A method, apparatus and computer program, the method comprising: receiving a first biopotential signal obtained by a first capacitive sensor; receiving a second biopotential signal obtained by a second capacitive sensor, the first capacitive sensor and the second capacitive sensor being positioned at different locations on a subject; synchronising biopotential signals obtained by the first capacitive sensor and the second capacitive sensor by applying a time adjustment to biopotential signals obtained by at least one of the first capacitive sensor or the second capacitive sensor; wherein features in at least one of the first biopotential signal and the second biopotential signal are used to synchronise the biopotential signals obtained by the first capacitive sensor and the second capacitive sensor.

A method, apparatus and computer program, the method comprising: receiving a first biopotential signal obtained by a first capacitive sensor; receiving a second biopotential signal obtained by a second capacitive sensor, the first capacitive sensor and the second capacitive sensor being positioned at different locations on a subject; synchronising biopotential signals obtained by the first capacitive sensor and the second capacitive sensor by applying a time adjustment to biopotential signals obtained by at least one of the first capacitive sensor or the second capacitive sensor; wherein features in at least one of the first biopotential signal and the second biopotential signal are used to synchronise the biopotential signals obtained by the first capacitive sensor and the second capacitive sensor.

An automatic recognition and classification method for electrocardiogram heartbeat based on artificial intelligence, comprising: processing a received original electrocardiogram digital signal to obtain heartbeat time sequence data and lead heartbeat data; cutting the lead heartbeat data according to the heartbeat time sequence data to generate lead heartbeat analysis data; performing data combination on the lead heartbeat analysis data to obtain a one-dimensional heartbeat analysis array; performing data dimension amplification and conversion according to the one-dimensional heartbeat analysis array to obtain four-dimensional tensor data; and inputting the four-dimensional tensor data to a trained LepuEcgCatNet heartbeat classification model, to obtain heartbeat classification information. The method overcomes the defect that the conventional method only depends on single lead independent analysis for result summary statistics and thus classification errors are more easily obtained, and the accuracy of the electrocardiogram heartbeat classification is greatly improved.

The present disclose generally relates to systems and methods for active titration of one or more cranial or peripheral nerve stimulators to treat obstructive sleep apnea. The active titration can be accomplished in an automated fashion by a closed-loop process. The closed-loop process can be executed by a computing device that includes a non-transitory memory storing instructions and a processor to execute the instructions to perform operations. The operations can include defining initial parameters for the one or more cranial or peripheral nerve stimulators for a patient; receiving sensor data from sensors associated with the patient based on a stimulation with the one or more cranial or peripheral stimulators programmed according to the initial parameters; and adjusting the initial parameters based on the sensor data.

The present disclose generally relates to systems and methods for active titration of one or more cranial or peripheral nerve stimulators to treat obstructive sleep apnea. The active titration can be accomplished in an automated fashion by a closed-loop process. The closed-loop process can be executed by a computing device that includes a non-transitory memory storing instructions and a processor to execute the instructions to perform operations. The operations can include defining initial parameters for the one or more cranial or peripheral nerve stimulators for a patient; receiving sensor data from sensors associated with the patient based on a stimulation with the one or more cranial or peripheral stimulators programmed according to the initial parameters; and adjusting the initial parameters based on the sensor data.

The illustrated embodiments include a method of operating a SAW sensor to detect a sample in a fluid which includes the steps of: providing a SAW sensor with a functionalized detection lane in a handheld, portable assay device and sensor system; maintaining the functionalized detection lane of the SAW sensor dry until the sample is fluidically disposed in the detection lane; fluidically disposing the sample in the functionalized detection lane; removing fluid the functionalized detection lane to concentrate the sample in the functionalized detection lane to increase the probability of a specific antibody-antigen interaction; washing the functionalized detection lane so that substantially only the specific antigen-antibody interaction remains in the functionalized detection lane; removing fluid from the functionalized detection lane again; and measuring concentration of the sample while the functionalized detection lane is fluid-free.

The illustrated embodiments include a method of operating a SAW sensor to detect a sample in a fluid which includes the steps of: providing a SAW sensor with a functionalized detection lane in a handheld, portable assay device and sensor system; maintaining the functionalized detection lane of the SAW sensor dry until the sample is fluidically disposed in the detection lane; fluidically disposing the sample in the functionalized detection lane; removing fluid the functionalized detection lane to concentrate the sample in the functionalized detection lane to increase the probability of a specific antibody-antigen interaction; washing the functionalized detection lane so that substantially only the specific antigen-antibody interaction remains in the functionalized detection lane; removing fluid from the functionalized detection lane again; and measuring concentration of the sample while the functionalized detection lane is fluid-free.

The present disclosure relates to a device, method and system for calculating, estimating, or monitoring the blood pressure of a subject based on physiological features and personalized models. At least one processor, when executing instructions, may perform one or more of the following operations. A first signal representing a pulse wave relating to heart activity of a subject may be received. A plurality of second signals representing time-varying information on a pulse wave of the subject may be received. A personalized model for the subject may be designated. Effective physiological features of the subject based on the plurality of second signals may be determined. A blood pressure of the subject based on the effective physiological features and the designated model for the subject may be calculated.

The present disclosure relates to a device, method and system for calculating, estimating, or monitoring the blood pressure of a subject based on physiological features and personalized models. At least one processor, when executing instructions, may perform one or more of the following operations. A first signal representing a pulse wave relating to heart activity of a subject may be received. A plurality of second signals representing time-varying information on a pulse wave of the subject may be received. A personalized model for the subject may be designated. Effective physiological features of the subject based on the plurality of second signals may be determined. A blood pressure of the subject based on the effective physiological features and the designated model for the subject may be calculated.

An implantable monitoring device includes first sensors to measure state information of one or both of a posture and an activity of a user and second sensors to measure bioinformation of two or more of an electrocardiogram (ECG) of a heart of the user, a pulmonary impedance of a lung of the user, a movement of the heart, a movement of a thorax including the lung, and a respiratory quotient (RQ) of the lung, two electrodes to detect bioinformation to measure one or both of the ECG and the pulmonary impedance, an analog circuit to process the detected bioinformation to measure the one or both of the ECG and the pulmonary impedance, and a processor to monitor an abnormal state of the heart and the lung of the user based on the state information and the bioinformation.