Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6951410 | Biomedical Signal Processing and Control | 2015 | 11 Pages |
Abstract
In this paper, a non-invasive optical signal, the finger photoplethysmogram (PPG) obtained before and after reactive hyperemia is investigated to discriminate between subjects with different CAD conditions. To this end, the PPG from both index fingers and standard 3-lead ECG of 48 patients (16 females, age 54.3 ± 9.6 years and 32 males, age 59.9 ± 10.6 years) scheduled for diagnostic angiography were recorded. The coronary condition of each subject was determined by three expert cardiologists (ground truth) based on these coronary angiograms. Of the 48 patients, 18 were diagnosed as having no disease (normal coronary - NC), 3 were diagnosed as having mild stenosis (MLD), 11 had single-vessel disease (SVD), 5 had two-vessel disease (2VD) and the remaining 11 were reported to have three-vessel disease (3VD). A vessel disease was determined when a significant (more than 50%) stenosis of the lumen cross-sectional area was observed. The 48 subjects were first grouped into two classes, namely high-risk: Class 1 = {2VD, 3VD} (N = 16) and low-risk: Class 2 = {NC, Mild, SVD} (N = 32). Using this approach, classification using a k-Nearest Neighbor classifier leads to an accuracy of 81.5%, a sensitivity of 82.0% and a specificity of 80.9%. Then all 48 subjects were regrouped slightly differently by moving the SVD subjects from the second (low-risk) to the first (high-risk) class. Therefore for the second approach high-risk: Class 1 = {SVD, 2VD, 3VD} (N = 27), whereas low-risk: Class 2 = {NC, Mild} (N = 21). This second approach resulted in an accuracy of 78.8%, a sensitivity of 79.3% and a specificity of 78.3%. We submit that this technique can be employed to implement an efficient triage system for scheduling coronary angiography, as it is able to identify non-invasively patients at greater risk of coronary stenosis.
Keywords
PPV2VDThree-vessel disease3VDSingle-vessel diseasePPGMSCTFMDCTMk-NNMLDSVDTMEPTTHDLACCCMRIVUSk-nearest neighborEndothelial dysfunctionpositive predictive valuenegative predictive valueRising velocityElectrocardiographyECGMRICardiac magnetic resonanceFlow-mediated dilationSPECTcoronary artery diseaseTriagetriglycerideMagnetic resonance imagingcoronary stenosissingle photon emission computed tomographycomputed tomographystroke volumeSensitivityAmplitudeAccuracyPulse transit timeintravascular ultrasoundHeart rateCADPhotoplethysmogramUltrasoundminimum lumen diameterhigh density lipoproteinlow density lipoproteinLDLNPV یا negative predictive valueSpecificityMulti-slice computed tomography
Related Topics
Physical Sciences and Engineering
Computer Science
Signal Processing
Authors
Zahra Sadat Hosseini, Edmond Zahedi, Hamid Movahedian Attar, Hossein Fakhrzadeh, Mohammad Habib Parsafar,