ArticleJournal of neuroscience methods2022
Automated sleep state classification of wide-field calcium imaging data via multiplex visibility graphs and deep learning.
Article in Journal of neuroscience methods, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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15 citing papers in PubMed, 37 citations in OpenAlex.
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- Attention-based CNN-BiLSTM for sleep state classification of spatiotemporal wide-field calcium imaging data.Journal of neuroscience methods · 2024Article
- A medical image classification method based on self-regularized adversarial learning.Medical physics · 2024Article
- Spatiotemporal relationships between neuronal, metabolic, and hemodynamic signals in the awake and anesthetized mouse brain.Cell reports · 2024Article
- Direct lingam and visibility graphs for analyzing brain connectivity in BCI.Medical & biological engineering & computing · 2024Article
- Network Representation of fMRI Data Using Visibility Graphs: The Impact of Motion and Test-Retest Reliability.Neuroinformatics · 2024Article
- Gershgorin circle theorem-based feature extraction for biomedical signal analysis.Frontiers in neuroinformatics · 2024Article
- Sleep Action Recognition Based on Segmentation Strategy.Journal of imaging · 2023Article
- A RUSBoosted tree method for k-complex detection using tunable Q-factor wavelet transform and multi-domain feature extraction.Frontiers in neuroscience · 2023Article
- A study on feature selection using multi-domain feature extraction for automated k-complex detection.Frontiers in neuroscience · 2023Article
- Visibility graph analysis for brain: scoping review.Frontiers in neuroscience · 2023Article
- Controlling the familywise error rate in widefield optical neuroimaging of functional connectivity in mice.Neurophotonics · 2023Article
- Validation of Deep Learning-based Sleep State Classification.microPublication biology · 2022Article
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Authors and funding
9 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundWide-field calcium imaging (WFCI) allows for monitoring of cortex-wide neural dynamics in mice. When applied to the study of sleep, WFCI data are manually scored into the sleep states of wakefulness, non-REM (NREM) and REM by use of adjunct EEG and EMG recordings. However, this process is time-consuming and often suffers from low inter- and intra-rater reliability and invasiveness. Therefore, an automated sleep state classification method that operates on WFCI data alone is needed. NEW
methodA hybrid, two-step method is proposed. In the first step, spatial-temporal WFCI data is mapped to multiplex visibility graphs (MVGs). Subsequently, a two-dimensional convolutional neural network (2D CNN) is employed on the MVGs to be classified as wakefulness, NREM and REM.
resultsSleep states were classified with an accuracy of 84% and Cohen's κ of 0.67. The method was also effectively applied on a binary classification of wakefulness/sleep (accuracy=0.82, κ = 0.62) and a four-class wakefulness/sleep/anesthesia/movement classification (accuracy=0.74, κ = 0.66). Gradient-weighted class activation maps revealed that the CNN focused on short- and long-term temporal connections of MVGs in a sleep state-specific manner. Sleep state classification performance when using individual brain regions was highest for the posterior area of the cortex and when cortex-wide activity was considered. COMPARISON WITH EXISTING
methodOn a 3-hour WFCI recording, the MVG-CNN achieved a κ of 0.65, comparable to a κ of 0.60 corresponding to the human EEG/EMG-based scoring.
conclusionsThe hybrid MVG-CNN method accurately classifies sleep states from WFCI data and will enable future sleep-focused studies with WFCI.
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