Lead quality monitoring for detection of the Optimal Snapshot Time to record resting ECG

This study presents a multichannel ECG quality monitoring system, which continuously scans the leads' status (valid/lead-off) and quality (0-100%), according to the ECG components in the low, medium and high frequency bands. The system aims to detect the optimal moment to start the record of a 10s resting ECG within the 1st minute of signal acquisition - the earliest in time, the best in all leads' quality, named `Optimal Snapshot Time' (OST) and `Best Snapshot Quality' (BestSQ). The system compares the current leads' quality to an adaptive quality threshold (AQT) whose decreasing trend is trained on 375 ECGs. The validation over 267 ECGs in the test database shows that: 87.2% of the ECGs would be recorded with a quality ≥ 95%BestSQ; 33.1% at the optimal moment OST±2.5s; 29.3% would be started earlier due to their sufficient quality >AQT; 37.2% would be recorded with a delay >2.5s due to their compromised BestSQ, not reaching the AQT level in the vicinity of OST.

[1]  Jukka Kortelainen,et al.  Electrocardiogram quality classification based on robust best subsets linear prediction error , 2011, 2011 Computing in Cardiology.

[2]  Irena Jekova,et al.  Recognition of diagnostically useful ECG recordings: Alert for corrupted or interchanged leads , 2011, 2011 Computing in Cardiology.

[3]  L. Y. Di Marco,et al.  An algorithm for assessment of quality of ECGs acquired via mobile telephones , 2011, 2011 Computing in Cardiology.

[4]  Hagen Malberg,et al.  CinC challenge — Assessing the usability of ECG by ensemble decision trees , 2011, 2011 Computing in Cardiology.

[5]  Xiaopeng Zhao,et al.  Computer algorithms for evaluating the quality of ECGs in real time , 2011, 2011 Computing in Cardiology.

[6]  G D Clifford,et al.  Signal quality indices and data fusion for determining acceptability of electrocardiograms collected in noisy ambulatory environments , 2011, 2011 Computing in Cardiology.

[7]  Benjamin E Moody Rule-based methods for ECG quality control , 2011, 2011 Computing in Cardiology.

[8]  Lenka Lhotska,et al.  Simple scoring system for ECG quality assessment on Android platform , 2011, 2011 Computing in Cardiology.

[9]  Peng Li,et al.  Real-time signal quality assessment for ECGs collected using mobile phones , 2011, 2011 Computing in Cardiology.

[10]  Yaron Kinar,et al.  Using machine learning to detect problems in ECG data collection , 2011, 2011 Computing in Cardiology.

[11]  Ikaro Silva,et al.  Improving the quality of ECGs collected using mobile phones: The PhysioNet/Computing in Cardiology Challenge 2011 , 2011, 2011 Computing in Cardiology.

[12]  Dieter Hayn,et al.  ECG quality assessment for patient empowerment in mHealth applications , 2011, 2011 Computing in Cardiology.

[13]  Vito Starc Could determination of equivalent dipoles from 12 lead ECG help in detection of misplaced electrodes , 2011, 2011 Computing in Cardiology.

[14]  Lars Johannesen,et al.  Assessment of ECG quality on an Android platform , 2011, 2011 Computing in Cardiology.

[15]  Irena Jekova,et al.  Threshold-based system for noise detection in multilead ECG recordings. , 2012, Physiological measurement.

[16]  Thomas Ho Chee Tat,et al.  Physionet Challenge 2011: Improving the quality of electrocardiography data collected using real time QRS-complex and T-Wave detection , 2011, 2011 Computing in Cardiology.