A method includes determining a heart beat signal during acquisition of MR images obtained at a plurality of cardiac cycles; determining at least one physiological parameter of a heart obtained at the plurality of cardiac cycles; determining a model including, determining, in each of the cardiac cycles, a variable time interval of variable duration and at least one additional time interval based on the heart beat signal and the at least one physiological parameter, the at least one additional time interval having a lower variability in duration than the variable time interval; determining a duration of the variable time interval and a duration of the cardiac cycle for each of the cardiac cycles based on the heart beat signal and the at least one physiological parameter; and assigning the MR images to the different cardiac phases based on the variable time interval and each of the cardiac cycles.

A method includes determining a heart beat signal during acquisition of MR images obtained at a plurality of cardiac cycles; determining at least one physiological parameter of a heart obtained at the plurality of cardiac cycles; determining a model including, determining, in each of the cardiac cycles, a variable time interval of variable duration and at least one additional time interval based on the heart beat signal and the at least one physiological parameter, the at least one additional time interval having a lower variability in duration than the variable time interval; determining a duration of the variable time interval and a duration of the cardiac cycle for each of the cardiac cycles based on the heart beat signal and the at least one physiological parameter; and assigning the MR images to the different cardiac phases based on the variable time interval and each of the cardiac cycles.

A system for establishing a model for cardiac ventricular hypertrophy (VH) screening includes a storage and a processor. The storage stores multiple pieces of subject data respectively associated with multiple subjects. Each of the pieces of subject data contains a basic physiological parameter group, an electrocardiographic parameter group, and an actual VH condition that corresponds to a left or right ventricle of the subject associated with the piece of subject data. The processor is electrically connected to the storage, splits the pieces of subject data into a training set and a test set, and establishes the model for VH screening based on the pieces of subject data in the training set by using machine learning techniques.

A system and a method for identifying a patient with a threshold number of distinct ECG abnormalities. The system and the method include an ECG monitoring device; a server; a database; a network; a memory containing machine readable medium comprising a machine executable code having stored thereon instructions for identifying patients with a threshold number of distinct ECG abnormalities; and a processor coupled to the memory, the processor configured to execute the machine executable code to cause the processor to: receive an ECG data output from the ECG monitoring device; process the ECG data output to identify abnormalities in the ECG data; and analyze the abnormalities in the ECG data in order to output an indication of whether the patient has depressed LVEF, wherein the ECG monitoring device, the server, the database, the memory, and the processor are coupled to the network via communication links.

A system and a method for identifying a patient with a threshold number of distinct ECG abnormalities. The system and the method include an ECG monitoring device; a server; a database; a network; a memory containing machine readable medium comprising a machine executable code having stored thereon instructions for identifying patients with a threshold number of distinct ECG abnormalities; and a processor coupled to the memory, the processor configured to execute the machine executable code to cause the processor to: receive an ECG data output from the ECG monitoring device; process the ECG data output to identify abnormalities in the ECG data; and analyze the abnormalities in the ECG data in order to output an indication of whether the patient has depressed LVEF, wherein the ECG monitoring device, the server, the database, the memory, and the processor are coupled to the network via communication links.

A physiological information acquisition device includes: a reception interface configured to receive waveform data corresponding to a measured waveform of a physiological parameter of a subject from a sensor; a notifier configured to output an alarm indicating that the physiological parameter is not normally acquired; a processor configured to cause the notifier to output the alarm based on the waveform data; and a predictor configured to predict, based on the waveform data, a probability that the physiological parameter is erroneously calculated. The processor is configured to cause the notifier to perform a notification of at least one of a quality of the waveform data, a state of the sensor, and an action shall be taken by a user, based on the probability.

A physiological information acquisition device includes: a reception interface configured to receive waveform data corresponding to a measured waveform of a physiological parameter of a subject from a sensor; a notifier configured to output an alarm indicating that the physiological parameter is not normally acquired; a processor configured to cause the notifier to output the alarm based on the waveform data; and a predictor configured to predict, based on the waveform data, a probability that the physiological parameter is erroneously calculated. The processor is configured to cause the notifier to perform a notification of at least one of a quality of the waveform data, a state of the sensor, and an action shall be taken by a user, based on the probability.

Provided are a system and method for informing of attachment positions of electrocardiogram (ECG) electrodes to improve the quality of ECG data. The system includes a judgment indicator extractor configured to extract a plurality of judgment indicator values from ECG data obtained through ECG electrodes, a reference value setter configured to, in a user-specific reference value setting mode, collect the judgment indicator values for a plurality of pieces of ECG data extracted by the judgment indicator extractor and set user-specific reference values for each judgment indicator, a similarity determiner configured to, in an ECG measurement mode, determine similarity by comparing a plurality of judgment indicator values extracted by the judgment indicator extractor with the user-specific reference values, and an electrode attachment position guide configured to inform a user of attachment positions of the ECG electrodes according to a similarity determination result of each of the judgment indicator values.

Provided are a system and method for informing of attachment positions of electrocardiogram (ECG) electrodes to improve the quality of ECG data. The system includes a judgment indicator extractor configured to extract a plurality of judgment indicator values from ECG data obtained through ECG electrodes, a reference value setter configured to, in a user-specific reference value setting mode, collect the judgment indicator values for a plurality of pieces of ECG data extracted by the judgment indicator extractor and set user-specific reference values for each judgment indicator, a similarity determiner configured to, in an ECG measurement mode, determine similarity by comparing a plurality of judgment indicator values extracted by the judgment indicator extractor with the user-specific reference values, and an electrode attachment position guide configured to inform a user of attachment positions of the ECG electrodes according to a similarity determination result of each of the judgment indicator values.

Systems and methods for ambulatory detection of Q wave-to-T wave (QT) interval prolongation are discussed. A medical-device system comprises a controller circuit and a user interface device. The controller circuit includes a long QT syndrome (LQTS) detector that measures a QT interval from a subcutaneous cardiac signal sensed from a patient using implantable electrodes, and detects an indication of QT prolongation using the measured QT time interval and a programmable threshold received as a user input from the user interface. The control circuit can adjust device operation based on the detected indication of QT prolongation. An output unit can generate a programmable alert of the QT prolongation corresponding to the user input of the programmable threshold.