Sleep Spindle Detection Research Articles

Unveil sleep spindles with concentration of frequency and time (ConceFT)

Objective. Sleep spindles contain crucial brain dynamics information. We introduce the novel non-linear time-frequency (TF) analysis tool ‘Concentration of Frequency and Time’ (ConceFT) to create an interpretable automated algorithm for sleep spindle annotation in EEG data and to measure spindle instantaneous frequencies (IFs). Approach. ConceFT effectively reduces stochastic EEG influence, enhancing spindle visibility in the TF representation. Our automated spindle detection algorithm, ConceFT-Spindle (ConceFT-S), is compared to A7 (non-deep learning) and SUMO (deep learning) using Dream and Montreal Archive of Sleep Studies (MASS) benchmark databases. We also quantify spindle IF dynamics. Main results. ConceFT-S achieves F1 scores of 0.765 in Dream and 0.791 in MASS, which surpass A7 and SUMO. We reveal that spindle IF is generally nonlinear. Significance. ConceFT offers an accurate, interpretable EEG-based sleep spindle detection algorithm and enables spindle IF quantification.

Retraction Note: Automatic detection of sleep spindles with the use of STFT, EMD and DWT methods

Retraction Note: Automatic detection of sleep spindles with the use of STFT, EMD and DWT methods

Method and system for automated detection of sleep spindles using a single EEG channels based TEO and EMD

As a hallmark of N2 sleep stage, sleep spindle detection based on electroencephalogram (EEG) recordings plays a crucial role in analyzing sleep. Hence, how to effectively automatically detect sleep spindle is crucially important. However, many automatic detection methods assume that the signal is stationary, which is inconsistent with the observation that spindle signals have a significant change in amplitude. Thus, some important non-stationary information and the presented evident dynamic characteristics has been ignored.To overcome the aforementioned shortcoming, we propose a novel methodology based on Teager Energy Operator (TEO) and Empirical Mode Decomposition (EMD) to determine the exact location of sleep spindles using single-channel EEG signals. Because the TEO is sensitive to amplitude variations and energy features, our proposed method is suitable for capturing sleep spindles. We conduct plenty of experiments to evaluate our method. The experiments are conducted to validate the effectiveness of our method. On the DREAMS Sleep Spindles Database, our method achieves 91.83% ± 1.1%, 94.12% ± 3.46%, and 83.38% ± 9.32% for accuracy, specificity, and sensitivity, respectively. Experimental results show that the performance of the proposed methodology presented herein achieves better performance as compared to other methods. Moreover, owing to its usage of a single channel of EEG signal, the proposed method will be suitable for edge-device implementation to expedite sleep disorder diagnosis.

Desktop app for detecting sleep spindles from sleep EEG data

Desktop app for detecting sleep spindles from sleep EEG data

A personalized semi-automatic sleep spindle detection (PSASD) framework

BackgroundSleep spindles are distinct electroencephalogram (EEG) patterns of brain activity that have been posited to play a critical role in development, learning, and neurological disorders. Manual scoring for sleep spindles is labor-intensive and tedious but could supplement automated algorithms to resolve challenges posed with either approaches alone. New methodsA Personalized Semi-Automatic Sleep Spindle Detection (PSASD) framework was developed to combine the strength of automated detection algorithms and visual expertise of human scorers. The underlying model in the PSASD framework assumes a generative model for EEG sleep spindles as oscillatory components, optimized to EEG amplitude, with remaining signals distributed into transient and low-frequency components. ResultsA single graphical user interface (GUI) allows both manual scoring of sleep spindles (model training data) and verification of automatically detected spindles. A grid search approach allows optimization of parameters to balance tradeoffs between precision and recall measures. Comparison with existing methodsPSASD outperformed DETOKS in F1-score by 19% and 4% on the DREAMS and P-DROWS-E datasets, respectively. It also outperformed YASA in F1-score by 25% in the P-DROWS-E dataset. Further benchmarking analysis showed that PSASD outperformed four additional widely used sleep spindle detectors in F1-score in the P-DROWS-E dataset. Titration analysis revealed that four 30-second epochs are sufficient to fine-tune the model parameters of PSASD. Associations of frequency, duration, and amplitude of detected sleep spindles matched those previously reported with automated approaches. ConclusionsOverall, PSASD improves detection of sleep spindles in EEG data acquired from both younger healthy and older adult patient populations.

