.png){kind=icon}
.png){kind=icon}
Return
Efficient sleep spindle detection algorithm with decision tree
.png){kind=icon}
PRE
AI
DOI：10.1016/j.eswa.2009.01.061.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
In this study, an efficient sleep spindle detection algorithm based on decision tree is proposed. After analyzing the EEG waveform, the decision algorithm determines the exact location of sleep spindle by evaluating the Outputs of three different methods namely: Short Time Fourier Transform (STFT), Multiple Signal Classification (MUSIC) algorithm and Teager Energy Operator (TEO). The EEG records collected from patients used in this study have been recorded at the Sleep Research Center in Department of Psychiatry of Gulhane Military Medicine Academy. The obtained results are in agreement with the visual analysis of EEG evaluated by expert physicians. The method is applied to 16 distinct patients, 420,570 minutes long EEG records and the performance of the algorithm was assessed for the sleep spindles detection with 96.17% sensitivity and 95.54% specificity. As a result, it is found that the proposed sleep spindle detection algorithm is an efficient method to detect sleep spindles on EEG records. (C) 2009 Elsevier Ltd. All rights reserved.
Keywords:
Sleep spindle detection
EEG
Short Time Fourier Transform
Wavelet Transform
MUSIC algorithm
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
7.5
Papers:
2.9W
Citations:
10.2W
View more organizations