Application of the entropy of approximation for the nonlinear characterizationin patients with Chagas disease

Chagas disease American trypanosomiasis is caused by a flagellated parasite: Trypanosoma cruzi, transmitted by an insect of the genus Triatoma and also by blood transfusions. In Latin America, the number of infected people is approximately 6 million, with a population exposed to the risk of infection of 550000. It is our interest to develop a non-invasive and low-cost methodology, capable of detecting any early cardiac alteration that also allows us to see dysautonomia or dysfunction within 24 hours and with this it could be used to detect any cardiac alteration caused by T early Cruzi. For this, we analyzed the 24-hour Holter ECG records in 107 patients with ECG abnormalities (CH2), 102 patients without ECG alterations (CH1) who had positive serological results for Chagas disease and 83 volunteers without positive serological results for Chagas disease (CONTROL). Approximate entropy was used to quantify the regularity of electrocardiograms (ECG) in the three groups. We analyzed 288 ECG segments per patient. Significant differences were found between the CONTROL-CH1, CONTROL-CH2 and CH1-CH2 groups.Postprint (published version

Cepstrum Feature Selection for the Classification of Sleep Apnea-Hypopnea Syndrome based on Heart Rate Variability

Abstract Cepstrum Coefficients are analyzed in order to study its performance in Sleep Apnea Introduction The Sleep Apnea Hypopnea Syndrome (SAHS) is a respiratory disorder characterized by frequent breathing pauses and a collapse of pharynx during sleep. If breathing ceases completely, then the event is called apnea. In case breathing does not cease but there is a reduction in the volume of air entering the lungs, then the event is called hipopnea. Previous studies have tried to diagnostic SAHS with the RR series obtained from the electrocardiogram (ECG) [1] with good performance, anyway the underlying regulatory mechanisms during apnea are still poorly understood. This fact makes necessary to explore appropriate feature estimation techniques in order to extract as much information as possible. In previous contribution [2] we have used cepstrum features without taking into consideration any selection criteria. In this paper we apply forward feature selection in order to improve apnea screening performance and find coefficients which describe with more detail the RR pattern in presence of SAHS. We have selected features from a specific cepstrum coefficients set composed by the first 60 elements containing information about periodic structures of the RR series but also about the system modelled by the filter-type elements. Linear Discriminant Analysis (LDA) and Quadratic Discriminant Analysis (QDA) have been proposed in order to quantify apnea minutes. The system will provide also a global score of the presence of clinically significant apnea based on the minute by minute apnea detection. A subject will be classified globally as SAHS is the percentage of minutes with apnea is at least 16%. Database The database was provided by Prof. Dr. Thomas Penzel for Computers in Cardiology 2000 challenge [3]. The data have been divided divided into a learning set (L set) and a test set (T set) of equal size. Each set consists of 35 recordings, containing a single ECG signal digitized at 100 Hz with 12-bit resolution, continuously for approximately 8 hours. Each recording includes a set of reference annotations, one for each minute, which indicates the presence or absence of apnea during that minute. These reference annotations were made by human experts on the basis of simultaneously recorded respiration signals. Group A (apnea) contains recordings with at least 10

Heart Diseases Diagnosis Using Artificial Neural Networks

Information technology has virtually altered every aspect of human life in the present era. The application of informatics in the health sector is rapidly gaining prominence and the benefits of this innovative paradigm are being realized across the globe. This evolution produced large number of patients’ data that can be employed by computer technologies and machine learning techniques, and turned into useful information and knowledge. This data can be used to develop expert systems to help in diagnosing some life-threating diseases such as heart diseases, with less cost, processing time and improved diagnosis accuracy. Even though, modern medicine is generating huge amount of data every day, little has been done to use this available data to solve challenges faced in the successful diagnosis of heart diseases. Highlighting the need for more research into the usage of robust data mining techniques to help health care professionals in the diagnosis of heart diseases and other debilitating disease conditions. Based on the foregoing, this thesis aims to develop a health informatics system for the classification of heart diseases using data mining techniques focusing on Radial Basis functions and emerging Neural Networks approach. The presented research involves three development stages; firstly, the development of a preliminary classification system for Coronary Artery Disease (CAD) using Radial Basis Function (RBF) neural networks. The research then deploys the deep learning approach to detect three different types of heart diseases i.e. Sleep Apnea, Arrhythmias and CAD by designing two novel classification systems; the first adopt a novel deep neural network method (with Rectified Linear unit activation) design as the second approach in this thesis and the other implements a novel multilayer kernel machine to mimic the behaviour of deep learning as the third approach. Additionally, this thesis uses a dataset obtained from patients, and employs normalization and feature extraction means to explore it in a unique way that facilitates its usage for training and validating different classification methods. This unique dataset is useful to researchers and practitioners working in heart disease treatment and diagnosis. The findings from the study reveal that the proposed models have high classification performance that is comparable, or perhaps exceed in some cases, the existing automated and manual methods of heart disease diagnosis. Besides, the proposed deep-learning models provide better performance when applied on large data sets (e.g., in the case of Sleep Apnea), with reasonable performance with smaller data sets. The proposed system for clinical diagnoses of heart diseases, contributes to the accurate detection of such disease, and could serve as an important tool in the area of clinic support system. The outcome of this study in form of implementation tool can be used by cardiologists to help them make more consistent diagnosis of heart diseases