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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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Introduction
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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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Dysrhythmias, also known as arrhythmias, are disturbances in the heart's rhythm that range from benign to life-threatening. A thorough evaluation is crucial for appropriate management and involves a comprehensive medical history, physical examination, and various diagnostic tests.Medical HistorySymptoms: Collect detailed information on palpitations, dizziness, syncope, chest pain, and fatigue. Note their onset, frequency, and triggers.Previous Cardiac Issues: Document any history of heart...
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Angina manifests as chest pain, tightness, or squeezing discomfort typically located behind the breastbone. It can radiate to the neck, jaw, shoulders, and inner aspects of the upper arms, most commonly the left arm. Patients may experience shortness of breath, fatigue, profuse sweating, dizziness, indigestion, heartburn, palpitations, anxiety, and vomiting as accompanying symptoms. This pain often lasts a few minutes and is triggered by physical exertion, emotional stress, heavy meals, or cold...
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Labeling Emotion01:20

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Emotional labeling is a cognitive process that involves identifying and naming one's emotions, such as anger, fear, happiness, or sadness. It allows individuals to recognize and express their internal emotional states, a critical aspect of emotional regulation and communication. Labeling emotions requires more than mere recognition; it also involves drawing upon memory and contextual cues to understand the current situation and apply a corresponding emotional label. For instance, feeling...
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Using the Electrocardiogram for Pain Classification under Emotional Contexts.

Pedro Silva1, Raquel Sebastião2

  • 1DFis, University of Aveiro, 3810-193 Aveiro, Portugal.

Sensors (Basel, Switzerland)
|February 11, 2023
PubMed
Summary
This summary is machine-generated.

Objective pain assessment using electrocardiograms (ECG) is feasible. This study shows ECG analysis can accurately detect pain, even with emotional variations, paving the way for improved pain management.

Keywords:
electrocardiogramemotional contextsmachine learningpainphysiological features

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Area of Science:

  • Biomedical Engineering
  • Physiological Monitoring
  • Pain Research

Background:

  • Current pain assessment relies on subjective patient reporting.
  • Objective pain measurement is crucial for accurate diagnosis and treatment.
  • Electrocardiogram (ECG) analysis offers a potential avenue for objective pain detection.

Purpose of the Study:

  • To analyze electrocardiogram (ECG) signals for pain classification under neutral and fear emotional states.
  • To investigate the feasibility of objective pain recognition using physiological data.
  • To evaluate the impact of emotional context on ECG-based pain detection.

Main Methods:

  • Collected ECG data from participants under neutral and fear conditions.
  • Developed and evaluated machine learning models (including Random Forest) for pain classification.
  • Employed various strategies: combined emotional contexts, emotionally independent models, and participant-independent approaches with leave-one-out cross-validation.

Main Results:

  • Achieved high balanced accuracy (up to 97.4%) for pain classification using combined emotional data.
  • Demonstrated robust performance (up to 97.7% balanced accuracy) with emotionally independent models.
  • Showcased significant accuracy (up to 94.9%) in participant-independent scenarios, validating the approach.
  • Identified Random Forest as the top-performing model across learning strategies.

Conclusions:

  • Physiological responses to pain are discernible via ECG, even amidst emotional variations.
  • ECG analysis presents a viable method for objective pain identification, supporting clinical applications.
  • The findings support the potential for developing non-invasive, objective pain assessment tools.