Automated Ecg Analysis
Automated Ecg 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.
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.
10:39
Automated Analysis of Intracellular Phenotypes of Salmonella Using ImageJ
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Cited by 3 •
2022
Salmonella invades and replicates inside intestinal epithelial cells both in Salmonella-specific vacuoles and free in the cytosol (hyper-replication). A high-throughput fluorescence microscopy-based protocol is described here to quantify the intracellular phenotypes of Salmonella by two complementary image analyses through ImageJ, reaching single-cell resolution and scoring.
06:27
Automated Analysis of C. elegans Fluorescence Images using SegElegans
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2025
Here we provide instructions on effectively utilizing SegElegans, a deep learning system we developed for the automated segmentation of individual worms in widefield microscopy images, for subsequent use in image analysis software such as ImageJ. We provide ways to use the system both online and offline.
FAQs
Signal quality determines whether software can reliably detect beats, waveforms, intervals, and amplitudes. Poor-quality digitized signals may interfere with measurement and classification, increasing the need for clinical review. For this reason, performance assessments should consider signal quality rather than treating every ECG as equally suitable for automated interpretation.
Machine-learning performance depends on training data that adequately represent the ECG patterns the system will encounter. Appropriate validation then tests whether the model performs consistently beyond the data used for development. Representative data and transparent evaluation are therefore important safeguards when assessing whether automated findings can support medical screening, triage, or monitoring.
Rule-based algorithms and machine-learning models provide different computational approaches to ECG classification. Rule-based systems apply specified interpretive criteria, whereas machine-learning systems learn patterns from training data. The overview supports both approaches as tools for identifying rhythms and flagging abnormalities, but either approach still requires evaluation for performance and clinical confirmation.
Automated findings are decision-support outputs rather than substitutes for clinical judgment. A clinician must confirm flagged rhythms or abnormalities in the context of the patient and the quality of the recorded signal. This review helps distinguish useful alerts from findings that require clarification, and it supports safer integration of software into medical care.
A typical workflow begins with digitizing the ECG, followed by software detection of beats and characteristic waveforms. The system then measures intervals and amplitudes before applying rule-based algorithms or machine-learning models to classify rhythms and flag abnormalities. The resulting assessment can be reviewed by a clinician, especially when signal quality or findings raise uncertainty.
The method can support arrhythmia screening, triage, patient monitoring, and review of large clinical datasets. Its value comes from helping process ECG information quickly and consistently across these settings. Automated outputs remain most appropriate as an aid to assessment, with clinicians confirming findings before they inform patient-care decisions.