Novel algorithms for cardiovascular parameters' estimation for long term monitoring systems

University of Technology, Sydney. Faculty of Engineering and Information Technology.In daily life every person needs continuous monitoring of temperature, heart rate, oxygen saturation level, blood pressure parameters and other parameters to have some idea about one's body systems performance and to assist doctors to diagnose one's health status. This information is more necessary for aged and unhealthy people, while it is also necessary for healthy person, who represents the undiagnosed subject. Usually, the healthy and unhealthy subjects are advised to measure their cardiovascular parameters at home at various times in a day to avoid any bad developments for their health status. Available self-measurement devices give only discrete readings and have not provided accurate information of heart rate, oxygen saturation level, and blood pressure parameters in many situations since most of them do not consider the body's movement or the uncertainty associated with the reading. Moreover, Blood pressure parameters (BPP): Systolic, Diastolic, and Mean Blood Pressures, have some types of correlation with the heart rate. This relationship is nonlinear and has many levels of uncertainty. The Type-2 Fuzzy system has a capacity to deal with nonlinear and uncertainty systems. The estimate of Blood pressure parameters based on heart rate can be categorized under such systems that fuzzy system can deal with. This thesis presents a novel algorithm to measure photo-plethysmography signal, heart rate and the oxygen saturation level and also to estimate BPPs for healthy and unhealthy subjects based on a prototype transducer, particle swarm optimization and type-2 Fuzzy System. The measured values of heart rate, oxygen saturation level, systolic, diastolic and mean blood pressures by utilizing the novel algorithn1 are compared with the clinical readings of heart rate, oxygen saturation level, systolic, diastolic and mean blood pressure. Very encouraging results have been achieved for estimating heart rate, oxygen saturation level, systolic, diastolic and mean blood pressures and the accuracy of estimated results for that parameters for healthy subjects, by our designed algorithm, are 99.53%,98.91 %,97.76%,91.81 % and 96.43%, respectively. Add to that, the accuracy of estimated results systolic, diastolic and mean arterial blood pressure for unhealthy subjects are 94.51 %,91.48% and 94.79%, respectively. On the contrary, the mean arterial blood pressure is estimated based on same heart rate and existing algorithm. This algorithm can only estimate mean arterial blood pressure. The accuracy of estimated mean arterial blood pressure equals 53.83%. The proposed model achieves very encouraging results; since all accuracies of the blood pressure parameters for unhealthy and healthy subjects are more than 91.4%. Moreover, the proposed algorithm can be utilized to determine heart rate, oxygen saturation level, systolic, diastolic and mean blood pressures

Multi agent system for estimation of cardiovascular parameters

Many cardiovascular diseases can be avoided by continuous monitoring cardiovascular parameters. Heart rate, electrocardiogram, blood pressure and pulse wave velocity are the most important and popular cardiovascular parameters. These parameters can be measured by different sensors that have been developed and improved to achieve reliable, accurate and continuous measurements. A part of the processing of theses sensors data, to get the related information, is parameters estimation. This paper presents a new concept to estimate cardiovascular parameters via using a new multi-agent system; that combines two independent methods; first method depends on pulse wave velocity (PWV), while second method depends on heart rate and artery resistance. The outcome of this multi-agent system is a continuous and reliable estimation of cardiovascular parameters by using non-invasive, Cuffless cheap sensors. © 2005 IEEE

Competing models of quality management and financial performance improvement.