Switching state-space modeling of neural signal dynamics.

Linear parametric state-space models are a ubiquitous tool for analyzing neural time series data, providing a way to characterize the underlying brain dynamics with much greater statistical efficiency than non-parametric data analysis approaches. However, neural time series data are frequently time-varying, exhibiting rapid changes in dynamics, with transient activity that is often the key feature of interest in the data. Stationary methods can be adapted to time-varying scenarios by employing fixed-duration windows under an assumption of quasi-stationarity. But time-varying dynamics can be explicitly modeled by switching state-space models, i.e., by using a pool of state-space models with different dynamics selected by a probabilistic switching process. Unfortunately, exact solutions for state inference and parameter learning with switching state-space models are intractable. Here we revisit a switching state-space model inference approach first proposed by Ghahramani and Hinton. We provide explicit derivations for solving the inference problem iteratively after applying a variational approximation on the joint posterior of the hidden states and the switching process. We introduce a novel initialization procedure using an efficient leave-one-out strategy to compare among candidate models, which significantly improves performance compared to the existing method that relies on deterministic annealing. We then utilize this state inference solution within a generalized expectation-maximization algorithm to estimate model parameters of the switching process and the linear state-space models with dynamics potentially shared among candidate models. We perform extensive simulations under different settings to benchmark performance against existing switching inference methods and further validate the robustness of our switching inference solution outside the generative switching model class. Finally, we demonstrate the utility of our method for sleep spindle detection in real recordings, showing how switching state-space models can be used to detect and extract transient spindles from human sleep electroencephalograms in an unsupervised manner.

0263 Feasibility of Measuring Sleep Spindles Using a Wearable EEG Headband in Adolescents with Neurodevelopmental Disorders

Abstract Introduction There is compelling evidence that sleep spindles, electroencephalogram (EEG) signatures of non-rapid eye movement sleep, are reliable markers of thalamocortical dysfunction in Schizophrenia. While promising, this research is limited by traditional sleep EEG methods, which cultivate small, non-representative samples. Wearable EEG headbands could be used to study naturalistic sleep across several nights in individuals who cannot tolerate standard sleep assessments, but this has yet to be tested. We aim to test feasibility of using a dry-EEG headband to measure sleep spindles in adolescents with a neurodevelopmental disorder, 22q11.2 Deletion Syndrome (22q11DS). Methods Adolescents with 22q11DS (n=7; 3 Females; Age: 15-28 years) were asked to sleep in their own bed wearing a dry-EEG headband (Dreem 3). Feasibility of data acquisition was determined by the ability of the participants to record at least 3 consecutive nights. We performed visual artifact detection and examined the ability to detect sleep spindles using the well-validated Ferrarelli Spindle Detection Algorithm on the F7-O2 electrode. Results All subjects completed at least 3 nights of recording (range: 3-5 nights), suggesting the device was well-tolerated. The data contained minimal artifact; 17 out of the 22 nights available for analysis contained artifact in < 20% of sleep epochs. The best night contained artifact in only 6% of sleep epochs. Sleep spindles were successfully detected using an automatic detector (M= 1.92 spindles/minute). Conclusion This pilot study suggests dry-EEG headbands offer a feasible method to measure sleep spindles in individuals with neurodevelopmental disorders. This new technology could allow for scalable biomarkers of atypical neurodevelopment associated with psychiatric disorders. Support (if any) RO1 MH085953; U01MH10171; UCLA Center for Autism Research Pilot Grant; Dreem Coup de Coeur Award

Hierarchical fusion detection algorithm for sleep spindle detection.

