Automated Sleep-Spindle Detection in Healthy Children Polysomnograms

Abstract

We present a new methodology to detect and characterize sleep spindles (SSs), based on the nonlinear algorithms, empirical-mode decomposition, and Hilbert-Huang transform, which provide adequate temporal and frequency resolutions in the electroencephalographic analysis. In addition, the application of fuzzy logic allows to emulate expert's procedures. Additionally, we built a database of 56 all-night polysomnographic recordings from children for training and testing, which is among the largest annotated databases published on the subject. The database was split into training (27 recordings), validation (10 recordings), and testing (19 recordings) datasets. The SS events were marked by sleep experts using visual inspection, and these marks were used as golden standard. The overall SS detection performance on the testing dataset of continuous all-night sleep recordings was 88.2% sensitivity, 89.7% specificity, and 11.9% false-positive (FP) rate. Considering only non-REM sleep stage 2, the results showed 92.2% sensitivity, 90.1% specificity, and 8.9% FP rate. In general, our system presents enhanced results when compared with most systems found in the literature, thus improving SS detection precision significantly without the need of hypnogram information.

Sleep spindle detection through amplitude–frequency normal modelling

“…Causa et al (2010) proposed an algorithm with 92.2% sensitivity and 90.1% specificity, hence more sensitive but less selective than ours. Estévez et al (2007) proposed an algorithm, tested on a single infant, with 96.3% accuracy and an 89.7% detection rate.…”

“…Fewer studies have investigated spindle detection in children (Grigg-Damberger et al, 2007;Causa et al, 2010). However, assessing automated spindles detection, specifically in childhood, seems important as spindles show modification in amplitude, frequency, length, density, interspindle interval and topological distribution from infancy to adolescence (Nagata et al, 1996;Shinomiya et al, 1999;Scholle et al, 2007); therefore, automated algorithms should be able to adapt to those variations.…”

“…However, assessing automated spindles detection, specifically in childhood, seems important as spindles show modification in amplitude, frequency, length, density, interspindle interval and topological distribution from infancy to adolescence (Nagata et al, 1996;Shinomiya et al, 1999;Scholle et al, 2007); therefore, automated algorithms should be able to adapt to those variations. Published spindle detection algorithms in children and in infants are based on empirical-mode decomposition and Hilbert-Huang transform (Causa et al, 2010), expert procedure and fuzzy logic (Held et al, 2004(Held et al, , 2006, peak identification , merge neural gas model (Estévez et al, 2007), and neuro fuzzy approach .…”

On-line empirical mode decomposition biomedical microprocessor for Hilbert Huang transform

“…Hilbert-Huang transform (HHT), proposed by Huang et al [1], is an adaptive data analysis method, composed of a decomposition method called, empirical mode decomposition (EMD), and Hilbert spectrum analysis. Recently, HHT has been widely used for biomedical engineering, such as sleep analysis [2], signal de-noising [3] and heart analysis [4], etc. Besides, hardware implementation of EMD using DSP and FPGA was proposed [5].…”

Cubic spline interpolation with overlapped window and data reuse for on-line Hilbert Huang transform biomedical microprocessor

“…Hilbert-Huang Transform (HHT), proposed by Huang et al [3], is an adaptive data analysis method and provides a possible solution for non-linear and nonstationary signal analysis. Recently, HHT has been widely used for biomedical engineering, such as sleep analysis [4], brain computer interface (BCI) [5], noise removing [6] and seizure prediction [7].…”