Techniques are disclosed for using both feature delineation and machine learning to detect cardiac arrhythmia. A computing device receives cardiac electrogram data of a patient sensed by a medical device. The computing device obtains, via feature-based delineation of the cardiac electrogram data, a first classification of arrhythmia in the patient. The computing device applies a machine learning model to the received cardiac electrogram data to obtain a second classification of arrhythmia in the patient. As one example, the computing device uses the first and second classifications to determine whether an episode of arrhythmia has occurred in the patient. As another example, the computing device uses the second classification to verify the first classification of arrhythmia in the patient. The computing device outputs a report indicating that the episode of arrhythmia has occurred and one or more cardiac features that coincide with the episode of arrhythmia.

The invention relates to a control method for a neuroprosthetic device, allowing to monitor and reduce pathological tremors in users via the stimulation of the peripheral muscles and modulation of the afferent pathways.

A system and method for non-invasively monitoring the hemodynamic state of a patient by determining on a beat-by-beat basis the ratio of lusitropic function to inotropic function as an index of myocardial well-being or pathology for use by clinicians in the hospital or by the patient at home. In one embodiment of the system a smartphone running an application program that is connected through the internet to the cloud processes electronic signals, first, from an electrocardiogram device monitoring electrical cardiac activity, and second, from a seismocardiogram device monitoring mechanical cardiac activity in order to determine such ratio as an instantaneous measurement of the hemodynamic state of the patient, including such states as sepsis, myocardial ischemia, and heart failure.

A system and method for non-invasively monitoring the hemodynamic state of a patient by determining on a beat-by-beat basis the ratio of lusitropic function to inotropic function as an index of myocardial well-being or pathology for use by clinicians in the hospital or by the patient at home. In one embodiment of the system a smartphone running an application program that is connected through the internet to the cloud processes electronic signals, first, from an electrocardiogram device monitoring electrical cardiac activity, and second, from a seismocardiogram device monitoring mechanical cardiac activity in order to determine such ratio as an instantaneous measurement of the hemodynamic state of the patient, including such states as sepsis, myocardial ischemia, and heart failure.

A physiological sensor device and system, and a correction method are provided. The physiological sensor device includes a physiological signal sensor, a first compensation sensor, and a signal processing device. The physiological signal sensor is attached to an object to be detected to sense a physiological signal value. The first compensation sensor is disposed on the physiological signal sensor. The signal processing device is coupled to the physiological signal sensor and the first compensation sensor. The signal processing device obtains through the first compensation sensor a failure region of the physiological signal sensor partially detached from the object to be detected and obtains a first failure compensation value according to the failure region, so as to compensate the physiological signal value sensed by the physiological signal sensor.

A physiological sensor device and system, and a correction method are provided. The physiological sensor device includes a physiological signal sensor, a first compensation sensor, and a signal processing device. The physiological signal sensor is attached to an object to be detected to sense a physiological signal value. The first compensation sensor is disposed on the physiological signal sensor. The signal processing device is coupled to the physiological signal sensor and the first compensation sensor. The signal processing device obtains through the first compensation sensor a failure region of the physiological signal sensor partially detached from the object to be detected and obtains a first failure compensation value according to the failure region, so as to compensate the physiological signal value sensed by the physiological signal sensor.

A computer implemented method and system for detecting arrhythmias in cardiac activity are provided. The method is under control of one or more processors configured with specific executable instructions. The method obtains far field cardiac activity (CA) signals and applies a direction related responsiveness (DRR) filter to the CA signals to produce DRR filtered signals. The method compares a current sample from the CA signals to a prior sample from the DRR filtered signals to identify a direction characteristic of the CA signals and defines the DRR filter based on a timing constant that is set based on the direction characteristic identified. The method analyzes the CA signals in connection with the DRR filtered signals to identify a peak characteristic of the CA signals and determines peak to peak intervals between successive peak characteristic. The method detects at least one of noise or an arrhythmia based on the peak to peak intervals and records results of the detecting.

A computer implemented method and system for detecting arrhythmias in cardiac activity are provided. The method is under control of one or more processors configured with specific executable instructions. The method obtains far field cardiac activity (CA) signals and applies a direction related responsiveness (DRR) filter to the CA signals to produce DRR filtered signals. The method compares a current sample from the CA signals to a prior sample from the DRR filtered signals to identify a direction characteristic of the CA signals and defines the DRR filter based on a timing constant that is set based on the direction characteristic identified. The method analyzes the CA signals in connection with the DRR filtered signals to identify a peak characteristic of the CA signals and determines peak to peak intervals between successive peak characteristic. The method detects at least one of noise or an arrhythmia based on the peak to peak intervals and records results of the detecting.

Systems, methods, and devices are described herein for evaluation, adjustment, and delivery of adaptive cardiac therapy. The systems, methods, and devices may utilize electrical heterogeneity information to determine and/or select one or more pacing settings and pacing type or configurations for a plurality of different heart rates. The adaptive cardiac therapy may deliver cardiac therapy at selected pacing settings such as, for example, A-V and/or V-V intervals, according to a presently measured heart rate and switch between left ventricular-only or biventricular cardiac pacing therapy also according to the presently measured heart rate.

A system and method for rehabilitation and recovery using adaptive surface electromyographic sensing and visualization is disclosed. The system uses surface electromyography (sEMG) sensors to identify signals of intent from patients with physical disabilities and then uses these signals to interact with a computer system designed to create repetitive practice in a manner that promotes neurological recovery. A machine teaming module analyses body signals picked up during patient movement attempts and converts these body signals to a visual representation of the intended movement by way of a virtual body or virtual body part displayed on a computer display, display glasses, or the like. The system thus allows for very early patient therapy, providing early benefits to rehabilitation therapy not heretofore possible. The virtual reality or augmented reality environment provides a patient with very early visual reinforcement of beneficial muscle activation patterns.