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Related Concept Videos

Heart Failure IV: Classification and Diagnostic Evaluation01:30

Heart Failure IV: Classification and Diagnostic Evaluation

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Heart failure can be classified in various ways, with the most common classifications based on physical activity limitations, disease progression, severity, and treatment strategies.The Functional Classification of Heart Failure divides patients into four categories based on physical activity limitation due to symptom burden.Class I: Patients in this class have cardiac disease but no physical activity limitations. Ordinary activities like walking, climbing stairs, or routine tasks do not cause...
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Pulse rhythm01:30

Pulse rhythm

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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.
Conversely, an irregular pulse pattern is termed dysrhythmia, stemming from disruptions in cardiac...
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Imaging Studies for Cardiovascular System I:Echocardiography01:17

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Cardiac imaging studies encompass a wide range of noninvasive and minimally invasive techniques designed to visualize the heart's structure and function in detail. One such technique is echocardiography, which uses high-frequency ultrasound waves to produce detailed images of the heart, known as echocardiograms.
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The meaning of illness is individualized to each person who experiences an alteration in health. In contrast, disease is a medical term indicating a pathological change in the structure and function of the body or mind. It is a condition that has specific symptoms and boundaries.
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Cardiomyopathy I: Introduction and Classification01:25

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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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Holter Monitor: 24-Hour Monitoring01:23

Holter Monitor: 24-Hour Monitoring

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Holter monitoring is a continuous electrocardiography (ECG) recording that tracks the heart's electrical activity over an extended period, generally 24 to 48 hours. This noninvasive diagnostic tool detects irregular heart rhythms that may not be captured during a standard ECG performed in a clinical setting.DeviceThe Holter monitor is a portable, small device connected to several electrodes on the patient's chest. These electrodes detect the heart's electrical signals and transmit them to the...
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Hydra, a Computer-Based Platform for Aiding Clinicians in Cardiovascular Analysis and Diagnosis
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A Novel Feature Selection with Hybrid Deep Learning Based Heart Disease Detection and Classification in the

Dwarakanath B1, Latha M1, Annamalai R2

  • 1Department of Information Technology, SRM Institute of Science and Technology, Ramapuram, Chennai, India.

Computational Intelligence and Neuroscience
|October 10, 2022
PubMed
Summary

A novel hybrid deep learning model (FSHDL-HDDC) enhances early heart disease (HD) detection. This approach uses elite opposition-based squirrel search algorithm for feature selection and an attention-based CNN-LSTM network for accurate classification.

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Area of Science:

  • Artificial Intelligence in Healthcare
  • Machine Learning for Medical Diagnosis
  • E-healthcare Systems

Background:

  • Online disease diagnosis services leverage data mining, wearables, and cloud computing to improve e-healthcare quality.
  • Early identification of diseases, particularly heart disease (HD), is crucial for reducing mortality rates and improving patient survival.
  • Accurate diagnosis and classification of HD are vital for effective clinical data analysis.

Purpose of the Study:

  • To introduce a novel feature selection with hybrid deep learning-based heart disease detection and classification (FSHDL-HDDC) model.
  • To enhance the accuracy and efficiency of heart disease detection within the e-healthcare environment.
  • To develop a robust system for early and precise classification of heart disease.

Main Methods:

  • The FSHDL-HDDC model incorporates data normalization and missing value imputation as primary preprocessing steps.
  • A feature selection method based on the elite opposition-based squirrel search algorithm (EO-SSA) is developed to identify optimal features.
  • An attention-based convolutional neural network (ACNN) integrated with a long short-term memory (LSTM) network (ACNN-LSTM) is employed for HD detection using medical data.

Main Results:

  • The FSHDL-HDDC technique demonstrated improved classification performance through extensive experimental validation.
  • Comparative analysis confirmed the superiority of the FSHDL-HDDC method over existing techniques across various performance metrics.
  • The proposed FSHDL-HDDC system achieved a maximum accuracy of 0.9772 in heart disease detection and classification.

Conclusions:

  • The FSHDL-HDDC model offers a promising advancement in e-healthcare for early and accurate heart disease detection.
  • The hybrid deep learning approach, combining advanced feature selection and ACNN-LSTM, significantly enhances diagnostic capabilities.
  • This research contributes to reducing heart disease mortality through improved diagnostic accuracy in digital health platforms.