Automated Sleep Spindle Detection using IIR filters and a Gaussian Mixture Model

作者:Patti Chanakya Reddy*; Penzel Thomas; Cvetkovic Dean
来源:37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2015-08-25 to 2015-08-29.

摘要

Sleep spindle detection using modern signal processing techniques such as the Short-Time Fourier Transform and Wavelet Analysis are common research methods. These methods are computationally intensive, especially when analysing data from overnight sleep recordings. The authors of this paper propose an alternative using pre-designed IIR filters and a multivariate Gaussian Mixture Model. Features extracted with IIR filters are clustered using a Gaussian Mixture Model without the use of any subject independent thresholds. The Algorithm was tested on a database consisting of overnight sleep PSG of 5 subjects and an online public spindles database consisting of six 30 minute sleep excerpts. An overall sensitivity of 57% and a specificity of 98.24% was achieved in the overnight database group and a sensitivity of 65.19% at a 16.9% False Positive proportion for the 6 sleep excerpts.

  • 出版日期2015