Sharif Digital Repository

In this thesis we have investigated and improved the signal processing techniques which are used for monitoring the heart abnormalities in terms of ECG (ElectroCardioGram) signals in order to detect heart attacks before they occur. De-noising ECG signals are one of the most important research topics in computer and electrical engineering fields. There are many different algorithms for de-noising signals in various domains. It usually is needed to propose a suitable algorithm for each specific system. In some cases instead of developing a new algorithm, we could modify the available ones for de-noising in our system. ECG signals are output from an electrocardiograph which measures electrical... 

The goal of the research presented in this thesis is removing noise from electrocardiogram (ECG) signals. The electrocardiogram is a test that measures the electrical activity of the heart. The information obtained from an electrocardiogram can be used to diagnose different types of heart disease. It may be useful for seeing how well the patient is responding to treatment. The extraction of high resolution ECG signals from noisy measurements is among the most tempting open problems of biomedical signal processing. Extracting useful clinical information from the real (noisy) ECG requires reliable signal processing techniques. Numerous methods have been reported to denoise ECG signals based on... 

The human heart is a complex system that contains many clues about its function in its electrocardiogram (ECG) signal. Due to the high mortality rate of heart diseases, detection and recognition of ECG arrhythmias is essential. The most difficult problem faced by ECG analysis is the vast variations among morphologies of ECG signals. In this study, we propose an approach for y detection of abnormal beats and data visualization with respect to ECG morphologies by using manifold learning. In order to do so, a nonlinear dimensionality reduction method based on the Laplacian Eigenmaps is used to reduce the high dimensions of the ECG signals, followed by the application of Bayesian and FLDA method... 

Apnea-bradycardia is a medical term for prolonged respiratory pause accompanied with a heart rate reduction which is a common event among preterm infants. Repetition of apnea-bradycardia episodescompromises oxygenation and tissue perfusion and may lead to neurological impairment or even short-term morbi-mortality. Main solution to this breathing-related disorder is continues monitoring of infants in neonatal intensive care units in order to detect apnea-bradycardia event, generate an alarm and warn available nurse or physician to initiate quick nursing actions. Various studies have been done in this area and different methods are proposed which mainly focus on cardiac signal processing. This... 

Apnea Bradycardia (AB) episodes (breathing pauses associated with a significant fall in heart rate) are the most common disease in preterm infants. Consequences associated with apnea-bradycardia episodes involve a compromise in oxygenation and tissue perfusion, a poor neuromotor prognosis at childhood and a predisposing factor to sudden-death syndrome in preterm newborns. It is therefore important that these episodes are recognized (early detected or predicted if possible), to start an appropriate treatment and to prevent the associated risks. In this thesis, we propose two Bayesian Network (BN) approaches (Markovian and Switching Kalman Filter) for the early detection of apnea bradycardia... 

Cardiovascular diseases are one of the major causes of mortality in humans. One way to diagnose heart diseases and abnormalities is processing of cardiac signals such as ECG.In many of these processes, inter-beat and intra-beat features of ECG signal must be extracted. These features include peak, onset and offset of ECG waves,meaningful intervals and segments that can be defined for ECG signal. ECG fiducial point (FP) extraction refers to identifying the location of the peak as well as the onset and offset of the P-wave,QRS complex and T-wave which convey clinically useful information. However, the precise segmentation of each ECG beat is a difficult task, even for experienced... 

Respiration signal is one of the biological information required to monitor patient respiratory activities. Noninvasive respiratory monitoring is an extensive field of research, which has seen widespread interest for several years. It is well known that the respiratory activity influences electrocardiographic measurements (ECG) in various ways. Therefore, different signal processing techniques have been developed for extracting this respiratory information from the ECG, namely ECG derived respiratory (EDR). Potential advantages of such techniques are their low cost, high convenience and the ability to simultaneously monitor cardiac and respiratory activity. One of the aims of this thesis is... 

Cardiovascular diseases are the leading cause of death globally so early diagnosis of them is important. Many researchers focused on this field. First signs of cardiac diseases appear in the electrocardiogram signal. This signal represents the electrical activity of the heart so it’s primarily used for the detection and classification of cardiac arrhythmias. Permanent monitoring of this signal is not possible for specialists so we should do this by means of Artificial Intelligence. In this thesis, we use recurrent neural networks to classify electrocardiogram’s arrhythmias. This deep learning method, use two sources of data to learn from. The first part of data is global for everyone and the... 

