Building an open-source community to enhance autonomic nervous system signal analysis: DBDP-autonomic.

IF 3.2 Q1 HEALTH CARE SCIENCES & SERVICES Frontiers in digital health Pub Date : 2025-01-09 eCollection Date: 2024-01-01 DOI:10.3389/fdgth.2024.1467424
Jessilyn Dunn, Varun Mishra, Md Mobashir Hasan Shandhi, Hayoung Jeong, Natasha Yamane, Yuna Watanabe, Bill Chen, Matthew S Goodwin
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Abstract

Smartphones and wearable sensors offer an unprecedented ability to collect peripheral psychophysiological signals across diverse timescales, settings, populations, and modalities. However, open-source software development has yet to keep pace with rapid advancements in hardware technology and availability, creating an analytical barrier that limits the scientific usefulness of acquired data. We propose a community-driven, open-source peripheral psychophysiological signal pre-processing and analysis software framework that could advance biobehavioral health by enabling more robust, transparent, and reproducible inferences involving autonomic nervous system data.

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构建一个增强自主神经系统信号分析的开源社区:DBDP-autonomic。
智能手机和可穿戴传感器提供了前所未有的能力,可以在不同的时间尺度、环境、人群和模式下收集外围心理生理信号。然而,开源软件的开发还没有跟上硬件技术和可用性的快速发展,这造成了一个分析障碍,限制了所获取数据的科学用途。我们提出了一个社区驱动的、开源的外周心理生理信号预处理和分析软件框架,它可以通过实现涉及自主神经系统数据的更稳健、透明和可重复的推断来促进生物行为健康。
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CiteScore
4.20
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0.00%
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审稿时长
13 weeks
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