An adaptive neuro-fuzzy inference system for sleep spindle detection

Sheng Fu Liang, Chih En Kuo, Yu Han Hu, Chun Yu Chen, Yu Hung Li

Research output: Contribution to conferencePaper

3 Citations (Scopus)

Abstract

In this paper, an adaptive neuro-fuzzy inference system (ANFIS) for sleep spindle detection was developed. Two input variables including teager energy operator (TEO) and sigma index analyses of the EEG signals were extracted. 1180 training samples (0.5 s) of 15 subjects were used to ANFIS training, include 397 spindle and 783 non-spindle waveform. Then the 1519 epochs (30s) of other 15 subjects were used to evaluate the performance of ANFIS. The overall sensitivity and specificity of the ANFIS are 94.09% and 96.76%, respectively. Although the overall false positive rate is 38.58%, spindle and non-spindle successful detection rate could almost reach 90% for each subject. This method can integrate with various PSG systems for sleep monitoring in cognitive enhancements or sleep efficiency.

Original languageEnglish
Pages369-373
Number of pages5
DOIs
Publication statusPublished - 2012 Dec 1
Event2012 International Conference on Fuzzy Theory and Its Applications, iFUZZY 2012 - Taichung, Taiwan
Duration: 2012 Nov 162012 Nov 18

Other

Other2012 International Conference on Fuzzy Theory and Its Applications, iFUZZY 2012
CountryTaiwan
CityTaichung
Period12-11-1612-11-18

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All Science Journal Classification (ASJC) codes

  • Logic

Cite this

Liang, S. F., Kuo, C. E., Hu, Y. H., Chen, C. Y., & Li, Y. H. (2012). An adaptive neuro-fuzzy inference system for sleep spindle detection. 369-373. Paper presented at 2012 International Conference on Fuzzy Theory and Its Applications, iFUZZY 2012, Taichung, Taiwan. https://doi.org/10.1109/iFUZZY.2012.6409733