Cardiovascular diseases are the first leading cause of death in the world also in IRAN. Early detection of such problems can decrease the costs also can help to cure the patient but it needs continuous monitoring and automated classification of hearbeats. Mobile devices and wearable gadgets are good solutions which can help patients before visiting the doctor.In this research, an algorithm is introduced which with the help of ECG signal detects dangerous myocardial problems. Our approach is using deep learning method which were not considered much before. In the proposed algorithm ECG signal is processed in order to get features and with dimensionality reduction, input of the network gets... 

Spiking Neural Networks(SNN) are networks which are consisted of layers of neurons, like other typical artificial neural networks. The main difference between SNN and other neural networks is the type of data transportation among neurons which is done by spikes. Spiking neural networks and their models are considered as the nearest networks and neurons to animals’ nervous systems. In aspects of hardware implementation, the type of data transportation in SNN causes them to be ultra-low power. So, implementation of these networks on chips like FPGA and also usage of SNN in applications with high processing load have startling germination, recently. In this work, we have tried to propose some... 

Continuous positive airway pressure (CPAP) is a standard treatment for patients with obstructive sleep apnea (OSA), a sleep-related breathing disorder characterized by full or partial occlusion of the upper airway during sleep. CPAP pressure adjust by a sleep technologist during attended laboratory polysomnography (PSG) to eliminate obstructive respiratory-related events (apneas, hypopneas). Because of changes in environment and lifestyle patients needed new adjustments for the device that brings discomfort and extra money for treatment. In recent, lots of methods have been proposed to replace PSG and minimized the number of biological signals to detect apnea, best results came from deep... 

Cardiovascular diseases (CVDs) have ranked first cause of deaths globally. In 2016, about 17.7 million people died from CVDs representing 31% of all world deaths. So, early intelligent detection of cardiovascular disease could help to save many lives in worldwide. There are several methods to analyze heart activity and to detect any abnormalities including Electrocardiogram, Stress test, Echocardiography, cardiac catheterization and coronary angiography.Among all methods, Electrocardiogram (ECG) is the most common and convenient type where it measures heart electrical activity and records it as a series of pulses. Analyzing these pulses would provide useful information about normal and... 

The blood pressure (BP) is one of the most vital signs to monitor human health. Hypertension is a major public health problem that is associated with morbidity and mortality. In the past decades, the number of people with hypertension disease has been increased. According to Ministry of Health, 54 percent of hypertensive patients are unaware of this health threat. Therefore, accurate and continuous measurement of BP is essential for its prevention and treatment. Traditional non-invasive methods of BP monitoring use an inflatable cuff, which causes discomfort due to occlusion of the artery and can not be used continuously. Numerous studies have attempted to present methods without using a... 

An inseparable problem from human daily life is stress that causes problems such as heart disease and depression, so stress management and control is essential for the health of the individual and society. This thesis explores the possibility of stress detection using vital signs and machine learning algorithms. First, by examining the potential of unsupervised learning algorithms for stress detection, a general method is developed and the accuracy of the algorithm is evaluated with the ECG signals of a smart wristband made in Sharif University of Technology Biosen group as well as WESAD data set. The self-organizing map structure is created based on stress-related features and final result... 

By continuously measuring blood pressure, we can prevent the irreversible effects of high blood pressure. With the traditional method of using a cuff, it is not possible to measure blood pressure continuously during the day, so for continuous monitoring of blood pressure, it is necessary to use a method without the need for a cuff. Based on previous studies, to estimate blood pressure, Photoplethysmogram and ECG signal features, or temporal and morphological features of Photoplethysmogram signal have been used. In methods that use ECG signals, signal recording is difficult, and methods that use both PPG and ECG signals are even more complex. Using only PPG signals also has its problems.... 

Cardiovascular diseases are currently the most common disease and the first cause of death in Iran and the world. It will increase in the future due to improper diet and lifestyle. Therefore, the prevention and treatment of these diseases is very important for the health of the country. One of the effective ways to diagnose heart diseases is to analyze the electrical signals of the heart and detect heart rhythm irregularities from it. The electrical signal of the heart called ECG is created by the electro-mechanical activity of the atria, ventricles and the valves between them. Therefore, this signal is useful for detecting the correct functioning of different parts of the heart and...