Methods and devices are provided that collect intra-cardiac electrogram (EGM) signals over first and second sensing channels (channel-1 and channel-2 EGM signals, respectively) associated with an event of interest that includes a right ventricle (RV) and a left ventricle (LV), determine first, second and third global characteristics (GC) from the channel-1 and channel-2 EGM signals, and define a QRS start time within at least one of the EGM signals; and determine a threshold crossing. The methods and systems compare at least one of the first, second and third GC to the threshold crossing, select one of the first, second and third GC based on the comparing; defining a QRS end time, within at least one of the channel-1 and channel-2 EGM signals based on the one of the first, second and third GC selected, and calculate a QRS duration based on the QRS start time and QRS end time.

Methods and systems are described for detecting the likelihood of patent ductus arteriosus (PDA) in an infant using electrocardiogram and photoplethysmographic pulse signals obtained from the upper body and foot of the infant.

Apparatuses and methods for extracting, de-noising, and analyzing electrocardiogram signals. Any of the apparatuses described herein may be implemented as a (or as part of a) computerized system. For example, described herein are apparatuses and methods of using them or performing the methods, for extracting and/or de-noising ECG signals from a starting signal. Also described herein are apparatuses and methods for analyzing an ECG signal, for example, to generate one or more indicators or markers of cardiac fitness, including in particular indicators of atrial fibrillation. Described herein are apparatuses and method for determining if a patient is experiencing a cardiac event, such as an arrhythmia.

An apparatus for monitoring a biosignal of a cyclist includes an electrocardiogram measuring sensor measuring an electrocardiogram signal of a cyclist, an electromyogram measuring sensor measuring an electromyogram signal of the cyclist, and a determination module determining muscle fatigue of the cyclist, based on at least one of a change in a heart rate of the measured electrocardiogram signal and a change in a frequency component of the measured electromyogram signal.

Systems, devices, and methods for monitoring and assessing blood glucose level in a patient are discussed. An exemplary system receives physiologic information from a patient using an ambulatory medical device. The physiologic information is correlated to, and different from, a direct glucose level measurement. The system determines a glucose index indicative of an abnormal blood glucose level using the received physiologic information by the two or more physiologic sensors. The system may use the glucose index to initiate or adjust a therapy, or to trigger a glucose sensor, separate from the two or more physiologic sensors, to directly measure blood glucose concentration.

An artificial intelligence-based interference recognition method for an electrocardiogram, comprising: cutting and sampling heart beat data of a first data amount, and inputting the heart beat data to be recognized that is obtained by cutting and sampling into an interference recognition binary classification model for interference recognition; in a sequence of the heart beat data, performing signal anomaly determination on a heart beat data segment where an inter-beat interval is greater than or equal to a preset interval determination threshold value, so as to determine whether the heart beat data segment is an abnormal signal; if the heart beat data segment is not an abnormal signal, determining a starting data point and an ending data point of sliding sampling in the heart beat data segment according to a set time with a preset time width, and performing sliding sampling on the data segment from the starting data point until the ending data point so as to obtain multiple sampling data segments; and using each sampling data segment as heart beat data to be recognized for interference recognition.

Disclosed are a system and a method for determining a probability for a person to have arrhythmia. The system (100) comprises means for measuring heartbeat interval of the person (102) for a period of time; an accelerometer; means for measuring an electrocardiogram (116) of the person (102); a user interface (114) for providing information and alerts, and a processor (112). The processor (112) is configured to detect a period of rest of the person (102), based on measurement data from the accelerometer; analyse the measured heartbeat interval to determine a probability of having arrhythmia for the person; generate an alert to the user interface (114), if the probability exceeds a predetermined threshold value, to alert the person to measure the electrocardiogram with the means for measuring an electrocardiogram (116); analyse the measured electrocardiogram to determine if the probable arrhythmia is confirmed; and indicate the confirmed arrhythmia to the person (102) via the user interface (114).

A method and system for measuring respiratory rate are disclosed. In a first aspect, the method comprises measuring at least one respiration signal and filtering the respiration signal using a lowpass filter. The method includes peak-picking the respiration signal to determine the respiratory rate and determining a quality metric of the respiratory rate. In a second aspect, the system comprises a wireless sensor device coupled to a user via at least one electrode, wherein the wireless sensor device includes a processor and a memory device coupled to the processor, wherein the memory device stores an application which, when executed by the processor, causes the processor to carry out the steps of the method.

The exemplary systems and methods may be configured to monitor electrical activity from a patient using a plurality of external electrodes. The exemplary systems and methods may be further configured to provide a model heart representative of the patient's heart based on at least one of a plurality of patient characteristics. The model heart can include a plurality of segments. The exemplary systems and methods may be further configured to determine a value of electrical activity for each of a plurality of anatomic regions of the model heart based on the mapped electrical activity. Each of the plurality of anatomic regions can include a subset of the plurality of segments.

A method for assessing electrocardiogram signal quality, the method comprising: receiving heartbeat analysis data by processing electrocardiogram data acquired from an electrocardiogram monitoring device; extracting position information and width information of a QRS complex in the heartbeat analysis data; extracting an RR interval signal between two adjacent QRS complex signals; performing QRS complex signal cancellation processing on the RR interval signal to obtain an RR interval signal for which the QRS complex signal is removed; filtering the processed RR interval signal without the QRS complex signal, and performing envelope calculation on the filtered signal to obtain the average power of a noise signal of the RR interval signal for which the QRS complex signal is removed; obtaining a signal quality evaluation index according to the average power of the noise signal and the power of the QRS complex signal.