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.
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A multimodal dataset is presented for the cognitive fatigue assessment of physiological minimally invasive sensory data of Electrocardiography (ECG) and Electrodermal Activity (EDA) and self-reporting scores of cognitive fatigue during HRI. Data were collected from 16 non-STEM participants, up to three visits each, during which the subjects interacted with a robot to prepare a meal and get ready for work. For some of the visits, a well-established cognitive test was used to induce cognitive fatigue.
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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.
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52 Views
The large number and scale of natural and man-made disasters have led to an urgent demand for technologies that enhance the safety and efficiency of search and rescue teams. Semi-autonomous rescue robots are beneficial, especially when searching inaccessible terrains, or dangerous environments, such as collapsed infrastructures. For search and rescue missions in degraded visual conditions or non-line of sight scenarios, radar-based approaches may contribute to acquire valuable, and otherwise unavailable information.
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3358 Views
<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.
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This is a MATLAB-based tool to convert electrocardiography (ECG) waveforms from paper-based ECG records into digitized ECG signals that is vendor-agnostic. The tool is packaged as an open-source standalone graphical user interface (GUI) based application. This open-source digitization tool can be used to digitize paper ECG records thereby enabling new prediction
algorithms.
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1071 Views
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.
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929 Views
For more information please take a look at the corresponding paper (DOI: 10.1109/JBHI.2019.2963786)
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This dataset provides the ECG signals recorded in ambulatory (moving) conditions of subjects. The ambulatory ECG (A-ECG) data acquired with two different recorders viz. Biopac MP36 Acquisition system and a self-developed wearable ECG recorder are made available. Total 10 subjects' (with avg. age of 27 years, 1 female and 9 males) ECG signals with four body movements- Left & Right arm up/down, Sitting down & standing up and Waist twist are uploaded.
An EEG signals dataset is also provided here.
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2183 Views
For research purposes, the ECG signals were obtained from the PhysioNet service (http://www.physionet.org) from the MIT-BIH Arrhythmia database. The created database with ECG signals is described below. 1) The ECG signals were from 29 patients: 15 female (age: 23-89) and 14 male (age: 32-89). 2) The ECG signals contained 17 classes: normal sinus rhythm, pacemaker rhythm, and 15 types of cardiac dysfunctions (for each of which at least 10 signal fragments were collected).
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10544 Views