A method for accurately extracting an abnormal potential within a QRS, comprising: in an ideal electrocardiographic signal pre-estimation stage, pre-estimating an ideal electrocardiographic signal using a non-linear transformation technology;

according to the pre-estimated ideal electrocardiographic signal, further estimating the ideal electrocardiographic signal by using a spline method, so as to accurately estimate the ideal electrocardiographic signal; and according to the accurately estimated ideal electrocardiographic signal, accurately extracting an abnormal potential within the QRS by means of a mobile standard deviation analysis technology. The method can be used not only on an average electrocardiographic signal after multiple superimposition, but also on a single beat electrocardiographic signal.

The present disclosure relates to a wearable monitor device and methods and systems for using such a device. In certain embodiments, the wearable monitor records cardiac data from a mammal and extracts particular features of interest. These features are then transmitted and used to provide health-related information about the mammal.

This document discusses, among other things, systems and methods to receive cardiac electrical information and premature ventricular contraction (PVC) information of a subject, detect atrial fibrillation (AF) of the subject using the received cardiac electrical information, and adjust AF detection using the received PVC information.

This document discusses, among other things, systems and methods to detect an initial arrhythmia event indication and, after a threshold amount of detection window intervals detecting the initial arrhythmia event indication, adjust a set of arrhythmia parameters or at least one of a respective set of parameter thresholds to increase sensitivity of an extended arrhythmia event indication detection.

A system and method for accelerating confirmation of cardiac arrhythmias is provided. The system includes memory to store specific executable instructions. One or more processors are configured to execute the specific executable instructions for obtaining a far field cardiac activity (CA) data set that includes far field CA signals for a series of beats. The CA data set includes a first CA data subset and a remainder CA data subset. The system analyzes the CA data set for an arrhythmia of interest utilizing a primary detection process having primary criteria. During a first phase of the primary detection process the system analyzes the first CA data subset to determine whether the first CA data subset satisfies at least a portion of the primary criteria. When the first CA data subset satisfies at least the portion of the primary criteria, the system initiates a secondary confirmation process. Parallel and contemporaneous in time the system i) analyzes the first CA data subset utilizing secondary criteria associated with the secondary confirmation process and ii) analyzes the remainder CA data subset utilizing the primary criteria. The system declares the CA data set to exhibit an arrhythmia episode when the first CA data subset satisfies the secondary criteria and the remainder CA data subset satisfies a remainder of the primary criteria.

A computation apparatus, a cardiac arrhythmia assessment method thereof and a non-transitory computer-readable recording medium are provided. In the method, electrocardiography (ECG) signal is obtained. Whether the ECG signal is conformed to a first abnormal rhythm symptom is determined. Then, whether the ECG signal is conformed to a second abnormal rhythm symptom different from the first abnormal rhythm symptom is determined based on the determined result of the first abnormal rhythm symptom. Accordingly, multiple abnormal rhythm assessments are integrated, the subsequent assessment is speeded-up and optimized according to the determined result of a previous assessment, so as to enable to implement on a handheld apparatus.

A method for evaluating heart function, comprising receiving a digitized cardiac signal, computing a power spectral density (PSD) or multiscale entropy (MSE) of the digitized cardiac signal, and evaluating heart function based on the PSD or MSE is provided. Systems and computer program products for doing same are also provided.

The exemplary systems and methods may be configured for use in the determination of ectopic beat-compensated electrical heterogeneity information. Electrical activity can be monitored by a plurality of external electrodes. Ectopic beat information can be detected. Ectopic beat-compensated electrical heterogeneity information can be generated based on the monitored electrical activity and the detected ectopic beat information.

The present specification discloses systems and methods of patient monitoring in which multiple sensors are used to detect physiological parameters and the data from those sensors are correlated to determine if an alarm should, or should not, be issued, thereby resulting in more precise alarms and fewer false alarms. Electrocardiogram readings can be combined with invasive blood pressure, non-invasive blood pressure, and/or pulse oximetry measurements to provide a more accurate picture of pulse activity and patient respiration. In addition, the monitoring system can also use an accelerometer or heart valve auscultation to further improve accuracy.

This disclosure provides a monitoring apparatus and a statistical display method for physiological parameter(s) thereof. The method may include receiving statistical setting information including a time range, a time interval, a classification rule, and a target parameter, and the classification rule may define one or more types of the target parameter; obtaining N group of target parameter result corresponding to N time interval from a result of historical physiological parameter, where the N time interval is included in the time range, and the N group of target parameter result may be a physiological parameter result corresponding to the target parameter in the result of historical physiological parameter; counting the number of each type of the target parameter in the N group of target parameter result according to the classification rule, and obtaining N group of statistical result corresponding to the N time interval for each target parameter