Sleep spindles are a vital sign implying that human beings have entered the second stage of sleep. In addition, they can effectively reflect a person's learning and memory ability, and clinical research has shown that their quantity and density are crucial markers of brain function. The "gold standard" of spindle detection is based on expert experience; however, the detection cost is high, and the detection time is long. Additionally, the accuracy of detection is influenced by subjectivity. To improve detection accuracy and speed, reduce the cost, and improve efficiency, this paper proposes a layered spindle detection algorithm. The first layer used the Morlet wavelet and RMS method to detect spindles, and the second layer employed an improved k-means algorithm to improve spindle detection efficiency. The fusion algorithm was compared with other spindle detection algorithms to prove its effectiveness. The hierarchical fusion spindle detection algorithm showed good performance stability, and the fluctuation range of detection accuracy was minimal. The average value of precision was 91.6%, at least five percentage points higher than other methods. The average value of recall could reach 89.1%, and the average value of specificity was close to 95%. The mean values of accuracy and F1-score in the subject sample data were 90.4 and 90.3%, respectively. Compared with other methods, the method proposed in this paper achieved significant improvement in terms of precision, recall, specificity, accuracy, and F1-score. A spindle detection method with high steady-state accuracy and fast detection speed is proposed, which combines the Morlet wavelet with window RMS and an improved k-means algorithm. This method provides a powerful tool for the automatic detection of spindles and improves the efficiency of spindle detection. Through simulation experiments, the sampled data were analyzed and verified to prove the feasibility and effectiveness of this method.

Convolutional Multiple Instance Learning for Sleep Spindle Detection With Label Refinement

Sleep spindles are closely associated with cognitive functions and neurological disorders; thus, spindle detection has been an important topic in sleep medicine. Recently, machine learning approaches have shown the potential in automatic sleep spindle detection by learning optimized features in a data-driven way, while they highly rely on labeled data, and the performance can be degraded when labels are inaccurate. However, accurate annotation of the spindle is usually difficult to obtain and high intraexpert and interexpert variance exist. In this work, we propose a convolutional neural network (CNN) with a label refinement component to learn an effective spindle detector with imperfect labels. Our approach consists of two stages: 1) a feature learning stage and 2) a label refinement stage. In the feature learning stage, a CNN-based multiple instance learning framework (CNN-MIL) is built for spindle feature learning. By assuming only parts of each labeled spindle segment contain true spindle patterns, the CNN-MIL model can learn most-likely spindle-related features from ambiguous labels. In the label refinement stage, we adjust the spindle labels by merging the original labels and labels predicted by CNN-MIL, and the modified labels are then used in the next round CNN-MIL feature learning. The two stages perform alternately for detector optimization. Extensive experiments demonstrated that our approach achieved the state-of-the-art performance.

Sleep spindles, stress and PTSD: The state of the science and future directions

Sleep spindles are a signature feature of non-REM (NREM) sleep, with demonstrated relationships to sleep maintenance and learning and memory. Because PTSD is characterized by disturbances in sleep maintenance and in stress learning and memory, there is now a growing interest in examining the role of sleep spindles in the neurobiology of PTSD. This review provides an overview of methods for measuring and detecting sleep spindles as they pertain to human PTSD and stress research, presents a critical review of early findings examining sleep spindles in PTSD and stress neurobiology, and proposes several directions for future research. In doing so, this review underscores the extensive heterogeneity in sleep spindle measurement and detection methods, the wide range of spindle features that may be and have been examined, the many persisting unknowns about the clinical and functional relevance of those features, and the problems considering PTSD as a homogeneous group in between-group comparisons. This review also highlights the progress that has been made in this field and underscores the strong rationale for ongoing work in this area.

