Spindler: a framework for parametric analysis and detection of spindles in EEG with application to sleep spindles

LaRocco, John; Franaszczuk, Piotr J.; Kerick, Scott E.; Robbins, Kay A.

doi:10.1088/1741-2552/aadc1c

Cited by 22 publications

(39 citation statements)

References 32 publications

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“…We compared SpindleNet with two recently published open-source spindle detection methods: McSleep (Parekh et al 2017) and Spindler (LaRocco et al 2018). We chose these two algorithms because they have been tested on the MASS and/or DREAMS datasets, and have been compared against a wide range of spindle detection methods (see Parekh et al 2017) for details).…”

Section: Resultsmentioning

confidence: 99%

A deep learning approach for real-time detection of sleep spindles

Kulkarni¹,

Xiao²,

Robinson³

et al. 2019

J. Neural Eng.

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Objective. Sleep spindles have been implicated in memory consolidation and synaptic plasticity during NREM sleep. Detection accuracy and latency in automatic spindle detection are critical for real-time applications. Approach. Here we propose a novel deep learning strategy (SpindleNet) to detect sleep spindles based on a single EEG channel. While the majority of spindle detection methods are used for off-line applications, our method is well suited for online applications. Main results. Compared with other spindle detection methods, SpindleNet achieves superior detection accuracy and speed, as demonstrated in two publicly available expert-validated EEG sleep spindle datasets. Our real-time detection of spindle onset achieves detection latencies of 150-350 ms (~2-3 spindle cycles) and retains excellent performance under low EEG sampling frequencies and low signal-to-noise ratios. SpindleNet has good generalization across different sleep datasets from various subject groups of different ages and species. Significance. SpindleNet is ultra-fast and scalable to multichannel EEG recordings, with an accuracy level comparable to human experts, making it appealing for long-term sleep monitoring and closed-loop neuroscience experiments.

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Section: Resultsmentioning

confidence: 99%

A deep learning approach for real-time detection of sleep spindles

Kulkarni¹,

Xiao²,

Robinson³

et al. 2019

J. Neural Eng.

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“…To identify the sleep stages accurately, several studies have been conducted to design classifiers based on the features extracted from special waveform, time-frequency, time-frequency image, amplitude, spectral and so on. The k-complex [7], [8] is one of the important parameters in determining the epoch as N-REM stage-2, which would help the automatic sleep staging system in a great deal. Yucelbas [9] have presented a fusing-method with logic AND operations and Ranjan et al [10] propounded a fuzzy neural network approach to find out whether there were k-complex in related epochs using the time and frequency analysis, automatically.…”

Section: Introductionmentioning

confidence: 99%

An Accurate Sleep Stages Classification Method Based on State Space Model

Shen

Guez

et al. 2019

IEEE Access

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The classification of sleep stages is the process which helps to evaluate the quality of sleep and detect the sleep related disorders. Through analyzing the electroencephalography, the sleep stages can be discriminated manually by specialists. However, this can be a laboriousness work because of the huge datasets. Until now, several studies have been conducted based on the automatic analysis of electroencephalography. Still, as the development of wearable technology, there is a need for an accurate and single-channel electroencephalography based sleep stages identification system. In this paper, a statespace based sleep stages classification method is proposed using the proposed model based essence features extraction method. This method employed the state-space model to establish the intrinsic models based on the single-channel electroencephalography, from which the features used for further classification can be extracted. For 2-stage to 6-stage classification of sleep states, the verification system can achieve 98.6%, 94.9%, 93.0%, 92.3%, 91.8% accuracy on the Sleep-EDF database, and also reach 94.9%, 87.7%, 82.7%, 80.9%, 78.2% on Dreams Subjects database.

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“…4) Spindle events: During the N2 and N3 stages, a series of attributes of spindle wave events, including the onset time and the proportion of events per minute, were obtained by using the previously studied and published automatic spindle wave event detection algorithm [20]. This open source algorithm enables us to obtain the peak value, absolute power of events and other attributes.…”

Section: Data Analysis and Statisticsmentioning

confidence: 99%

Low Frequency Transcranial Alternating Current Stimulation Accelerates Sleep Onset Process

Xie¹,

Wang²,

Xiao³

et al. 2021

IEEE Trans. Neural Syst. Rehabil. Eng.

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The aim of this study is to find a kind of low frequency oscillation transcranial alternating current stimulation, which is directly applied to the scalp epidermal, to stimulate the cerebral cortex with a large spatial range of electric field oscillation across the brain hemisphere, and then trigger the start of the Top-Down processing of sleep homeostasis, in the daytime nap. Methods: Thirty healthy subjects, to take naps, underwent an intervention of electrical stimulation at 5 Hz, applied to the dorsal lateral prefrontal cortex. The subjects in the experiments were strictly controlled, and opened their eyes when stimulation was transmitted. Subsequently, after 15 min transcranial alternating current stimulation, subjects entered the experimental procedure of sleep. Electroencephalograph was taken at baseline and during sleep. Behavioral indicators were also added to the experiment. Results: We found that the total power of Electroencephalograph activity in the theta band, as well as low-frequency power at 1-7 Hz, was significantly entrained and increased, and that alpha activity was attenuated faster and spindle activity active earlier. Even more, the transition from awake to Non-rapid eye movement stages occurs earlier. Alertness also decreased when the subjects woke up after brief sleep. Conclusion: The intervention of low frequency brain rhythmic transcranial alternating current stimulation may induce accelerated effect on sleep onset process, thereby possibly alleviating the problems related to sleep disorders such as difficulty to reach the real sleep state quickly after lying down.

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Spindler: a framework for parametric analysis and detection of spindles in EEG with application to sleep spindles

Cited by 22 publications

References 32 publications

A deep learning approach for real-time detection of sleep spindles

A deep learning approach for real-time detection of sleep spindles

An Accurate Sleep Stages Classification Method Based on State Space Model

Low Frequency Transcranial Alternating Current Stimulation Accelerates Sleep Onset Process

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