GI-SleepNet: A Highly Versatile Image-Based Sleep Classification Using a Deep Learning Algorithm

Abstract

Sleep-stage classification is essential for sleep research. Various automatic judgment programs, including deep learning algorithms using artificial intelligence (AI), have been developed, but have limitations with regard to data format compatibility, human interpretability, cost, and technical requirements. We developed a novel program called GI-SleepNet, generative adversarial network (GAN)-assisted image-based sleep staging for mice that is accurate, versatile, compact, and easy to use. In this program, electroencephalogram and electromyography data are first visualized as images, and then classified into three stages (wake, NREM, and REM) by a supervised image learning algorithm. To increase its accuracy, we adopted GAN and artificially generated fake REM sleep data to equalize the number of stages. This resulted in improved accuracy, and as little as one mouse’s data yielded significant accuracy. Due to its image-based nature, the program is easy to apply to data of different formats, different species of animals, and even outside sleep research. Image data can be easily understood; thus, confirmation by experts is easily obtained, even when there are prediction anomalies. As deep learning in image processing is one of the leading fields in AI, numerous algorithms are also available.

Intelligent automatic sleep staging model based on CNN and LSTM

“…Delimayanti et al ( 23 ) demonstrated the utilization of features extracted from EEG signals via FFT to improve the performance of automated sleep stage classification through machine learning methods. Gao et al ( 24 ) developed a novel program called GI-SleepNet, a generative adversarial network (GAN)-assisted image-based sleep staging for mice that is accurate, versatile, compact, and easy to use. Loh et al ( 25 ) proposed a deep learning model based on a one-dimensional convolutional neural network (1D-CNN) for CAP detection and homogenous 3-class classification for sleep stages: wakefulness (W), rapid eye movement (REM), and NREM sleep.…”

Optimization of real-time analysis of sleep-wake cycle in mice

“…Alternatively, MC-SleepNet combines two types of deep neural networks that can automatically score sleep stages with an accuracy of 96.6% [2] , whereas mixture z-scoring, a machine learning algorithm used in AccuSleep, demonstrated an accuracy of 96.8% [3] . A very high accuracy of determining sleep stages was also achieved with automated sleep stage scoring using a k-nearest neighbors classifier [4] , a supervised deep convolutional neural network called WaveSleepNet [5] , a generative adversarial network-assisted image-based sleep staging program named GI-SleepNet [6] , and a neural network architecture that is able to distinguish between the three main stages Wake, REM and NREM as well as the infrequent stages pre-REM and artifact [7] . Although the accuracy of automated sleep staging is excellent, analysis of sleep-wake cycles in mice remains a time-consuming and laborious process.…”