Provided is an exercise electrocardiogram data analysis method, which includes: acquiring and analyzing exercise electrocardiogram data to obtain a high-frequency QRS waveform curve; selecting a first reference point and a second reference point from the high-frequency QRS waveform curve; according to the first reference point, the second reference point and the high-frequency QRS waveform curve, determining the area of a waveform descent area; and according to the area of the waveform descent area, determining the attention level.

In an aspect, the present disclosure provides a method comprising: (a) identifying a first negative and positive electromagnetic dipoles in a first electromagnetic field map associated with a heart of the individual at a first time; (b) identifying a second negative and positive electromagnetic dipoles in a second electromagnetic field map associated with the heart of the individual at a second time; (c) determining a first angle based on the first negative and positive electromagnetic dipoles; (d) determining a second angle based on the second negative and positive electromagnetic dipoles; and (e) determining a presence, an absence, or a likelihood of coronary artery disease in the individual, based at least in part on (i) whether the first angle differs from the second angle by at least 100 degrees, or (ii) whether there is a presence of a third electromagnetic dipole in the first or the second electromagnetic field map.

In an aspect, the present disclosure provides a method comprising: (a) identifying a first negative and positive electromagnetic dipoles in a first electromagnetic field map associated with a heart of the individual at a first time; (b) identifying a second negative and positive electromagnetic dipoles in a second electromagnetic field map associated with the heart of the individual at a second time; (c) determining a first angle based on the first negative and positive electromagnetic dipoles; (d) determining a second angle based on the second negative and positive electromagnetic dipoles; and (e) determining a presence, an absence, or a likelihood of coronary artery disease in the individual, based at least in part on (i) whether the first angle differs from the second angle by at least 100 degrees, or (ii) whether there is a presence of a third electromagnetic dipole in the first or the second electromagnetic field map.

This application discloses a premature beat detection method, an electronic device. The premature beat detection method in this application includes: performing premature beat detection on a user by using a premature beat detection function; determining a premature beat type of the user by using a premature beat type determining function when detecting that the user has a premature beat by using the premature beat detection function, and calculating premature beat load of the user based on detection data obtained by the premature beat detection function; and reminding the user of a premature beat risk when the calculated premature beat load is greater than a premature beat load threshold corresponding to the premature beat type determined by the premature beat type determining function.

This application discloses a premature beat detection method, an electronic device. The premature beat detection method in this application includes: performing premature beat detection on a user by using a premature beat detection function; determining a premature beat type of the user by using a premature beat type determining function when detecting that the user has a premature beat by using the premature beat detection function, and calculating premature beat load of the user based on detection data obtained by the premature beat detection function; and reminding the user of a premature beat risk when the calculated premature beat load is greater than a premature beat load threshold corresponding to the premature beat type determined by the premature beat type determining function.

In an aspect, the present disclosure provides a method comprising: (a) identifying a first negative and positive electromagnetic dipoles in a first electromagnetic field map associated with a heart of the individual at a first time; (b) identifying a second negative and positive electromagnetic dipoles in a second electromagnetic field map associated with the heart of the individual at a second time; (c) determining a first angle based on the first negative and positive electromagnetic dipoles; (d) determining a second angle based on the second negative and positive electromagnetic dipoles; and (e) determining a presence, an absence, or a likelihood of coronary artery disease in the individual, based at least in part on (i) whether the first angle differs from the second angle by at least 100 degrees, or (ii) whether there is a presence of a third electromagnetic dipole in the first or the second electromagnetic field map.

In an aspect, the present disclosure provides a method comprising: (a) identifying a first negative and positive electromagnetic dipoles in a first electromagnetic field map associated with a heart of the individual at a first time; (b) identifying a second negative and positive electromagnetic dipoles in a second electromagnetic field map associated with the heart of the individual at a second time; (c) determining a first angle based on the first negative and positive electromagnetic dipoles; (d) determining a second angle based on the second negative and positive electromagnetic dipoles; and (e) determining a presence, an absence, or a likelihood of coronary artery disease in the individual, based at least in part on (i) whether the first angle differs from the second angle by at least 100 degrees, or (ii) whether there is a presence of a third electromagnetic dipole in the first or the second electromagnetic field map.

Provided herein are computer implemented methods and systems for prediction of major adverse cardiovascular events (MACE) and/or detection of CAD in a patient, based on phases of exercise electrocardiogram (ECG) test (EET) data.

Provided herein are computer implemented methods and systems for prediction of major adverse cardiovascular events (MACE) and/or detection of CAD in a patient, based on phases of exercise electrocardiogram (ECG) test (EET) data.