Using Combined Descriptive and Predictive Methods of Data Mining for Coronary Artery Disease Prediction: a Case Study Approach

Heart disease is one of the major causes of morbidity in the world. Currently, large proportions of healthcare data are not processed properly, thus, failing to be effectively used for decision making purposes. The risk of heart disease may be predicted via investigation of heart disease risk factors coupled with data mining knowledge. This paper presents a model developed using combined descriptive and predictive techniques of data mining that aims to aid specialists in the healthcare system to effectively predict patients with Coronary Artery Disease (CAD). To achieve this objective, some clustering and classification techniques are used. First, the number of clusters are determined using clustering indexes. Next, some types of decision tree methods and Artificial Neural Network (ANN) are applied to each cluster in order to predict CAD patients. Finally, results obtained show that the C&RT decision tree method performs best on all data used in this study with 0.074 error. All data used in this study are real and are collected from a heart clinic database

Détection de signatures temps-fréquence sur des crises d'epilepsie

Parmi les méthodes d'investigation en épilepsie, la stéréo-électroencéphalographie (SEEG) fournit des informations capitales sur des interactions entre structures cérébrales. Ce papier présente une application de l'analyse temps-fréquence sur des signaux SEEG dans le but d'étudier la variabilité de signatures temps-fréquence présentes sur différentes voies d'enregistrement et différentes crises d'un patient donné. Ce problème se pose sous la forme d'un problème de détection de signatures ressemblant à une signature de référence. Quatre statistiques basées sur les informations temps-fréquence de la signature de référence et du signal sont présentées et leurs performances comparées. Les résultats montrent que le lissage des représentations temps-fréquence, (surtout celui effectué par la représentation temps-fréquence dépendant du signal), permet de détecter des signatures ressemblantes

Estimation de la pression pulmonaire par analyses spectrale et temps-fréquence du deuxième son cardiaque

Cette étude a pour but d'évaluer différentes méthodes de sélection du deuxième son cardiaque et de ses composantes aortique et pulmonaire et d'estimation de leurs paramètres spectraux pour la mesure de la pression artérielle pulmonaire. Trois méthodes basées sur la transformée rapide de Fourier (FFT) et 5 méthodes basées sur la distribution Wigner-Ville (DWV) ont été testées à l'aide de signaux enregistrés chez 27 patients. La performance de chaque méthode a été évaluée par l'indice de corrélation entre la mesure de la pression artérielle pulmonaire obtenue à partir de quatre paramètres spectraux extraits du deuxième son cardiaque et de ses composantes aortique et pulmonaire et celle mesurée par échocardiographie Doppler. Les résultats démontrent que la meilleure méthode basée sur la transformée rapide de Fourier et celle basée sur la représentation temps-fréquence (RTF) ont des performances similaires. Les deux méthodes nécessitent une séparation préalable des composantes aortique et pulmonaire du deuxième son cardiaque avant d'effectuer l'analyse spectrale ou l'analyse temps-fréquence

Monitoring of salt iodisation programme in Iran; Health outcomes, shortages and perspective

Iodine deficiency disorders include a wide range of metabolic and nonmetabolic disorders including goiter. To control IDDs, the World Health Organization and responsible agencies in countries established daily iodine uptake. Almost all the countries in the world provide the required iodine through salt iodisation. IDDs are not completely eradicable, so monitoring the salt iodisation programme is necessary for control of IDDs. In Iran, a salt iodisation programme was started in 1996. In this study, we took salt samples from all legally produced salt brands in Iran in 30 provinces and measured iodine concentration. The results of the monitoring programme for iodine concentration in schoolchildren's urine was used to compare accessibility to iodized salts and health outcomes. The results show that more than 80 of available salts have a suitable or acceptable concentration of iodine. Despite large variance in iodine concentration in available salt in some provinces, the median of iodine concentration in salts is within an acceptable range. Also, the urinary concentration of iodine (national median = 161) confirms that shortage of iodine intake is very low in Iran. The high rate of salt consumption of the Iranian people also has a significant effect on iodine uptake, but can lead to hyperthyroidism and hypertension that must be controlled. © 2018 Elsevier Gmb

Higher order spectral regression discriminant analysis (HOSRDA): A tensor feature reduction method for ERP detection

