These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


Pubmed for Handhelds

PUBMED FOR HANDHELDS

Search MEDLINE/PubMed

  • Title: Intra-QRS spectral changes accompany ST segment changes during episodes of myocardial ischemia.
    Author: Gramatikov B, Iyer V.
    Journal: J Electrocardiol; 2015; 48(1):115-22. PubMed ID: 25266140.
    Abstract:
    BACKGROUND: Coronary artery disease and myocardial ischemia cause substantial morbidity and mortality. While ischemia is traditionally diagnosed on the 12-lead electrocardiogram (ECG) by shifts in the ST segment, electrical changes are also produced within the QRS complex during depolarization of ischemic ventricular tissue, though these are often of small amplitude and can be missed in traditional ECG analysis. We explore the utility of an easily implemented spectral analysis method for detecting intra-QRS changes during episodes of myocardial ischemia, using Holter recordings from the European ST-T database. METHODS: Time-frequency distributions of QRS complexes from each recording were computed using the continuous wavelet transform. Indices corresponding to frequency content of four overlapping frequency bands were computed: F1 (24-35Hz), F2 (30-45Hz), F3 (40-60Hz), and F4 (50-80Hz). Values of these indices were compared during annotated episodes of ST change and during a baseline during the recording. RESULTS: Marked changes in intra-QRS frequency content were identified during ischemia, grouped by ECG lead analyzed. In lead III, a pronounced and statistically significant increase in the highest frequency sub-bands (F3 and F4) was consistently observed. Analysis of anterior precordial leads also showed significant increases in F4. CONCLUSIONS: Intra-QRS time-frequency analysis using the continuous wavelet transform can identify a spectral signature corresponding to myocardial ischemia in the range 24-80Hz. Intra-QRS spectral analysis has the potential for many clinical applications.
    [Abstract] [Full Text] [Related] [New Search]