{'fraction_non_alphanumeric': 0.036076662908680945, 'fraction_numerical': 0.020856820744081173, 'mean_word_length': 5.407942238267148, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Electrocardiogram signal quality assessment using an artificially reconstructed target lead

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.