An atrial fibrillation analysis device includes: a hardware processor that: acquires P-wave data from only one of either: a first electrocardiogram in a lead of one direction on a plane including a body-axis direction and a left-right direction with respect to a subject, or a second electrocardiogram in leads of two directions orthogonal to each other on the plane; extracts P-wave fragments from the acquired P-wave data; and analyzes a possibility of development of atrial fibrillation based on at least one of a number of the P-wave fragments and a duration of the P-wave fragments.

An atrial fibrillation analysis device includes: a hardware processor that: acquires P-wave data from only one of either: a first electrocardiogram in a lead of one direction on a plane including a body-axis direction and a left-right direction with respect to a subject, or a second electrocardiogram in leads of two directions orthogonal to each other on the plane; extracts P-wave fragments from the acquired P-wave data; and analyzes a possibility of development of atrial fibrillation based on at least one of a number of the P-wave fragments and a duration of the P-wave fragments.

A computer implemented method for detecting arrhythmias in cardiac activity including obtaining far field cardiac activity (CA) signals for a series of beats. For at least a portion of the beats, the one or more processors perform, on a beat by beat basis: a) identifying first and second feature of interests (FOI) from a segment of the CA signal that corresponds to a current beat; and b) classifying the current beat into one of first and second groups. The method also includes designating one of the first and second groups to be a primary group based on a relation between the first and second groups, and for the beats in the primary group, selecting one of the first and second FOIs as the R-wave FOI. The method also includes rejecting an arrhythmia detection based on the P-waves detected.

Systems and methods are disclosed to determine, in response to a detected atrial sense event in a first cardiac cycle, a ventricular blanking period for the first cardiac cycle and to detect a ventricular sense event in the first cardiac cycle using the received electrical information following the determined ventricular blanking period.

An artificial intelligence self-learning-based static electrocardiography analysis method and apparatus, the method comprising data preprocessing, heartbeat detection, heartbeat classification based on a depth learning method, heartbeat verification, heartbeat waveform feature detection, measurement and analysis of electrocardiography events, and finally automatic output of reporting data, realizing an automated static electrocardiograph analysis method having a complete and rapid flow. The static electrocardiography analysis method can also record modification information of an automatic analysis result, collect modified data, and feed same back to the depth learning model to continue training, thereby continuously making improvements and improving the accuracy of the automatic analysis method.

Systems, methods, devices, and techniques for analyzing and applying features of a T-wave derived from an electrocardiogram. A computing system can receive a set of data that characterizes an electrocardiogram of a patient. The system can analyze the set of data to identify a T-wave that occurs in the electrocardiogram. The system can determine values of one or more features of the T-wave and provide the information that identifies the values of the one or more features of the T-wave to a user.

Systems, methods, devices, and techniques for analyzing and applying features of a T-wave derived from an electrocardiogram. A computing system can receive a set of data that characterizes an electrocardiogram of a patient. The system can analyze the set of data to identify a T-wave that occurs in the electrocardiogram. The system can determine values of one or more features of the T-wave and provide the information that identifies the values of the one or more features of the T-wave to a user.

A method for automatically and independently associating a cardiovascular disease with an electrocardiogram, ECG, signal includes receiving the ECG signal; denoising the ECG signal using a wavelet transform; determining peaks of the denoised ECG signal by applying a combination of (1) two event-related moving averages and (2) fractional-Fourier-transform (FrFT) to the denoised ECG signal; and passing the peaks through a classifier for identifying the cardiovascular disease that corresponds to the ECG signal.

A method for automatically and independently associating a cardiovascular disease with an electrocardiogram, ECG, signal includes receiving the ECG signal; denoising the ECG signal using a wavelet transform; determining peaks of the denoised ECG signal by applying a combination of (1) two event-related moving averages and (2) fractional-Fourier-transform (FrFT) to the denoised ECG signal; and passing the peaks through a classifier for identifying the cardiovascular disease that corresponds to the ECG signal.

An apparatus for measuring the size of an electrocardiography signal using a Hilbert transform, according to one embodiment of the present invention, may comprise: a receiving unit for receiving a measured electrocardiography signal; a transform unit for Hilbert-transforming the received electrocardiography signal; and a measurement unit for obtaining the size of the electrocardiography signal on the basis of the Hilbert-transformed electrocardiography signal.