Development and comparison of four sleep spindle detection methods (original) (raw)
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Detecting sleep spindles in EEGs using wavelet fourier analysis and statistical features
Biomedical Signal Processing and Control, 2019
One of the more difficult tasks in sleep stage scoring is the detection of sleep spindles. Developing an effective method to identify these transitions in sleep electroencephalogram (EEG) recordings is an ongoing challenge, as there are typically hundreds of such transitions in each recording. This paper proposes a statistical model and a method based on wavelet Fourier analysis to detect sleep spindles. In this work, spindle detection is achieved in two phases: a training phase and a testing phase. An EEG signal is first divided into segments, using a sliding window technique. The size of the window is 0.5 s, with an overlap of 0.4 s. Then, each EEG segment is decomposed using a discrete wavelet transform into different levels of decompositions. The wavelet detail coefficient at level 3 (D3) is selected from these parameters, and this is passed through a fast Fourier transform to identify the desired frequency bands {␣, , , ı, ␥}. Ten statistical characteristics are extracted from each band. Nonparametric Kruskal-Wallis one-way analysis of variance is used to select the important features, representing each of the 0.5 s EEG segments. To detect all possible occurrences of sleep spindles in the original EEG signals, four different window sizes of 0.25, 1.0, 1.5 and 2.0 s are also tested. Finally, the extracted features are used as the input to four classifiers to detect the sleep spindles: a least-squares support vector machine (LS-SVM), K-nearest neighbours, a K-means algorithm and a C4.5 decision tree. The obtained results demonstrate that the proposed method yields optimal results with a window size of 0.5 s. The maximum averages of accuracy, sensitivity and specificity are 97.9%, 98.5% and 97.8%, respectively. This method can efficiently detect spindles in EEG signals, and can assist sleep experts in analysing EEG signals.
Optimization of sigma amplitude threshold in sleep spindle detection
Journal of Sleep Research, 2000
Sleep spindles are transient EEG waveforms of non-rapid eye movement sleep. There is considerable intersubject variability in spindle amplitudes. The problem in automatic spindle detection has been that, despite this fact, a ®xed amplitude threshold has been used. Selection of the spindle detection threshold value is critical with respect to the sensitivity of spindle detection. In this study a method was developed to estimate the optimal recording-speci®c threshold value for each all-night recording without any visual scorings. The performance of the proposed method was validated using four test recordings each having a very dierent number of visually scored spindles. The optimal threshold values for the test recordings could be estimated well. The presented method seems very promising in providing information about sleep spindle amplitudes of individual all-night recordings.
Combining time-frequency and spatial information for the detection of sleep spindles
Frontiers in human neuroscience, 2015
EEG sleep spindles are short (0.5-2.0 s) bursts of activity in the 11-16 Hz band occurring during non-rapid eye movement (NREM) sleep. This sporadic activity is thought to play a role in memory consolidation, brain plasticity, and protection of sleep integrity. Many automatic detectors have been proposed to assist or replace experts for sleep spindle scoring. However, these algorithms usually detect too many events making it difficult to achieve a good tradeoff between sensitivity (Se) and false detection rate (FDr). In this work, we propose a semi-automatic detector comprising a sensitivity phase based on well-established criteria followed by a specificity phase using spatial and spectral criteria. In the sensitivity phase, selected events are those which amplitude in the 10-16 Hz band and spectral ratio characteristics both reject a null hypothesis (p < 0.1) stating that the considered event is not a spindle. This null hypothesis is constructed from events occurring during rapi...
2004
An automated system for sleep spindles detection within EEG background activity, combining two different approaches, is presented. The first approach applies detection criteria on the sigma-band filtered EEG signal, including fuzzy thresholds. The second approach mimics an expert's procedure. A sleep spindle detection is validated if both approaches agree. The method was applied on a testing set, consisting of continuous sleep recordings of two patients, totaling 1132 epochs (pages). A total of 803 sleep spindles events were marked by the experts. Results showed an 87.7% agreement between the detection system and the medical experts.
