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Ventricular arrhythmias are one of the most important causes of annual deaths in the world, which may lead to sudden cardiac deaths. Accurate and early diagnosis of ventricular arrhythmias in heart diseases is essential for preventing mortality in cardiac patients. Ventricular activity on the electrocardiogram (ECG) signal is in the interval from the beginning of QRS complex to T wave end. Variations in the ECG signal and its features may indicate heart condition of patients. The first step to extract features of ECG in time domain is finding R peaks. In this paper, a combination of two algorithms of Pan-Tompkins and state logic machine has been used to find R peaks in heart signals for normal sinus signals and ventricular abnormalities. Then, a healthy or sick beat may be realized by comparing the difference between R peaks obtained from two algorithms in each beat. The morphological features of the ECG signal in the range of QRS complex are evaluated. Ventricular tachycardia (VT), ventricular flutter (VFL), ventricular fibrillation (VFI), ventricular escape beat (VEB), and premature ventricular contractions (PVCs) are abnormalities studied in this paper. In the classification step, the support vector machine (SVM) classifier with Gaussian kernel (one in front of everyone) is used. Accuracy percentages of ventricular abnormalities mentioned above and normal sinus rhythm are respectively obtained as 95.8%, 92.8%, 94.5, 98.9%, 91.5%, and 100%. The database of this paper has been taken from normal sinus rhythm and MIT-SCD banks available on Physionet.org. [ABSTRACT FROM AUTHOR]

Because of increasing risk of diabetes, the measurement along with control of blood sugar has been of great importance in recent decades. In type I diabetes, because of the lack of insulin secretion, the cells cannot absorb glucose leading to low level of glucose. To control blood glucose (BG), the insulin must be injected to the body. This paper proposes a method for BG level regulation in type I diabetes. The control strategy is based on nonlinear model predictive control. The aim of the proposed controller optimized with genetics algorithms is to measure BG level each time and predict it for the next time interval. This merit causes a less amount of control effort, which is the rate of insulin delivered to the patient body. Consequently, this method can decrease the risk of hypoglycemia, a lethal phenomenon in regulating BG level in diabetes caused by a low BG level. Two delay differential equation models, namely Wang model and Enhanced Wang model, are applied as controller model and plant, respectively. The simulation results exhibit an acceptable performance of the proposed controller in meal disturbance rejection and robustness against parameter changes. As a result, if the nutrition of the person decreases instantly, the hypoglycemia will not happen. Furthermore, comparing this method with other works, it was shown that the new method outperforms previous studies. [ABSTRACT FROM AUTHOR]

X-ray coronary angiography has been a gold standard in the clinical diagnosis and interventional treatment of coronary arterial diseases for decades. In angiography, a sequence of images is obtained, a few of which are suitable for physician inspection. This paper proposes an automatic algorithm for the extraction of one or more frames from an angiogram sequence, which is most suitable for diagnosis and analysis by experts or processors. The algorithm consists of two stages: In the first stage, the background and illumination in the angiogram sequence are omitted. By analyzing the histogram of the sequence, a feature is attributed to each frame. These features, determining the visibility of the vessel tree, are clustered by a fuzzy c-means method. In the second stage, the cardiac phase for each frame is specified. Using the results of both stages, the best frames in an angiogram sequence are obtained. To evaluate the proposed method, it has been tested on angiogram sequences from several patients. The results demonstrate the accuracy of the method. The performance and speed of our method indicate its usefulness in clinical applications. [ABSTRACT FROM AUTHOR]