Described herein are methods, devices and system for determining whether an R-wave detection should be classified as a false R-wave detection due to T-wave oversensing (TWO) or P-wave oversensing (PWO). One such method includes comparing a specific morphological characteristic (e.g., peak amplitude) associated with the R-wave detection to the specific morphological characteristic associated with each R-wave detection in a first set of earlier detected R-wave detections to thereby determine whether first TWO or PWO morphological criteria are met, and in a second set of earlier detected R-wave detections to thereby determine whether second TWO or PWO morphological criteria are met, wherein the second set differs from the first set but may have some overlap with the first set. The method also includes determining whether to classify the R-wave detection as a false R-wave detection, based on whether one of the first or second TWO or PWO morphological criteria are met.

Described herein are methods, devices and system for determining whether an R-wave detection should be classified as a false R-wave detection due to T-wave oversensing (TWO) or P-wave oversensing (PWO). One such method includes comparing a specific morphological characteristic (e.g., peak amplitude) associated with the R-wave detection to the specific morphological characteristic associated with each R-wave detection in a first set of earlier detected R-wave detections to thereby determine whether first TWO or PWO morphological criteria are met, and in a second set of earlier detected R-wave detections to thereby determine whether second TWO or PWO morphological criteria are met, wherein the second set differs from the first set but may have some overlap with the first set. The method also includes determining whether to classify the R-wave detection as a false R-wave detection, based on whether one of the first or second TWO or PWO morphological criteria are met.

A medical having a motion sensor is configured to set an atrial event sensing parameter used for sensing atrial event signals from a motion signal produced by the motion sensor. The medical device sets an atrial event sensing parameter by applying a sensing window during each one of multiple ventricular cycles, determining a feature of the motion signal during the sensing window for at least a portion of the ventricular cycles, and setting the atrial event sensing parameter based on the determined features. The medical device may sense the atrial event from the motion signal according to the atrial event sensing parameter.

This disclosure is directed to systems and techniques for determining an evidence level of an atrial tachycardia (AT) episode based on heart beat intervals in the cardiac activity data over a pre-determined time period. Based on a determination that the evidence level indicates relatively regular heart beat intervals, the example techniques apply a first set of AT detection criteria and indicate a detection of an AT episode based on satisfaction of at least one of the first set of AT detection criteria. Based on a determination that the evidence level indicates relatively irregular heart beat intervals, the example techniques apply a second set of AT detection criteria and indicate a detection of an AT episode based on based on satisfaction of at least one of the second set of AT detection criteria.

This disclosure is directed to systems and techniques for determining an evidence level of an atrial tachycardia (AT) episode based on heart beat intervals in the cardiac activity data over a pre-determined time period. Based on a determination that the evidence level indicates relatively regular heart beat intervals, the example techniques apply a first set of AT detection criteria and indicate a detection of an AT episode based on satisfaction of at least one of the first set of AT detection criteria. Based on a determination that the evidence level indicates relatively irregular heart beat intervals, the example techniques apply a second set of AT detection criteria and indicate a detection of an AT episode based on based on satisfaction of at least one of the second set of AT detection criteria.

A computer implemented system and method include one or more processors configured to receive a plurality of electrocardiogram (ECG) signals from one or more subcutaneous implantable medical devices (IMDs) and combine at least two of the plurality of ECG signals to form a first composite ECG signal.

A computer implemented system and method include one or more processors configured to receive a plurality of electrocardiogram (ECG) signals from one or more subcutaneous implantable medical devices (IMDs) and combine at least two of the plurality of ECG signals to form a first composite ECG signal.

An improved wearable device for detecting progression of Cardiac Amyloidosis based on changes in relative values of characteristics of P-wave and R-wave is disclosed. In an embodiment of the invention, two electrodes the device are connected to user's skin surface to obtain traces of ECG signals. Thereafter, correction factors are determined for the obtained traces of ECG signals. A microprocessor included in the device applies correction factors on the traces of ECG signals to obtain characteristics of P-wave and R-wave. Finally, the microprocessor determines the ratio of the characteristics (such as amplitude) of the P-wave to the characteristics (such as amplitude) of the R-wave and records said ratio. Still further, the microprocessor compares all such recorded ratios or features, to determine and display if there is disease progression.

An improved wearable device for detecting progression of Cardiac Amyloidosis based on changes in relative values of characteristics of P-wave and R-wave is disclosed. In an embodiment of the invention, two electrodes the device are connected to user's skin surface to obtain traces of ECG signals. Thereafter, correction factors are determined for the obtained traces of ECG signals. A microprocessor included in the device applies correction factors on the traces of ECG signals to obtain characteristics of P-wave and R-wave. Finally, the microprocessor determines the ratio of the characteristics (such as amplitude) of the P-wave to the characteristics (such as amplitude) of the R-wave and records said ratio. Still further, the microprocessor compares all such recorded ratios or features, to determine and display if there is disease progression.

An electrocardiogram information dynamic monitoring method and dynamic monitoring system. The method includes a dynamic monitoring device receiving monitoring reference data input by a user or issued by a server; the data collection on a tested object so as to obtain electrocardiogram data of the tested object; the characteristic identification on the electrocardiogram data so as to obtain characteristic signals of the electrocardiogram data, implementing cardiac activity classification on the electrocardiogram data according to the characteristic signals, obtaining cardiac activity classification information according to electrocardiogram basic rule reference data, and generating electrocardiogram event data, wherein the electrocardiogram event data comprises device ID information of the dynamic monitoring device; the dynamic monitoring device determining corresponding electrocardiogram event information according to the electrocardiogram event data, and determining whether the electrocardiogram event information is electrocardiogram abnormality event information; and outputting alarm information when the electrocardiogram event information is electrocardiogram abnormality event information.