Surface electrocardiogram (ECG) dataset recorded during relaxation in 70 healthy subjects
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<strong>Study Sample and Ethics Statement</strong> The sample consisted of 71 university students, average age 20.38 years (<em>SD</em> = 2.96), 78.8% female. Subjects with previous cardio-vascular disorders and irregular ECG were excluded. The study has been approved by the Institutional Review Board of the Department of Psychology, University of Belgrade No. 2018-19. All participants signed Informed Consents in accordance with the Declaration of Helsinki. In the course of visual examination, it was decided to discard ECG from one subject due to the presence of bigeminial arythmia, so further analysis was performed on 70 subjects instead of 71. <strong>Measurement Setup</strong> BIOPAC sensors (Biopac Systems Inc., Camino Goleta, CA, USA) were used for recording biosignals in another study (Bjegojević et al., 2020). Here, we used only ECG signals recorded in sitting relaxed position from standard bipolar Lead I using the BIOPAC MP150 unit with AcqKnowledge software and ECG 100C module with surface H135SG Ag/AgCl electrodes (Kendall/Covidien, Dublin, Ireland). In order to decrease skin-electrode impedance, the skin was cleaned with Nuprep gel (Weaver & Co., Aurora, USA) to reduce skin-electrode impedance. The sampling frequency was set at 2000 Hz and gain was set to 1000. <strong>Dataset, Code, and Feature Instructions</strong> analysisECG.R, function with analysis procedures written in R programming language anec12919-sup-0001-supinfo.pdf, detailed ECG processing and feature extraction procedure (also available as supplementary material for article [1]) ecg_70.txt, .txt data file, text format mainECG.R, main program written in R programming language Please, note that test personality results are not available in the current dataset. We are planning to open them in our future research. For more information please contact corresponding author by e-mail (nadica.miljkovic@etf.rs). <strong>Citing Instruction</strong> If you find these signals and code useful for your own research or teaching class, please cite both relevant dataset and papers: Boljanić, T., Miljkovic, N., Lazarević, L. B., Knezevic, G., & Milašinović, G. (2021). Relationship between electrocardiogram-based features and personality traits: Machine learning approach. <em>Annals of Noninvasive Electrocardiology</em>, 00, e12919. https://doi.org/10.1111/anec.12919 Bjegojević, B., Milosavljević, N., Dubljević, O., Purić, D., & Knežević, G. (2020). In pursuit of objectivity: Physiological measures as a means of emotion induction procedure validation. <em>XXIVI Scientific Conference on Empirical Studies in Psychology</em>, p. 17-19. Boljanić, T., Miljković, N., Lazarević B. Lj., Knežević, G., & Milašinović, G. (2021). Surface electrocardiogram (ECG) dataset recorded during relaxation in 70 healthy subjects (Version 1) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.5599239
<strong>研究样本与伦理声明</strong> 本研究样本共71名大学生，平均年龄20.38岁（标准差<em>SD</em> = 2.96），其中女性占比78.8%。排除既往患有心血管疾病及心电图（Electrocardiogram, ECG）异常的受试者。本研究已获得贝尔格莱德大学心理学系伦理审查委员会批准，批准编号为2018-19。所有参与者均按照《赫尔辛基宣言》签署了知情同意书。在视觉检查过程中，由于1名受试者存在二联律心律失常，故剔除其心电图数据，最终实际纳入后续分析的有效样本为70名，而非最初的71名。 <strong>测量设置</strong> 本研究此前的一项研究（Bjegojević等，2020）曾使用BIOPAC传感器（Biopac系统公司，美国加利福尼亚州戈莱塔市）记录生物信号。本次研究仅使用BIOPAC MP150采集设备搭配AcqKnowledge软件，通过标准双极导联I、ECG 100C模块及表面H135SG Ag/AgCl电极（Kendall/Covidien，爱尔兰都柏林市），采集受试者放松坐姿下的心电图信号。为降低皮肤-电极阻抗，使用Nuprep凝胶（Weaver公司，美国奥罗拉市）擦拭受试者皮肤。采样频率设置为2000 Hz，增益设置为1000。 <strong>数据集、代码及特征提取说明</strong> 本次公开的资源包含以下文件： 1. analysisECG.R：采用R编程语言编写的分析流程函数； 2. anec12919-sup-0001-supinfo.pdf：详细的心电图处理及特征提取流程（同时作为文献[1]的补充材料公开）； 3. ecg_70.txt：纯文本格式的数据文件； 4. mainECG.R：采用R编程语言编写的主程序。 请注意，当前数据集未包含人格测试结果，我们计划在未来的研究中公开该部分数据。如需更多信息，请通过电子邮件联系通讯作者：nadica.miljkovic@etf.rs。 <strong>引用规范</strong> 若您认为本数据集与代码对您的研究或课堂教学有所帮助，请同时引用以下相关数据集与论文： 1. Boljanić, T., Miljković, N., Lazarević, L. B., Knezević, G., & Milašinović, G. (2021). 基于心电图特征与人格特质的关联：机器学习方法. 《无创心电图学年鉴（Annals of Noninvasive Electrocardiology）》, 00, e12919. https://doi.org/10.1111/anec.12919 2. Bjegojević, B., Milosavljević, N., Dubljević, O., Purić, D., & Knežević, G. (2020). 追求客观性：生理测量作为情绪诱发程序验证的手段. 第24届心理学实证研究科学会议, 第17-19页。 3. Boljanić, T., Miljković, N., Lazarević B. Lj., Knežević, G., & Milašinović, G. (2021). 70名健康受试者放松状态下的表面心电图（Electrocardiogram, ECG）数据集（版本1）[数据集]. Zenodo. https://doi.org/10.5281/zenodo.5599239
