What is the diagnostic criteria for spectral electrocardiography?

Spectral electrocardiography (ECG), usually refers to an advanced analytical technique based on traditional electrocardiography. It converts time-domain ECG signals into the frequency domain through mathematical transformations, thereby revealing information about different frequency components within the cardiac electrical signals. The diagnostic criteria for spectral ECG mainly include the following aspects:

1. Abnormal electrocardiographic spectral energy: When abnormalities in spectral energy occur simultaneously in three adjacent leads, this typically reflects significant myocardial ischemia in the corresponding cardiac region.

2. Large information content and high sensitivity: Spectral ECG, after digital processing, provides comprehensive parameter information with high sensitivity, enabling the detection of dynamic changes that conventional ECG cannot reveal.

3. Frequency component analysis: Focuses on changes in the frequency components of the ECG signal within the range of 0.2 Hz to 25 Hz. Abnormalities such as arrhythmias or myocardial ischemia may cause increases or decreases in energy within specific frequency ranges.

4. Pattern recognition: Using machine learning and artificial intelligence algorithms, specific cardiac disease patterns can be automatically identified from large volumes of spectral data, thereby improving diagnostic accuracy and efficiency.