Improved detection of congestive heart failure via probabilistic symbolic pattern recognition and heart rate variability metrics

Ruhi Mahajan, Teeradache Viangteeravat, Oguz Akbilgic

Research output: Contribution to journalArticle

8 Citations (Scopus)

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.

Original languageEnglish (US)
Pages (from-to)55-63
Number of pages9
JournalInternational Journal of Medical Informatics
Volume108
DOIs
StatePublished - Dec 1 2017

Fingerprint

Heart Failure
Heart Rate
Sensitivity and Specificity
Decision Trees

All Science Journal Classification (ASJC) codes

  • Health Informatics

Cite this

@article{347c0d7c61444672b056252b080791b4,
title = "Improved detection of congestive heart failure via probabilistic symbolic pattern recognition and heart rate variability metrics",
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.",
author = "Ruhi Mahajan and Teeradache Viangteeravat and Oguz Akbilgic",
year = "2017",
month = "12",
day = "1",
doi = "10.1016/j.ijmedinf.2017.09.006",
language = "English (US)",
volume = "108",
pages = "55--63",
journal = "International Journal of Medical Informatics",
issn = "1386-5056",
publisher = "Elsevier Ireland Ltd",

}

TY - JOUR

T1 - Improved detection of congestive heart failure via probabilistic symbolic pattern recognition and heart rate variability metrics

AU - Mahajan, Ruhi

AU - Viangteeravat, Teeradache

AU - Akbilgic, Oguz

PY - 2017/12/1

Y1 - 2017/12/1

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=85028999805&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85028999805&partnerID=8YFLogxK

U2 - 10.1016/j.ijmedinf.2017.09.006

DO - 10.1016/j.ijmedinf.2017.09.006

M3 - Article

VL - 108

SP - 55

EP - 63

JO - International Journal of Medical Informatics

JF - International Journal of Medical Informatics

SN - 1386-5056

ER -