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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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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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Arrhythmia or dysrhythmia refers to an abnormal heart rhythm caused by a defect in the heart's conduction system. It can cause the heart to beat irregularly, too quickly, or too slowly, leading to symptoms like chest pain, shortness of breath, and fainting. Factors such as stress, caffeine, alcohol, nicotine, cocaine, certain drugs, congenital defects, diseases, and electrolyte abnormalities can trigger arrhythmias.
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Pulse rhythm01:30

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Pulse rhythm refers to the pattern of pulsations within specific intervals, offering valuable insights into the regularity or irregularity of the heart's beats as observed through the pattern of pulsation within specific intervals. A regular pulse exhibits a consistent heart rate with uniform waveforms and pulsation force, variations of which can be classified as normal, weak, or bounding.
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Epilepsy is primarily characterized by unpredictable seizures, either provoked by an identifiable factor, such as injury or illness, or unprovoked, occurring spontaneously without apparent cause.
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The normal cardiac rhythm is a synchronized electrical activity that facilitates the regular and coordinated contraction of the heart muscle. This process is essential for efficient blood circulation throughout the body. The fundamental elements involved in establishing and maintaining this rhythm include the unique electrical properties of cardiac muscle cells, the sinoatrial (SA) node's pacemaker function, the specialized conducting system, and the ionic mechanisms underlying each phase...
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Updated: Oct 3, 2025

Semi-automated Optical Heartbeat Analysis of Small Hearts
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A VLSI Chip for the Abnormal Heart Beat Detection Using Convolutional Neural Network.

Yuan-Ho Chen1,2, Szi-Wen Chen1,3, Pei-Jung Chang1

  • 1Department of Electronics Engineering, Chang Gung University, Taoyuan 333, Taiwan.

Sensors (Basel, Switzerland)
|February 15, 2022
PubMed
Summary
This summary is machine-generated.

This study developed an AI-based system using a convolutional neural network (CNN) for detecting abnormal heartbeats from ECG signals. The efficient hardware design achieved a 96.3% discrimination rate, suitable for wearable healthcare devices.

Keywords:
convolutional neural network (CNN)electrocardiogram (ECG)very large scale integration implementation (VLSI)

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Area of Science:

  • Biomedical Engineering
  • Artificial Intelligence
  • Cardiology

Background:

  • Electrocardiograms (ECG) are crucial for diagnosing heart conditions by monitoring the heart's electrical activity.
  • Early detection and timely treatment of heart diseases are vital for patient outcomes.

Purpose of the Study:

  • To design and implement an artificial intelligence (AI)-based system for detecting abnormal heartbeats.
  • To enable early detection and timely treatment of heart diseases using advanced algorithms.

Main Methods:

  • A convolutional neural network (CNN) was designed for fast and accurate abnormal heartbeat identification.
  • Modular processing element units and activation function modules were developed for circuit scalability.
  • The CNN was implemented using TSMC 0.18 μm CMOS technology.

Main Results:

  • The implemented CNN operated at 60 MHz with a chip area of 1.42 mm² and power dissipation of 4.4 mW.
  • Performance evaluation using the MIT-BIH arrhythmia database showed a discrimination rate of 96.3%.
  • The system demonstrated high computational efficiency.

Conclusions:

  • The AI-based abnormal heartbeat detection system is efficient and accurate.
  • The hardware implementation is suitable for integration into wearable healthcare devices for continuous monitoring.