A detailed analysis of the effects of various combinations of heart rate variability indices in congestive heart failure

The major purpose of the heart is to circulate blood, which carries oxygen and nutrients to the body. Heart Failure is a decreased ability of the heart to either fill itself with blood or emptying it. Because the congestion, which is the fluid accumulation in various parts of the body, is common in the patients with heart failure, this disease is also named as Congestive Heart Failure (CHF). Although, at times, the diagnosis of heart failure is straightforward, it often challenges physicians because particular aspects of the syndrome lead to confusion. When heart failure is suspected, certain elements of the physical examination aid in the diagnosis. Unfortunately, the examination often does not yield enough information for confirmation. Although several diagnostic criteria schemes are available, their clinical utility is questionable, and their concordance is poor.The physicians have long relied as a gold standard on echocardiography for the diagnosis of CHF patients. It has not been possible to use a simple method as ECG for this purpose because of the many difficulties in interpreting the ECG output. Therefore, it would be very helpful both for physicians and patients alike if it is possible to diagnose CHF from an ECG record. The main purpose of this thesis is accomplishing such a purpose, i.e. detecting CHF from an ECG output.Heart Rate Variability (HRV) analysis has been the subject of many studies of clinical origin. Majority of these studies have used HRV measures as predictors of the risk of mortality (prognosis) for cardiac patients. Only a few studies have been focused on using HRV measures for diagnostic purpose. This thesis is focused on exploring advanced techniques of HRV analysis in an attempt to develop robust methods for diagnosing patients with CHF from an ECG records. This study considers presenting new feature extraction method, developing new preprocessing techniques, and finding an optimal k-Nearest Neighbors classifier to discriminate the patients with CHF from the normals and to discriminate systolic versus diastolic dysfunction in CHF patients. The wavelet entropy, which has been used in the other biomedical signal classification schemes like EEG spike detection, is also used as an HRV measure to enhance the performance of the classifier. Furthermore, lagged Poincare plot measures are also included in the study. A new preprocessing technique, called as heart rate normalization, is also used to enhance the performance in discriminating the CHF patients from normals and determining the type of dysfunctionality as systolic or diastolic in CHF patients. In addition, Genetic Algorithm is used to select the optimal features from among a large set. The whole process is summarized as a single flowchart, which will be a useful guide for novice researchers.In order to conduct these studies, open-source databases from MIT/BIH are used to discriminate the patients with CHF from normal subjects and ECG records from the Faculty of Medicine in Dokuz Eylül University to discriminate systolic CHF patients from diastolic ones. The results show that heart rate normalized analysis of HRV can be used to achieve more accurate results for diagnosing the patients with CHF. In addition, wavelet-entropy based frequency-domain measures seem to be useful in the diagnosis. On the other hand, higher-lagged Poincare plot measures seem to be useless in the diagnosis. As a result, this study achieves the overall accuracies of 93.98% in discriminating the CHF patients from normal subjects and 100% in discriminating systolic versus diastolic dysfunctionality in CHF patients, which are the highest values in the literature.