A method for preparing an audiogram of a test subject by use of a hearing instrument. A sound signal is recorded in an auditory canal of the test subject at least partially closed by the hearing instrument by a first electroacoustic input transducer of the hearing instrument. A first input signal is generated therefrom. A test sound is generated by an electroacoustic output transducer of the hearing instrument and output into the auditory canal of the test subject at least partially closed by the hearing instrument. A hearing threshold of the test subject at at least one test frequency is ascertained on a basis of a reaction of the test subject to the test sound and by use of the first input signal.

A method for machine learning based artifact rejection is provided. The method may include applying a machine learning model to identify artefactual independent components in transcranial magnetic stimulation electroencephalogram data collected during a transcranial magnetic stimulation procedure. Clean transcranial magnetic stimulation electroencephalogram data is generated by removing, from the transcranial magnetic stimulation electroencephalogram data, the artefactual independent components. Real-time adjustments to parameters of the transcranial magnetic stimulation procedure may be performed based on the clean transcranial magnetic stimulation electroencephalogram data. Related systems and articles of manufacture, including computer program products, are also provided.

Systems, devices, and techniques are configured for analyzing evoked compound action potentials (ECAP) signals to assess the effect of a delivered electrical stimulation signal. In one example, a system includes processing circuitry configured to receive ECAP information representative of an ECAP signal sensed by sensing circuitry, and determine, based on the ECAP information, that the ECAP signal includes at least one of an N2 peak, P3 peak, or N3 peak. The processing circuitry may then control delivery of electrical stimulation based on at least one of the N2 peak, P3 peak, or N3 peak.

The embodiments described herein relate to a self-positioning, quick-deployment low profile transvenous electrode system for sequentially pacing both the atrium and ventricle of the heart in the “dual chamber” mode, and methods for deploying the same.

Computer implemented methods and systems for detecting noise in cardiac activity are provided. The method and system obtain a far field cardiac activity (CA) data set that includes far field CA signals for a series of beats, overlay a segment of the CA signals with a noise search window, and identify turns in the segment of the CA signals. The method and system determine whether the turns exhibit a turn characteristic that exceed a turn characteristic threshold, declare the segment of the CA signals as a noise segment based on the determining operation, shift the noise search window to a next segment of the CA signal and repeat the identifying, determining and declaring operations; and modify the CA signals based on the declaring the noise segments.

A system and associated methods for round-the-clock monitoring of an animal's health status includes an animal harness that is worn by the animal, and one or both of a mobile device and a remote server. The animal harness includes a plurality of sensors for collecting health measurements of the animal. The animal harness also includes a transceiver that communicates the heath measurements to one or both of the mobile device and the remote server, where a user may view the health measurements. Firmware in the animal harness, an application running in the mobile device, and software in the remote server processes and corrects the health measurements to generate a health status of the animal and notifications are generated when the animal's health is not within a safe range defined by the user.

Described herein are methods, devices, and systems that monitor heart rate and/or for arrhythmic episodes based on sensed intervals that can include true R-R intervals as well as over-sensed R-R intervals. True R-R intervals are initially identified from an ordered list of the sensed intervals by comparing individual sensed intervals to a sum of an immediately preceding two intervals, and/or an immediately following two intervals. True R-R intervals are also identified by comparing sensed intervals to a mean or median of durations of sensed intervals already identified as true R-R intervals. Individual intervals in a remaining ordered list of sensed intervals (from which true R-R intervals have been removed) are classified as either a short interval or a long interval, and over-sensed R-R intervals are identified based on the results thereof. Such embodiments can be used, e.g., to reduce the reporting of and/or inappropriate responses to false positive tachycardia detections.

Medical device systems include processing circuitry configured to acquire sensed cardiac signals associated with cardiac activity of a heart of a patient, and to analyze the sensed cardiac signals to determine if a noise signal is present within the cardiac signals.

A blood pressure monitor configured to removably mount to a cuff in a substantially symmetrical position with respect to a width of the cuff can include a housing defining an interior, a first port, and a second port. The first port can: secure to a first prong of the cuff when the cuff is mounted in a first orientation; receive and secure to a second prong of the cuff when the cuff is mounted in a second orientation; and enable fluid communication between the interior and at least one of a first fluid passage within the first prong and a second fluid passage within the second prong. The second port can: secure to the second prong of the cuff when the cuff is mounted in the first orientation; and receive and secure to the first prong of the cuff when the cuff is mounted in the second orientation.

Sense amplifier circuits particularly useful in sensing neural responses in an Implantable Pulse Generator (IPG) are disclosed. The IPG includes a plurality of electrodes, with one selected as a sensing electrode and another selected as a reference to differentially sense the neural response in a manner that subtracts a common mode voltage (e.g., stimulation artifact) from the measurement. The circuits include a differential amplifier which receives the selected electrodes at its inputs, and comparator circuitries to assess each differential amplifier input to determine whether it is of a magnitude that is consistent with the differential amplifier's input requirements. Based on these determinations, an enable signal is generated which informs whether the output of the differential amplifier validly provides the neural response at any point in time. Further, clamping circuits are connected to the differential amplifier inputs to clamp these inputs in magnitude to prevent the differential amplifier from damage.