Tensors are valuable tools to represent Electroencephalogram (EEG) data. Tucker decomposition is the most used tensor decomposition in multidimensional discriminant analysis and tensor extension of Linear Discriminant Analysis (LDA), called Higher Order Discriminant Analysis (HODA), is a popular tensor discriminant method used for analyzing Event Related Potentials (ERP). In this paper, we introduce a new tensor-based feature reduction technique, named Higher Order Spectral Regression Discriminant Analysis (HOSRDA), for use in a classification framework for ERP detection. The proposed method (HOSRDA) is a tensor extension of Spectral Regression Discriminant Analysis (SRDA) and casts the eigenproblem of HODA to a regression problem. The formulation of HOSRDA can open a new framework for adding different regularization constraints in higher order feature reduction problem. Additionally, when the dimension and number of samples is very large, the regression problem can be solved via efficient iterative algorithms. We applied HOSRDA on data of a P300 speller from BCI competition III and reached average character detection accuracy of 96.5% for the two subjects. HOSRDA outperforms almost all of other reported methods on this dataset. Additionally, the results of our method are fairly comparable with those of other methods when 5 and 10 repetitions are used in the P300 speller paradigm

Seizure detection in EEG signals: a comparison of different approaches.

In this paper, the performance of traditional variance-based method for detection of epileptic seizures in EEG signals are compared with various methods based on nonlinear time series analysis, entropies, logistic regression,discrete wavelet transform and time frequency distributions.We noted that variance-based method in compare to the mentioned methods had the best result (100%) applied on the same database

Synthetic ECG generation and Bayesian filtering using a Gaussian wave-based dynamical model

In this paper, we describe a Gaussian wave-based state space to model the temporal dynamics of electrocardiogram (ECG) signals. It is shown that this model may be effectively used for generating synthetic ECGs as well as separate characteristic waves (CWs) such as the atrial and ventricular complexes. The model uses separate state variables for each CW, i.e. P, QRS and T, and hence is capable of generating individual synthetic CWs as well as realistic ECG signals. The model is therefore useful for generating arrhythmias. Simulations of sinus bradycardia, sinus tachycardia, ventricular flutter, atrial fibrillation and ventricular tachycardia are presented. In addition, discrete versions of the equations are presented for a model-based Bayesian framework for denoising. This framework, together with an extended Kalman filter and extended Kalman smoother, was used for denoising the ECG for both normal rhythms and arrhythmias. For evaluating the denoising performance, the signal-to-noise ratio (SNR) improvement of the filter outputs and clinical parameter stability were studied. The results demonstrate superiority over a wide range of input SNRs, achieving a maximum 12.7 dB improvement. Results indicate that preventing clinically relevant distortion of the ECG is sensitive to the number of model parameters. Models are presented which do not exhibit such distortions. The approach presented in this paper may therefore serve as an effective framework for synthetic ECG generation and model-based filtering of noisy ECG recordings

Detection of rhythmic discharges in newborn EEG signals.

This paper presents a scalp eletroencephalogram (EEG) rhythmic pattern detection scheme based on neural networks. rhythmic discharges detection is applicable to the majority of seizures seen in newborns, and is listed as detecting 90% of all the seizures. In this approach some features based on various methods are extracted and compared by a modified multilayer neural network in order to find rhythmic discharges. Statistical performance comparison with seizure detection schemes of Gotman et al. and Liu et al. is performed

Filtering noisy ECG signals using the extended Kalman filter based on a modified dynamic ECG model

In this paper an Extended Kalman Filter (EKF) has been proposed for the filtering of noisy ECG signals. The method is based on a modified nonlinear dynamic model, previously introduced for the generation of synthetic ECG signals. An automatic parameter selection method has also been suggested, to adapt the model with a vast variety of normal and abnormal ECG signals. The results show that the EKF output is able to track the original ECG signal shape even in the most noisiest epochs of the ECG signal. The proposed method may serve as an efficient filtering procedure for applications such as the noninvasive extraction of fetal cardiac signals from maternal abdominal signals. 1

Effect of intravenous injection of galanin on plasma concentrations of growth hormone, thyroid hormones and milk production in the Saanen goat

The goal of this study was to determine whether intravenously galanin injection effect on plasma concentrations of growth hormone (GH), thyroxine (T4), triiodothyronine (T3) and milk production in theSaanen goats. Fifteen Saanen goats were randomly divided into 5 groups (n = 3 in each group). Each group received daily injection of 0, 0.2, 0.4, 0.8 and 1.6 g galanin/Kg for 10 days (period of injection).Blood and milk samples were collected daily on d-2 to d-1 (before treatment), d0 to d10 (during treatment), and d11 to d12 (after treatment) each morning. Injection of galanin significantly (P < 0.05)increased plasma concentrations of GH. Also, galanin decreased plasma concentrations of T3 and T4 throughout the experiment period, while it had no significant effect on milk production. The result of this study indicated that galanin may increase the plasma concentration of GH, and decrease the plasma concentration of T3 and T4, but fail to alter milk production in the Saanen goats