title: Classification of heart rate signals of healthy and pathological subjects using threshold based symbolic entropy.
abstract: The dynamical fluctuations of biological signals provide a unique window to construe the underlying mechanism of the biological systems in health and disease. Recent research evidences suggest that a wide class of diseases appear to degrade the biological complexity and adaptive capacity of the system. Heart rate signals are one of the most important biological signals that have widely been investigated during the last two and half decades. Recent studies suggested that heart rate signals fluctuate in a complex manner. Various entropy based complexity analysis measures have been developed for quantifying the valuable information that may be helpful for clinical monitoring and for early intervention. This study is focused on determining HRV dynamics to distinguish healthy subjects from patients with certain cardiac problems using symbolic time series analysis technique. For that purpose, we have employed recently developed threshold based symbolic entropy to cardiac inter-beat interval time series of healthy, congestive heart failure and atrial fibrillation subjects. Normalized Corrected Shannon Entropy (NCSE) was used to quantify the dynamics of heart rate signals by continuously varying threshold values. A rule based classifier was implemented for classification of different groups by selecting threshold values for the optimal separation. The findings indicated that there is reduction in the complexity of pathological subjects as compared to healthy ones at wide range of threshold values. The results also demonstrated that complexity decreased with disease severity.
authors.affiliation: University of Azad Kashmir Department of Computer Sciences and Information Technology 13100 Azad Kashmir Pakistan.
authors.forename: Wajid
authors.initials: W
authors.lastname: Aziz
authors.affiliation: University of Azad Jammu and Kashmir Muzaffarbad Department of Physics 13100 Azad Kashmir Pakistan.
authors.forename: M
authors.initials: M
authors.lastname: Rafique
authors.affiliation: University of Azad Jammu and Kashmir Muzaffarbad Department of Mathematics 13100 Azad Kashmir Pakistan.
authors.forename: I
authors.initials: I
authors.lastname: Ahmad
authors.affiliation: Um-Alqura University College of Computer and Information System, Al-Abidiyya Building P.O. Box 715 Makkah Saudi Arabia.
authors.forename: M
authors.initials: M
authors.lastname: Arif
authors.affiliation: University of Azad Jammu and Kashmir Muzaffarbad Department of Sociology and Rural Development 13100 Azad Kashmir Pakistan.
authors.forename: Nazneen
authors.initials: N
authors.lastname: Habib
authors.affiliation: University of Azad Kashmir Department of Computer Sciences and Information Technology 13100 Azad Kashmir Pakistan.
authors.forename: M S A
authors.initials: MS
authors.lastname: Nadeem
mesh_terms: D000328:Adult; D000368:Aged; D001281:Atrial Fibrillation / Q000175:diagnosis / Q000503:physiopathology*; D016022:Case-Control Studies; D004562:Electrocardiography; D015716:Electrocardiography, Ambulatory; D019277:Entropy; D005260:Female; D006333:Heart Failure / Q000175:diagnosis / Q000503:physiopathology*; D006339:Heart Rate; D006801:Humans; D008297:Male; D008875:Middle Aged; D011237:Predictive Value of Tests; D012720:Severity of Illness Index; D012815:Signal Processing, Computer-Assisted; D013997:Time Factors; D055815:Young Adult
keywords: HRV analysis; biological signals; complexity; optimal variability