Electrocardiogram Signal Quality Assessment Based on Structural Image Similarity Metric

• Published in: IEEE transactions on biomedical engineering 2018-04, Vol.65 (4), p.745-753
• Main Authors: Shahriari, Yalda, Fidler, Richard, Pelter, Michele M., Bai, Yong, Villaroman, Andrea, Hu, Xiao
• Format: Article
• Language: English
• Subjects: Alarm fatigue; Alarm systems; Algorithms; Arrhythmia; Biomedical monitoring; Cluster Analysis; Clustering; Data Accuracy; Data processing; Discriminant analysis; Echocardiography; EKG; electrocardiogram (ECG); Electrocardiography; Electrocardiography - methods; Electrocardiography - standards; Humans; Image Processing, Computer-Assisted; Image quality; image quality index (IQI); Indexes; intensive care unit (ICU); Quality; Quality assessment; Quality control; Reproducibility of Results; Signal processing; Signal Processing, Computer-Assisted; Signal quality; Similarity; structural similarity index measure (SSIM)
• ISSN: 0018-9294; 1558-2531
• DOI: 10.1109/TBME.2017.2717876

Objective: We developed an image-based electrocardiographic (ECG) quality assessment technique that mimics how clinicians annotate ECG signal quality. Methods: We adopted the structural similarity measure (SSIM) to compare images of two ECG records that are obtained from displaying ECGs in a standard scale. Then, a subset of representative ECG images from the training set was selected as templates through a clustering method. SSIM between each image and all the templates were used as the feature vector for the linear discriminant analysis classifier. We also employed three commonly used ECG signal quality index (SQI) measures: baseSQI, kSQI, and sSQI to compare with the proposed image quality index (IQI) approach. We used 1926 annotated ECGs, recorded from patient monitors, and associated with six different ECG arrhythmia alarm types which were obtained previously from an ECG alarm study at the University of California, San Francisco (UCSF). In addition, we applied the templates from the UCSF database to test the SSIM approach on the publicly available PhysioNet Challenge 2011 data. Results: For the UCSF database, the proposed IQI algorithm achieved an accuracy of 93.1% and outperformed all the SQI metrics, baseSQI, kSQI, and sSQI, with accuracies of 85.7%, 63.7%, and 73.8% respectively. Moreover, evaluation of our algorithm on the PhysioNet data showed an accuracy of 82.5%. Conclusion : The proposed algorithm showed better performance for assessing ECG signal quality than traditional signal processing methods. Significance: A more accurate assessment of ECG signal quality can lead to a more robust ECG-based diagnosis of cardiovascular conditions.