Patient Specific Congestive Heart Failure Detection From Raw ECG signal

TL;DR

This paper presents a method for detecting congestive heart failure from raw ECG signals using non-linear features and machine learning classifiers, achieving perfect accuracy with neural networks.

Contribution

Introduces a novel feature extraction from raw ECG using SODP and demonstrates high accuracy in CHF detection with neural networks.

Findings

Neural network classifier achieved 100% accuracy.

Feature extraction from SODP effectively distinguishes CHF patients.

Two validation methods confirmed robustness of the system.

Abstract

In this study; in order to diagnose congestive heart failure (CHF) patients, non-linear second-order difference plot (SODP) obtained from raw 256 Hz sampled frequency and windowed record with different time of ECG records are used. All of the data rows are labelled with their belongings to classify much more realistically. SODPs are divided into different radius of quadrant regions and numbers of the points fall in the quadrants are computed in order to extract feature vectors. Fisher's linear discriminant, Naive Bayes, Radial basis function, and artificial neural network are used as classifier. The results are considered in two step validation methods as general k-fold cross-validation and patient based cross-validation. As a result, it is shown that using neural network classifier with features obtained from SODP, the constructed system could distinguish normal and CHF patients with 100% accuracy rate. Keywords