Telectronics Pacing Systems, Inc.

Pacesetter, Inc.

John P Wickham

David Bassin

Anthony J Murphy

Paul Lunsmann

Peter M Nickolls

Robert Dodd

Barry Flower

Tony Murphy

Geoff Drane

William M Somerville

An implantable cardiac device wherein a reliability indicator is developed for cardiac classification based on the presence or absence of noise. If the classification is unreliable a robust therapy is applied until the cardiac condition is confirmed.

Arrhythmia classification system with reliability indication that allows

An implantable cardiac device includes electrodes for sensing and pacing the heart, and optionally, defibrillation electrodes. The IECG with a noise component sensed in the heart is processed to derive raw sense signals indicative of whether the sensed QRS complexe's range is outside a certain range. This range is dynamically adjusted to compensate for the noise component. Preferably this range is defined by a threshold signal generator which generates a threshold signal including a first part which varies dynamically in accordance with a preset criteria and a second part which is at least equal to the peak amplitude of the noise.

Apparatus with noise classification in an implantable cardiac device by

An implantable cardioverter/defibrillator device having an improved sensing system for automatically and rapidly adjusting to changing amplitudes of intracardiac electrogram 'ICEG' signals during transitions between normal sinus rhythm and ventricular tachycardia/ventricular fibrillation of a patient's heart is disclosed. ICEG signals are sensed, amplified and filtered to remove high frequency and low frequency noise and artifacts therefrom. Crossings of positive and negative threshold levels by the filtered signals are detected and corresponding positive and negative output signals representative thereof are provided. The positive and negative threshold levels are varied in accordance with corresponding variations of fractions of the levels of the filtered signals so that the positive and negative threshold levels move independently of one another in response to changes in the levels of the filtered signals; and, the positive and negative output signals generated during each heart beat are rationalized so that only one output signal is provided per heart beat.

Intracardiac electrogram sensing in an arrhythmia control system

A method, useable in implantable cardioverter defibrillators, for classifying heart rhythms of a patient by means of electrogram morphology. A number of features (metrics) of the electrogram are measured to form a description of the shape of individual electrograms, and the metrics are then converted into a cardiac rhythm diagnosis by means of a pattern classification technique.

Method of classifying heart rhythms by analyzing several morphology

An improved implantable pacemaker/defibrillator device in which a first and relatively simplistic technique is employed to detect an abnormal heart rhythm and a second more precise algorithm is utilized for the more difficult problem of confirming the arrythmia. Since the complex, higher power consuming device is utilized only for confirmation, overall power consumption is minimized. Optionally, the precise algorithm may be utilized periodically to adjust the sensitivity of the detection circuitry.

Cardioverting defibrillating device with off-line ECG analysis

An apparatus and method for the detection and treatment of arrhythmias using a processor having a neural network with a hierarchical arrangement including a first lower level for classifying individual waveforms, a second higher level for diagnosing detected arrhythmias and a third higher level for the application of therapy in response to a diagnosed arrhythmia. The neural network may be a back propogation neural network or an associative memory type neural network. The arrhythmias detected may be at least one of bradycardia, tachycardia and fibrillation. The apparatus may include a cardioverting/defibrillating pacemaker. In general, the apparatus acquires physiological signals representative of heart activity in a patient. A neural network receives the physiological signals and determines if any arrhythmia is present, and if present, selects therapy to be applied to the heart. A therapy generator then applies the therapy selected by the neural network. The physiological signals may be processed or unprocessed ECG signal, signals indicative of the properties of the blood including the presence of gases, blood temperature, and blood flow signals or signals representative of ventricular wall impedance or ventricular volume.

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Five Dock, Australia

John P Wickham