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Related Concept Videos

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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Cardiomyopathy, or CMP, is a group of diseases affecting the myocardial structure, impairing its ability to pump blood effectively. This condition can lead to arrhythmias, heart failure, or sudden cardiac death.Cardiomyopathies are classified into primary and secondary categories:Primary Cardiomyopathy refers to conditions involving only the heart muscle that are often idiopathic (of unknown cause) or genetic. They primarily affect the myocardium without the involvement of other systemic...
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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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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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Assessing a patient's pulse is a fundamental skill in healthcare, but certain situations require special attention:
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Cardiovascular Disease Recognition Based on Heartbeat Segmentation and Selection Process.

Mehrez Boulares1,2, Reem Alotaibi1, Amal AlMansour1

  • 1Information System Department, Computing College, King Abdulaziz University, Jeddah, Makkah 21589, Saudi Arabia.

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Summary

This study introduces an artificial intelligence model using convolutional neural networks (CNNs) for accurate cardiovascular disease (CVD) detection from heart sounds. The AI model achieved high accuracy, demonstrating its potential for early CVD diagnosis.

Keywords:
CVDPCGconvolutional neural networkdeep learningdenoisingheart soundssegmentation

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Area of Science:

  • Cardiology
  • Biomedical Engineering
  • Artificial Intelligence

Background:

  • Cardiovascular disease (CVD) diagnosis often relies on cardiac auscultation, a method requiring expert interpretation of heart sounds (Phonocardiogram - PCG).
  • Automated analysis of PCG signals using artificial intelligence (AI) offers a promising approach to aid physicians in preliminary CVD diagnosis.

Purpose of the Study:

  • To develop an accurate cardiovascular disease recognition model utilizing unsupervised and supervised machine learning methods, specifically a convolutional neural network (CNN).
  • To evaluate the performance of the proposed AI model on publicly available heart sound datasets (PASCAL and PhysioNet).

Main Methods:

  • The study employed a convolutional neural network (CNN) architecture for analyzing Phonocardiogram (PCG) signals.
  • Both unsupervised and supervised machine learning techniques were utilized within the CNN framework.
  • The model's performance was rigorously evaluated on the PASCAL and PhysioNet heart sound datasets.

Main Results:

  • Heart cycle segmentation and segment selection significantly impacted model performance metrics, including accuracy, sensitivity (TPR), precision (PPV), and specificity (TNR).
  • On the PASCAL dataset, the model achieved an overall accuracy of 0.87, precision of 0.81, and sensitivity of 0.83.
  • On the PhysioNet dataset, the model demonstrated superior performance with 0.97 accuracy, 0.946 sensitivity, 0.944 precision, and 0.946 specificity.

Conclusions:

  • The developed AI model, based on CNNs, shows significant potential for accurate and reliable cardiovascular disease detection from heart sound analysis.
  • The findings highlight the critical role of signal processing techniques like segmentation and segment selection in optimizing AI-driven diagnostic tools.
  • The high performance on both PASCAL and PhysioNet datasets suggests the generalizability and clinical utility of the proposed approach for automated CVD screening.