The University of British Columbia

A software approach has been developed for detecting electrocautery noise in the electrocardiogram (ECG) using a wavelet decomposition of the signal. With this approach, a clinical monitoring expert system can be forewarned of potential artifacts in trend values derived from the ECG, allowing it to proceed with caution when making decisions based on these trends. In 15 operations spanning 38.5 h of ECG data, we achieved a false positive rate of 0.71% and a false negative rate of 0.33%. While existing hardware approaches detect the source of the noise without any ability to assess its impact on the measured ECG, our software approach detects the presence of noise in the signal itself. Furthermore, the software approach is cheaper and easier to implement in a clinical environment than existing hardware approaches