High Frequency Noise Detection and Handling in ECG Signals.

After acquisition of new clinical electrocardiogram (ECG) signals the first step is often to preprocess and have a signal quality assessment to uncover noise. There might be restriction on the signal length and other issue that impose limitation where it is not possible to discard the whole signal if noise is present. Thus there is a great need to retain as much noise free regions as possible. A noise detection method is evaluated on a manually annotated subset (2146 leads) of a data base of 12-lead ECG recordings from 1006 bicycle race participants. The aim is to apply the noise detector on the unlabelled part of the data set before any further analysis is conducted. The proposed noise detector can be divided into 3 parts: 1) Select a high frequency signal as a base signal. 2) Apply a thresholding strategy on the base signal. 3) Use a noise detection strategy. In this work receiver operating characteristic (ROC) curve and area under the curve (AUC) will be used to assess a high frequency noise detector designed for ECG signals. Even though ROC analysis is widely used to assess prediction models, it has its own limitation. However, it is a good starting point to assess discriminatory ability. To generate the ROC curve the performance evaluation is based on sample-level. That is, each sample has a label whether it is noise or not. The threshold strategy and the chosen threshold will be the varying factor to generate ROC curves. The best model has an average AUC of 0.862, which shows a good detector to discriminate noise. This threshold strategy will be used for noise detection on the unlabelled part of the data set.