Development of dynamics preserving method for extraction of imaging photoplethysmogram signal from a conventional camera's video recordings.

开发动态保存方法，用于从传统相机的视频记录中提取成像光电体积描记图信号。

• 批准号: 19K14589
• 负责人: スヴィリドヴァ ニーナ
• 金额: $ 2.58万
• 依托单位: Tokyo University of Science
• 依托单位国家: 日本
• 项目类别: Grant-in-Aid for Early-Career Scientists
• 财政年份: 2019
• 资助国家: 日本
• 起止时间: 2019-04-01 至 2024-03-31
• 项目状态: 已结题
• 来源: https://kaken.nii.ac.jp/en/grant/KAKENHI-PROJECT-19K14589/
• 关键词: photoplethysmogram; time series analysis; nonlinear dynamics; chaos; chaos theory; recurrence plot; Photoplethysmogram; deterministinc chaos; filtering; image processing; biomedical signal

In FY 2022, the dynamics of the photoplethysmogram signals were furtherly investigated. Thoughtful investigation of the data collection parameters effects, such as time series length and sampling, were studied. The results demonstrated the dependence of the dynamical characteristics, extracted by nonlinear time series analysis, from the time series length and sampling rate. Moreover, it was found that the obtained results can be used to detect the minimal time series length required for reliable feature extraction. Furthermore, these results were validated on complex nonlinear models; thus, these results' applicability can be extended to other nonlinear complex signals besides the photoplethysmogram.Additionally, in this FY imaging photoplethysmogram (iPPG) signals were investigated. First, iPPG signals were successfully extracted from video recordings obtained from an iPad camera. Then its dynamical characteristics were investigated. The results demonstrated significant similarities between reference signals recorded by a conventional pulse oximeter and signals extracted from an iPad camera. This unveils that a smartphone/iPad camera can be used to record photoplethysmographic signals with dynamical characteristics similar to those of conventional pulse oximeters, thus enabling possibilities for photoplethysmogram-based health monitoring applications, such as physiological and mental health monitoring, without any specialized devices.

于2022财年，进一步研究了光电容积描记图信号的动态。研究了时间序列长度和采样等数据采集参数的影响。结果表明，非线性时间序列分析提取的动力学特性，从时间序列长度和采样率的依赖性。此外，它被发现，所获得的结果可以用来检测可靠的特征提取所需的最小时间序列长度。此外，本文还在复非线性模型上对上述结果进行了验证，从而将这些结果的适用性扩展到光容积脉搏波以外的其他非线性复信号。首先，iPPG信号成功地从iPad摄像头获得的视频记录中提取出来。研究了其动力学特性。结果表明，传统脉搏血氧仪记录的参考信号与iPad摄像头提取的信号之间存在显著的相似性。这揭示了智能手机/iPad相机可用于记录具有与传统脉搏血氧仪相似的动态特性的光电容积脉搏波信号，从而实现基于光电容积脉搏波的健康监测应用的可能性，例如生理和心理健康监测，而无需任何专用设备。

项目成果

Discrimination of deterministic chaos and noisy periodicity by the recurrence plot with variable threshold.

通过具有可变阈值的递归图来区分确定性混沌和噪声周期性。

• 发表时间: 2021
• 期刊: Proceedings of 2021 RISP International Workshop on Nonlinear Circuits, Communications and Signal Processing.
• 作者: Shiki Kanamaru;Nina Sviridova;Yutaka Shimada;Tohru Ikeguchi
• 通讯作者: Tohru Ikeguchi

Detection of Age-Related Changes in the Dynamics of Short Photoplethysmogram Recordings.

检测短光电体积描记图动态中与年龄相关的变化。

• 发表时间: 2020
• 期刊: Proceedings of The Institute of Electronics, Information and Communication Engineers (IEICE) General Conference 2020.
• 作者: Nina Sviridova;Tohru Ikeguchi
• 通讯作者: Tohru Ikeguchi

Application of Recurrence Quantification Analysis to Hypertension Photoplethysmograms.

复发量化分析在高血压光电体积描记图中的应用。

• 发表时间: 2020
• 期刊: Proceedings of International Symposium on Nonlinear Theory and its Applications 2020, NOLTA 2020.
• 作者: Nina Sviridova;Tohru Ikeguchi
• 通讯作者: Tohru Ikeguchi

Effect of recording length on the extraction of the photoplethysmogram dynamical characteristics by recurrence quantification analysis.

通过递归量化分析记录长度对光电体积描记图动态特征提取的影响。

• 发表时间: 2021
• 作者: Sviridova Nina;Artoni Pietro;Fagiolini Michela;Hensch Takao K.;Aihara Kazuyuki;Nina Sviridova
• 通讯作者: Nina Sviridova

Dynamical Characteristics of Wild-Type Mouse Spontaneous Pupillary Fluctuations

野生型小鼠瞳孔自发波动的动力学特征

• DOI: 10.1109/embc46164.2021.9630747
• 发表时间: 2021
• 期刊: 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)
• 作者: Sviridova Nina;Artoni Pietro;Fagiolini Michela;Hensch Takao K.;Aihara Kazuyuki
• 通讯作者: Aihara Kazuyuki