Clinical Evaluation of the ECG Noise Extraction Tool as a Component of ECG Analysis Algorithms Evaluation

The aim of this work is to demonstrate the performance of the ECG noise extraction tool (ECGNExT) which provides estimates of ECG noise that are not significantly different from the inherent noise in an ECG generated by motion artifacts and other sources. In addition, this paper elaborates on use of ECGNExT in an algorithm evaluation context comparing two QRS detection algorithms.

140 simultaneous pairs of clean ECGs and ECGs corrupted with motion-induced noise from 29 participants under five different and separate motion conditions were collected and analyzed. Estimates of the noise component of the ECGs recorded with noise were obtained using ECGNExT and were then added to the clean ECGs yielding estimated ECGs with noise. Root mean squared error (RMSE) between the recorded and estimated ECGs with noise was calculated for temporal comparison, and band powers of the signals were calculated for spectral comparison.

A t-test revealed that the mean RMSE < 150-microvolts with p-value < 0.001 and ${\bm{\alpha \ }} = {\bm{\ }}0.01$, and equivalence tests showed that the band powers of the two ECGs were statistically equivalent with ${\bm{\alpha \ }} = {\bm{\ }}0.01$.

ECGNExT can reliably estimate the underlying ECG noise while preserving temporal and spectral features.

We previously proposed ECGNExT as a component of ECG analysis algorithm testing during noise conditions and reported its performance based on simulated ECG data. This work provides additional support of the performance and functionality of the ECGNExT algorithm from a study with pairs of simultaneously recorded ECGs with and without noise from human subjects.