Exemplified method and system facilitates monitoring and/or evaluation of disease or physiological state using mathematical analysis and machine learning analysis of a biopotential signal collected from a single electrode. The exemplified method and system creates, from data of a singularly measured biopotential signal, via a mathematical operation (i.e., via numeric fractional derivative calculation of the signal in the frequency domain), one or more mathematically-derived biopotential signals (e.g., virtual biopotential signals) that is used in combination with the measured biopotential signals to generate a multi-dimensional phase-space representation of the body (e.g., the heart). By mathematically modulating (e.g., by expanding or contracting) portions of a given biopotential signal, in the frequency domain, the numeric-based operation gives emphasis or de-emphasis to certain measured frequencies of the biopotential signals, which, when coupled with machine learning, facilitates improved diagnostics of certain pathologies.

Techniques are disclosed for monitoring a patient for the occurrence of cardiac arrhythmias. A computing system obtains a cardiac electrogram (EGM) strip for a current patient. Additionally, the computing system may apply a first cardiac rhythm classifier (CRC) with a segment of the cardiac EGM strip as input. The first CRC is trained on training cardiac EGM strips from a first population. The first CRC generates first data regarding an aspect of a cardiac rhythm of the current patient. The computing system may also apply a second CRC with the segment of the cardiac EGM strip as input. The second CRC is trained on training cardiac EGM strips from a smaller, second population. The second CRC generates second data regarding the aspect of the cardiac rhythm of the current patient. The computing system may generate output data based on the first and/or second data.

Techniques are disclosed for monitoring a patient for the occurrence of cardiac arrhythmias. A computing system obtains a cardiac electrogram (EGM) strip for a current patient. Additionally, the computing system may apply a first cardiac rhythm classifier (CRC) with a segment of the cardiac EGM strip as input. The first CRC is trained on training cardiac EGM strips from a first population. The first CRC generates first data regarding an aspect of a cardiac rhythm of the current patient. The computing system may also apply a second CRC with the segment of the cardiac EGM strip as input. The second CRC is trained on training cardiac EGM strips from a smaller, second population. The second CRC generates second data regarding the aspect of the cardiac rhythm of the current patient. The computing system may generate output data based on the first and/or second data.

Techniques are disclosed for automatically calibrating a reference orientation of an implantable medical device (IMD) within a patient. In one example, sensors of an IMD sense a plurality of orientation vectors of the IMD with respect to a gravitational field. Processing circuitry of the IMD processes the plurality of orientation vectors to identify an upright vector that corresponds to an upright posture of the patient. The processing circuitry classifies the plurality of orientation vectors with respect to the upright vector to define a sagittal plane of the patient and a transverse plane of the patient. The processing circuitry determines, based on the upright vector, the sagittal plane, and the transverse plane, a reference orientation of the IMD within the patient. As the orientation of the IMD within the patient changes over time, the processing circuitry may recalibrate its reference orientation and accurately detect a posture of the patient.

Techniques are disclosed for automatically calibrating a reference orientation of an implantable medical device (IMD) within a patient. In one example, sensors of an IMD sense a plurality of orientation vectors of the IMD with respect to a gravitational field. Processing circuitry of the IMD processes the plurality of orientation vectors to identify an upright vector that corresponds to an upright posture of the patient. The processing circuitry classifies the plurality of orientation vectors with respect to the upright vector to define a sagittal plane of the patient and a transverse plane of the patient. The processing circuitry determines, based on the upright vector, the sagittal plane, and the transverse plane, a reference orientation of the IMD within the patient. As the orientation of the IMD within the patient changes over time, the processing circuitry may recalibrate its reference orientation and accurately detect a posture of the patient.

Systems and methods are disclosed for linking a point-list to a three-dimensional anatomy of the heart. Techniques disclosed include recording a point-list, where each entry in the point-list comprises data elements, and is associated with a location in the heart and a measurement. The associated measurement is acquired by an electrode of a catheter that is placed at the associated location in the heart. Techniques disclosed further include selecting one or more anchor points associated with a region of interest in the heart, then, for each entry in the recorded point-list, computing a data element of distance between the entry's associated location in the heart and the selected one or more anchor points, and manipulating entries in the point-list based on their respective data elements.

Systems and methods are disclosed for linking a point-list to a three-dimensional anatomy of the heart. Techniques disclosed include recording a point-list, where each entry in the point-list comprises data elements, and is associated with a location in the heart and a measurement. The associated measurement is acquired by an electrode of a catheter that is placed at the associated location in the heart. Techniques disclosed further include selecting one or more anchor points associated with a region of interest in the heart, then, for each entry in the recorded point-list, computing a data element of distance between the entry's associated location in the heart and the selected one or more anchor points, and manipulating entries in the point-list based on their respective data elements.

A leadless cardiac pacemaker device is configured to provide HIS bundle pacing and contains a housing having a tip, a first electrode arranged on the housing in the vicinity of the tip, the first electrode being configured to engage with intra-cardiac tissue, and a second electrode arranged on the housing at a distance from the tip of the housing. A processor is enclosed in the housing and operatively connected to the first electrode and the second electrode. The processor is configured to process a reception signal received by at least one of the first electrode and the second electrode and to generate a pacing signal to be emitted using at least one of the first electrode and the second electrode.

A medical device, such as an extra-cardiovascular implantable cardioverter defibrillator (ICD), senses R-waves from a first cardiac electrical signal by a first sensing channel and stores a time segment of a second cardiac electrical signal acquired by a second sensing channel in response to each sensed R-wave. The ICD determines morphology match scores from the stored time segments of the second cardiac electrical signal and, based on the morphology match scores, withholds detection of a tachyarrhythmia episode. In some examples, the ICD detects T-wave oversensing based on the morphology match scores and withholds detection of a tachyarrhythmia episode in response to detecting the T-wave oversensing.

A medical device, such as an extra-cardiovascular implantable cardioverter defibrillator (ICD), senses R-waves from a first cardiac electrical signal by a first sensing channel and stores a time segment of a second cardiac electrical signal acquired by a second sensing channel in response to each sensed R-wave. The ICD determines morphology match scores from the stored time segments of the second cardiac electrical signal and, based on the morphology match scores, withholds detection of a tachyarrhythmia episode. In some examples, the ICD detects T-wave oversensing based on the morphology match scores and withholds detection of a tachyarrhythmia episode in response to detecting the T-wave oversensing.