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.
Highlights
Heart failure is a stage when the heart unable to pump sufficient amount of blood that tissues need or just able to perform this with high filling pressures (Braunwald E, Zipes DP, Libby P 2004)
In order to examine amount of information in a window time, the records are divided into four different window time that are 10 sec, 7 sec, 5 sec and 3 sec. for 10 sec of window time, 150 windowed sample are extracted from record
Total of 33 ECG records (15 congestive heart failure (CHF) and 18 normal ECG) are used. In literature both frequencies are used to analyse. Both normal and CHF patient’s ECG records with 256 Hz sampling frequency are considered in this study for analysing
Summary
Heart failure is a stage when the heart unable to pump sufficient amount of blood that tissues need or just able to perform this with high filling pressures (Braunwald E, Zipes DP, Libby P 2004). Causes of approximately 45000 patients death are declared as heart failure, and with each passing year, this number is increasing due to population aging and rising rates of cardiovascular disease-free survival. Some of them considered Heart Rate Variability (HRV) of ECG signals (Işler & Kuntalp 2007; Kamath 2012b; Işler Y n.d.; Kannathal et al 2006; Thuraisingham 2010) In both cases, hidden important information (such as wavelet, eigenvector methods, Poincare, RR interval, etc.) in the ECG record are revealed and used as feature vectors for detecting CHF patients.
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