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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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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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Holter Monitor: 24-Hour Monitoring01:23

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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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Semi-automated Optical Heartbeat Analysis of Small Hearts
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Real-Time Heart Arrhythmia Detection Using Apache Spark Structured Streaming.

Sadegh Ilbeigipour1, Amir Albadvi1, Elham Akhondzadeh Noughabi1

  • 1Department of Information Technology Engineering, Industrial and Systems Engineering Faculty, Tarbiat Modares University, Tehran, Iran.

Journal of Healthcare Engineering
|May 10, 2021
PubMed
Summary

This study introduces a real-time cardiac arrhythmia detection system using Apache Spark Structured Streaming. The machine learning pipeline significantly reduces detection delays and improves classification performance for critical heart rhythm monitoring.

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Area of Science:

  • Biomedical Engineering
  • Computer Science
  • Cardiology

Background:

  • Cardiac arrhythmias are a leading cause of mortality worldwide.
  • Electrocardiogram (ECG) analysis is crucial for diagnosing arrhythmias, but real-time monitoring is needed due to intermittent symptoms.
  • Timely detection of arrhythmias through continuous ECG analysis can prevent life-threatening incidents.

Purpose of the Study:

  • To implement a machine learning pipeline for real-time cardiac arrhythmia detection using Apache Spark Structured Streaming.
  • To evaluate the impact of Structured Streaming on classification performance and detection delay.
  • To compare the performance of different machine learning classifiers for arrhythmia detection.

Main Methods:

  • Utilized the Apache Spark Structured Streaming module for real-time ECG data analysis.
  • Developed and compared three multiclass classifiers: decision trees, random forest, and logistic regression.
  • Trained and evaluated models on ECG data from the MIT/BIH database, focusing on normal beats, RBBB, and atrial fibrillation.

Main Results:

  • The random forest classifier demonstrated superior performance compared to decision trees and logistic regression.
  • The implemented pipeline achieved significant reductions in runtime, especially with an increased number of class labels.
  • The study validated previous findings in classification model performance metrics.

Conclusions:

  • Apache Spark Structured Streaming offers a viable and efficient platform for real-time cardiac arrhythmia detection.
  • The random forest classifier is effective for classifying arrhythmias in real-time ECG data.
  • This approach enhances timely arrhythmia detection, potentially improving patient outcomes and reducing healthcare burdens.