An Efficient Sleep Spindle Detection Algorithm Based on MP and LSBoost

An Efficient Sleep Spindle Detection Algorithm Based on MP and LSBoost

Automated real-time EEG sleep spindle detection for brain-state-dependent brain stimulation.

Sleep spindles are a hallmark electroencephalographic feature of non-rapid eye movement sleep, and are believed to be instrumental for sleep-dependent memory reactivation and consolidation. However, direct proof of their causal relevance is hard to obtain, and our understanding of their immediate neurophysiological consequences is limited. To investigate their causal role, spindles need to be targeted in real-time with sensory or non-invasive brain-stimulation techniques. While fully automated offline detection algorithms are well established, spindle detection in real-time is highly challenging due to their spontaneous and transient nature. Here, we present the real-time spindle detector, a robust multi-channel electroencephalographic signal-processing algorithm that enables the automated triggering of stimulation during sleep spindles in a phase-specific manner. We validated the real-time spindle detection method by streaming pre-recorded sleep electroencephalographic datasets to a real-time computer system running a Simulink® Real-Time™ implementation of the algorithm. Sleep spindles were detected with high levels of Sensitivity (~83%), Precision (~78%) and a convincing F1-Score (~81%) in reference to state-of-the-art offline algorithms (which reached similar or lower levels when compared with each other), for both naps and full nights, and largely independent of sleep scoring information. Detected spindles were comparable in frequency, duration, amplitude and symmetry, and showed the typical time-frequency characteristics as well as a centroparietal topography. Spindles were detected close to their centre and reliably at the predefined target phase. The real-time spindle detection algorithm therefore empowers researchers to target spindles during human sleep, and apply the stimulation method and experimental paradigm of their choice.

Deep-spindle: An automated sleep spindle detection system for analysis of infant sleep spindles

Background:Sleep spindles are an indicator of the development and integrity of the central nervous system in infants. Identifying sleep spindles manually in EEG is time-consuming and typically requires experienced experts. Automated detection of sleep spindles would greatly facilitate this analysis. Deep learning methods have been widely used recently in EEG analysis. Method:We have developed a deep learning-based automated sleep spindle detection system, Deep-spindle, which employs a convolutional neural network (CNN) combined with a bidirectional Long Short-Term Memory (LSTM) network, which could assist in the analysis of infant sleep spindles. Deep-spindle was trained on the EEGs of ex-term infants to estimate the number and duration of sleep spindles. The ex-term EEG on channel F4-C4 was split into training (N=81) and validation (N=30) sets. An additional 30 ex-term EEG and 54 ex-preterm infant EEGs (channel F4-C4 and F3-C3) were used as an independent test set. Result:Deep-spindle detected the number of sleep spindles with 91.9% to 96.5% sensitivity and 95.3% to 96.7% specificity, and estimated sleep spindle duration with a percent error of 13.1% to 19.1% in the independent test set. For each detected spindle event, the user is presented with amplitude, power spectral density and the spectrogram of the corresponding spindle EEG, and the probability of the event being a sleep spindle event, providing the user with insight into why the event is predicted as a sleep spindle to provide confidence in the predictions. Conclusion:The Deep-spindle system can reduce physicians’ workload, demonstrating the potential to assist physicians in the automated analysis of sleep spindles in infants.

The Portiloop: A deep learning-based open science tool for closed-loop brain stimulation.

Closed-loop brain stimulation refers to capturing neurophysiological measures such as electroencephalography (EEG), quickly identifying neural events of interest, and producing auditory, magnetic or electrical stimulation so as to interact with brain processes precisely. It is a promising new method for fundamental neuroscience and perhaps for clinical applications such as restoring degraded memory function; however, existing tools are expensive, cumbersome, and offer limited experimental flexibility. In this article, we propose the Portiloop, a deep learning-based, portable and low-cost closed-loop stimulation system able to target specific brain oscillations. We first document open-hardware implementations that can be constructed from commercially available components. We also provide a fast, lightweight neural network model and an exploration algorithm that automatically optimizes the model hyperparameters to the desired brain oscillation. Finally, we validate the technology on a challenging test case of real-time sleep spindle detection, with results comparable to off-line expert performance on the Massive Online Data Annotation spindle dataset (MODA; group consensus). Software and plans are available to the community as an open science initiative to encourage further development and advance closed-loop neuroscience research [https://github.com/Portiloop].

