Reconstructing damaged fNIRS signals with a generative deep learning model

IF 10.7 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Yingxu Zhi, Baiqiang Zhang, Bingxin Xu, Fei Wan, Peisong Niu, Haijing Niu
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引用次数: 0

Abstract

Functional near-infrared spectroscopy (fNIRS) imaging offers a promising avenue for measuring brain function in both healthy and diseased cohorts. However, signal quality in fNIRS data frequently encounters challenges, such as low signal-to-noise ratio or substantial motion artifacts in one or multiple measurement channels, impeding the comprehensive exploitation of the data. Developing a valid method to improve the quality of damaged fNIRS signals is crucial, particularly given the extensive use of wearable fNIRS devices in natural settings where noise issues are even more unavoidable. Here, we proposed a generative deep learning approach to recover damaged fNIRS signals in one or more measurement channels. The model captured spatial and temporal variations in the time series of fNIRS data by integrating multiscale convolutional layers, gated recurrent units (GRUs), and linear regression analyses. We trained the model on a resting-state fNIRS dataset from healthy elderly individuals and evaluated its performance in terms of reconstruction accuracy and functional connectivity matrix similarity. Collectively, the proposed model exhbited an excellent performance for the reconstruction of damaged fNIRS time series. In individual channel-level, the model can accurately reconstruct damaged fNIRS time series (mean correlation = 0.80 ± 0.14) while preserving intervariable relationships (correlation = 0.93). In multiple channel-level, the model maintained robust reconstruction accuracy and consistency in terms of functional connectivity. Our findings underscore the potential of generative deep learning techniques in reconstructing damaged fNIRS signals, providing a novel perspective for the efficient utilization of data in clinical diagnosis and brain research.

用生成式深度学习模型重建受损的近红外信号
功能近红外光谱(fNIRS)成像为测量健康和患病人群的脑功能提供了一种很有前途的途径。然而,fNIRS数据的信号质量经常遇到挑战,如一个或多个测量通道中的低信噪比或大量运动伪影,阻碍了数据的综合利用。开发一种有效的方法来提高受损fNIRS信号的质量是至关重要的,特别是考虑到在自然环境中广泛使用可穿戴fNIRS设备,而噪音问题更是不可避免的。在这里,我们提出了一种生成式深度学习方法来恢复一个或多个测量通道中受损的近红外光谱信号。该模型通过整合多尺度卷积层、门控循环单元(gru)和线性回归分析,捕捉了fNIRS数据时间序列的时空变化。我们在健康老年人的静息状态fNIRS数据集上训练该模型,并从重建精度和功能连接矩阵相似性方面评估其性能。综上所述,该模型在重建受损的近红外时间序列方面表现出良好的性能。在单个通道水平上,该模型可以准确地重建受损的fNIRS时间序列(平均相关= 0.80±0.14),同时保持变量间关系(相关= 0.93)。在多通道级,该模型在功能连通性方面保持了较好的重建精度和一致性。我们的研究结果强调了生成式深度学习技术在重建受损的近红外光谱信号方面的潜力,为临床诊断和大脑研究中有效利用数据提供了一个新的视角。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Artificial Intelligence Review
Artificial Intelligence Review 工程技术-计算机:人工智能
CiteScore
22.00
自引率
3.30%
发文量
194
审稿时长
5.3 months
期刊介绍: Artificial Intelligence Review, a fully open access journal, publishes cutting-edge research in artificial intelligence and cognitive science. It features critical evaluations of applications, techniques, and algorithms, providing a platform for both researchers and application developers. The journal includes refereed survey and tutorial articles, along with reviews and commentary on significant developments in the field.
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