SiamQuality: a ConvNet-based foundation model for photoplethysmography signals.

IF 2.3 4区 医学 Q3 BIOPHYSICS
Cheng Ding, Zhicheng Guo, Zhaoliang Chen, Randall J Lee, Cynthia Rudin, Xiao Hu
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引用次数: 0

Abstract

Objective. Physiological data are often low quality and thereby compromises the effectiveness of related health monitoring. The primary goal of this study is to develop a robust foundation model that can effectively handle low-quality issue in physiological data.Approach. We introduce SiamQuality, a self-supervised learning approach using convolutional neural networks (CNNs) as the backbone. SiamQuality learns to generate similar representations for both high and low quality photoplethysmography (PPG) signals that originate from similar physiological states. We leveraged a substantial dataset of PPG signals from hospitalized intensive care patients, comprised of over 36 million 30 s PPG pairs.Main results. After pre-training the SiamQuality model, it was fine-tuned and tested on six PPG downstream tasks focusing on cardiovascular monitoring. Notably, in tasks such as respiratory rate estimation and atrial fibrillation detection, the model's performance exceeded the state-of-the-art by 75% and 5%, respectively. The results highlight the effectiveness of our model across all evaluated tasks, demonstrating significant improvements, especially in applications for heart monitoring on wearable devices.Significance. This study underscores the potential of CNNs as a robust backbone for foundation models tailored to physiological data, emphasizing their capability to maintain performance despite variations in data quality. The success of the SiamQuality model in handling real-world, variable-quality data opens new avenues for the development of more reliable and efficient healthcare monitoring technologies.

SiamQuality:基于 ConvNet 的光心动图信号基础模型。
目的:生理数据通常质量不高,从而影响了相关健康监测的有效性。本研究的主要目标是开发一种稳健的基础模型,以有效处理生理数据的低质量问题:我们引入了 SiamQuality,这是一种以卷积神经网络(CNN)为骨干的自我监督学习方法。SiamQuality 通过学习,为源自相似生理状态的高质量和低质量光电血压计(PPG)信号生成相似的表示。我们利用了来自住院重症监护患者的大量 PPG 信号数据集,其中包括超过 3600 万对 30 秒的 PPG 信号:在对 SiamQuality 模型进行预训练后,对其进行了微调,并在六项以心血管监测为重点的 PPG 下游任务中进行了测试。值得注意的是,在呼吸频率估计和心房颤动检测等任务中,该模型的性能分别比先进水平高出 75% 和 5%。结果凸显了我们的模型在所有评估任务中的有效性,特别是在可穿戴设备的心脏监测应用中,表现出显著的改进:这项研究强调了 CNN 作为针对生理数据定制的基础模型的稳健支柱的潜力,同时也强调了 CNN 在数据质量发生变化时仍能保持性能的能力。SiamQuality 模型在处理真实世界中不同质量数据方面的成功,为开发更可靠、更高效的医疗监控技术开辟了新的途径。
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来源期刊
Physiological measurement
Physiological measurement 生物-工程:生物医学
CiteScore
5.50
自引率
9.40%
发文量
124
审稿时长
3 months
期刊介绍: Physiological Measurement publishes papers about the quantitative assessment and visualization of physiological function in clinical research and practice, with an emphasis on the development of new methods of measurement and their validation. Papers are published on topics including: applied physiology in illness and health electrical bioimpedance, optical and acoustic measurement techniques advanced methods of time series and other data analysis biomedical and clinical engineering in-patient and ambulatory monitoring point-of-care technologies novel clinical measurements of cardiovascular, neurological, and musculoskeletal systems. measurements in molecular, cellular and organ physiology and electrophysiology physiological modeling and simulation novel biomedical sensors, instruments, devices and systems measurement standards and guidelines.
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