Key result
A proposed ECG signal quality assessment algorithm combining energy-concavity index analysis and a correlation-based neural network achieved a final accuracy of 93.60%.
A novel ECG signal quality assessment algorithm using ECI and neural network-based correlation achieved high accuracy (93.6%) in detecting signal disturbances.
May enhance automated ECG preprocessing in research settings; leaves open prospective clinical validation before practice adoption.
In real applications, even the most accurate electrocardiogram (ECG) analysis algorithm, based on research databases, might breakdown completely if a quality measurement technique is not applied precisely before the analysis. The major concentration of this study is to describe and develop a reliable ECG signal quality assessment technique. The proposed algorithm includes three major stages: preprocessing, energy-concavity index (ECI) analysis and a correlation-based examination subroutine. The preprocessing step includes the removal of baseline wanders and high-frequency disturbances. The quality measurement based on ECI includes two separate stages according to the energy and concavity of the ECG signal. The correlation-based quality measurement step is mainly established by using the correlation between ECG leads estimated by applying a suitably trained neural network. The operating characteristics of the proposed ECI are sensitivity (Se) of 77.04% with a positive predictive value (PPV) of 90.53% for detecting high-energy noise. The correlation-based technique achieved the best scores (Se = 100%; PPV = 98.92%) for detecting high-energy noise and for recognising any other kind of disturbances (Se = 92.36%; PPV = 94.77%). Although ECI analysis acts effectively against high-energy disturbances, very poor performance is obtained in cases where the energy of the disturbances is not considerable. However, the correlation-based method is able to find all kinds of disturbances. For officially evaluating the proposed algorithm, an entry was sent to the Computing-in-Cardiology Challenge 2011 on 27 February 2012; a final score (accuracy) of 93.60% was achieved.
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Naseri et al. (2014) studied ECG signal quality assessment. Proposed ECG signal quality assessment algorithm was evaluated on Accuracy in the Computing-in-Cardiology Challenge 2011. A proposed ECG signal quality assessment algorithm combining energy-concavity index analysis and a correlation-based neural network achieved a final accuracy of 93.60%.
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