The main aim of our work was to assess the reliability of indirect abdominal electrocardiography as an alternative to the commonly used Doppler ultrasound monitoring technique. As a reference method, we used direct fetal electrocardiography. Direct and abdominal signals were acquired simultaneously, using dedicated instrumentation. The developed method of maternal signal suppression as well as fetal QRS complexes detection was presented. Recordings were collected during established labors, each consisted of four signals from the maternal abdomen and the reference signal acquired directly from the fetal head. After assessing the performance of the QRS detector, the accuracy of fetal heart rate measurement was evaluated. Additionally, to reduce the influence of inaccurately detected R-waves, some validation rules were proposed. The obtained results revealed that the indirect method is able to provide an accuracy sufficient for a reliable assessment of fetal heart rate variability. However, the method is very sensitive to recording conditions, influencing the quality of signals. Our investigations confirmed that abdominal electrocardiography, even in its current stage of development, offers an accuracy equal to or higher than an ultrasound method, at the same time providing some additional features.

Atrial fibrillation (AF) is a serious heart arrhythmia leading to a significant increase of the risk for occurrence of ischemic stroke. Clinically, the AF episode is recognized in an electrocardiogram. However, detection of asymptomatic AF, which requires a long-term monitoring, is more efficient when based on irregularity of beat-to-beat intervals estimated by the heart rate (HR) features. Automated classification of heartbeats into AF and non-AF by means of the Lagrangian Support Vector Machine has been proposed. The classifier input vector consisted of sixteen features, including four coefficients very sensitive to beat-to-beat heart changes, taken from the fetal heart rate analysis in perinatal medicine. Effectiveness of the proposed classifier has been verified on the MIT-BIH Atrial Fibrillation Database. Designing of the LSVM classifier using very large number of feature vectors requires extreme computational efforts. Therefore, an original approach has been proposed to determine a training set of the smallest possible size that still would guarantee a high quality of AF detection. It enables to obtain satisfactory results using only 1.39% of all heartbeats as the training data. Post-processing stage based on aggregation of classified heartbeats into AF episodes has been applied to provide more reliable information on patient risk. Results obtained during the testing phase showed the sensitivity of 98.94%, positive predictive value of 98.39%, and classification accuracy of 98.86%.

The following chapter describes the basic concepts of fuzzy systems and approximate reasoning. The study focuses mainly on fuzzy models based on Zadeh’s compositional rule of inference. The presentation begins with an introduction of fundamental ideas of fuzzy conditional (if-then) rules. A collection of fuzzy if-then rules formulates the so-called knowledge base, which formally represents the knowledge to be processed during approximate reasoning. The subsequent sections present formal definitions related to the compositional rule of inference and approximate reasoning using a knowledge base. Theoretical considerations are supplemented with practical examples of fuzzy systems as the foundation of many modern structures. The description includes fuzzy systems proposed by Mamdani and Assilan, Takagi, Sugeno and Kang, and Tsukamoto.