Search

There has been an alarmingly high mortality rate because of cardiovascular diseases since the past few years and some innovative research has been done by scientists and research scholars all over the world to make the pre-emptive detection of these diseases efficient, cheap and accessible to the public. In this paper, we have studied the research and experimentation that has been done on detection and classification of Cardiovascular diseases using Phonocardiogram signals. There are many problems that come with the cheap and democratized process of heart monitoring using Phonocardiogram signals, like environment noise, lung sounds etc. sever a innovative classification and transformation methods that solved these problems and yielded promising results were found in literature. This paper aims at providing a detailed view on such techniques and optimal classification models which yield comparable results and are computationally cheaper. [ABSTRACT FROM AUTHOR]

In recent years, health issues such as cardiac failure, lung failures, and heart-related disorders have been on the rise at an alarming pace. To resolve these issues, continuous health monitoring is needed. Diseases may be detected and treated early with routine health screening. Wirelessly tracking a patient's wellbeing is a new technology that is gaining traction. The implementation of the body temperature sensor, ECG sensor, oxygen sensor, and pulse sensor for an IoT-based health monitoring device is described in this paper. The sensor's data was relayed to the Arduino, which is hence used to send data to the cloud through a WIFI system. The data was further analyzed using an algorithm, and classification was completed. [ABSTRACT FROM AUTHOR]

Cardiovascular Diseases (CVDs) had arisen as deadly disease. This was the biggest reason for the world's enormous number of deaths over the last few eras. More accuracy, perfection and precision were needed for prediction of heart diseases at early stage. To address this issue, a predictive system was needed. In medicinal arena, machine learning could be used as a tool for analysis, discovery, and prediction of numerous syndromes. Machine learning helps to make choices and forecasts effectively from vast number of records provided by healthcare field. The intent of this paper is to pinpoint threats of heart disease and to predict heart disease using Logistic Regression algorithm. The results offer the probabilities of occurrence of heart disease based on percentage. The metrics to evaluate the model for considered dataset using contingency table are Accuracy, Precision, Recall and F1 measure. Heart illness prediction using logistic regression provided enhanced accuracy than other techniques. The datasets taken are categorized based on the constraints interrelated with heart disease. This system assesses those constraints by means of machine learning practices. [ABSTRACT FROM AUTHOR]

To measure the circulatory system of skin using infrared light, PPG is extensively utilized. PPG has a lot of innate focal points like affordability, non-prominent in nature and goes probably as a versatile demonstrative gadget. It helps in the assessment of circulatory strain, oxygen inundation levels, heart yield and for administering distinctive other autonomic components of the body. For the amazing screening of different pathologies, PPG fills in as a huge promising framework. The improvement of blood in the vessel which spreads from the heart to the toes and fingertips is reflected by the PPG signals. In this paper, PPG signals are utilized for a singular patient who is encountering cardio vascular issues. For the PPG signals, the Firefly clusters and Dragonfly selector algorithms is utilized. In this study an in depth analysis of classifiers in detection of cardiovascular diseases (CVD) is done with the help of capnobase database. The following classifiers are used in this paper linear regression, Nonlinear Regression, Logistic regression, Bayesian linear discriminant classifier (BDLC) and Gaussian Mixture Model (GMM) and firefly. The plan and execution of a PPG are very economical and have a simple support. It is also used for the peripheral blood fluorescent and venous filling time. Results show that the Linear Regression classifier exceeds the other five classifiers in terms of accuracy (65.85%), F1 score (68.18%), MCC (0.316), Jaccard metric (51.72%), and error rate (34.15%). [ABSTRACT FROM AUTHOR]

To measure the circulatory system of skin using infrared light, PPG is extensively utilized. PPG has a lot of innate focal points like affordability, non-prominent in nature and goes probably as a versatile demonstrative gadget. It helps in the assessment of circulatory strain, oxygen inundation levels, heart yield and for administering distinctive other autonomic components of the body. For the amazing screening of different pathologies, PPG fills in as a huge promising framework. The improvement of blood in the vessel which spreads from the heart to the toes and fingertips is reflected by the PPG signals. In this paper, PPG signals are utilized for a singular patient who is encountering cardio vascular issues. For the PPG signals, the Firefly clusters and Dragonfly selector algorithms is utilized. In this study an in depth analysis of classifiers in detection of cardiovascular diseases (CVD) is done with the help of capnobase database. The following classifiers are used in this paper linear regression, Nonlinear Regression, Logistic regression, Bayesian linear discriminant classifier (BDLC) and Gaussian Mixture Model (GMM) and firefly. The plan and execution of a PPG are very economical and have a simple support. It is also used for the peripheral blood fluorescent and venous filling time. Results show that the Linear Regression classifier exceeds the other five classifiers in terms of accuracy (65.85%), F1 score (68.18%), MCC (0.316), Jaccard metric (51.72%), and error rate (34.15%). [ABSTRACT FROM AUTHOR]

Detection of the heart rate disturbances is one of the main functions of the ECG monitoring systems. Atrial fibrillation represents one of the most dangerous and widespread cardiac arrhythmias that may cause development of apoplexy and even lead to the patient's death. Many algorithms for detection of this decease with the use of ECG analysis are proposed by now, but the best of them demonstrate accuracy of atrial fibrillation detection no more than about 94.5 %. This paper is devoted to the new method of this arrhythmia detection showing high level of specificity that permit to avoid the decease hyper diagnostics. [ABSTRACT FROM AUTHOR]

A heartbeat is the consequence of cardiac tissue deforming in response to tension generated in cardiac fibres. This tension is a result of biochemical reactions that occur as part of cardiac electrophysiology. This coupled electro-mechanical system is usually modelled by coupling a model of tissue deformation with a tissue level model of cardiac electrophysiology such as the bidomain equations [1]. One difficulty with computing a numerical solution of this coupled system is numerical stability [3, 7]. In this paper we describe how numerical stability may be attained by embedding the model of generation of active tension within the tissue deformation model. [ABSTRACT FROM AUTHOR]

In this paper the goal was to measure the contrast to noise ratio (CNR) of fast spin echo (FSE) magnetic resonance imaging (MRI) sequences in detecting thermal lesions created by high intensity focused ultrasound (HIFU) in rabbit kidney, liver, heart, and brain and lamb pancreas. A spherically focused transducer was used which is navigated inside MRI by a custom made positioning device. A simple simulation model was developed which predicts the CNR for the two FSE MRI sequences. The maximum contrast measured with T1-W FSE ranges from 10 to 25. For all 5 tissues of interest if one uses TR between 400 and 500 ms the contrast is maximized. The T1, and T2 value of lesion depends strongly on the host tissue and is always lower than the host tissue. The greater the difference in T1 value, the greater the CNR. The simulated model for predicting the CNR was proven successful. The CNR measured with T2-W FSE varies between 12 and 15 for all 5 tissues. With T2-W FSE if one uses TE between 40 and 50 ms, the contrast is maximized. [ABSTRACT FROM AUTHOR]