BIOSINGNALS. INSTICC, Prague, Czech Republic: SciTePress.
  • Saraiva AA, Castro FMJ, Sousa JVM, Valente A, Fonseca FNM. 2018b. Comparative study between the walsh hadamard transform and discrete cosine transform. Antalya,Turkey: 7th International Conference on Advanced Technologies. Vol. 2.
  • Saraiva AA, Fonseca Ferreira NM, Valente A. New bioinspired filter of dicom images. Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: BIODEVICES. INSTICC, SciTePress. p. 258–265. ISBN 978-989-758-277-6. doi: 10.5220/0006723802580265.
  • Schomer DL, Da Silva FL. 2012. Niedermeyer’s electroencephalography: basic principles, clinical applications, and related fields. Philadelphia, PA: AAN Enterprises
  • Swarnkar A, Kumar R, Kumar A, Khanna P. 2017. Performance of different threshold function for ECG compression using slantlet transform. Noida, Delhi-NCR: Signal Processing and Integrated Networks (spin). Vol 37; Feb. p. 375–379. doi: 10.1109/SPIN.2017.8049977.
  • Teplan M, et al. 2002. Fundamentals of EEG measurement. Meas Sci Rev. 20(2):1–11.
  • Willmott CJ, Matsuura K. 2008. Advantages of the mean absolute error (MAE) over the root mean square error (rmse) in assessing average model performance. Clim Res. JSTOR.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.