Examining cross-database global training to evaluate five different methods for ventricular beat classification

Artificial Intelligence and Women Researchers in the Czech Republic

Artificial intelligence as a research area has been continuously growing for several decades. Many applications were developed in various domains. Medicine and health care have attracted more intensive attention thanks to rapid technological development that has accelerated generation of large volumes of data requiring intelligent analysis and evaluation. This article illustrates, through examples of women researchers and selected AI projects in medicine, the wide spectrum of applications developed during the last fifteen years in the Czech Republic, and in particular at the Czech Technical University in Prague. Women researchers played an important and irreplaceable role since the advent of AI research in the Czech Republic. By their example, they motivated many young female students to join the community and start their research career in the AI area. They frequently participated in research projects led by the senior women researchers. The presented overview of projects illustrates the diversity of the medical area and the potential of AI methods that can be used for solving data- and knowledge-intensive problems. We briefly touch on the AI study programs. In conclusion, we point out the future challenges in AI and its applications in medicine and health care.

NEURO — GENETIC APPROACH TO CLASSIFICATION OF CARDIAC ARRYTHMIAS

The premature ventricular contraction (PVC) is a cardiac arrhythmia which is widely encountered in the cardiologic field. It can be detected using the electrocardiogram signal parameters. In general the use of multilayered feed forward neural networks has been hampered by the lack of a training algorithm which reliably finds a nearly globally optimal set of weights. Genetic algorithms are a class of optimization procedures which are good at exploring a large and complex space in an intelligent way to find values close to the global optimum. This paper deals with designing a neural network classifier of PVC beats whose weights are genetically evolved using a genetic algorithm. We developed two classifiers. First, the Classical neural classifier (CNC) was trained by the back-propagation (BP) method. Second neuro-genetic classifier (NGC) was trained by genetic algorithm technique. Performance and accuracy of the two techniques are presented and compared. Our results illustrate the improvements gained by using a genetic algorithm rather than BP. We use the medical database (MIT-BIH) to validate our results.

Evolving neural networks using a genetic algorithm for heartbeat classification

This study investigates the effectiveness of a genetic algorithm (GA) evolved neural network (NN) classifier and its application to the classification of premature ventricular contraction (PVC) beats. As there is no standard procedure to determine the network structure for complicated cases, generally the design of the NN would be dependent on the user's experience. To prevent this problem, we propose a neural classifier that uses a GA for the determination of optimal connections between neurons for better recognition. The MIT-BIH arrhythmia database is employed to evaluate its accuracy. First, the topology of the NN was determined using the trial and error method. Second, the genetic operators were carefully designed to optimize the neural network structure. Performance and accuracy of the two techniques are presented and compared.