Detection Of Noise Type In Electrocardiogram

Physicians use electrocardiogram (ECG) to diagnose cardiovascular diseases. It is mainly used in hospital environment; however, with advancements in ambulatory ECG, it is now available outside of hospital environment. Ambulation can lead to contamination of ECG with various noises leading to signal corruption, misdiagnosis, or false alarms. Removal of noise from ECG is possible; however, blindly applying noise removal techniques may reduce the fidelity of the ECG. As such, identification of the noise in the ECG and applying targeted techniques minimize information loss. In this study, a machine learning approach is used to identify the type of noise in ECG. ECG from Physionet's Normal Sinus Rhythm Database was contaminated with noise (baseline wandering, electrode motion, and electromyography) from Physionet's MIT-BIH Noise Stress Test Database at different levels and combinations. The chosen machine learning algorithm was Random Forest with 1024 estimators. The Random Forest had a precision and recall of 1.0 when identifying clean ECG. The average precision and recall were 0.47 and 0.63, respectively, for segments with a single type of noise. The average precision and recall were were 0.44 and 0.27, respectively, for segments with multiple types of noise. The drop in precision and recall was due to the misclassification of the ECG with multiple noises as ECG with a single noise; as an example, classification of an ECG with baseline wandering and electromyography as an ECG with baseline wandering. The classifier performed well at identifying any of the noises in segments with multiple types of noises with an average precision and recall of 0.81 and 0.70, respectively. The classifier generally performed well in identifying types of noise in ECG allowing for future work in developing a framework for identification and mitigation of noise.