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Related Concept Videos

Stress-Strain Diagram01:10

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A stress-strain diagram is a crucial tool that graphically displays a material's mechanical characteristics. This diagram is derived from a tensile test performed on a carefully prepared cylindrical specimen. The specimen has two gauge marks inscribed on its central part, and the distance between these marks is known as the gauge length. The cylindrical specimen is placed in a testing machine, which applies an increasing centric load. As this load grows, so does the gauge length. This...
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A material's elastic behavior is characterized by the disappearance of stress once the load is removed, allowing the material to return to its original state. However, when stress surpasses the yield point, yielding commences, marking the onset of plastic deformation or permanent set. This change from elastic to plastic behavior is influenced by the peak stress value and the duration before the load is removed. An intriguing observation occurs when a specimen is loaded, unloaded, and...
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Stress-Strain Diagram - Ductile Materials01:24

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The stress-strain relationship in ductile materials such as structural steel or aluminium is intricate and progresses through several stages. When a specimen is loaded, it initially exhibits a linear length increase, depicted by a steep straight line on the stress-strain diagram. It indicates the material is elastically deforming and will return to its original shape once unloaded. However, when a critical stress value is reached, plastic deformation begins. This stage sees substantial...
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Related Experiment Video

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Intermediate Strain Rate Material Characterization with Digital Image Correlation
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Characterizing artifacts in RR stress test time series.

Fabian Astudillo-Salinas, Kenneth Palacio-Baus, Lizandro Solano-Quinde

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |March 9, 2017
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces an automated method to detect artifacts in electrocardiographic (ECG) stress test data. The approach effectively classifies RR time series quality, improving automatic heartbeat labeling by identifying usable ECG leads.

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    Area of Science:

    • Cardiology
    • Biomedical Engineering
    • Signal Processing

    Background:

    • Electrocardiographic (ECG) stress test records frequently contain artifacts that compromise data quality.
    • Accurate analysis of RR time series is crucial for reliable cardiac diagnostics during stress tests.

    Purpose of the Study:

    • To develop and validate a simple, automated method for characterizing artifact levels in unprocessed RR stress test time series.
    • To classify ECG leads into quality categories (Very good, Good, Low quality, Useless) for improved data handling.

    Main Methods:

    • RR time series from 65 ECG stress test records (8 leads each) were analyzed.
    • An automated method divided time series into windows, flagging noisy windows exceeding a standard deviation threshold (SDT).
    • Series were classified based on the percentage of noisy windows, with SDT around 20% of the mean yielding optimal results.

    Main Results:

    • The automated method achieved over 63% agreement with expert annotation, comparable to inter-annotator agreement (70.77%).
    • A standard deviation threshold (SDT) close to 20% of the mean provided the best classification accuracy.
    • Combining 'Very good' and 'Good' lead classifications shows potential for enhancing automatic heartbeat labeling.

    Conclusions:

    • The proposed automated method offers a reliable approach to quantify artifacts in ECG stress test RR time series.
    • This technique can aid in distinguishing between high-quality and low-quality ECG data, facilitating better clinical interpretation.
    • Improved artifact detection and lead classification can significantly enhance the accuracy of automated cardiac analysis tools.