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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.
Three major waveforms are present in a typical ECG recording: the P wave, the QRS complex, and...
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ECG Interpretation of Arrhythmias II: Atrial, Junctional and Ventricular Arrhythmias01:25

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Arrhythmia is a condition characterized by an irregular heart rhythm, with ECG changes that differ based on its origin and nature. The types of arrhythmias discussed below include atrial, junctional, and ventricular arrhythmias.Atrial ArrhythmiasPremature Atrial Complexes (PACs): PACs are early atrial beats caused by stress, caffeine, alcohol, electrolyte imbalances, hypoxia, hyperthyroidism, or certain medications (e.g., bronchodilators and decongestants). The ECG shows early P waves with an...
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Parkinson's Disease: Overview01:15

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Neurodegenerative disorders are progressive diseases that cause irreversible damage and loss to neurons in specific brain areas. Examples of these disorders include Parkinson's disease, Alzheimer's disease, Multiple Sclerosis (MS), and Amyotrophic Lateral Sclerosis (ALS). These disorders share characteristics such as proteinopathies, selective neuronal vulnerability, and a complex interplay between genetic and environmental factors. The primary therapeutic goal for these conditions is...
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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.
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Neural Regulation01:37

Neural Regulation

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Digestion begins with a cephalic phase that prepares the digestive system to receive food. When our brain processes visual or olfactory information about food, it triggers impulses in the cranial nerves innervating the salivary glands and stomach to prepare for food.
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Electrocardiogram Fundamentals01:28

Electrocardiogram Fundamentals

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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.
Parts of an ECG
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Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease
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Electrocardiographic changes predate Parkinson's disease onset.

Oguz Akbilgic1,2, Rishikesan Kamaleswaran3, Akram Mohammed4

  • 1Deparment of Health Informatics and Data Science, Parkinson School of Health Informatics and Public Health, Loyola University Chicago, Maywood, IL, USA. oakbilgic@luc.edu.

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Electrocardiogram (ECG) analysis can identify early Parkinson's disease (PD) risk. Cardiac electrical activity, especially using Probabilistic Symbolic Pattern Recognition, shows promise in detecting prodromal PD before motor symptoms appear.

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Area of Science:

  • Cardiology
  • Neurology
  • Biomedical Engineering

Background:

  • Autonomic nervous system dysfunction is an early indicator in Parkinson's disease (PD).
  • Identifying prodromal PD is crucial for timely intervention and research.
  • Cardiac electrical activity offers a potential non-invasive biomarker for neurodegenerative diseases.

Purpose of the Study:

  • To develop a proof-of-concept model for identifying individuals at high risk of developing Parkinson's disease.
  • To investigate the utility of cardiac electrical activity analysis for early PD detection.
  • To compare novel feature extraction methods with traditional heart rate variability metrics.

Main Methods:

  • Analysis of 10-second electrocardiogram (ECG) recordings from 60 subjects (10 PD, 25 prodromal PD, 25 controls).
  • Feature extraction using heart rate variability (HRV) metrics, signal processing, and Probabilistic Symbolic Pattern Recognition (PSPR).
  • Stepwise logistic regression and cross-validation to build and assess predictive models.

Main Results:

  • Four PSPR-derived features were identified as significant predictors of PD.
  • The final logistic regression model achieved an Area Under the Curve (AUC) of 0.90 [0.80, 0.99].
  • Cross-validation yielded an average AUC of 0.835 [0.831, 0.839], indicating robust performance.

Conclusions:

  • Cardiac electrical activity contains valuable information for predicting future PD risk.
  • PSPR analysis of ECGs can outperform classical HRV metrics in identifying prodromal PD.
  • Machine learning applied to ECG data holds potential for early detection of individuals at high risk for Parkinson's disease.