MONITORING SYSTEM FOR DETECTION OF ABRUPT CHANGES IN BIO-ELECTRIC SIGNALS
The article is devoted to the description of a monitoring system that allows detecting abrupt changes in the state of a human operator based on quasi-periodic electrophysiological data. As is known, many electrophysiological signals, including electrocardiogram (ECG), photoplethysmogram (PPG), breathing curves, etc. are quasi-periodic. Considering that psychophysiological states are reflected in the dynamic characteristics of biosignals, it is advisable to use mathematical methods of nonlinear dynamics to recognize abrupt changes. The essence of the so-called phase-flat method is to reflect the functional dependence of the speed on the instantaneous value of a periodic signal in the form of a superposition pattern. The abrupt change recognition algorithm is based on statistical hypotheses about changes in the statistical characteristics of the distributions of the considered signals. The article presents the possibilities of using a portable device based on a network of microcontrollers that performs the functions of monitoring the dynamics of quasi-periodic electrophysiological signals. To ensure the reliability and flexibility of the proposed monitoring system, it is designed as a three-level hierarchical system. Where the topmost level provides the interactivity of the given system. The system has the ability, if necessary, by the user, to interrupt the real monitoring process, actively change the system metaparameters and thereby adapt the system to the individual characteristics of the human operator.