A vital signs monitoring system includes a peak pattern detection module configured to output a peak prediction signal from sensor signals based on a peak prediction algorithm; a vital sign estimating module configured to estimate a vital sign based on the peak prediction signal; an activity and context detector module configured to output a context signal based on at least one environmental condition and/or activity level of the person; and a concept drift detection module configured to output a drift signal based on drift detected in the estimated vital sign. The peak pattern prediction module is configured to update the peak prediction algorithm based on the context signal and the drift signal.

A vital signs monitoring system includes a peak pattern detection module configured to output a peak prediction signal from sensor signals based on a peak prediction algorithm; a vital sign estimating module configured to estimate a vital sign based on the peak prediction signal; an activity and context detector module configured to output a context signal based on at least one environmental condition and/or activity level of the person; and a concept drift detection module configured to output a drift signal based on drift detected in the estimated vital sign. The peak pattern prediction module is configured to update the peak prediction algorithm based on the context signal and the drift signal.

An apparatus and a method for monitoring a bio-signal measuring condition are disclosed. The apparatus includes a bio-signal receiver configured to receive a bio-signal that is measured from a user, and a processor configured to extract any one or any combination of a waveform feature, a period feature, and an amplitude feature, from the received bio-signal, determine whether the extracted any one or any combination of the waveform feature, the period feature, and the amplitude feature are normal, using at least one predetermined determination reference corresponding to the extracted any one or any combination of the waveform feature, the period feature, and the amplitude features, and monitor a measuring condition of the received bio-signal, based on whether the extracted any one or any combination of the waveform feature, the period feature, and the amplitude feature are determined to be normal.

A display method applied to a monitor is disclosed. The method includes acquiring a first partition configuration on a display interface of a touch display screen of the monitor; displaying at least one physiological data in the first physiological data display area; displaying data other than the at least one physiological data in the first function information display area; detecting a touch screen operation received by the touch display screen; acquiring a second partition configuration of the display interface if a first touch screen operation that meets a preset re-partitioning rule is detected, the second partition configuration including a second physiological data display area, a second function information display area, and a third function information display area; partially or fully displaying at least one physiological data in the second physiological data display area; and displaying data other than the at least one physiological data in the second function information display area.

A display method applied to a monitor is disclosed. The method includes acquiring a first partition configuration on a display interface of a touch display screen of the monitor; displaying at least one physiological data in the first physiological data display area; displaying data other than the at least one physiological data in the first function information display area; detecting a touch screen operation received by the touch display screen; acquiring a second partition configuration of the display interface if a first touch screen operation that meets a preset re-partitioning rule is detected, the second partition configuration including a second physiological data display area, a second function information display area, and a third function information display area; partially or fully displaying at least one physiological data in the second physiological data display area; and displaying data other than the at least one physiological data in the second function information display area.

Aspects of the present disclosure provide methods, apparatuses, and systems for closed-loop sleep protection and/or sleep regulation. According to an aspect, a biosignal parameter and ambient noise are measured. The biosignal parameter is used to determine sleep condition of a subject. The sleep condition is determined based on one or more of personalized sleep data or historical sleep data collected using a subset of society. Based on the sleep condition, an arousal threshold is determined. Based on the ambient noise and the determined sleep condition, one or more actions are taken to regulate sleep and avoid sleep disruption.

Aspects of the present disclosure provide methods, apparatuses, and systems for closed-loop sleep protection and/or sleep regulation. According to an aspect, a biosignal parameter and ambient noise are measured. The biosignal parameter is used to determine sleep condition of a subject. The sleep condition is determined based on one or more of personalized sleep data or historical sleep data collected using a subset of society. Based on the sleep condition, an arousal threshold is determined. Based on the ambient noise and the determined sleep condition, one or more actions are taken to regulate sleep and avoid sleep disruption.

Described herein are neural network-based systems, methods and instrumentalities associated with cardiac assessment. An apparatus as described herein may obtain electrocardiographic imaging (ECGI) information associated with a human heart and magnetic resonance imaging (MRI) information associated with the human heart, and integrate the ECGI and MRI information using a machine-learned model. Using the integrated ECGI and MRI information, the apparatus may predict target ablation sites, estimate electrophysiology (EP) measurements, and/or simulate the electrical system of the human heart.

Described herein are neural network-based systems, methods and instrumentalities associated with cardiac assessment. An apparatus as described herein may obtain electrocardiographic imaging (ECGI) information associated with a human heart and magnetic resonance imaging (MRI) information associated with the human heart, and integrate the ECGI and MRI information using a machine-learned model. Using the integrated ECGI and MRI information, the apparatus may predict target ablation sites, estimate electrophysiology (EP) measurements, and/or simulate the electrical system of the human heart.

Systems and methods for positioning a patient during radiological measurements are provided. A biofeedback signal from the patient is received. While receiving the biofeedback signal from the patient and while the patient is positioned at a first position by the fixture, determining whether the biofeedback signal from the patient is indicative of the patient breathing at rest. Further, in accordance with a determination that the biofeedback signal from the patient is not indicative of the patient breathing at rest, articulating the patient using the fixture from the first position to a second position. In accordance with a determination that the biofeedback signal from the patient at the second position is indicative of the patient breathing at rest, obtaining radiological measurements of the patient with the patient positioned at the second position.