A medical system including processing circuitry configured to receive a cardiac signal indicative of a cardiac characteristic of a patient from sensing circuitry and configured to receive a glucose signal indicative of a glucose level of the patient. The processing circuitry is configured to formulate a training data set including one or more training input vectors using the cardiac signal and one or more training output vectors using the glucose signal. The processing circuitry is configured to train a machine learning algorithm using the formulated training data set. The processing circuitry is configured to receive a current cardiac signal from the patient and determine a representative glucose level using the current cardiac signal and the trained machine learning algorithm.

Described herein are implantable medical devices and systems, and methods for use therewith, for reducing T-wave oversensing and arrythmia undersensing that occur due to inappropriate filtering of a signal indicative of cardiac electrical activity. A method includes obtaining a signal indicative of cardiac electrical activity, and using a first bandpass filter to produce a first filtered version thereof, using a second bandpass filter to produce a second filtered version thereof, wherein the first bandpass filter passes frequencies within a first frequency range, and the second bandpass filter passes frequencies within a second frequency range that is wider than the first frequency range. The method also includes selectively changing from using the first filtered version of the signal to monitor for a VS event, to using the second filtered version of the signal to monitor for a VS event, based on first criteria, and vice versa, based on second criteria.

Described herein are implantable medical devices and systems, and methods for use therewith, for reducing T-wave oversensing and arrythmia undersensing that occur due to inappropriate filtering of a signal indicative of cardiac electrical activity. A method includes obtaining a signal indicative of cardiac electrical activity, and using a first bandpass filter to produce a first filtered version thereof, using a second bandpass filter to produce a second filtered version thereof, wherein the first bandpass filter passes frequencies within a first frequency range, and the second bandpass filter passes frequencies within a second frequency range that is wider than the first frequency range. The method also includes selectively changing from using the first filtered version of the signal to monitor for a VS event, to using the second filtered version of the signal to monitor for a VS event, based on first criteria, and vice versa, based on second criteria.

A medical system including processing circuitry configured to assess a blood glucose level of a patient. The processing circuitry is configured to use a cardiac signal indicative of the electrical activity of the patient's heart and a temperature signal indicative of a body temperature of the patient. The cardiac signal may be, for example, an electrocardiogram (ECG), an electrogram (EGM), or another measure. The medical system is configured to determine a representative cardiac measure indicative of the cardiac signal and determine a representative temperature measure indicative of the temperature signal. The medical system is configured to assess when a blood glucose level of the patient may be outside a euglycemic range based on the representative cardiac measure and the representative temperature measure determined.

A medical system including processing circuitry configured to assess a blood glucose level of a patient. The processing circuitry is configured to use a cardiac signal indicative of the electrical activity of the patient's heart and a temperature signal indicative of a body temperature of the patient. The cardiac signal may be, for example, an electrocardiogram (ECG), an electrogram (EGM), or another measure. The medical system is configured to determine a representative cardiac measure indicative of the cardiac signal and determine a representative temperature measure indicative of the temperature signal. The medical system is configured to assess when a blood glucose level of the patient may be outside a euglycemic range based on the representative cardiac measure and the representative temperature measure determined.

A medical device is configured to deliver anti-tachycardia pacing (ATP) in the presence of T-wave alternans. The device is configured to detect a ventricular tachyarrhythmia from a cardiac electrical signal received by the medical device. In response to the detected ventricular tachyarrhythmia, the device delivers a plurality of ATP pulses at alternating time intervals. The alternating time intervals comprise at least a first ATP time interval separating a first pair of the ATP pulses and a second ATP time interval different than the first ATP time interval. The second ATP time interval consecutively follows the first ATP time interval and separates a second pair of the ATP pulses.

A filter method for reducing interferences of a measuring signal, caused by magnetic fields of a rotatable medical imaging system while measuring bioelectric signals in a differential voltage measuring system, the filter method including: capturing a frequency value of a rotation of a gantry of the rotatable medical imaging system; generating a virtual reference signal as a function of the frequency value captured; estimating, via an adaptive signal filter, an amplitude and a constant phase offset of an estimated interference signal, based upon the virtual reference signal generated and a measuring signal; and filtering the measuring signal with the adaptive signal filter by subtracting the estimated interference signal from the measuring signal. A filter apparatus is also described. Furthermore a voltage measuring system is described. Furthermore, a rotating medical imaging system is described.

Provided is a contactless electrocardiogram measurement device which may perform a high-quality sleep monitoring while improving a sleep quality of an object person. The contactless electrocardiogram measurement device includes a measurement unit disposed between a vibration medium and a support member to measure vibration generated from a body of an object person that is transmitted from the vibration medium, wherein the measurement unit includes a plate-shaped cover portion interposed between the vibration medium and the support member, and a vibration sensor for detecting the vibration generated in the cover portion.

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.

A medical system including processing circuitry configured to receive a cardiac signal indicative of a cardiac characteristic of a patient from sensing circuitry and configured to receive a glucose signal indicative of a glucose level of the patient. The processing circuitry is configured to formulate a training data set including one or more training input vectors using the cardiac signal and one or more training output vectors using the glucose signal. The processing circuitry is configured to train a machine learning algorithm using the formulated training data set. The processing circuitry is configured to receive a current cardiac signal from the patient and determine a representative glucose level using the current cardiac signal and the trained machine learning algorithm.