A method for generating electrocardiogram (ECG) signals includes detecting at least one cardiac motion induced signal. The at least one cardiac motion induced signal is a seismocardiography (SCG) signal. The method includes transforming the at least one detected cardiac motion induced signal into at least one ECG signal. Multiple channel-specific signals of a multi-channel ECG signal are determined by the transformation from the at least one SCG signal.

Systems and apparatus for synthesizing (generating) 12-lead ECG dataset from 3-lead ECG data. In particular, one or more transformation parameters may be determined that may be applied to 3-lead ECG dataset to generate 12-lead ECG data with particular speed and accuracy. The transformation parameters, which may include a plurality of matrices, may be determined from a synchronized patient's 12-lead ECG dataset and 3-lead ECG data. The 12-lead ECG dataset may be collected at a different time than the 3-lead ECG data. In some embodiments, the 12-lead ECG dataset and/or the 3-lead ECG dataset may be resampled prior to determining the transformation parameters.

One embodiment of an exemplary ambulatory seizure monitoring method calculates a phase lock value synchrony level of a neurological signal of an individual; detects an onset of a seizure event for the individual by comparing the phase lock value synchrony level with a patient threshold for the individual; and transmits a notification to a remote communication device indicating the onset of the seizure event for the individual.

In accordance with one embodiment, a method for determining changes in health of a user is disclosed. The method includes sensing multi-dimensional motion of a body part of a user to generate a first multi-dimensional motion signal at a first time and date; in response to the first multi-dimensional motion signal, generating a first neuro-mechanical fingerprint; generating a first health measure in response to the first NFP and user calibration parameters; sensing multi-dimensional motion of the body part of the user to generate another multi-dimensional motion signal at another time and date; in response to the another multi-dimensional motion signal, generating another neuro-mechanical fingerprint; generating another health measure in response to the another NFP and the user calibration parameters; and comparing the first health measure with the another health measure to determine a difference representing the health degradation of the user.

The embodiment of the present disclosure provides a method and a system for identifying a user action. The method and system may obtain user action data collected from a plurality of measurement positions on a user, the user action data corresponding to an unknown user action, identify that the user action includes a target action when obtaining the user action data based on at least one set of target reference action data, the at least one set of target reference action data corresponding to the target action, and send information related to the target action to the user.

A system and method for determining cardiovascular parameters can include: receiving a plethymogram (PG) dataset, removing noise from the PG dataset, segmenting the PG dataset, extracting a set of fiducials from the PG dataset, and transforming the set of fiducials to determine the cardiovascular parameters.

An apnea analysis system may include a photoplethysmographic (PPG) sub-system, a breath detection sub-system, and an apnea analysis module. An apnea analysis system includes a photoplethysmographic (PPG) sub-system, a breath detection sub-system, and an apnea analysis module. The PPG sub-system is configured to be operatively connected to an individual and output a PPG signal from the individual. The breath detection sub-system is configured to be operatively connected to the individual and output a breath signal from the individual. The apnea analysis module is in communication with the PPG sub-system and the breath detection sub-system. The apnea analysis module analyzes the breath signal and a respiratory component of the PPG signal and, based on the analysis, identifies a presence of apnea, differentiates between obstructive apnea and central apnea, and provides an indication of the identified apnea.

A disease prediction model construction apparatus is provided. The disease prediction model construction apparatus may include a spectral data acquisition unit that emits near-infrared rays toward a skin of a subject to acquire near-infrared spectral data and a prediction model construction unit that constructs a disease prediction model based on a time-blood glucose graph generated from the acquired near-infrared spectral data.

An embodiment of the present invention is provided with a projective transform model including a plurality of nodes and a projection table, the plurality of nodes each holding a reference vector having a dimension corresponding to the dimension of multi-dimensional data. The projection table indicates the correspondence relation between the number of each node and a coordinate in a two-dimensional space as a projection target of the reference vector held by the node. First in a learning phase, multi-dimensional input data of a positive example and a negative example is acquired, the amplitude characteristic amounts thereof are calculated, and this amplitude characteristic amount data is learned as the reference vectors of the nodes for each sample. Subsequently, the Euclidean distance between coordinates when the nodes learned based on the amplitude characteristic amount data of the positive example and the nodes learned based on the amplitude characteristic amount data of the negative example are projected into the two-dimensional space in accordance with the projection table is calculated, and coordinates in the projection table are updated so that the calculated Euclidean distance becomes equal to or larger than a threshold value.

A heart-rate associated with a heartbeat signal is determined. A transform is selected based on the determined heart-rate associated with the heartbeat signal and a reference heart-rate associated with a dictionary of a sparse approximation model. The transform is selected independent of other factors associated with generation of the heartbeat signal. The selected transform is applied to the dictionary of the sparse approximation model, generating an adjusted dictionary of the sparse approximation model. Anomalous heartbeats in the heartbeat signal are detected using the adjusted dictionary of the sparse approximation model.