Ruhi Mahajan1, Teeradache Viangteeravat2, Oguz Akbilgic3. 1. UTHSC-ORNL, Center for Biomedical Informatics, University of Tennessee Health Science Center, Memphis, TN 38103, USA; Department of Pediatrics, University of Tennessee Health Science Center, Memphis, TN 38103, USA. 2. Department of Pediatrics, University of Tennessee Health Science Center, Memphis, TN 38103, USA; Children's Foundation Research Institute, Le Bonheur Children's Hospital, Memphis, TN 38103, USA. 3. UTHSC-ORNL, Center for Biomedical Informatics, University of Tennessee Health Science Center, Memphis, TN 38103, USA; Department of Pediatrics, University of Tennessee Health Science Center, Memphis, TN 38103, USA; Department of Preventive Medicine, University of Tennessee Health Science Center, Memphis, TN 38103, USA. Electronic address: oakbilg1@uthsc.edu.
Abstract
OBJECTIVE: A timely diagnosis of congestive heart failure (CHF) is crucial to evade a life-threatening event. This paper presents a novel probabilistic symbol pattern recognition (PSPR) approach to detect CHF in subjects from their cardiac interbeat (R-R) intervals. METHOD: PSPR discretizes each continuous R-R interval time series by mapping them onto an eight-symbol alphabet and then models the pattern transition behavior in the symbolic representation of the series. The PSPR-based analysis of the discretized series from 107 subjects (69 normal and 38 CHF subjects) yielded discernible features to distinguish normal subjects and subjects with CHF. In addition to PSPR features, we also extracted features using the time-domain heart rate variability measures such as average and standard deviation of R-R intervals. RESULTS: An ensemble of bagged decision trees was used to classify two groups resulting in a five-fold cross-validation accuracy, specificity, and sensitivity of 98.1%, 100%, and 94.7%, respectively. However, a 20% holdout validation yielded an accuracy, specificity, and sensitivity of 99.5%, 100%, and 98.57%, respectively. Results from this study suggest that features obtained with the combination of PSPR and long-term heart rate variability measures can be used in developing automated CHF diagnosis tools.
OBJECTIVE: A timely diagnosis of congestive heart failure (CHF) is crucial to evade a life-threatening event. This paper presents a novel probabilistic symbol pattern recognition (PSPR) approach to detect CHF in subjects from their cardiac interbeat (R-R) intervals. METHOD: PSPR discretizes each continuous R-R interval time series by mapping them onto an eight-symbol alphabet and then models the pattern transition behavior in the symbolic representation of the series. The PSPR-based analysis of the discretized series from 107 subjects (69 normal and 38 CHF subjects) yielded discernible features to distinguish normal subjects and subjects with CHF. In addition to PSPR features, we also extracted features using the time-domain heart rate variability measures such as average and standard deviation of R-R intervals. RESULTS: An ensemble of bagged decision trees was used to classify two groups resulting in a five-fold cross-validation accuracy, specificity, and sensitivity of 98.1%, 100%, and 94.7%, respectively. However, a 20% holdout validation yielded an accuracy, specificity, and sensitivity of 99.5%, 100%, and 98.57%, respectively. Results from this study suggest that features obtained with the combination of PSPR and long-term heart rate variability measures can be used in developing automated CHF diagnosis tools.
Authors: Jong Hyun Jhee; SungHee Lee; Yejin Park; Sang Eun Lee; Young Ah Kim; Shin-Wook Kang; Ja-Young Kwon; Jung Tak Park Journal: PLoS One Date: 2019-08-23 Impact factor: 3.240
Authors: Oguz Akbilgic; Liam Butler; Ibrahim Karabayir; Patricia P Chang; Dalane W Kitzman; Alvaro Alonso; Lin Y Chen; Elsayed Z Soliman Journal: Eur Heart J Digit Health Date: 2021-10-09