Design of hybrid architectures based on neural classifier and RBF pre-processing for ECG analysis

Assessment and comparison of different methods for heartbeat classification

The most common way to diagnose cardiac dysfunctions is the ECG signal analysis, usually starting with the assessment of the QRS complex as the most significant wave in the electrocardiogram. Many methods for automatic heartbeats classification have been applied and reported in the literature but the use of different ECG features and the training and testing on different datasets, makes their direct comparison questionable. This paper presents a comparative study of the learning capacity and the classification abilities of four classification methods--Kth nearest neighbour rule, neural networks, discriminant analysis and fuzzy logic. They were applied on 26 morphological parameters, which include information of amplitude, area, interval durations and the QRS vector in a VCG plane and were tested for five types of ventricular complexes--normal heart beats, premature ventricular contractions, left and right bundled branch blocks, and paced beats. One global, one basic and two local learning sets were used. A small-sized learning set, containing the five types of QRS complexes collected from all patients in the MIT-BIH database, was used either with or without applying the leave one out rule, thus representing the global and the basic learning set, respectively. The local learning sets consisted of heartbeats only from the tested patient, which were taken either consecutively or randomly. Using the local learning sets the assessed methods achieved high accuracies, while the small size of the basic learning set was balanced by reduced classification ability. Expectedly, the worst results were obtained with the global learning set.

Ranking of pattern recognition parameters for premature ventricular contractions classification by neural networks

Detection and classification of ventricular complexes from a limited number of ECG leads is of considerable importance in critical care or operating room patient monitoring. Beat-to-beat detection allows the heart rhythm evolution to be followed and various types of arrhythmia to be recognized. A quantitative analysis is proposed of pattern recognition parameters for classification of normal QRS complexes and premature ventricular contractions (PVC). Twenty-six parameters have been defined: the width of the QRS complex, three vectorcardiogram parameters and 11 from two ECG leads. These parameters include: amplitudes of positive and negative peaks, area of positive and negative waves, various time-interval durations, amplitude and angle of the QRS vector, etc. They are measured for all QRS complexes annotated as 'normals' and 'PVCs' from the 48 ECG recordings of the MIT-BIH arrhythmia database. Neural networks (NN) are shown to be a useful instrument for the analysis of large quantities of parameters. Separate ranking of any parameter and homogeneous group ranking (amplitude, area, interval, slope and vector) were performed. From the two ECG leads, the first three ranked parameter groups for clustering of PVCs are amplitude, slope and interval, while for N clustering they are vector, amplitude and area. Considering the entire parameter set, we obtained N = 99.7% correct detection of normal QRS complexes and PVC = 98.5% of premature ventricular complexes. The study also shows that simultaneous analysis of two ECG channels yields better accuracy compared to using a single channel: the improvement is 0.1% in the classification of N beats and 4.5% for PVC beats.

An interactive framework for an analysis of ECG signals

In this study, we introduce and discuss a development of a highly interactive and user-friendly environment for an ECG signal analysis. The underlying neural architecture being a crux of this environment comes in the form of a self-organizing map. This map helps discover a structure in a set of ECG patterns and visualize a topology of the data. The role of the designer is to choose from some already visualized regions of the self-organizing map characterized by a significant level of data homogeneity and substantial difference from other regions. In the sequel, the regions are described by means of information granules-fuzzy sets that are essential in the characterization of the main relationships existing in the ECG data. The study introduces an original method of constructing membership functions that incorporates class membership as an important factor affecting changes in membership grades. The study includes a comprehensive descriptive modeling of highly dimensional ECG data.