Automatic detection of ECG electrode misplacement: a tale of two algorithms.
Artifacts in an electrocardiogram (ECG) due to electrode misplacement can lead to wrong diagnoses. Various computer methods have been developed for automatic detection of electrode misplacement. Here we reviewed and compared the performance of two algorithms with the highest accuracies on several databases from PhysioNet. These algorithms were implemented into four models. For clean ECG records with clearly distinguishable waves, the best model produced excellent accuracies (> = 98.4%) for all misplacements except the LA/LL interchange (87.4%). However, the accuracies were significantly lower for records with noise and arrhythmias. Moreover, when the algorithms were tested on a database that was independent from the training database, the accuracies may be poor. For the worst scenario, the best accuracies for different types of misplacements ranged from 36.1% to 78.4%. A large number of ECGs of various qualities and pathological conditions are collected every day. To improve the quality of health care, the results of this paper call for more robust and accurate algorithms for automatic detection of electrode misplacement, which should be developed and tested using a database of extensive ECG records.
Department of Mechanical, Aerospace, and Biomedical Engineering, University of Tennessee, Knoxville, TN 37996, USA.
Henian
H
Xia
Gabriel A
GA
Garcia
Xiaopeng
X
Zhao
D000465:Algorithms; D016477:Artifacts; D001331:Automation; D016208:Databases, Factual; D004562:Electrocardiography / Q000295:instrumentation*; D004566:Electrodes; D006801:Humans; D016571:Neural Networks, Computer