A wearable device includes a wearable device main body, a sensor part configured to contact a skin surface of a user of the wearable device, and measure a bio-signal of the user, and a shock absorber that is interposed between the wearable device main body and the sensor part to mechanically connect the wearable device main body and the sensor part, and that is configured to reduce motion transmission between the wearable device main body and the sensor part to permit the wearable device main body to move independently from the sensor part.

An apparatus includes data acquisition circuitry and a processor. The data acquisition circuitry is configured to acquire multiple signals using multiple respective electrodes of an array of electrodes coupled to one of an organ of a patient and tissue or a cell culture. The processor is configured to hold a definition of a mixed-norm that is defined as a function of relative positions of the electrodes in the array, and jointly compress the multiple signals in a compressed-sensing (CS) process that minimizes the mixed-norm.

An apparatus includes data acquisition circuitry and a processor. The data acquisition circuitry is configured to acquire multiple signals using multiple respective electrodes of an array of electrodes coupled to one of an organ of a patient and tissue or a cell culture. The processor is configured to hold a definition of a mixed-norm that is defined as a function of relative positions of the electrodes in the array, and jointly compress the multiple signals in a compressed-sensing (CS) process that minimizes the mixed-norm.

An active implantable stimulating device (10) includes: (a) a tissue coupling unit (40) for being implanted directly onto a vagus nerve (Vn) of a patient, (b) an EEG-unit (70) for measuring an electroencephalogram of the patient, (c) an encapsulation unit (50) configured for being subcutaneously implanted, (d) an energy transfer lead (30) for transferring pulses of electrical and/or optical energy, (e) a signal transfer lead (60) for transferring signals between the EEG unit and the encapsulation unit. EEG electrodes (70a-70d) monitor the electric activity of the brain of a patient. The EEG signal is conveyed to the electronic circuit (53) in the form of EEG conditioned data. The electronic circuit analyses the EEG conditioned data to yield analysis results. The electronic circuit takes a decision to trigger energy pulses to stimulate the vagus nerve (VN).

An active implantable stimulating device (10) includes: (a) a tissue coupling unit (40) for being implanted directly onto a vagus nerve (Vn) of a patient, (b) an EEG-unit (70) for measuring an electroencephalogram of the patient, (c) an encapsulation unit (50) configured for being subcutaneously implanted, (d) an energy transfer lead (30) for transferring pulses of electrical and/or optical energy, (e) a signal transfer lead (60) for transferring signals between the EEG unit and the encapsulation unit. EEG electrodes (70a-70d) monitor the electric activity of the brain of a patient. The EEG signal is conveyed to the electronic circuit (53) in the form of EEG conditioned data. The electronic circuit analyses the EEG conditioned data to yield analysis results. The electronic circuit takes a decision to trigger energy pulses to stimulate the vagus nerve (VN).

A data processing method for detecting and classifying a segment of a signal that is obtained from a single-channel EEG-recording as a target signal segment or as a non-target signal segment. The method includes a voting process to determine whether classification of a first detected segment of the signal as a target signal segment or classification of a second detected segment of the signal as a non-target signal segment is correct. A device and a system that are configured and arranged to perform the data processing method.

A data processing method for detecting and classifying a segment of a signal that is obtained from a single-channel EEG-recording as a target signal segment or as a non-target signal segment. The method includes a voting process to determine whether classification of a first detected segment of the signal as a target signal segment or classification of a second detected segment of the signal as a non-target signal segment is correct. A device and a system that are configured and arranged to perform the data processing method.

The invention relates to methods and systems for determining the intention of a subject to perform a voluntary action based on the analysis of the subject's respiratory phases and neuroelectrical signals.

The invention relates to methods and systems for determining the intention of a subject to perform a voluntary action based on the analysis of the subject's respiratory phases and neuroelectrical signals.

The present disclosure provides systems and methods that include or otherwise leverage a machine-learned brain injury location model to predict locations of brain injury in a patient based on test data associated with the patient, such as, for example, behavioral test data. For example, the machine-learned brain injury location model can be trained on training data associated with a corpus of patients, where the training data includes sets of example test data (e.g., behavioral test data) respectively labeled with ground truth brain injury locations.