Key result
A threshold-based system for ECG noise detection achieved an area under the receiver operating characteristic curve of 0.968, offering adjustable settings for high sensitivity or high specificity.
Effect estimate: AUC 0.968
A novel threshold-based algorithm effectively detects various types of noise in 12-lead ECGs, offering flexible implementations for high sensitivity or high specificity depending on application requirements.
Could enhance ECG quality control in devices; leaves open prospective validation across clinical settings.
This paper presents a system for detection of the most common noise types seen on the electrocardiogram (ECG) in order to evaluate whether an episode from 12-lead ECG is reliable for diagnosis. It implements criteria for estimation of the noise corruption level in specific frequency bands, aiming to identify the main sources of ECG quality disruption, such as missing signal or limited dynamics of the QRS components above 4 Hz; presence of high amplitude and steep artifacts seen above 1 Hz; baseline drift estimated at frequencies below 1 Hz; power-line interference in a band ±2 Hz around its central frequency; high-frequency and electromyographic noises above 20 Hz. All noise tests are designed to process the ECG series in the time domain, including 13 adjustable thresholds for amplitude and slope criteria which are evaluated in adjustable time intervals, as well as number of leads. The system allows flexible extension toward application-specific requirements for the noise levels in acceptable quality ECGs. Training of different thresholds' settings to determine different positive noise detection rates is performed with the annotated set of 1000 ECGs from the PhysioNet database created for the Computing in Cardiology Challenge 2011. Two implementations are highlighted on the receiver operating characteristic (area 0.968) to fit to different applications. The implementation with high sensitivity (Se = 98.7%, Sp = 80.9%) appears as a reliable alarm when there are any incidental problems with the ECG acquisition, while the implementation with high specificity (Sp = 97.8%, Se = 81.8%) is less susceptible to transient problems but rather validates noisy ECGs with acceptable quality during a small portion of the recording.
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Jekova et al. (2012) studied ECG noise detection (n=1,000). Threshold-based system for noise detection vs. Different threshold settings was evaluated on Receiver operating characteristic area for noise detection (AUC 0.968). A threshold-based system for ECG noise detection achieved an area under the receiver operating characteristic curve of 0.968, offering adjustable settings for high sensitivity or high specificity.
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