Journal of electrocardiology · 2018
A method to extract realistic artifacts from electrocardiogram recordings for robust algorithm testing.
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Galeotti Loriano, Scully Christopher G
Аннотация
OBJECTIVE: Recordings of signal noise and artifacts can be added to clean electrocardiogram (ECG) records to assess the performance of ECG and arrhythmia analysis algorithms in the presence of noise. We present a method to estimate device-specific signal noise and artifacts from ECG records. This method can be applied to obtain noise estimates from healthy subjects on any ECG lead, allowing a simple device-specific recording. The proposed approach is assessed using the MIT-BIH Noise Stress Test Database recordings combined with simulated ECGs.
METHODS: The proposed noise-estimation method is based on the subtraction of a time-aligned median beat from a noisy ECG recording. To test our method, electrode motion and muscle artifact noise from MIT-BIH Noise Stress Test database were added to simulated ECG signals at signal-noise ratios (SNR) from -6 to 20 dB. A comparison between noise and estimated noise signal statistical characteristics was made including root-mean squared error and assessment of the power content in three frequency bands (cardiac [0.5-5 Hz], mid [5-25 Hz], and high [25-40 Hz]).
RESULTS: Visual assessment and frequency analysis demonstrate the good quality of noise estimation. Root-mean squared error between noise and estimated noise signals was <0.5 Normalized Units across all SNR levels. Band power error was stable across SNR levels with median percentage error between noise and estimate noise signals of <10% for cardiac and mid frequency bands.
CONCLUSION: Estimating noise from ECG records is a viable approach to generate noise and artifacts-only signals. These signals are device-specific and easy to collect from healthy subjects without requiring special electrode set-ups. Therefore, they may be suitable for use with annotated ECG databases to assess the robustness of ECG analysis algorithms in the presence of noise.
METHODS: The proposed noise-estimation method is based on the subtraction of a time-aligned median beat from a noisy ECG recording. To test our method, electrode motion and muscle artifact noise from MIT-BIH Noise Stress Test database were added to simulated ECG signals at signal-noise ratios (SNR) from -6 to 20 dB. A comparison between noise and estimated noise signal statistical characteristics was made including root-mean squared error and assessment of the power content in three frequency bands (cardiac [0.5-5 Hz], mid [5-25 Hz], and high [25-40 Hz]).
RESULTS: Visual assessment and frequency analysis demonstrate the good quality of noise estimation. Root-mean squared error between noise and estimated noise signals was <0.5 Normalized Units across all SNR levels. Band power error was stable across SNR levels with median percentage error between noise and estimate noise signals of <10% for cardiac and mid frequency bands.
CONCLUSION: Estimating noise from ECG records is a viable approach to generate noise and artifacts-only signals. These signals are device-specific and easy to collect from healthy subjects without requiring special electrode set-ups. Therefore, they may be suitable for use with annotated ECG databases to assess the robustness of ECG analysis algorithms in the presence of noise.