An algorithm for assessment of quality of ECGs acquired via mobile telephones

Philip Langley, Luigi Yuri Di Marco, Susan King, David Duncan, C. Di Maria, Wenfeng Duan, Marjan Bojarnejad, D. Zheng, J. Allen, Alan Murray

Research output: Contribution to journalArticlepeer-review

40 Citations (Scopus)


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%.
Original languageEnglish
Pages (from-to)281-284
Number of pages4
JournalComputing in Cardiology
Publication statusPublished - 2011
Externally publishedYes
EventComputing in Cardiology Conference - Zhejiang University, Hangzhou, China
Duration: 18 Sept 201121 Sept 2011


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