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一个通过使用罗斯勒混沌方程得到的肱二头肌肌电信号刺激数据集。

A dataset of a stimulated biceps muscle of electromyogram signal by using rossler chaotic equation.

作者信息

Khodadadi Vahid, Rahatabad Fereidoun Nowshiravan, Sheikhani Ali, Dabanloo Nader Jafarnia

机构信息

Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.

Engineering Research Center in Medicine and Biology, Science and Research Branch, Islamic Azad University, Tehran, Iran.

出版信息

Data Brief. 2023 Jul 20;49:109438. doi: 10.1016/j.dib.2023.109438. eCollection 2023 Aug.

Abstract

Biological systems, composed of various interrelated components, are nonlinear systems. Improved disease diagnosis and the application of efficient treatment and therapeutic aids are the direct outcomes of possessing a deep understanding of such systems. Therefore, by employing diverse biological system simulations and subsequently analyzing their responses and characteristics, we can diagnose diseases. In this particular study, a novel stimulation method was utilized for the first time, employing the Rossler equation, to record the electromyogram (EMG) signals of the biceps muscle in ten participants. The presented dataset enables the extraction of biological, computational, and chaotic features, which can be utilized for disease classification and diagnosis. Furthermore, this dataset can be employed for the training, validation, and testing of neural networks.

摘要

由各种相互关联的组件组成的生物系统是非线性系统。深入了解此类系统的直接成果是改善疾病诊断以及有效治疗和治疗辅助手段的应用。因此,通过采用各种生物系统模拟并随后分析其反应和特征,我们可以诊断疾病。在这项具体研究中,首次使用了一种新颖的刺激方法,即采用罗斯勒方程来记录十名参与者肱二头肌的肌电图(EMG)信号。所呈现的数据集能够提取生物、计算和混沌特征,可用于疾病分类和诊断。此外,可以将此数据集用于神经网络的训练、验证和测试。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8f0b/10369377/4d39af46b0cb/gr1.jpg

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