University of Massachusetts Medical School Faculty Publications

Title

Motion and Noise Artifact-Resilient Atrial Fibrillation Detection using a Smartphone

UMMS Affiliation

Department of Medicine, Division of Cardiovascular Medicine

Publication Date

2018-06-01

Document Type

Article

Disciplines

Analytical, Diagnostic and Therapeutic Techniques and Equipment | Biomedical Devices and Instrumentation | Cardiology | Cardiovascular Diseases | Telemedicine

Abstract

We have recently found that our previously-developed atrial fibrillation (AF) detection algorithm for smartphones can give false positives when subjects' fingers or hands move, as we rely on proper finger placement over the smartphone camera to collect the signal of interest. Specifically, smartphone camera pulsatile signals that are obtained from normal sinus rhythm (NSR) subjects but are corrupted by motion and noise artifacts (MNAs) are frequently detected as AF. AF and motion-corrupted episodes have the similar characteristic that pulse-to-pulse intervals (PPIs) are irregular. We have developed an MNA-resilient smartphone-based AF detection algorithm that first discriminates and eliminates MNA-corrupted episodes in smartphone camera recordings, and then detects AF in MNA-free recordings. We found that MNA-corrupted episodes have highly-varying pulse slope, large turning point ratio, or large kurtosis values in smartphone signals compared to MNA-free AF and NSR episodes. We first use these three metrics for MNA discrimination and exclusion. Then, AF is detected in MNA-free signals using our previous algorithm. The capability to discriminate MNAs and AFs separately in smartphone signals increases the specificity of AF detection. To evaluate the performance of the proposed MNA-resilient AF algorithm, 99 subjects, including 88 study participants with AF at baseline and in NSR after electrical cardioversion as well as 11 participants with MNA-corrupted NSR, were recruited. Using iPhone 4S, 5S, and 6S models, we collected 2-minute pulsatile time series from each subject. The clinical results show that the accuracy, sensitivity and specificity of the proposed AF algorithm are 0.97, 0.98, 0.97, respectively, which are higher than those of the previous AF algorithm.

Keywords

atrial fibrillation, motion and noise artifact, root mean square of successive RR differences (RMSSD), Shannon entropy, support vector machine (SVM)

DOI of Published Version

10.1109/JETCAS.2018.2818185

Source

Chong JW, Cho CH, Tabei F, Le-Anh D, Esa N, McManus DD, Chon KH. Motion and Noise Artifact-Resilient Atrial Fibrillation Detection using a Smartphone. IEEE J Emerg Sel Top Circuits Syst. 2018 Jun;8(2):230-239. doi: 10.1109/JETCAS.2018.2818185. Epub 2018 Mar 22. PMID: 30687580; PMCID: PMC6345530. Link to article on publisher's site

Related Resources

Link to Article in PubMed

Journal/Book/Conference Title

IEEE journal on emerging and selected topics in circuits and systems

PubMed ID

30687580

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