Lookup NU author(s): Dr Philip Langley,
Costanzo Di Maria,
Dr Ding Chang Zheng,
Dr John Allen,
Professor Alan Murray
Full text for this publication is not currently held within this repository. Alternative links are provided below where available.
For the application of acquiring ECGs from mobile telephones by unskilled users it would be beneficial if the mobile device could assess ECG quality and inform the user if the quality was acceptable.Using the PhysioNet/Computing in Cardiology Challenge 2011 dataset we identified several ECG features that were commonly observed in the training set 'unacceptable' category for algorithmic development: flat baseline (FB), saturation (SA), baseline drift (BD), low amplitude (LA), high amplitude (HA) and steep slope (SS).For the training set with each feature detection applied separately the following scores were achieved: FB 76.2%, SA 80.9%, BD 61.3%, LA 75.6%, HA 74.1% and SS 77.5%. With all features combined a score of 91.4% was achieved. For the test set the algorithm classified 181 records as unacceptable and 319 records as acceptable and the score was 85.7%.
Author(s): Langley P, Di Marco LY, King S, Duncan D, Di Maria C, Duan W, Bojarnejad M, Zheng D, Allen J, Murray A
Publication type: Conference Proceedings (inc. Abstract)
Publication status: Published
Conference Name: Computing in Cardiology
Year of Conference: 2011