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The goal of this project is to use support vector clustering to enhance the performance of a coronary heart disease detection model in comparison to the aXgboost Classifier. Substances and Techniques: The Support Vector Clustering Method and Xgboost Classifier Algorithms are used for cardiac disease prediction. In this study, we split our participants evenly into two groups of five. Identification and forecasting are performed using a cardiac data set. There are 14 indicators in the dataset that could be used to determine if a patient has cardiac problems. Results: Two statistically reliable classifiers were used to produce the results: The observed significance level for accuracy is 0.002 (two-tailed) (p0.05). When it comes to cardiac disease prediction, Support Vector Clustering Technique appears to far outperform Xgboost Classifier. The xgboost Classifier Algorithm has an accuracy of 79.60 percent, while the Support Vector Clustering Method achieves an accuracy of 82.84 percent. [ABSTRACT FROM AUTHOR]

This work employed MRI images and unique Naive Bayes (NB) and Linear Regression (LR) algorithms to improve the accuracy and specificity of prenatal heart disease diagnosis. Materials and Strategies: This study compares Innovative Naive Bayes (N=20) to Linear Regression. The total sample size was obtained using g power with an alpha of 0.05, an enrollment ratio of 0.1, a confidence interval of 95 percent, and pre-test power of 80 percent. The groundbreaking Naive Bayes classifier has 88 percent accuracy and 84 percent specificity, whereas the Linear Regression classifier has 84 percent accuracy and 82 percent specificity. The SPSS analysis revealed statistically significant accuracy rate (p=0.001) and specificity (p=0.006). The innovative Naive Bayes classifier detects foetal cardiac disease more effectively than Linear regression, according to a study. [ABSTRACT FROM AUTHOR]

The effort of the community in early checkups for coronary heart disease is still very lacking. That is due to the less awareness and cost constraints so that heart disease is handled too late which causes the heart condition to get worse and even complications. A diagnosis of coronary heart risk based on facial texture has been done lately by researchers in China and produces pretty good output, but no similar study has been found in Indonesia. Therefore, this study aims to design several machine learning models and find out the performance of algorithms in diagnosing the risk of coronary heart disease with facial imagery. The research dataset was extracted using the Gray Level Co-occurrence Matrix on specific areas of the face. The areas taken are right crow's feet, right canthus, bridge nose, forehead, left canthus and left crow's feet. The main focus of this study was on the performance of the Support Vector Machine, Decision Tree, and Neural Network models. Dataset processing procedures are divided into two, namely model making (training) and performance testing (testing). The findings showed that the best performing model was NN with an AUC score of 92.8%, followed by SVM with 85.6%, and the lowest was DT with 68% AUC. NN became the model with the best performance with 76.9% accuracy, 81.7% precision, 76.9% recall, and an F1 score of 77.5%. [ABSTRACT FROM AUTHOR]

Heart disease is now one of the leading causes of death. As a result of the high difficulty of diagnosing and treating heart disease, as well as a lack of effective medical diagnostic equipment, medical practitioners, and other services, the mortality rate is rising dramatically, especially in developing countries. Since many preventable heart diseases are on the rise, large-scale adequate preventive measures are needed. In today's digital world, many clinical decision support systems to prevent heart disease have been developed by various scientists to simplify and ensure effective diagnosis. This paper examines the state-of-the-art support systems for different clinical decisions to prevent heart disease. This support system is proposed by various researchers using different preprocessing (ECG signal denoising), feature extraction and classification techniques for disease diagnosis. [ABSTRACT FROM AUTHOR]

In comparison to a Bagging classifier, the goal is to enhance the accuracy of a novel coronary heart disease prediction model using support vector clustering. Support Vector Clustering Technique and Bagging Classifier Algorithms are used to predict heart disease and for identification and prediction, a cardiac dataset is employed. The dataset comprises 14 variables that can be used to determine whether or not a patient has heart disease. The research article ha s 2 groups; each group has N=5 samples. The results were produced using two classifiers having statistical significance: For accuracy, the sig (2-tailed) p-value observed is 0.001 (p<0.05). In terms of heart disease prediction, the Support Vector Clustering Technique appears to perform significantly better than Bagging Classifier. In predicting novel coronary heart disease Support Vector Clustering Technique has an accuracy of 82.19 percent, which appears to be higher than the 74.21 percent accuracy of the Bagging Classifier. [ABSTRACT FROM AUTHOR]

