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Related Concept Videos

ECG Interpretation of Arrhythmias I: Sinus Arrhythmias01:16

ECG Interpretation of Arrhythmias I: Sinus Arrhythmias

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Arrhythmias are disturbances in the heart's rhythm that lead to abnormal heartbeats. These irregularities can originate from different parts of the heart and are classified based on their origin and nature.
Types of Arrhythmias
Sinus Node Arrhythmias
Sinus Bradycardia: Originating from the sinoatrial (SA) node, sinus bradycardia involves slower impulses, resulting in a heart rate of less than 60 beats per minute (bpm). Causes include sleep, vagal stimulation, beta-blockers, hypothyroidism,...
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ECG Interpretation of Arrhythmias II: Atrial, Junctional and Ventricular Arrhythmias01:25

ECG Interpretation of Arrhythmias II: Atrial, Junctional and Ventricular Arrhythmias

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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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lncRNA - Long Non-coding RNAs02:39

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In humans, more than 80% of the genome gets transcribed. However, only around 2% of the genome codes for proteins. The remaining part produces non-coding RNAs which includes ribosomal RNAs, transfer RNAs, telomerase RNAs, and regulatory RNAs, among other types. A large number of regulatory non-coding RNAs have been classified into two groups depending upon their length – small non-coding RNAs, such as microRNA, which are less than 200 nucleotides in length, and long non-coding RNA...
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Mechanism of Cardiac Arrhythmias01:28

Mechanism of Cardiac Arrhythmias

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Arrhythmias are irregular heart rhythms occurring when the heart's electrical impulses become abnormal. These disturbances can lead to various symptoms, depending on their severity and the underlying cause. Some common factors contributing to arrhythmias include hypoxia, ischemia, electrolyte imbalances, excessive catecholamine exposure, drug toxicity, and muscle overstretching. Arrhythmias can be classified into two main types based on the rate and site of origin of abnormal heart rhythms.
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Protein Networks02:26

Protein Networks

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An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
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Related Experiment Video

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A new approach for arrhythmia classification using deep coded features and LSTM networks.

Ozal Yildirim1, Ulas Baran Baloglu1, Ru-San Tan2

  • 1Department of Computer Engineering, Munzur University, Tunceli, Turkey.

Computer Methods and Programs in Biomedicine
|June 16, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces a novel method using convolutional auto-encoders (CAE) and long-short term memory (LSTM) networks for efficient electrocardiogram (ECG) analysis. The approach significantly reduces data size and classification time for detecting heart arrhythmias.

Keywords:
Arrhythmia detectionAutoencodersDeep learningECG compressionLSTM

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Area of Science:

  • Cardiology
  • Biomedical Engineering
  • Signal Processing

Background:

  • Electrocardiogram (ECG) monitoring is crucial for diagnosing heart rhythm disorders.
  • Current Holter monitors have hardware limitations, necessitating automated arrhythmia detection.
  • Expert cardiologists manually analyze long-term ECG recordings, which is time-consuming.

Purpose of the Study:

  • To present a novel approach for automatic detection and compression of arrhythmic ECG signals.
  • To improve diagnostic capacity and reduce the computational cost of ECG analysis.
  • To address the limitations of current hardware in ECG monitoring devices.

Main Methods:

  • Implementation of a convolutional auto-encoder (CAE) for nonlinear compression of ECG signals.
  • Utilizing long-short term memory (LSTM) classifiers for automated arrhythmia recognition.
  • Deep feature coding of ECG signals using the CAE network.

Main Results:

  • Significant reduction in storage requirements and classification time for ECG data.
  • ECG signals compressed with an average 0.70% PRD rate using the MIT-BIH arrhythmia database.
  • Achieved over 99.0% accuracy in automatic arrhythmia recognition.

Conclusions:

  • The proposed approach effectively reduces analysis time for LSTM networks in ECG data.
  • A novel and efficient method for ECG signal compression and high-performance automatic recognition was developed.
  • The technique offers a low computational cost solution for advanced ECG analysis.