Pacesetter, Inc.

Daniel Cooper

Chih-ming James Chiang

Saul E Greenhut

Tibor A Nappholz

Bruce M Steinhaus

A rate responsive implantable pacemaker generates a metabolic demand parameter which is converted into a corresponding metabolic indicated parameter. The parameter is used to determine certain characteristics related to an exercise period and the patient, including exercise duration and intensity. These characteristics are used to dynamically calculate an exercise recovery period during which the pacing rate is elevated above a rate indicated by the metabolic parameter. Preferably, fuzzy logic circuitry is used for this purpose.

Rate responsive pacemaker with exercise recovery using minute volume

A rate responsive pacemaker with improved response to exercise onset includes a detector for detecting a metabolic parameter indicative of the patient's metabolic demand. The metabolic demand parameter derived therefrom is analyzed either using a heart rate sensitive zero crossing detector or a derivative calculator and used to generate an optimized pacing rate.

Rate-responsive pacemaker with rapid minute volume determination

A rate responsive implantable pacemaker generates a metabolic demand parameter such as a thoracic impedance which is converted into a corresponding metabolic indicated parameter. During the conversion, intermediate parameters, such as either tidal volume and respiration rate, or a derivative of the metabolic demand parameter are used by a cross-check circuit to insure that artefacts due for example to stroke volume noise are eliminated. The peak limiter is used to limit the metabolic indicated rate to an acceptable range. Both protection circuits use fuzzy logic components.

Rate-responsive pacemaker with noise-rejecting minute volume

A diagnostic device makes use of knowledge based rule system for making a prognosis of a cardiac malfunction based on operation parameters and/or patient characteristics received from a cardiac implant. The device generates a symptoms vector from the information received from the implant, and using the expert rules to generate an appropriate prognosis. Both pathological problems and programming problems may be detected in this manner.

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Hauguenau, France

Daniel Cooper