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Evaluation of a Holter system to record ST-segment changes.

S C Hammill1

  • 1Mayo Clinic, Rochester, MN.

Journal of Electrocardiology
|October 1, 1987
PubMed
Summary
This summary is machine-generated.

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The Avionics 445B recorder and Innovator 750 scanner accurately detect ST-segment changes during treadmill tests. This Holter monitoring system demonstrates high sensitivity and specificity for ambulatory ECG monitoring.

Area of Science:

  • Cardiology
  • Medical Devices
  • Diagnostic Technology

Background:

  • ST-segment changes during exercise are critical indicators of myocardial ischemia.
  • Accurate detection of these changes is essential for timely diagnosis and treatment.
  • Evaluating the performance of ambulatory ECG monitoring devices is crucial for clinical practice.

Purpose of the Study:

  • To assess the accuracy of the Avionics 445B recorder and Innovator 750 scanner for identifying ST-segment changes.
  • To compare the diagnostic performance of a Holter monitoring system against a standard 12-lead ECG during treadmill exercise testing.

Main Methods:

  • 143 patients undergoing treadmill exercise testing were monitored using both 12-lead ECG and a 2-lead Holter system (Avionics 445B recorder).
  • Holter recordings were analyzed using the Innovator 750 scanner with automated ST-segment analysis.

Related Experiment Videos

  • The 12-lead ECG recorded during exercise served as the reference standard.

    • Main Results:

    • The Holter system achieved 89% sensitivity, 96% specificity, and 91% positive predictive accuracy in detecting ST-segment changes.
    • Forty-two patients showed positive ST-segment changes on both systems; 92 showed no changes on either.
    • The system demonstrated reliable performance in identifying ST-segment deviations.

    Conclusions:

    • The Avionics 445B recorder and Innovator 750 scanner provide accurate ST-segment change detection during ambulatory ECG monitoring.
    • This Holter system is a reliable tool for identifying ischemic episodes during exercise.
    • The findings support the use of this technology in clinical settings for diagnosing cardiac conditions.