Investigation of automatic spindle detection in sleep EEG signals contaminated with noise and artifact sources

AbstractElectroencephalography (EEG) serves as the gold standard for noninvasive diagnosis of different types of sleep disorders such as sleep apnea, insomnia, narcolepsy, restless leg syndrome, and parasomnias. In this study, a novel automated cascade filter is introduced as a preprocessing tool for suppressing all noise and artifact interferences from sleep EEG signals before detecting sleep spindles. The multi-stage filter employs the Multi-Kernel Normalized Least Mean Square with Coherence-based Sparsification (MKNLMS-CS) algorithm in the first step to remove all artifact interferences while applying the 1-D patch-based Non-Local Means (NLM) algorithm in the subsequent step to remove all noise components. Three state-of-the-art automated spindle detection algorithms, namely Mc-Sleep, Spinky, and Spindler, are examined in EEG signals contaminated with noise and artifact components individually and concurrently. The spindle detection performance is investigated with real EEG data taken from the well-known DREAMS database, and the experimental results demonstrate the importance of the proposed multi-stage filter in enhancing the performance of spindle detection using the three spindle detection algorithms. This elucidates the robustness of the suggested multi-stage filter in providing high-resolution sleep EEG data from noisy EEG recordings. Also, experimental results reveal that Spinky algorithm outperforms Mc-Sleep and Spindler methods in detecting spindles for filtered EEG signals using several evaluation metrics, including accuracy (94.8% versus 92.0% and 94.6%), precision (53.4% versus 36.4% and 47.5%), specificity (97.3% versus 93.9% and 96.1%) and F1-score (58.2% versus 41.3% and 50.9%), respectively. This shows that combining the proposed multi-stage filter with Spinky algorithm outperforms the other two methods in detecting spindles in EEG signals, and it represents an efficient automated spindle detection system that achieves high diagnosis performance in terms of accuracy (94.8%), specificity (97.3%), and F1-score (58.2%).

Toward Sleep Study Automation: Detection Evaluation of Respiratory-Related Events.

The diagnosis of sleep disordered breathing depends on the detection of respiratory-related events: apneas, hypopneas, snores, or respiratory event-related arousals from sleep studies. While a number of automatic detection methods have been proposed, their reproducibility has been an issue, in part due to the absence of a generally accepted protocol for evaluating their results. With sleep measurements this is usually treated as a classification problem and the accompanying issue of localization is not treated as similarly critical. To address these problems we present a detection evaluation protocol that is able to qualitatively assess the match between two annotations of respiratory-related events. This protocol relies on measuring the relative temporal overlap between two annotations in order to find an alignment that maximizes their F1-score at the sequence level. This protocol can be used in applications which require a precise estimate of the number of events, total event duration, and a joint estimate of event number and duration. We assess its application using a data set that contains over 10,000 manually annotated snore events from 9 subjects, and show that when using the American Academy of Sleep Medicine Manual standard, two sleep technologists can achieve an F1-score of 0.88 when identifying the presence of snore events. In addition, we drafted rules for marking snore boundaries and showed that one sleep technologist can achieve F1-score of 0.94 at the same tasks. Finally, we compared this protocol against the protocol that is used to evaluate sleep spindle detection and highlighted the differences.

