{"title":"SleepSIM:基于条件 GAN 的非快速眼动睡眠脑电图信号发生器。","authors":"Sajila D Wickramaratne, Ankit Parekh","doi":"10.1109/EMBC40787.2023.10341043","DOIUrl":null,"url":null,"abstract":"<p><p>Synthetic data generation has become increasingly popular with the increasing use of generative networks. Recently, Generative Adversarial Network (GAN) architectures have produced exceptional results in synthetic image generation. However, time series generation still needs to be studied. This paper proposes a Conditional GAN-based system to generate unique samples of non-REM sleep electroencephalographic (EEG) signals. The CGAN model had a 1-D Convolution Neural Network based architecture. The model was trained using real EEG from healthy controls. The trained model can generate an artificial 30-second epoch of non-REM sleep whose power spectrum is identical to that of a real sleep EEG.Clinical relevance- Sleep EEG simulation can be used to train and enhance the skillset of fellows and technicians in the sleep medicine field. Variations in EEG signals can be highly complex to model mathematically; however, here, we harness the power of deep learning, using generative models such as CGANs to train, model complex data distributions, and generate diverse and artificial but realistic EEG signals during non-REM sleep.</p>","PeriodicalId":72237,"journal":{"name":"Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"SleepSIM: Conditional GAN-based non-REM sleep EEG Signal Generator.\",\"authors\":\"Sajila D Wickramaratne, Ankit Parekh\",\"doi\":\"10.1109/EMBC40787.2023.10341043\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Synthetic data generation has become increasingly popular with the increasing use of generative networks. Recently, Generative Adversarial Network (GAN) architectures have produced exceptional results in synthetic image generation. However, time series generation still needs to be studied. This paper proposes a Conditional GAN-based system to generate unique samples of non-REM sleep electroencephalographic (EEG) signals. The CGAN model had a 1-D Convolution Neural Network based architecture. The model was trained using real EEG from healthy controls. The trained model can generate an artificial 30-second epoch of non-REM sleep whose power spectrum is identical to that of a real sleep EEG.Clinical relevance- Sleep EEG simulation can be used to train and enhance the skillset of fellows and technicians in the sleep medicine field. Variations in EEG signals can be highly complex to model mathematically; however, here, we harness the power of deep learning, using generative models such as CGANs to train, model complex data distributions, and generate diverse and artificial but realistic EEG signals during non-REM sleep.</p>\",\"PeriodicalId\":72237,\"journal\":{\"name\":\"Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-07-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/EMBC40787.2023.10341043\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/EMBC40787.2023.10341043","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
摘要
随着生成网络的应用日益广泛,合成数据生成也变得越来越流行。最近,生成对抗网络(GAN)架构在合成图像生成方面取得了卓越的成果。然而,时间序列生成仍有待研究。本文提出了一种基于条件 GAN 的系统,用于生成独特的非快速眼动睡眠脑电图(EEG)信号样本。CGAN 模型采用基于一维卷积神经网络的架构。该模型使用健康对照组的真实脑电图进行训练。训练后的模型可生成 30 秒的非快速眼动睡眠人工时程,其功率谱与真实睡眠脑电图相同。临床意义--睡眠脑电图模拟可用于培训和提高睡眠医学领域研究员和技术人员的技能。脑电信号的变化可能是非常复杂的数学模型;但是,在这里,我们利用深度学习的力量,使用 CGANs 等生成模型进行训练,为复杂的数据分布建模,并在非快速眼动睡眠期间生成多样、人工但真实的脑电信号。
SleepSIM: Conditional GAN-based non-REM sleep EEG Signal Generator.
Synthetic data generation has become increasingly popular with the increasing use of generative networks. Recently, Generative Adversarial Network (GAN) architectures have produced exceptional results in synthetic image generation. However, time series generation still needs to be studied. This paper proposes a Conditional GAN-based system to generate unique samples of non-REM sleep electroencephalographic (EEG) signals. The CGAN model had a 1-D Convolution Neural Network based architecture. The model was trained using real EEG from healthy controls. The trained model can generate an artificial 30-second epoch of non-REM sleep whose power spectrum is identical to that of a real sleep EEG.Clinical relevance- Sleep EEG simulation can be used to train and enhance the skillset of fellows and technicians in the sleep medicine field. Variations in EEG signals can be highly complex to model mathematically; however, here, we harness the power of deep learning, using generative models such as CGANs to train, model complex data distributions, and generate diverse and artificial but realistic EEG signals during non-REM sleep.