Objective of the project is to improve accuracy and predict Novel Coronary Heart Disease using Support Vector Clustering technique in comparison with Adaboost classifier. Support Vector Clustering Technique and Adaboost Classifier Algorithms are used to predict heart disease and for identification and prediction, a cardiac dataset is employed. Dataset comprises 14 variables that can be used to determine whether or not a patient has heart disease. Study contains two groups with sample size N = 5 for each group. Support Vector Clustering Technique has an accuracy of 83.19 percent, which appears to be higher than the 78.96 percent accuracy of the Adaboost Classifier. Results were obtained with statistical significance of two algorithms: sig (2-tailed) p-value observed is 0.001 (p < 0.05) for accuracy. In terms of heart disease prediction, the Support Vector Clustering Technique appears to perform significantly better than Adaboost. [ABSTRACT FROM AUTHOR]

In this paper, a model for heart disease diagnosis using semi-supervised learning methods has been proposed. The proposed method, uses Semi-Supervised Support Vector Machine (S3VM) for heart disease diagnosis. Also, Expectation Maximization (EM) method is used for handling missing values in patient's observed data; which can improve diagnosis accuracy greatly. Thus, our proposed method for diagnosing heart disease, consists of three steps: "preprocessing by EM", "extracting features by PCA" and "disease detection using S3VM". In the evaluation of the proposed method, a real dataset containing medical information of 270 patients was used. Each data record had 13 attributes, which were reduced to 10 using the PCA algorithm. The results of the proposed model has been compared with other classification algorithms. Experimental results shows that using the proposed system, we could diagnose heart disease with an average accuracy of 83%. The results of this study show that the proposed method has better performance in terms of accuracy as well as in terms of specificity and sensitivity criteria and can be a useful tool for early heart disease diagnosis. [ABSTRACT FROM AUTHOR]

In the current era, heart disease is a disease that is suffered by many people in the world. Therefore, early diagnosis and prediction of heart disease is a way to reduce disease risk and improve public health. Although several studies have been carried out related to the recommendation system, but none have focused on the recommendation of a heart disease prevention package with medical record data that focuses on the symptoms that appear. A recommendation system with a probability matrix of a disease can be based on past data. The calculation results show that the method used can overcome the problems of dispersion and scalability and can produce a fairly high accuracy compared to other methods, namely 81.01%. In future research, this research is expected to increase the risk that data such as geographic location and family factors can be taken into account so that accurate results are more accurate. [ABSTRACT FROM AUTHOR]

The major health issue is HD and it has affected a number of people worldwide. In recent times, heart disease becomes the key reason of the demise of individuals of different age groups. Heart disease affects the functionality of blood vessels, and causes coronary artery infections that weaken the body of the patient. Therefore, early prediction of heart disease is very important to effectively treat cardiac patients before a heart attack or stroke can occur. Cardiovascular diseases can be identified by conducting medical tests and using wearable sensors. The enhancement for predicting the heart attack is required in the health sector with the help of different DL methods. The commonly seen symptoms of cardiac disease is breath shortness, physical body illness and swelling in feet are swollen. Researchers make an attempt for discovering an effective method so that the heart disease can be detected because the existing diagnosis methods of heart disease are less efficient in early time identification. The heart disease prediction is the approach of predicting futuristic heart disease likelihoods using the existent knowledge. There are several steps that are included in the heart disease prediction. The pre-processing, feature extraction and classification are some of these steps. The random forest and logistic regression based the hybrid scheme is introduced. Various approaches for this task are reviewed in this paper. The result of analysis shows that the supervised algorithms are the most efficient for predicting cardiovascular disorders. [ABSTRACT FROM AUTHOR]

Many lives can be saved if disease can be detected at earlier stage. Heart disease diagnosis using traditional medical history is not reliable as your doctor will perform a series of tests to diagnose heart disease. Invasive methods of diagnosis are time consuming and costly. Medical field can be significantly benefited if we provide an accurate diagnosis of diseases using advanced Machine Learning (ML) approach. As per the new survey, from last 20 years heart problem has remained the major cause of mortality worldwide so in today's era, it is necessary to diagnose it quickly and accurately. The contents of paper intend to bring forth a comprehensive survey of ML techniques in projecting heart disease. We review representable research works that has been carried out in this field using machine learning approach for University of California, Irvin (UCI) database. Comparison is only possible if we have some common benchmark on the dataset. Therefore, we have chosen the studies that have implemented machine learning algorithms on the same dataset namely UCI database. Few unaddressed issues and challenges that comparatively received meager attention are discussed, highlighting the future prospects for heart disease prediction and providing pointers to the future research. [ABSTRACT FROM AUTHOR]

