一种周期性分裂吸引子重构方法有助于心血管信号诊断和阻塞性睡眠呼吸暂停综合征监测。
A periodic split attractor reconstruction method facilitates cardiovascular signal diagnoses and obstructive sleep apnea syndrome monitoring.
作者信息
Zhang Ze, Hirose Kayo, Yamada Katsunori, Sato Daisuke, Uchida Kanji, Umezu Shinjiro
机构信息
Graduate School of Creative Science and Engineering, Department of Modern Mechanical Engineering, Waseda University, 3-4-1 Okubo, Shinjuku-ku, Tokyo, 169-8555, Japan.
Department of Anesthesiology and Pain Relief Center, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan.
出版信息
Heliyon. 2024 Aug 3;10(15):e35623. doi: 10.1016/j.heliyon.2024.e35623. eCollection 2024 Aug 15.
Electrocardiogram (ECG) is a powerful tool to detect cardiovascular diseases (CVDs) and health conditions. We proposed a new method for evaluating ECG for efficient medical diagnosis in daily life. By splitting the signal according to the cardiac activity cycle, the periodic split attractor reconstruction (PSAR) method is proposed with time embedding, including three types of splitting methods to show its chaotic domain characteristics. We merged the CVDs dataset and the obstructive sleep apnea syndrome (OSAS) first-lead ECG signal dataset to validate the performance of PSAR for diagnosis and health monitoring using PSAR density maps as SE-ResNet input features. PSAR under 3 split methods showed different sensitivities for different CVDs. While in OSAS monitoring, PSAR showed good ability to recognize sleep abnormalities.
心电图（ECG）是检测心血管疾病（CVD）和健康状况的有力工具。我们提出了一种用于评估心电图的新方法，以便在日常生活中进行高效的医学诊断。通过根据心动周期对信号进行分割，提出了具有时间嵌入的周期性分割吸引子重建（PSAR）方法，包括三种分割方法以展示其混沌域特征。我们首先合并了心血管疾病数据集和阻塞性睡眠呼吸暂停综合征（OSAS）的首导联心电图信号数据集，以使用PSAR密度图作为SE-ResNet输入特征来验证PSAR在诊断和健康监测方面的性能。3种分割方法下的PSAR对不同的心血管疾病表现出不同的敏感性。而在OSAS监测中，PSAR表现出良好的识别睡眠异常的能力。
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