ECG

The Clarkson University Affective Data Set (CUADS) is a multi-modal affective dataset designed to assist in machine learning model development for automated emotion recognition. CUADS provides electrocardiogram, photoplethysmogram, and galvanic skin response data from 38 participants, captured under controlled conditions using Shimmer3 ECG and GSR sensors. ECG, GSR and PPG signals were recorded while each participant viewed and rated 20 affective movie clips. CUADS also provides big five personality traits for each participant.

This dataset is from our study that challenges the conventional interpretation of electrocardiogram (ECG) measurements, suggesting a paradigm shift in our understanding. Traditionally, ECGs are seen as reflections of the electric potential on the body's surface, but we propose an alternative hypothesis: ECGs may represent the gradient of the electric potential rather than the potential itself. To investigate this, we use computational methods based on the boundary element method (BEM) within the SCIRun numerical package.

<p><span style="font-size: 10.0pt; font-family: 'Times New Roman',serif; mso-fareast-font-family: 'Times New Roman'; border: none windowtext 1.0pt; mso-border-alt: none windowtext 0in; padding: 0in; mso-ansi-language: EN-US; mso-fareast-language: EN-US; mso-bidi-language: AR-SA;">Many neurophysiological measurements are affected by mental state tasks.

This is the dataset associated with the IEEE-JBHI submission "Synthesizing Electrocardiograms With Atrial Fibrillation Characteristics Using Generative Adversarial Networks". This dataset contains 4,768 synthesized atrial fibrillation (AF)-like ECG signals stored in PhysioNet MAT/HEA format.