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Correlation between ECG and Cardiac Cycle01:25

Correlation between ECG and Cardiac Cycle

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The electrical signals recorded on an electrocardiogram (ECG) occur before the mechanical processes of contraction and relaxation during the cardiac cycle.
A cardiac action potential originates in the SA node and spreads throughout the atria and the AV node in approximately 0.03 seconds. This results in the P wave in an ECG and triggers atrial contraction. The action potential is then briefly slowed at the AV node, allowing the atria to contract and fill the ventricles with blood before...
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Electrocardiogram01:29

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An electrocardiogram (ECG or EKG) is a critical diagnostic tool that records the electrical signals produced by the heart during each heartbeat. This recording is achieved through electrodes placed strategically on the arms, legs, and chest. The electrocardiograph amplifies these signals and produces 12 distinct tracings, offering a comprehensive understanding of the heart's electrical activity.
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Introduction
An electrocardiogram (ECG) is a diagnostic tool for identifying cardiac conditions such as arrhythmias, conduction abnormalities, and myocardial ischemia.
Definition
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Classification of Signals01:30

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In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
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The heart rate, or pulse rate, is a vital indicator of cardiovascular health. It reflects the number of times the heart beats per minute. Various physiological and environmental factors influence heart rate, increasing or decreasing cardiac output. Understanding these factors is crucial for assessing heart function and identifying potential health issues.
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An electrocardiogram (ECG)graphically represents the heart's electrical activity on ECG paper or a monitor.
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RETRACTED ARTICLE: Utilizing variable auto encoder-based TDO optimization algorithm for predicting loneliness from

R Bharathi Vidhya1, S Jerritta1

  • 1Department of ECE, Vels Institute of Science, Technology and Advanced Studies, Chennai, India.

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Summary
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Social isolation and loneliness negatively impact health. This study introduces a novel method using electrocardiogram (ECG) signals and advanced algorithms to accurately detect loneliness, improving health monitoring.

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Area of Science:

  • Biomedical Engineering
  • Artificial Intelligence in Healthcare
  • Signal Processing

Background:

  • Social isolation and loneliness affect a significant portion of the population, particularly seniors.
  • These conditions are linked to increased vulnerability, depression, and adverse health outcomes, including cardiovascular problems.
  • Early detection of loneliness is crucial for timely intervention and mitigating negative health impacts.

Purpose of the Study:

  • To develop and validate a novel approach for detecting loneliness using electrocardiogram (ECG) signals.
  • To leverage advanced machine learning techniques for accurate identification of loneliness from physiological data.
  • To explore the potential of ECG analysis as a non-invasive biomarker for mental well-being.

Main Methods:

  • Utilized undecimated discrete wavelet transform for preprocessing ECG signals.
  • Employed a variable autoencoder for extracting salient features from the preprocessed ECG data.
  • Applied a metaheuristic optimized Echo State Network (ESN) for the precise classification of loneliness.

Main Results:

  • The proposed method demonstrated improved accuracy and performance in identifying loneliness from ECG signals.
  • The combination of feature extraction and optimized ESN classification proved effective in capturing subtle patterns related to loneliness.
  • The study successfully validated the efficacy of ECG analysis for loneliness detection.

Conclusions:

  • The developed approach offers a promising, objective method for loneliness detection using readily available ECG data.
  • This technology has the potential to enhance mental health monitoring and support personalized interventions.
  • Further research can explore the integration of this method into broader health surveillance systems.