Short Time Fourier Transform and Automatic Visual Scoring for the Detection of Sleep Spindles
IFIP Advances in Information and Communication Technology, 2012
Sleep spindles are the most interesting hallmark of stage 2 sleep EEG. Their accurate identification in a polysomnographic signal is essential for sleep professionals to help them mark Stage 2 sleep. Visual spindle scoring however is a tedious workload. In this paper two different approaches are used for the automatic detection of sleep spindles: Short Time Fourier Transform and Automatic Visual Scoring. The results obtained using both methods are compared with human expert scorers.
Computer Methods and Programs in Biomedicine, 2005
An artificial neural network (ANN) based on the Multi-Layer Perceptron (MLP) architecture is used for detecting sleep spindles in band-pass filtered electroencephalograms (EEG), without feature extraction. Following optimum classification schemes, the sensitivity of the network ranges from 79.2% to 87.5%, while the false positive rate ranges from 3.8% to 15.5%. Furthermore, due to the operation of the ANN on time-domain EEG data, there is agreement with visual assessment concerning temporal resolution. Specifically, the total inter-spindle interval duration and the total duration of spindles are calculated with 99% and 92% accuracy, respectively. Therefore, the present method may be suitable for investigations of the dynamics among successive inter-spindle intervals, which could provide information on the role of spindles in the sleep process, and for studies of pharmacological effects on sleep structure, as revealed by the modification of total spindle duration.
Automated Sleep-Spindle Detection in Healthy Children Polysomnograms
IEEE Transactions on Biomedical Engineering, 2010
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.
An Automatic Sleep Spindle Detector based on WT, STFT and WMSD
Zenodo (CERN European Organization for Nuclear Research), 2012
Sleep spindles are the most interesting hallmark of stage 2 sleep EEG. Their accurate identification in a polysomnographic signal is essential for sleep professionals to help them mark Stage 2 sleep. Sleep Spindles are also promising objective indicators for neurodegenerative disorders. Visual spindle scoring however is a tedious workload. In this paper three different approaches are used for the automatic detection of sleep spindles: Short Time Fourier Transform, Wavelet Transform and Wave Morphology for Spindle Detection. In order to improve the results, a combination of the three detectors is presented and comparison with human expert scorers is performed. The best performance is obtained with a combination of the three algorithms which resulted in a sensitivity and specificity of 94% when compared to human expert scorers.
Journal of Neuroscience Methods, 2009
Evidence supports the robustness and stability of individual differences in non-rapid eye movement (NREM) sleep electroencephalogram (EEG) spectra with a special emphasis on the 9-16 Hz range corresponding to sleep spindle activity. These differences cast doubt on the universal validity of sleep spindle analysis methods based on strict amplitude and frequency criteria or a set of templates of natural spindles. We aim to improve sleep spindle analysis by the individual adjustments of frequency and amplitude criteria, the use of a minimum set of a priori knowledge, and by clear dissections of slow-and fast sleep spindles as well as to transcend the concept of visual inspection as being the ultimate test of the method's validity. We defined spindles as those segments of the NREM sleep EEG which contribute to the two peak regions within the 9-16 Hz EEG spectra. These segments behaved as slow-and fast sleep spindles in terms of topography and sleep cycle effects, while age correlated negatively with the occurrence of fast type events only. Automatic detections covered 92.9% of visual spindle detections (A&VD). More than half of the automatic detections (58.41%) were exclusively automatic detections (EADs). The spectra of EAD correlated significantly and positively with the spectra of A&VD as well as with the average (AVG) spectra. However, both EAD and A&VD had higher individual-specific spindle spectra than AVG had. Results suggest that the individual adjustment method (IAM) detects EEG segments possessing the individual-specific spindle spectra with higher sensitivity than visual scoring does.