OBJECTIVE: This paper aims to prove that automatic beat classification on ECG signals can be effectively solved with a pure knowledge-based approach, using an appropriate set of abstract features obtained from the interpretation of the physiological processes underlying the signal. METHODS: A set of qualitative morphological and rhythm features are obtained for each heartbeat as a result of the abductive interpretation of the ECG. Then, a QRS clustering algorithm is applied in order to reduce the effect of possible errors in the interpretation. Finally, a rule-based classifier assigns a tag to each cluster. RESULTS: The method has been tested with the MIT-BIH Arrhythmia Database records, showing a significantly better performance than any other automatic approach in the state-of-the-art, and even improving most of the assisted approaches that require the intervention of an expert in the process. CONCLUSION: The most relevant issues in ECG classification, related to a large extent to the variability of the signal patterns between different subjects and even in the same subject over time, will be overcome by changing the reasoning paradigm. SIGNIFICANCE: This paper demonstrates the power of an abductive framework for time-series interpretation to make a qualitative leap in the significance of the information extracted from the ECG by automatic methods.

Continuous followup of heart condition through long-term electrocardiogram monitoring is an invaluable tool for diagnosing some cardiac arrhythmias. In such context, providing tools for fast locating alterations of normal conduction patterns is mandatory and still remains an open issue. This paper presents a real-time method for adaptive clustering QRS complexes from multilead ECG signals that provides the set of QRS morphologies that appear during an ECG recording. The method processes the QRS complexes sequentially by grouping them into a dynamic set of clusters based on the information content of the temporal context. The clusters are represented by templates which evolve over time and adapt to the QRS morphology changes. Rules to create, merge, and remove clusters are defined along with techniques for noise detection in order to avoid their proliferation. To cope with beat misalignment, derivative dynamic time warping is used. The proposed method has been validated against the MIT-BIH Arrhythmia Database and the AHA ECG Database showing a global purity of 98.56% and 99.56%, respectively. Results show that our proposal not only provides better results than previous offline solutions but also fulfills real-time requirements.

In this paper, we propose the multivariable fuzzy temporal profile model (MFTP), which enables the projection of expert knowledge on a physical system over a computable description. This description may be used to perform automatic abstraction on a set of parameters that represent the temporal evolution of the system. This model is based on the constraint satisfaction problem (CSP) formalism, which enables an explicit representation of the knowledge, and on fuzzy set theory, from which it inherits the ability to model the imprecision and uncertainty that are characteristics of human knowledge vagueness. We also present an application of the MFTP model to the recognition of landmarks in mobile robotics, specifically to the detection of doors on ultrasound sensor signals from a Nomad 200 robot.

We take as a setting point the Fuzzy Temporal Profile (FTP) model, which is used to describe the temporal evolution of a certain physical parameter. This model defines the approximation of an evolution curve by means of a set of linear sections between a series of significant points (X0, X1, …, XN), and in which each section is defined by means of an imprecise constraint on the duration, the increase in value and the slope between the points connected by the section. Following the FTP model we present a language for the representation of trends, the semantics of which is projected onto a fuzzy definition of each one of the constraints of a profile of interest.