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Related Concept Videos

Electrocardiogram01:29

Electrocardiogram

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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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An electrocardiogram (ECG)graphically represents the heart's electrical activity on ECG paper or a monitor.
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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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Introduction
An electrocardiogram (ECG) is a diagnostic tool for identifying cardiac conditions such as arrhythmias, conduction abnormalities, and myocardial ischemia.
Definition
An electrocardiogram (ECG) visualizes the heart's electrical activity by tracing the electrical movement associated with each heartbeat on a graph or monitor. As the heart beats, an electrical wave passes through it, correlating with the cardiac cycle events.
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Evaluation of electrocardiogram: numerical vs. image data for emotion recognition system.

Sharifah Noor Masidayu Sayed Ismail1, Nor Azlina Ab Aziz2, Siti Zainab Ibrahim1

  • 1Faculty of Information Science & Technology, Multimedia University, Bukit Beruang,, Melaka, 75450, Malaysia.

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Summary

This study compared 1-D and 2-D electrocardiogram (ECG) data for emotion recognition systems (ERS). Both formats showed comparable performance, indicating the potential of both 1-D and 2-D ECG data for ERS applications.

Keywords:
DREAMEREmotion recognitionelectrocardiogramimage ECGnumerical ECG

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Area of Science:

  • Physiological signal processing
  • Affective computing
  • Machine learning for emotion recognition

Background:

  • Electrocardiogram (ECG) is vital for cardiovascular health monitoring.
  • Previous research explored 1-D ECG for emotion detection, but 2-D ECG image format is less studied.
  • A consensus on the impact of ECG input format (1-D vs. 2-D) on emotion recognition system (ERS) accuracy is lacking.

Purpose of the Study:

  • To investigate the effect of ECG input format on ERS performance.
  • To compare the accuracy of ERS using 1-D numerical ECG data versus 2-D ECG images.

Main Methods:

  • Utilized the DREAMER dataset comprising 23 ECG recordings during emotional elicitation.
  • Extracted features from 1-D ECG using AUBT and TEAP; extracted features from 2-D ECG images using ORB, SIFT, KAZE, AKAZE, BRISK, and HOG.
  • Applied Linear Discriminant Analysis (LDA) for dimensionality reduction and Support Vector Machine (SVM) for classifying valence and arousal.

Main Results:

  • 1-D ECG-based ERS achieved 65.06% accuracy and 75.63% F1-score for valence; 57.83% accuracy and 44.44% F1-score for arousal.
  • 2-D ECG-based ERS yielded a maximum of 62.35% accuracy and 49.57% F1-score for valence; 59.64% accuracy and 59.71% F1-score for arousal.
  • Both 1-D and 2-D ECG input formats demonstrated comparable performance in emotion classification.

Conclusions:

  • The study indicates that both 1-D and 2-D ECG data formats are viable for emotion recognition systems.
  • The findings highlight the potential of utilizing both numerical and image-based ECG data for enhanced ERS.
  • Further research can explore hybrid approaches or optimize feature extraction for each modality.