Now a day's maximum peoples are suffering from some diseases, it is very important to identify these diseases among the patients, to identify the diseases we need some classification algorithms to classify the patients into high-risk zone and no-risk zone. This work presents the experimental study for the heart diseases diagnosis using the classification techniques and convolution neural network techniques, comparative performance is measured in the form of result parameter i.e. accuracy and, applied with the UCI machine learning datasets. [ABSTRACT FROM AUTHOR]

Prolonged standing and sitting in the job environment are one of the principal causes of cardiovascular disease and musculoskeletal disorders. However, these diseases turn out to be just the results of sedentary postures and little has been analyzed regarding physiological indicators due to extended workdays. The main objective of this study was to do a systematic review and a meta-analysis using PRISMA strategy on the effects of prolonged standing and sitting postures in physiological biomarkers measured in work tasks, simulated workday activities and uninterrupted postures evaluated in laboratory. This study is intended to analyze different effects of prolonged standing and sitting in physiological indicators and its possible impact on human health due to these postures. A total of 46 studies for the seated posture and 29 for the standing posture were selected and included in the 95% IC meta-analysis. Results have proven statistical significance and reliable heterogeneity for the following biomarkers: Amplitude muscle twitch force indicator MTF (p=<0.001; I^2=0%), Mean arterial pressure (p=0.049; I^2=15.09%), Venous refilling time (<0.001; 43.86%), Lower limb volume (p=<0.001; I^2=0%) for prolonged standing in healthy population and Lower leg circumference (p=0.031; I^2=0%), Triglycerides (p=0.007; I^2=15.57%) for prolonged sitting posture in healthy and overweight population respectively. [ABSTRACT FROM AUTHOR]

In this research, an algorithm based on dynamic neural networks was designed to pre-diagnose cardiac diseases. The neural network learns from a database of signals with cardiac conditions (arrhythmias) and through real-time monitoring, the patterns are searched to identify whether the test subject is healthy or suffers of heart disease. The dynamic neural network functions as an estimator of states, through time, it learns the dynamics of the cardiac suffering signal, assimilating its behavior. Applying the mean square error, we can compare them and know if they are identical. Signals of interest are under a pre-define analysis and through their variations, information from it can be extracted. [ABSTRACT FROM AUTHOR]

Lorenz plot (LP) method gives a global view of long-time electrocardiogram signals, is an efficient simple visualization tool to analyze heart rate variability and cardiac arrhythmias. 50 records of RR-interval time series with frequent premature complexes exported from 24-hour Holter. Constructed threedimensional LP (3DLP) with three successive RR intervals as X, Y and Z axis in Cartesian coordinate system for each record, then stereographic projection it along the space diagonal. The radii of dots distinguished eccentric clusters (ECs) points from centric cluster's with the accuracy of 94±6.0%. The eccentric scatter-dots were separated and identified by the frequency distribution characteristic on azimuth, with the accuracy of 93±13.3% ECs. The APF < -2.8° of CPN EC supported the ventricular extrasystoles diagnosis, with excellent sensitivity (1.0) and specificity (0.92). The transformed coordinates (polar radius and angle) of 3DLP could extract and quantify clusters to diagnose arrhythmia, and might provide additional prognosis information. [ABSTRACT FROM AUTHOR]

Heart auscultation is a screening method done by listening using a stethoscope for early diagnosis of heart disease, it is low cost and non-invasive, but it has a limitation of human hearing. This paper presents a noise-robust feature extraction method by combining and selecting a heart sound (HS) feature in time and frequency domain. Wavelet decomposition (WD) is used for noise removal. Features are extracted by AR-PSD and used as inputs for classification. The nine types of abnormal HS that were taken from Michigan Heart Sound Database were classified into nine categories. In this research, statistics features in time and frequency domain are used as additional features. Correlation-based Feature Selection (CFS) is used to select the best feature among 13 features extracted. The performance of a new feature set is called Feature Set 3 compared to CFS. The result shows that the proposed feature set achieves the highest level of accuracy. [ABSTRACT FROM AUTHOR]

