Ecg Data Analysis
Ecg Data Analysis - Related Videos
11:17
Acquisition and Analysis of an ECG (electrocardiography) Signal
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2023
Source: Peiman Shahbeigi-Roodposhti and Sina Shahbazmohamadi, Biomedical Engineering Department, University of Connecticut, Storrs, Connecticut An electrocardiograph is a graph recorded by electric potential changes occurring between electrodes placed on a patient's torso to demonstrate cardiac activity. An ECG signal tracks heart rhythm and many cardiac diseases, such as poor blood flow to the heart and structural abnormalities. The action potential created by contractions of the heart wall...
08:00
Ambulatory ECG Recording in Mice
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Cited by 19 •
2010
Telemetric ECG has emerged as an essential tool in evaluating animal models for cardiac arrhythmias and sudden cardiac death. Here, we present a stepwise guide to telemetric ECG recordings for application in long-term ambulatory ECG monitoring in mice.
09:11
Implantation of Radiotelemetry Transmitters Yielding Data on ECG, Heart Rate, Core Body Temperature and Activity in Free-moving Laboratory Mice
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Cited by 56 •
2011
A surgical technique for implantation of commercially available telemetry transmitters used for continuous measurement of biopotential (one-lead ECG), heart rate, core body temperature and locomotor activity in freely moving mice is shown. Recommendations and protocols for post-operative care and pain relief, improving recovery, well being and survival rate are also presented.
05:15
Zebrafish Electrocardiography (ECG): A Minimally Invasive Assay to Evaluate Cardiac Function
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2023
This video shows how to perform electrocardiography (ECG or EKG) measurements, a test which measures the electrical activity of the heart, on a zebrafish.
06:35
Basics of Multivariate Analysis in Neuroimaging Data
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Cited by 37 •
2010
The current article describes the basics of multivariate analysis and contrasts it to the more commonly used voxel-wise univariate analysis. Both types of analysis are applied to a clinical-neuroscience data set. Supplementary split-half simulations show better replication of the multivariate results in independent data sets.
FAQs
R peaks mark prominent points in the cardiac waveform, while RR intervals measure the time between successive R peaks. These intervals support heart-rate calculations and provide the time series used to examine heart-rate variability. In behavioral studies, changes in these measures can be compared across tasks or conditions to evaluate differences in cardiac responses.
Noise and motion artifacts can obscure waveform features and make R-peak detection or interval measurement less reliable. Preprocessing is therefore important before calculating heart rate or heart-rate variability. Accounting for these disturbances helps distinguish recording-related variation from cardiac changes associated with stress, attention, emotion, physical activity, or social interaction.
Heart-rate variability captures changes in the spacing of successive heartbeats rather than treating heart rate as constant over time. This time-dependent information can reveal variation in cardiac responses during behavioral tasks. Examining it alongside heart rate allows researchers to describe autonomic patterns more fully when comparing stress, attention, emotional, or social conditions.
A typical workflow begins with reviewing the ECG recording, identifying and accounting for noise or motion artifacts, and locating waveform features such as R peaks. Researchers then derive RR intervals, heart rate, and heart-rate variability. Finally, they compare these measures across behavioral conditions and relate the resulting cardiac patterns to observed behavior.
Cardiac measures can provide physiological information alongside observations of behavior. Researchers may examine how heart rate or heart-rate variability changes during stress, attention, emotion, physical activity, or social interaction. Relating those measures to task conditions and observed actions helps evaluate whether cardiac arousal varies with behavioral context in laboratory or real-world settings.
Researchers can calculate comparable cardiac measures for different tasks or conditions, while maintaining attention to recording quality and motion-related artifacts. They can then examine changes in heart rate, RR intervals, or heart-rate variability alongside observed behavior. This approach supports interpretation of physiological arousal changes without treating any single cardiac measure as a complete account of behavior.