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Related Concept Videos

Acute Coronary Syndrome III: Diagnostic Studies01:30

Acute Coronary Syndrome III: Diagnostic Studies

Diagnosing acute coronary syndrome or ACS begins with a thorough patient history. Notable symptoms include central, crushing chest pain radiating to the left arm, neck, jaw, or back, along with shortness of breath, sweating (diaphoresis), nausea, vomiting, dizziness, and palpitations.It is crucial to note any history of cardiac illnesses and assess risk factors, including age, gender, smoking, hypertension, diabetes, hyperlipidemia, and a sedentary lifestyle.During physical examination, vital...
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Acute Coronary Syndrome (ACS) encompasses a spectrum of heart conditions caused by sudden obstruction of coronary arteries, typically resulting from the rupture of an atherosclerotic plaque and subsequent thrombus (blood clot) formation. This obstruction can lead to partial or complete blockage of blood flow, causing varying degrees of myocardial ischemia or infarction.ACS includes the following clinical entities:Unstable Angina (UA)Non-ST-Elevation Myocardial Infarction (NSTEMI)ST-Elevation...

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A Research Method For Detecting Transient Myocardial Ischemia In Patients With Suspected Acute Coronary Syndrome Using Continuous ST-segment Analysis
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A Research Method For Detecting Transient Myocardial Ischemia In Patients With Suspected Acute Coronary Syndrome Using Continuous ST-segment Analysis

Published on: December 28, 2012

A statistically based acute ischemia detection algorithm suitable for an implantable device.

Bruce Hopenfeld1, M Sasha John, Tim A Fischell

  • 1Angel Medical Systems, 1163 Shrewsbury Avenue, Shrewsbury, NJ 07702, USA. brhopenfeld@yahoo.com

Annals of Biomedical Engineering
|June 29, 2012
PubMed
Summary
This summary is machine-generated.

A new algorithm accurately detects acute myocardial ischemia using heart rate-dependent ST-segment deviation thresholds. This statistically driven approach shows high sensitivity for detecting ischemia during percutaneous coronary interventions (PCI).

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Area of Science:

  • Cardiology
  • Medical Devices
  • Signal Processing

Background:

  • Acute myocardial ischemia detection is crucial for timely intervention.
  • Current methods for ischemia detection can be limited by patient-specific variations.
  • Electrocardiogram (ECG) monitoring is a standard tool for assessing cardiac events.

Purpose of the Study:

  • To evaluate a novel, statistically driven algorithm for acute ischemia detection.
  • To assess the algorithm's performance using subject-specific, heart-rate dependent ST-deviation thresholds.
  • To determine the algorithm's accuracy in detecting ischemia induced by percutaneous coronary interventions (PCI).

Main Methods:

  • Algorithm development involving a start-up phase to establish normal ST-segment deviation ranges.
  • Generation of dynamic, subject-specific ST-deviation thresholds based on heart rate.
  • Testing the algorithm with 10-day Holter monitoring in a control group and during PCI procedures in a patient group.
  • Analysis of algorithm's detection rate for occlusions in Left Anterior Descending (LAD), Left Circumflex (LCX), and Right Coronary Artery (RCA) during PCI.

Main Results:

  • The algorithm demonstrated high detection rates for occlusions: 17/17 LAD, 7/8 LCX, and 8/9 RCA.
  • No false positive detections were observed in the 10-day control group data.
  • The algorithm successfully identified ischemia induced by PCI procedures.

Conclusions:

  • Statistically generated, subject-specific, heart-rate dependent ST-deviation thresholds can effectively detect PCI-induced myocardial ischemia.
  • The algorithm shows promise for accurate and reliable acute ischemia detection with minimal false positives.
  • This approach may improve the monitoring of cardiac patients undergoing interventions.