A model for estimation of noise tolerance in ECG parameters

Parameters extracted from ECG recordings show different noise tolerance levels. Some parameters may be slightly affected by noise, while the others could be inaccurate. Choosing a single noise threshold for all parameters may lead to adoption of invalid results or removal of valid parameters. In this study, we develop a statistical model between the Signal-to-Noise Ratio (SNR) and our Signal Quality Indicator (SQI) algorithm, and determine the noise-tolerance threshold for several ECG parameters statistically. Our dataset was based on the STAFF-III database with added physical noise recording segments from MIT-BIH database containing electrode motion, muscle artifact and baseline wander. We generated 3193 noise-added 12-lead ECG signals with SNR varying from -6dB to 24dB. For each 10-second segment, the noise level was measured by our SQI algorithm, and ECG parameters were measured by the Philips DXL ECG algorithm, allowing us to derive a noise model and the thresholds for noise tolerance of the ECG parameters. Varying thresholds suggest using parameter-specific thresholds in order to avoid reduction in accuracy.