Discrimination of Systolic and Diastolic Dysfunctions using Multi-Layer Perceptron in Heart Rate Variability Analysis

Abstract In this study, the heart rate variability (HRV) analysis is used to distinguish patients with systolic congestive heart failure (CHF) from patients with diastolic CHF. In the analysis performed, the best accuracy performances of short-term HRV measures are compared. These measures are calcu...

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Bibliographic Details
Published in:Computers in biology and medicine 2016-09, Vol.76, p.113-119
Main Author: Isler, Yalcin
Format: Article
Language:English
Subjects:
Adult
Aged
Algorithms
Diabetic neuropathy
Discrimination
Electrocardiography, Ambulatory
Entropy
Female
Heart attacks
Heart failure
Heart Failure - classification
Heart Failure - physiopathology
Heart rate
Heart Rate - physiology
Heart rate variability
Humans
Internal Medicine
Male
Middle Aged
Multi-layer perceptron
Nearest neighbor
Neural Networks (Computer)
Normalization
Other
Patients
Short term
Studies
Ultrasonic imaging
Young Adult
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Description
Summary:Abstract In this study, the heart rate variability (HRV) analysis is used to distinguish patients with systolic congestive heart failure (CHF) from patients with diastolic CHF. In the analysis performed, the best accuracy performances of short-term HRV measures are compared. These measures are calculated in four different ways with optional normalization methods of heart rate and data. The nearest neighbor and the multi-layer perceptron (MLP) are used to evaluate the performances in discriminating these two groups. The results point out that using both data and heart rate normalizations enhances the classifier performance. The maximum accuracy is obtained as 96.43% with MLP classifier.
ISSN:0010-4825
1879-0534
DOI:10.1016/j.compbiomed.2016.06.029