Improving accelerometer-based rate adaptive pacing by means of second-generation signal processing

Toward a neurodynamical understanding of ictogenesis

Although considerable information on cellular and network mechanisms of epilepsy exists, it is still not understood why, how, and when the transition from interictal to ictal state takes place. The authors review their work on nonlinear EEG analysis and provide consistent evidences that dynamical changes in the neural activity allows the characterization of a preictal state several minutes before seizure onset. This new neurodynamical approach of ictogenesis opens new perspectives for studying the basic mechanisms in epilepsy as well as for possible therapeutic interventions.

Characterizing neurodynamic changes before seizures

The study of dynamic changes in neural activity preceding epileptic seizure allows the characterization of a preictal state several minutes before seizure onset. This opens up new perspectives for studying the mechanisms of epileptogenesis as well as for possible therapeutic interventions, which represent a major breakthrough. In this review the authors present and discuss the results from their group in this domain using nonlinear analysis of brain signals, as well as the limitations of this topic and current questions.

Nonlinear EEG analysis and its potential role in epileptology

Deterministic chaos offers a striking explanation for apparently irregular behavior of the brain that is evidenced in the EEG. Recent developments in the physical-mathematical framework of the theory of nonlinear dynamics (colloquially often termed chaos theory) provide new concepts and powerful algorithms to analyze such time series. Because of its high versatility, nonlinear time series analysis has already gone beyond the physical sciences and, at present, is being successfully applied in a variety of disciplines, including cardiology, neurology, psychiatry, and epileptology. However, it is well known that different influencing factors limit the use of nonlinear measures to characterize EEG dynamics in a strict sense. Nevertheless, when interpreted with care, relative estimates of, e.g., the correlation dimension or the Lyapunov exponents, can reliably characterize different states of normal and pathologic brain function. In epileptology, extraction of nonlinear measures from the intracranially recorded EEG promises to be important for clinical practice. In addition to an immense reduction of information content of long-lasting EEG recordings, previous studies have shown that these measures enable (a) localization of the primary epileptogenic area in different cerebral regions during the interictal state, (b) investigations of antiepileptic drug effects, (c) analyses of spatio-temporal interactions between the epileptogenic zone and other brain areas, and (d) detection of features predictive of imminent seizure activity. Nonlinear time series analysis provides new and supplementary information about the epileptogenic process and thus contributes to an improvement in presurgical evaluation.

Developments in sensor-driven pacing

This article reviews the recent major developments in the field of rate adaptive pacing. Including, the improved instrumentation of existing sensors, the use of multiple sensors to enhance sensor specificity or sensitivity, and the automation of sensor calibration. The physiologic benefits and programming of rate adaptive pacing are reviewed.

A self-tuning effect of membership functions in a fuzzy-logic-based cardiac pacing system

This paper describes a self-tuning method of membership functions in a fuzzy-logic-based cardiac pacing system and validates its feasibility in a double sensor system which has minute ventilation and oxygen saturation level as its guides for the rate regulation. Though the agreement between the pacing rates (fuzzy rates) calculated with three linguistic variables for each parameter and the target rates were not satisfactory, it was improved significantly by tuning the membership functions. Almost the same evaluated values with those obtained by using six linguistic variables for each parameter were obtained. Time required for the self-tuning process was about 40 s (386CPU, 20 MHz) which was fast enough for the system. The smaller number of linguistic labels results in a smaller number of rules, which is beneficial in implantable cardiac pacemakers with limited memory capacity. A fuzzy-logic-based cardiac pacing system is promising for the realization of custom-made cardiac pacemakers.