Multifractal analysis of time series obtained of heartbeat intervals from healthy people and patients with congestive heart failure (CHF) was performed. In addition, we designed a Holter monitor with a maximum sampling frequency of 1500 Hz. In order to get the data saved in the Holter we developed a software, also this software concentrates all procedures to perform the multifractal analysis. Besides, the software gets a prediagnosis based on nonlinear dynamic techniques and traditional techniques. In the multifractal analysis we found differences between the multifractal index and the spectra symmetry of the series from healthy subjects and subjects with CHF. We observed that in healthy subjects the spectrum tends to be right skewed, for subjects with CHF it tends to be left skewed. These results confirm the possibility of characterizing the health state of a person by using parameters of nonlinear dynamics, particularly the multifractal parameters. [ABSTRACT FROM AUTHOR]

This work intends to define requirements to design, deploy, evaluate and validate the feasibility, efficiency and safety of an enriched telecardiology application for providing remotely ECG and Holter diagnostic/monitor services as atrial fibrillation screening. The platform must include supporting tools for e-learning that allow to operate the solution by general practitioners and patients for second opinion, best follow-up through home monitoring and on-line learning, boosted by an on-demand virtualized data recording and training infrastructure, based on a cloudified infrastructure. It can be understood as an experiment that will use a cloudified infrastructure to deploy new telecardiology related services, particularly for atrial fibrillation patients that could be monitorized from several ambulatories. [ABSTRACT FROM AUTHOR]

Technological advances in the last few decennia allow the monitoring of many physiological observables in a continuous way, which in physics is called a "time series". The best studied physiological time series is that of the heart rhythm, which can be derived from an electrocardiogram (ECG). Studies have shown that a healthy heart is characterized by a complex time series and high heart rate variability (HRV). In adverse conditions, the cardiac time series degenerates towards randomness (as seen in, e.g., fibrillation) or rigidity (as seen in, e.g., ageing), both corresponding to a loss of HRV as described by, e.g., Golberger et. al [1]. Cardiac and digestive rhythms are regulated by the autonomous nervous system (ANS), that consists of two antagonistic branches, the orthosympathetic branch (ONS) that accelerates the cardiac rhythm but decelerates the digestive system, and the parasympathetic brand (PNS) that works in the opposite way. Because of this reason, one might expect that the statistics of gastro-esophageal time series, as described by Gardner et. al. [2,3], reflects the health state of the digestive system in a similar way as HRV in the cardiac case, described by Minocha et. al. In the present project, we apply statistical methods derived from HRV analysis to time series of esophageal acidity (24h pHmetry). The study is realized on data from a large patient population from the Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán. Our focus is on patients with functional disease (symptoms but no anatomical damage). We find that traditional statistical approaches (e.g. Fourier spectral analysis) are unable to distinguish between different degenerations of the digestive system, such as gastric esophageal reflux disease (GERD) or functional gastrointestinal disorder (FGID). [ABSTRACT FROM AUTHOR]

In this work, the efficacy of various features on electrocardiogram (ECG) based biometric authentication process is thoroughly examined. In particular, the features acquired from temporal analysis, wavelet transformation, power spectral density estimation and QRS-complex detection over ECG signals are considered. These features are employed with two distinct classification algorithms, namely decision tree and Bayes network, specifically for gender, age and identity recognition problems. The biometric authentication framework is evaluated on a benchmark dataset that contains ECG records of 18 healthy people including 5 men, aged 26 to 45, and 13 women, aged 20 to 50. The results of the experimental analysis reveal that if all those features are used in combination rather than individually, better performance is attained for all classifiers in each recognition problem. [ABSTRACT FROM AUTHOR]

Artificial neural networks, used for practices of engineering, are strong tool for useful relationships between data. Particle swarm optimization is successfully carried out to train feedforward neural networks. In this study, heart diseases diagnosis was realized by using Electrocardiography (ECG) records that taken from MIT-BIH ECG database, which were used to classify 6 different arrhythmias for training and testing. These are normal sinus rhythm (NSR), ventricular tachycardia (VTK), sinus arrhythmia (SA), atrial premature contraction (APC), atrial fibrillation (AF) and ventricular trigeminy (VTI). Artificial Neural Networks (ANN) and Artificial Neural Networks trained by particle swarm optimization (PSO-NN) are used as a classifier and they are compared. Experimental results have revealed that PSO-NN model is better for classification of ECG signals than traditional ANN. [ABSTRACT FROM AUTHOR]

