Abstract: A device for confirming the presence of thread in nuts and other threaded parts has a threaded gage mounted on a holder which is connected through a clutch to a motor. The motor drives the holder to rotate the threaded gage to engage the threaded part. Switches are provided to stop and reverse the motor. The device identifies parts without threads and also damaged and incomplete threads. The device does not impart undue force levels to unthreaded, undersized parts or defective parts. The device confirms threads in threaded blind bores.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.