Optimal filtering and quality control of the signal-averaged ECG. High-fidelity 1-minute recordings.

BACKGROUND The clinical performance of the signal-averaged ECG (SAECG) for prediction of ventricular tachycardia (VT) depends on its quality, or final noise level. However, signal averaging is a statistical estimation procedure that is time-consuming and vulnerable to noise-induced error. The optimally filtered SAECG is proposed as a simple, quality-assured procedure requiring only 1 minute of data. METHODS AND RESULTS The optimally filtered SAECG is based on measures of signal variance and time-frequency representations. Forty subjects were studied to compare a 0.3-microV root-mean-square (RMS) noise endpoint SAECG with an optimally filtered 64-beat ensemble. Eight SAECGs were computed with noise endpoints of 1.0-through 0.3-microV RMS. Noise measurements were also made directly from the filtered SAECG. From these and previously published data, sensitivity was predicted as a function of noise endpoint. Measured QRS parameters and final noise were highly similar between the optimally filtered SAECG and the 0.3-microV RMS noise endpoint SAECG. CONCLUSIONS The optimally filtered 64-beat SAECG achieves a performance (equivalent noise reduction, signal definition, and mathematically predicted sensitivity for VT) similar to a 0.3-microV RMS noise endpoint average. Testing in a large clinical database is required to validate the method for routine clinical use. SAECGs terminated by use of different noise measurement techniques are not directly comparable because of measurement technique dependence. However, a formula is presented for comparison of statistics between studies that have used the most popular noise measurement techniques.