An electrosurgical generator may reduce unintended tissue damage by improving regulation of output power. The electrosurgical generator may control the power during a cycle, and react to a change in power if arcing occurs. Voltage sources, especially, demonstrate the tendency to have large, uncontrolled power excursions during normal electrosurgical use. The magnitude of the power excursions may be dependent on various factors. An exemplary electrosurgical generator control scheme reduces or minimizes the thermal spread by accurately supplying the specified power within a few cycles. Additionally, fast and accurate regulation provided by the constant voltage mode reduces or minimizes unintentional tissue charring. Thus, reduced thermal spread and charring should result in better surgical outcomes by reducing scarring and decreasing healing times. An electrosurgical generator controller may be configured to control both a DC-DC buck converter and a DC-AC boost inverter based in part on electrical parameters of the electrosurgical generator.

An electrosurgical system for providing an electrosurgical signal to a patient is disclosed. The electrosurgical system includes a control circuit, wherein the control circuit is programmed to: provide the electrosurgical signal to a first electrode and a second electrode, receive a plurality of input variables, wherein the plurality of input variables are indicative of a short being either present or absent between the first electrode and the second electrode, and apply a short detection algorithm to the plurality of input variables to indicate either a short circuit or no short circuit between the first electrode and the second electrode during the provision of the electrosurgical signal. The plurality of input variables may include at least one impedance level between the first electrode and the second electrodes during the provision of the electrosurgical signal. The short detection algorithm applied may include a fuzzy logic algorithm, a neural network algorithm, or neuro-fuzzy algorithm.

Various embodiments are directed to an electrosurgical systems and methods for providing an electrosurgical signal to a patient. An electrosurgical signal defining a plurality of pulses may be provided to first and second electrodes. A first reading may be received indicating an impedance between the first and second electrodes taken at a first point of a first pulse of the electrosurgical signal. A second reading may indicate the impedance a first point of a second pulse of the electrosurgical signal, where the first point of the first pulse and the first point of the second pulse are at equivalent positions within the first and second pulses. Based on a comparison of the first reading and the second reading, a short circuit may be determined and a signal indicating the short circuit may be generated.

The present disclosure relates to an apparatus and method for discriminating biological tissue, and a surgical apparatus using the same, the biological tissue discriminating method being capable of exactly discriminating the biological tissue by measuring an impedance value per frequency, teaching the measured impedance value per frequency in a single classifier according to learning algorithms that are different from one another having the measured impedance value per frequency as an input variable to discriminate the biological tissue, and re-teaching the biological tissue discriminated from each single classifier in a meta classifier to finally discriminate the biological tissue.

A computer implemented method for estimating tissue parameters, includes collecting data, from a surgical system including an instrument and an energy source, the data including at least one electrical parameter associated with delivering energy from the instrument to tissue, communicating the data to at least one machine learning algorithm, determining, using the at least one machine learning algorithm, a tissue parameter based upon the data, communicating the determined tissue parameter to a computing device associated with the energy source for use in formulating an energy-delivery algorithm for delivering energy from the instrument to tissue, and delivering energy from the instrument of the surgical system to tissue in accordance with the energy-delivery algorithm.

An apparatus for prediction of repeat ablation efficacy, the apparatus including an electrocardiogram device, wherein the electrocardiogram device is configured to detect post-ablation arrhythmic electrocardiogram (ECG) data representative of a post-ablation arrythmia of a patient who has previously undergone an ablation procedure and a processor configured to receive, from the electrocardiogram device, the post-ablation arrhythmic ECG data predict, using a repeat-ablation efficacy machine-learning model, a determination of a pulmonary vein reconnection by identifying features within the post-ablation arrhythmic ECG data that are historically representative of pulmonary vein reconnection determinations and transmit, for display, the determination.