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Dysrhythmias V: Evaluating Dysrhythmias01:30

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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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Introduction
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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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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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Development and Validation of Embedded Device for Electrocardiogram Arrhythmia Empowered with Transfer Learning.

Rizwana Naz Asif1, Sagheer Abbas1, Muhammad Adnan Khan2

  • 1School of Computer Science, National College of Business Administration and Economics, Lahore 54000, Pakistan.

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This study introduces a novel embedded device model (DVEEA-TL) for diagnosing heart abnormalities using electrocardiogram (ECG) signals. The transfer learning approach achieves high accuracy, outperforming previous methods for reliable arrhythmia detection.

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Area of Science:

  • Biomedical Engineering
  • Artificial Intelligence in Healthcare
  • Cardiology

Background:

  • Internet of Things (IoT) and cloud computing have enhanced healthcare data management and disease investigation.
  • Electrocardiogram (ECG) is crucial for diagnosing heart conditions.
  • Previous machine learning techniques for ECG analysis were feature-based and less accurate than transfer learning.

Purpose of the Study:

  • To develop and validate an embedded device model (DVEEA-TL) for accurate ECG arrhythmia diagnosis using transfer learning.
  • To improve the accuracy of heart abnormality detection compared to existing methods.

Main Methods:

  • Development of the DVEEA-TL model, integrating hardware and software components.
  • Creation of a fused dataset combining Kaggle and real-time healthy/unhealthy ECG data.
  • Application of the AlexNet transfer learning approach for ECG signal analysis.

Main Results:

  • The DVEEA-TL model achieved high accuracy in diagnosing heart abnormalities.
  • Achieved 99.9% accuracy during the training stage and 99.8% during the validation stage.
  • Demonstrated superior performance compared to previous research in ECG-based heart abnormality detection.

Conclusions:

  • The DVEEA-TL model offers a reliable and highly accurate approach for ECG arrhythmia diagnosis.
  • The integration of transfer learning and a fused dataset significantly enhances diagnostic accuracy.
  • This model represents a reliable advancement in automated heart condition monitoring.