Electroencephalogram (EEG) recordings provide a valuable, noninvasive method for measuring human brain activity. This protocol modi es our general protocol for EEG recording (Farrens et al., 2019) for use during the COVID-19 pandemic. It was created with the help of numerous experts, and it speci es a clear set of steps for interacting with research participants, using personal protective equipment (PPE), and disinfecting equipment, all with the goal of reducing the COVID-19 risks for both laboratory personnel and participants. It focuses on the use of EEG in relatively simple research studies of adults who can easily understand and follow instructions, yet can be readily adapted for studies using other types of EEG experiments or other participant populations.

This newly inaugurated research database for 12-lead electrocardiogram signals was created under the auspices of Chapman University and Shaoxing People’s Hospital (Shaoxing Hospital Zhejiang University School of Medicine) and aims to enable the scientific community in conducting new studies on arrhythmia and other cardiovascular conditions. Certain types of arrhythmias, such as atrial fibrillation, have a pronounced negative impact on public health, quality of life, and medical expenditures. As a non-invasive test, long term ECG monitoring is a major and vital diagnostic tool for detecting these conditions. This practice, however, generates large amounts of data, the analysis of which requires considerable time and effort by human experts. Advancement of modern machine learning and statistical tools can be trained on high quality, large data to achieve exceptional levels of automated diagnostic accuracy. Thus, we collected and disseminated this novel database that contains 12-lead ECGs of 10,646 patients with a 500 Hz sampling rate that features 11 common rhythms and 67 additional cardiovascular conditions, all labeled by professional experts. The dataset consists of 10-second, 12-dimension ECGs and labels for rhythms and other conditions for each subject. The dataset can be used to design, compare, and fine-tune new and classical statistical and machine learning techniques in studies focused on arrhythmia and other cardiovascular conditions.

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