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This paper proposed the heart disease diagnosis system using nonlinear ARX (NARX) model. The system uses neural network for model estimation and classification of Normal and several heart diseases based on heart sounds. In classification, a spectrogram was applied to the modeled heart sounds for features extraction and selection. The features were fed to the FFNN and trained using Resilient Backpropagation...
Liver biopsy is considered as mandatory for the management of patients infected with the hepatitis C virus (HCV), particularly for staging of fibrosis degree. However, due to its invasive nature and limitations of sampling error, the tendency is to substitute the liver biopsy with non-invasive method. The objective of this study is to combine the serum biomarkers and histopathological findings to...
This research is on presenting a new approach for cardiac arrhythmia disease classification. The proposed method uses Modular neural network (MNN) model to classify arrhythmia into normal and abnormal classes. We have performed experiments on UCI Arrhythmia data set. Missing attribute values of this data set are replaced by closest column value of the concern class. We have constructed neural network...
Intelligent environments have the ability to assist people in their everyday life, with the aim of improving their wellbeing. One aspect of a humans' wellbeing is their health. The ability to monitor, understand, and correct bodily processes in real time and in convenient way can help people to improve their state of health, foresee and prevent diseases, will promote wellbeing and a longer life. To...
Electrocardiogram (ECG) is a signal designed to work as result of contraction of the heart muscle that make up the electrical biopotentials, using body surface electrodes obtained by obtaining are marked. The result of the heart losing healty working conditions,disturbances on freguency and amplitude of ECG signals recorded by electrocardiograph according to the healty ECG signal, occurs. The study...
This study presents a preliminary analysis of the relationship between electroencephalographic (EEG) and electrocorticographic (ECoG) event-related potentials (ERPs) recorded from from a single patient using a brain-computer interface (BCI) speller. The patient had medically intractable epilepsy and underwent temporary placement of an intracranial ECoG grid electrode array to localize seizure foci...
This paper presents an evidential segmentation scheme of respiratory signals for the detection of the wheezing sounds. The segmentation is based on the modeling of the data by evidence theory which is well suited to represent such uncertain and imprecise data. In this paper, we particularly focus on the modelization of the data imprecision using the fuzzy theory. The modelization result is then used...
The problem is statistical prediction of the number of people that will be infected with a contagious illness in a closed population over time. The prediction is based on the Susceptible-Infectious-Recovered (SIR) model of epidemic dynamics with inhomogeneous population mixing. The paper presents a theoretical analysis of the predictive accuracy based on the Cramer-Rao lower bound (CRLB). The CRLB...
In this study we investigate a means of distinguishing between stable and more complex atrial fibrillation (AF) sources by analyzing ECG signals. For this purpose, 21 episodes of AF were generated by using a 3D biophysical model of the atria. The AF episodes were classified into two groups (with or without stable sources) by visual observation of the electrical propagation on the epicardial tissue...
Clinical standard 12-lead ECG recordings over 5 minutes on patients in atrial fibrillation or in atrial flutter were analyzed. After suppression of the signal components related to ventricular activity, the amplitude spectra of all leads were inspected. The spectrum of lead V1 clearly showed the presence of harmonics of a basic frequency, not necessarily at the modal frequency. The dominant basic...
Error estimation is fundamental in GSP applications, such as the discovery of biomarkers to classify disease, or the construction of genetic regulatory networks, especially in small sample settings. Braga-Neto and Dougherty proposed a kernel-based technique of error estimation, called bolstered error estimation, which was shown empirically to work well in low-dimensional spaces (Braga-Neto and Dougherty,...
The paper presents the results of a signal processing approach to detect and isolate systolic murmurs. The identification of the first and second heart sounds and separating systole and diastole from a complete cardiac cycle were successfully carried out through wavelet analysis using an orthogonal Daubechies (db6) wavelet as the mother wavelet. At the fifth level of decomposition, S1 and S2 were...
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