This work is focused on the development of intelligent classifiers in the area of biomedicine, focusing on the problem of diagnosing cardiac diseases based on the electrocardiogram (ECG), or more precisely on the differentiation of the types of atrial fibrillations.A novel methodology is presented for optimal placement and selections of knots, for approximating or fitting curves to data using smoothing splines. Due to the relevance of the placement of the knots in smoothing spline approximation, in order to have an optimal allocation of the number of knots and their positions, a Multi-Objective Genetic Algorithm has been developed, with the main purpose of avoiding a large number of local minima (in terms of approximation error for different system complexity or number of knots) existing in the problem of knots placement. With the information obtained with the b-spline approximation is possible to build a classifier that will be capable of distinguishing the different types Atrial Fibrillation pathology. [ABSTRACT FROM AUTHOR]

A correct diagnosis of the problem can only lead to the correct treatment. Use of various signals (biomedical signals) produced by the human body for the purpose of diagnosis is not new. It is well known that a Proper analysis of biomedical signals leads to a correct diagnosis of the problem. Presently the biomedical signals for ECG are collected in supine position which may not be comfortable for all the patients. In this paper, a method of analysing the ECG signals collected in various postures i.e. sitting, standing and supine has been presented. ECG signals of 16 patients have been collected in all the three postures and analysed by using Principal Component Analysis (PCA) and Independent Component Analysis (ICA). PCA and ICA together are suitable for signal analysis because PCA reduces dimension of the acquired data whereas ICA reduces noise of the dimension reduced signal. Eigen value variance for all three postures has been calculated and found the first principal component Eigen value variance is 99.95% in standing posture, 99.94% in sitting posture, 99.66% in supine posture. The result indicates a high scale reduction of the data with considerable accuracy. [ABSTRACT FROM AUTHOR]

Echocardiography is the safe, easy and fast technology for diagnosing the cardiac diseases. As in other ultrasound images these images also contain speckle noise. In some cases this speckle noise is useful such as in motion detection. But in general noise removal is required for better analysis of the image and proper diagnosis. Different Adaptive and anisotropic filters are included for statistical analysis. Statistical parameters such as Signal-to-Noise Ratio (SNR), Peak Signal-to-Noise Ratio (PSNR), and Root Mean Square Error (RMSE) calculated for performance measurement. One more important aspect that there may be blurring during speckle noise removal. So it is prefered that filter should be able to enhance edges during noise removal. [ABSTRACT FROM AUTHOR]

The electrocardiogram (ECG) is widely used for diagnosis of heart diseases. Good quality of ECG is utilized by physicians for interpretation and identification of physiological and pathological phenomena. However, in real situations, ECG recordings are often corrupted by artifacts or noise. Noise severely limits the utility of the recorded ECG and thus needs to be removed, for better clinical evaluation. In the present paper a new noise cancellation technique is proposed for removal of random noise like muscle artifact from ECG signal. A transform domain robust variable step size Griffiths' LMS algorithm (TVGLMS) is proposed for noise cancellation. For the TVGLMS, the robust variable step size has been achieved by using the Griffiths' gradient which uses cross-correlation between the desired signal contaminated with observation or random noise and the input. The algorithm is discrete cosine transform (DCT) based and uses symmetric property of the signal to represent the signal in frequency domain with lesser number of frequency coefficients when compared to that of discrete Fourier transform (DFT). The algorithm is implemented for adaptive line enhancer (ALE) filter which extracts the ECG signal in a noisy environment using LMS filter adaptation. The proposed algorithm is found to have better convergence error/misadjustment when compared to that of ordinary transform domain LMS (TLMS) algorithm, both in the presence of white/colored observation noise. The reduction in convergence error achieved by the new algorithm with desired signal decomposition is found to be lower than that obtained without decomposition. The experimental results indicate that the proposed method is better than traditional adaptive filter using LMS algorithm in the aspects of retaining geometrical characteristics of ECG signal. [ABSTRACT FROM AUTHOR]

Cardiovascular disease is the leading cause of death in America. Computer simulation of complicated dynamics of the heart could provide valuable quantitative guidance for diagnosis and treatment of heart problems. In this paper, we present an integrated numerical model which encompasses the interaction of cardiac electrophysiology, electromechanics, and mechanoelectrical feedback. The model is solved by finite element method on a Linux cluster and the Cray XT5 supercomputer, kraken. Dynamical influences between the effects of electromechanics coupling and mechanic-electric feedback are shown. [ABSTRACT FROM AUTHOR]