Six competing models of quality management and financial performance improvement are hypothesized and statistically tested, using data from a survey of general managers of 288 four- and five-star hotels in Egypt and structural equation modeling. The comparative analysis of the conceptually and structurally different models suggests that financial performance can be improved when quality management is viewed holistically as a commonality of its interconnected practices (top management leadership; employee management; customer focus; supplier management; process management; quality data and reporting). Managers must therefore integrate stakeholders into design and implementation of effective quality management systems. This study: advances knowledge of the roles of alternative models of quality management in improving financial performance; deepens our understanding of the main features of a quality management system capable of enhancing organizational performance; and contributes to ongoing debates in quality and service management literature on factors that impact financial performance

Blood Pressure Estimation with Considering of Stroke volume Effect

The mean arterial pressure (MAP) is a very important cardiovascular parameter for physicians to diagnose various cardiovascular diseases. Many algorithms were used to estimate MAP with different accuracy. These algorithms used different factors, such as blood level, pulses, and external applied pressure, photo-plethysmography (PPG) signal features, heart rate (HR), and other factors. In addition, some natural-based techniques were employed to minimize the difference between estimated and measured blood pressure, as well as to measure blood pressure continuously

Type-2 fuzzy classification of blood pressure parameters

Blood pressure measurement is a highly sensitive task, because even breathing can cause variation as high as lOmmHg in reading of blood pressure. Due to the presence of high level of uncertainty; the linear model for blood pressure classification is not appropriate. Fuzzy Logic Systems are capable of producing precise solutions from vague, incomplete, or approximate information, by accommodating the ambiguities and logic. This paper presents a novel type-2 Fuzzy Logic System to estimate and classify blood pressure parameters in appropriate linguistic description. Firstly, a type-2 fuzzy logic system for the classification of blood pressure parameters is designed. Secondly, the proposed model is demonstrated by graphical user interface. The designed fuzzy model for the classification of blood pressure parameters gives more realistic results than linear model. The outcome of this paper is a friendly graphic user interface (GUI). The proposed model takes crisp value of heart rate as an input and generates crisp reading of blood pressure along with its appropriate linguistic classification, e.g., normal, low, or high etc; to provide more clear information to the general public about their cardiac health. The system has been tested and the computed percentage is less than 10% error values of all ten subjects' systolic, diastolic and mean blood pressure. © 2007 IEEE

Data for : An Enhanced CNN-LSTM Based Multi-Stage Framework for PV and Load Short-Term Forecasting: DSO Scenarios

The provided data is linked to the paper titled "An Enhanced CNN-LSTM Based Multi-Stage Framework for PV and Load Short-Term Forecasting: DSO Scenarios." In this research, a novel multi-stage framework for PV and load Short-Term Forecasting (STF) is introduced, incorporating feature generation, feature selection, and optimal hyperparameter tuning preprocessing techniques. The final stage of the proposed framework presents an enhanced hybrid CNN-LSTM deep learning model architecture.The effectiveness of this framework is evaluated and compared against other state-of-the-art approaches across various DSO scenarios, encompassing multiple single-phase residential loads, three-phase feeders, and secondary substations. Remarkably, the proposed framework exhibits significant reductions in forecasting errors.The provided time series data serves the purpose of testing the proposed short-term forecasting methodology. It features a 5-minute resolution for one month (July 2021, a summer month) and consists of 8,920 data points for each data profile.This data can be categorized into two main categories: load data and PV data. The load data includes three sub-categories representing different DSO scenarios: Multiple residential loads (with 100 and 400 residential loads in separate datasets), three-phase feeder load demands, and three-phase substation load demands. On the other hand, the PV data folder contains PV data for one month (July), along with corresponding weather data.THIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOV

Continuous measurement of Oxygen Saturation Level using photoplethysmography signal

Measuring Oxygen Saturation Level is an important part in monitoring patient's health condition. This is commonly monitored by a pulse oximeter, which has been widely adopted around the world as a standard measure during anesthesia, neonatal care and post-operative recovery. However, measuring Oxygen level continuously is very important for aged people, pregnant women and in many other critical situations. This paper considers the problem of measuring Oxygen saturation level continuously, a method to measure Oxygen Level using Photoplethysmography (PPG) signal has been presented. A prototype transducer has been built to measure the PPG. The transducer needs to be in contact to the subject's finger. Developed algorithm is presented that achieved accurate oxygen level and an experiment was also carried out on subjects' finger © 2006 Research Publishing Services