Nine-day continuous recording of EEG and 2-hour of high-density EEG under chronic sleep restriction in mice

This work provides an EEG dataset collected from nine mice during the sleep deprivation (SD) paradigm for the sleep science community. It includes 9-day of continuous recording of the frontal and parietal EEG, accelerometer, and 2-hour of high-density EEG (HD-EEG) under SD and SD-free conditions. Eighteen hours of SD were conducted on 5 consecutive days. The HD-EEG data were saved in the EEGLAB format and stored as the brain imaging data structure (BIDS). These datasets can be used to (i) compare mouse HD-EEG to human HD-EEG, (ii) track oscillatory activities of the sleep EEG (e.g., slow waves, spindles) across the cortical regions under different conditions of sleep pressure, and (iii) investigate the cortical traveling waves in the mouse brain. We also provided Python code for basic analyses of this dataset, including the detection of slow waves and sleep spindles. We hope that our dataset will reveal hidden activities during sleep and lead to a better understanding of the functions and mechanisms of sleep.

Advanced sleep spindle identification with neural networks

Sleep spindles are neurophysiological phenomena that appear to be linked to memory formation and other functions of the central nervous system, and that can be observed in electroencephalographic recordings (EEG) during sleep. Manually identified spindle annotations in EEG recordings suffer from substantial intra- and inter-rater variability, even if raters have been highly trained, which reduces the reliability of spindle measures as a research and diagnostic tool. The Massive Online Data Annotation (MODA) project has recently addressed this problem by forming a consensus from multiple such rating experts, thus providing a corpus of spindle annotations of enhanced quality. Based on this dataset, we present a U-Net-type deep neural network model to automatically detect sleep spindles. Our model’s performance exceeds that of the state-of-the-art detector and of most experts in the MODA dataset. We observed improved detection accuracy in subjects of all ages, including older individuals whose spindles are particularly challenging to detect reliably. Our results underline the potential of automated methods to do repetitive cumbersome tasks with super-human performance.

Spindle-AI: Sleep Spindle Number and Duration Estimation in Infant EEG.

Sleep spindle features show developmental changes during infancy and have the potential to provide an early biomarker for abnormal brain maturation. Manual identification of sleep spindles in the electroencephalogram (EEG) is time-consuming and typically requires highly-trained experts. Automated detection of sleep spindles would greatly facilitate this analysis. Research on the automatic detection of sleep spindles in infant EEG has been limited to-date. We present a random forest-based sleep spindle detection method (Spindle-AI) to estimate the number and duration of sleep spindles in EEG collected from 141 ex-term born infants, recorded at 4 months of age. The signal on channel F4-C4 was split into a training set (81 ex-term) and a validation set (30 ex-term). An additional 30 ex-term infant EEGs (channel F4-C4 and channel F3-C3) were used as an independent test set. Fourteen features were selected for input into a random forest algorithm to estimate the number and duration of spindles and the results were compared against sleep spindles annotated by an experienced clinical physiologist. The prediction of the number of sleep spindles in the independent test set demonstrated 93.3% to 93.9% sensitivity, 90.7% to 91.5% specificity, and 89.2% to 90.1% precision. The duration estimation of sleep spindle events in the independent test set showed a percent error of 5.7% to 7.4%. Spindle-AI has been implemented as a web server that has the potential to assist clinicians in the fast and accurate monitoring of sleep spindles in infant EEGs.

Effect of a Recliner Chair with Rocking Motions on Sleep Efficiency.

In this study, we analyze the effect of a recliner chair with rocking motions on sleep quality of naps using automated sleep scoring and spindle detection models. The quality of sleep corresponding to the two rocking motions was measured quantitatively and qualitatively. For the quantitative evaluation, we conducted a sleep parameter analysis based on the results of the estimated sleep stages obtained on the brainwave and spindle estimation, and a sleep survey assessment from the participants was analyzed for the qualitative evaluation. The analysis showed that sleep in the recliner chair with rocking motions positively increased the duration of the spindles and deep sleep stage, resulting in improved sleep quality.