Patent classifications
A61N1/38
TREATMENT OF DEPRESSION USING MACHINE LEARNING
Provided herein are, inter alia, methods for identifying subjects suffering from depression that will respond to treatment with an antidepressant.
TREATMENT OF DEPRESSION USING MACHINE LEARNING
Provided herein are, inter alia, methods for identifying subjects suffering from depression that will respond to treatment with an antidepressant.
Treatment of depression using machine learning
Provided herein are, inter alia, methods for identifying subjects suffering from depression that will respond to treatment with an antidepressant.
Method and apparatus for discriminating tachycardia events in a medical device
A method and medical device for detecting a cardiac event that includes sensing cardiac signals from a plurality of electrodes, the plurality of electrodes forming a first sensing vector and a second sensing vector, identifying the cardiac event as one of a shockable event and a non-shockable event in response to first processing of a first interval sensed along the first sensing vector during a predetermined sensing window and a second interval simultaneously sensed along the second sensing vector, performing second processing of the first interval and the second interval, different from the first processing, in response to the cardiac event being identified as a shockable event, and determining whether to delay identifying the cardiac event being shockable in response to the second processing of the first interval and the second interval.
TREATMENT OF DEPRESSION USING MACHINE LEARNING
Provided herein are, inter alia, methods for identifying subjects suffering from depression that will respond to treatment with an antidepressant.
TREATMENT OF DEPRESSION USING MACHINE LEARNING
Provided herein are, inter alia, methods for identifying subjects suffering from depression that will respond to treatment with an antidepressant.
TREATMENT OF DEPRESSION USING MACHINE LEARNING
Provided herein are, inter alia, methods for identifying subjects suffering from depression that will respond to treatment with an antidepressant.
Treatment of depression using machine learning
Provided herein are, inter alia, methods for identifying subjects suffering from depression that will respond to treatment with an antidepressant.
SYSTEMS AND METHODS FOR ELECTROCONVULSIVE THERAPY
The present disclosure relates to methods of electroconvulsive therapy (ECT) for a patient's brain based on determining an optimal individualized current amplitude for a patient by (1) dividing an optimal E-field strength (optimal Ebrain) by a baseline E-field strength (baseline Ebrain) of the patient's brain, or (2) performing an initial treatment to determine a patient's amplitude titrated seizure threshold (STa), followed by use of a multiplier value multiplied by the STa for an individualized amplitude for subsequent treatments. Methods of ECT using optimal individualized current amplitude provide an antidepressant effect and reduce adverse cognitive effects on the patient's brain.
SYSTEMS AND METHODS FOR ELECTROCONVULSIVE THERAPY
The present disclosure relates to methods of electroconvulsive therapy (ECT) for a patient's brain based on determining an optimal individualized current amplitude for a patient by (1) dividing an optimal E-field strength (optimal Ebrain) by a baseline E-field strength (baseline Ebrain) of the patient's brain, or (2) performing an initial treatment to determine a patient's amplitude titrated seizure threshold (STa), followed by use of a multiplier value multiplied by the STa for an individualized amplitude for subsequent treatments. Methods of ECT using optimal individualized current amplitude provide an antidepressant effect and reduce adverse cognitive effects on the patient's brain.