Version 1
: Received: 27 October 2023 / Approved: 30 October 2023 / Online: 31 October 2023 (03:34:53 CET)
How to cite:
Ye, S. Wavelet Theories and Techniques for Brain Disease: Diagnosis, and Treatment. Preprints2023, 2023101986. https://doi.org/10.20944/preprints202310.1986.v1
Ye, S. Wavelet Theories and Techniques for Brain Disease: Diagnosis, and Treatment. Preprints 2023, 2023101986. https://doi.org/10.20944/preprints202310.1986.v1
Ye, S. Wavelet Theories and Techniques for Brain Disease: Diagnosis, and Treatment. Preprints2023, 2023101986. https://doi.org/10.20944/preprints202310.1986.v1
APA Style
Ye, S. (2023). Wavelet Theories and Techniques for Brain Disease: Diagnosis, and Treatment. Preprints. https://doi.org/10.20944/preprints202310.1986.v1
Chicago/Turabian Style
Ye, S. 2023 "Wavelet Theories and Techniques for Brain Disease: Diagnosis, and Treatment" Preprints. https://doi.org/10.20944/preprints202310.1986.v1
Abstract
This paper first introduces the main classification of brain diseases and the main causes of these diseases. It is followed by wavelet analysis, which is a mathematical function or wave-like pattern used to transform data, which can decompose the signal into different wavelet functions, each of which is related to a specific scale or frequency. Wavelet analysis has been applied in many fields such as image processing, data analysis and engineering. In addition, wavelet is also used in the analysis of biological signals. Wavelet analysis breaks down brain signals, such as EEG or fMRI data, into various frequency components at different scales. Wavelet analysis provides time-frequency localization. Pattern recognition can be enhanced by isolating salient features in the data. Wavelet denoising can effectively separate noise from underlying brain signals. Wavelet denoising usually adopts threshold method. Wavelet analysis can enhance pattern recognition by isolating salient features in the data. Researchers can train machine learning models based on these wavelet-derived features to recognize specific patterns associated with different neurological disorders. Wavelet analysis can track these changes by continuously evaluating the frequency content of the neuroimage data over time. Through continuous efforts, wavelet theory and technology have become valuable tools in the field of neuroscience and brain disease research.
Keywords
brain diseases; wavelet; feature extraction; signal denoising; classification and diagnosis; time-frequency analysis
Subject
Medicine and Pharmacology, Clinical Medicine
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
