Benchmarking matching pursuit to find sleep spindles

Pós Graduação em Clínica Médica da Universidade Federal do Rio Grande do Sul, Hospital de Clínicas de Porto Alegre, Brazil.
Journal of Neuroscience Methods (Impact Factor: 2.05). 10/2006; 156(1-2):314-21. DOI: 10.1016/j.jneumeth.2006.01.026
Source: PubMed


The aim of this study is to evaluate performance of Matching Pursuit (MP) algorithm against visual analysis for automatic sleep spindle (SS) detection in a sample of sleep stages 2-4 and REM pertaining to nine healthy young subjects. MP-SS voltage, frequency and duration characteristics were investigated for the amplitude threshold (AT) that maximized yield between test sensitivity and specificity. Parameter distribution curves were also built for correctly detected (true positive) and false-positive events. For sleep stage 2, MP reached 80.6% sensitivity and specificity for an AT value of 58.8. For all stages together, 81.2% sensitivity and specificity were reached for an AT value of 46.6. Specificity curves were adequate for all stages; sensitivity was lower for S3+4. Sigma frequency range activity with atypical characteristics was detected within REM sleep. Prevalence indexes obtained with MP were much higher than visual prevalence indexes for all stages; similar voltage, frequency and duration distribution curves were obtained for true positive and false positive events. For this sample of young male healthy subjects, the free-ware MP algorithm showed satisfactory performance for SS detection in sleep stage 2 as reported earlier, acceptable performance in sleep stages 3+4, although with lowered sensitivity, and sigma frequency range activity within REM sleep that needs better understanding. Within NREM sleep, correspondence between the MP automatic and the visual method was supported.

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Available from: Gunther Gerhardt
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    • "The idea of MP was used in sleep spindle detection as early as 1996 [11] and more recently in [16], [17]. The performance of MP, however, for sleep spindle detection of healthy male subjects was not at par with the other detectors employing more basic detection methods [27]. "
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    ABSTRACT: This paper proposes an EEG processor for sleep spindle detection algorithms. It non-linearly separates the raw EEG signal into non-oscillatory transient and sustained rhythmic oscillation components using long and short windows for the short-time Fourier transform. The processor utilizes the fact that sleep spindles can be sparsely represented via the inverse of a short-time Fourier transform. Five sleep spindle detectors were tested on the EEG database with and without the proposed EEG processor. We achieved an improvement of 13.3% in the by-sample F1 score, and 13.9% in the by-sample Matthews Correlation Coefficient score of these algorithms when the processed EEG was used for spindle detection. The processor was able to improve the scores by reducing the number of false positive spindles and increasing the number of true positive spindles detected.
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    • "For detection of spindles in children [12] presented a method using Hilbert-Huang transform while [13] used amplitude-frequency normal modelling to detect spindles in both children and adults. In another method, Schönwald et al. [14] evaluated the use of matching pursuit (MP) for spindle detection and achieved good results. Duman et al. [15] used Teager energy, maximum frequency and harmonic decomposition with a decision tree classifier to mark the presence of spindles. "
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    ABSTRACT: Sleep spindles are transient waveforms observed on the electroencephalogram (EEG) during the N2 stage of sleep. In this paper we evaluate the use of line length, an efficient and low-complexity time domain feature, for automatic detection of sleep spindles. We use this feature with a simple algorithm to detect spindles achieving sensitivity of 83.6% and specificity of 87.9%. We also present a comparison of these results with other spindle detection methods evaluated on the same dataset. Further, we implemented the algorithm on a MSP430 microcontroller achieving a power consumption of 56.7 μW. The overall detection performance, combined with the low power consumption show that line length could be a useful feature for detecting sleep spindles in wearable and resource-constrained systems.
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    • "only validated algorithm accuracy in capturing high frequency narrowband alpha on simulated data and not real EEG. Techniques based on matching pursuit (MP) have also been used by Schönwald et al. to identify alpha spindles in sleep by using a dictionary of Gabor, Fourier and Dirac delta functions [25]. They report sensitivity and specificity values of approximately 0.812 in detecting alpha sleep spindles across all stages of sleep. "
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    ABSTRACT: Rhythmic oscillatory activity is widely observed during a variety of subject behaviors and is believed to play a central role in information processing and control. A classic example of rhythmic activity is alpha spindles, which consist of short (0.5-2 s) bursts of high frequency alpha activity. Recent research has shown that alpha spindles in the parietal/occipital area are statistically related to fatigue and drowsiness. These spindles constitute sharp changes in the underlying statistical properties of the signal. Our hypothesis is that change point detection models can be used to identify the onset and duration of spindles in EEG. In this work we develop an algorithm that accurately identifies sudden bursts of narrowband oscillatory activity in EEG using techniques derived from change point analysis. Our motivating example is detection of alpha spindles in the parietal/occipital areas of the brain. Our goal is to develop an algorithm that can be applied to any type of rhythmic oscillatory activity of interest for accurate online detection. In this work we propose modeling the alpha band EEG time series using discounted autoregressive (DAR) modeling. The DAR model uses a discounting rate to weigh points measured further in the past less heavily than points more recently observed. This model is used together with predictive loss scoring to identify periods of EEG data that are statistically significant. Our algorithm accurately captures changes in the statistical properties of the alpha frequency band. These statistical changes are highly correlated with alpha spindle occurrences and form a reliable measure for detecting alpha spindles in EEG. We achieve approximately 95% accuracy in detecting alpha spindles, with timing precision to within approximately 150ms, for two datasets from an experiment of prolonged simulated driving, as well as in simulated EEG. Sensitivity and specificity values are above 0.9, and in many cases are above 0.95, for our analysis. Modeling the alpha band EEG using discounted AR models provides an efficient method for detecting oscillatory alpha activity in EEG. The method is based on statistical principles and can generally be applied to detect rhythmic activity in any frequency band or brain region.
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