An electrocardiogram or ECG (also known as EKG-abbreviated from the German word Elektro-Kardiographie), is an electrical recording of the heart and is used in the investigation of heart disease. 'An electrocardiogram (ECG) is a graphic tracing of the electric current generated by the heart muscle during a heartbeat'. The electrocardiogram (ECG)/(EKG) is a surface measurement of the electrical potential generated by electrical activity in cardiac tissue. It has been used extensively in medicine sine its inventions in the early 1900' sand has been proven to be invaluable in various diagnostics applications such as the detection of irregular heartbeat patterns (i.e. fibrillation & arrhythmia), hearts murmurs (other abnormal sounds), tissue/structural damage (such as valve malfunction) and coronary artery blockage. In this paper we made a circuit network by using this circuit we can acquire an ECG signal of the heart electrical activity. This is one of the cheapest circuit to acquire ECG signal. It's whole cost around Rs.250/- only. Instrumentation amplifier AD620AN, notch filter, various resistances, capacitors, wires etc. are used to made this circuit. [ABSTRACT FROM AUTHOR]

The last decade has witnessed major advancements in the direct application of functional imaging techniques to several clinical contexts. Unfortunately, this is not the case of Electrocardiology. As a matter of fact, epicardial maps, which can hit electrical conduction pathologies that routine surface ECG’s analysis may miss, can be obtained non invasively from body surface data through mathematical model-based reconstruction methods. But, their interpretation still requires highly specialized skills that belong to few experts. The automated detection of salient patterns in the map, grounded on the existing interpretation rationale, would therefore represent a major contribution towards the clinical use of such valuable tools, whose diagnostic potential is still largely unexploited. We focus on epicardial activation isochronal maps, which convey information about the heart electric function in terms of the depolarization wavefront kinematics. An approach grounded on the integration of a Spatial Aggregation (SA) method with concepts borrowed from Computational Geometry provides a computational framework to extract, from the given activation data, a few basic features that characterize the wavefront propagation, as well as a more specific set of features that identify an important class of heart rhythm pathologies, namely reentry arrhythmias due to block of conduction. [ABSTRACT FROM AUTHOR]

We present numerical results related to the direct and the inverse problems in electrocardiography. The electrical activity of the heart is described by the bidomain equations. The electrocardiograms (ECGs) recorded in different points on the body surface are obtained by coupling the bidomain equation to a Laplace equation in the torso. The simulated ECGs are quite satisfactory. As regards the inverse problem, our goal is to estimate the parameters of the bidomain-torso model. Here we present some preliminary results of a parameter estimation for the torso model. [ABSTRACT FROM AUTHOR]

Development of native Low Density Lipoprotein (LDL) biosensors is of great importance in clinical analysis because the LDL concentration, which is the main carrier of cholesterol, in the plasma, is a fundamental parameter for the prevention and diagnosis of a number of clinical disorders such as heart disease, hypertension and atherosclerosis. The optical properties of the Europium-Tetracycline Complex (EuTc) were investigated for the solutions containing LDL in their compositions. In this paper we show an enhancement in the europium luminescence of EuTc complex in the presence of LDL. The time-resolved fluorescence spectroscopy experimental results of the pure EuTc sample and samples with LDL (EuTc:LDL) reveal an increase in the europium emission lifetime in the lipoprotein-doped samples with respect to the pure EuTc sample. A calibration curve, reasonably well described by a linear function between 0 and 3 mg/mL of LDL, was obtained. The obtained limit of detection was 0.23 mg/mL. Sixteen blood plasma samples all of them contend approximately 90 mg/dL of LDL were studied and the LDL concentrations were calculated with our method. The average LDL concentration obtained was 94 mg/dL. The results show that the EuTc complex can be used as a sensor to determine LDL with fast response, compact design, and reproducible results. [ABSTRACT FROM AUTHOR]

Auscultation is one of the most ancient and useful techniques for the diagnosis of heart disease. However, phonocardiography has been waning and has largely been replaced by echocardiography. To visualize heart sounds and murmurs for a novice, a mobile graphic system has been developed using a smartphone with an external microphone attached to a chest piece. Original sound waves are visualized in real time and saved on a smartphone. Sound waves filtered with noise suppression also clarify the sounds. The system enables even novice clinicians to see and recognize heart sounds and murmurs at the patient's bedside in real time. [ABSTRACT FROM AUTHOR]