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基于动作信号的高阶谱和熵的睡眠期间运动检测的特征提取和相似性：西班牙裔社区健康研究/拉丁裔研究的结果。

Feature Extraction and Similarity of Movement Detection during Sleep, Based on Higher Order Spectra and Entropy of the Actigraphy Signal: Results of the Hispanic Community Health Study/Study of Latinos.

机构信息

Departamento de Telecomunicaciones, Universidad de Pinar del Río, Pinar del Río, Cuba, Martí #270, CP: 20100; Instituto Universitario de Matemática Pura y Aplicada, Universitat Politècnica de València (UPV), Camino de Vera s/n, 46022 Valencia, España.

Biomedical Data Science Lab (BDSLab), Instituto Universitario de Tecnologías de la Información y Comunicaciones (ITACA), Universitat Politècnica de València (UPV), Camino de Vera s/n, 46022 Valencia, España.

出版信息

Sensors (Basel). 2018 Dec 6;18(12):4310. doi: 10.3390/s18124310.

DOI:10.3390/s18124310
PMID:30563277
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6308588/
Abstract

The aim of this work was to develop a new unsupervised exploratory method of characterizing feature extraction and detecting similarity of movement during sleep through actigraphy signals. We here propose some algorithms, based on signal bispectrum and bispectral entropy, to determine the unique features of independent actigraphy signals. Experiments were carried out on 20 randomly chosen actigraphy samples of the Hispanic Community Health Study/Study of Latinos (HCHS/SOL) database, with no information other than their aperiodicity. The Pearson correlation coefficient matrix and the histogram correlation matrix were computed to study the similarity of movements during sleep. The results obtained allowed us to explore the connections between certain sleep actigraphy patterns and certain pathologies.

摘要

本工作旨在开发一种新的无监督探索方法，通过活动记录仪信号来描述特征提取和检测睡眠期间运动的相似性。我们在这里提出了一些基于信号双谱和双谱熵的算法，以确定独立活动记录仪信号的独特特征。实验是在西班牙语裔社区健康研究/拉丁裔研究（HCHS/SOL）数据库中随机选择的 20 个活动记录仪样本上进行的，除了它们的非周期性之外，没有其他信息。计算了 Pearson 相关系数矩阵和直方图相关系数矩阵，以研究睡眠期间运动的相似性。所得到的结果使我们能够探索某些睡眠活动模式与某些疾病之间的联系。

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