Title :
MRS Feature Extraction: Time-Frequency and Wavelet Analysis
Author :
Mahmoodabadi, S.Zarei ; Alireazie, J. ; Babyn, P. ; Kassner, A. ; Widjaja, E.
Author_Institution :
Mahmoodabadi Dept. of Electr. & Comput. Eng., Ryerson Univ., Toronto, ON
Abstract :
Magnetic Resonance Spectroscopy (MRS) signals are being used for diagnosis of various brain diseases. Feature extraction of the MRS data is the most important step in analyzing the data. In this study a fully automated system is developed to analyze the MRS signals. The wavelet analysis is utilized in extracting signal features and the time-frequency representations. Consulting specialists in the field, the sensitivity of 87.11% plusmn 18 and the positive predictivity of 88.97% plusmn 15 in extracting features have been achieved.
Keywords :
biomedical measurement; brain; diseases; feature extraction; magnetic resonance spectroscopy; medical signal processing; neurophysiology; patient diagnosis; time-frequency analysis; wavelet transforms; MRS data analysis; MRS feature extraction; brain disease diagnosis; consulting specialist; fully automated system; magnetic resonance spectroscopic signals; time-frequency analysis; wavelet analysis; Data analysis; Data mining; Diseases; Feature extraction; Magnetic analysis; Magnetic resonance; Signal analysis; Spectroscopy; Time frequency analysis; Wavelet analysis;
Conference_Titel :
Bioinformatics and Biomedical Engineering, 2008. ICBBE 2008. The 2nd International Conference on
Conference_Location :
Shanghai
Print_ISBN :
978-1-4244-1747-6
Electronic_ISBN :
978-1-4244-1748-3
DOI :
10.1109/ICBBE.2008.795
