An apparatus includes a network interface and a processor. The network interface is configured to communicate over a communication network. The processor is configured to receive (i) data, including a medical parameter acquired as a function of time, and (ii) a selection of one or more time intervals of interest within the time period. The processor is further configured to compress a first portion of the data, which is within the selected time intervals, at a first resolution, and compress a second portion of the data, which is outside the selected time intervals, at a second resolution, which is coarser than the first resolution. The processor is additionally configured to transmit the compressed first and second portions of the data, via the network interface, over the communication network.

According to an embodiment of the present disclosure, a vehicular electrocardiogram measurement device includes an electrocardiographic sensor which is installed in a seat of a vehicle and obtains a patch signal, and a controller, which, on the basis of the patch signal obtained by the electrocardiographic sensor, generates a first request signal for requesting contact of a user's body with a patch of the electrocardiographic sensor. At least one among an autonomous vehicle, a user terminal, or a server according to embodiments of the present disclosure may be combined or associated with an artificial intelligence module, an unmanned aerial vehicle (UAV), a robot, an augmented reality (AR) device, and a device related to virtual reality (VR) or 5G service.

Methods and apparatus provide Cheyne-Stokes respiration (CSR) detection based on a blood gas measurements such as oximetry. In some embodiments, a duration, such as a mean duration of contiguous periods of changing saturation or re-saturation occurring in an epoch taken from a processed oximetry signal, is determined. An occurrence of CSR may be detected from a comparison of the duration and a threshold derived to differentiate saturation changes due to CSR respiration and saturation changes due to obstructive sleep apnea. The threshold may be a discriminant function derived as a classifier by an automated training method. The discriminant function may be further implemented to characterize the epoch for CSR based on a frequency analysis of the oximetry data. Distance from the discriminant function may be utilized to generate probability values for the CSR detection.

Systems and methods include a cellular, WiFi, and Bluetooth transceiver coupled to a processor; an accelerometer or a motion sensor coupled to the processor; and a sensor coupled to the processor to sense mood or body vital sign.

Systems and methods for determining pulse arrival time utilizing sensors coupled to mobile electronic devices are described. A system embodiment includes, but is not limited to, a sensor configured to provide electrocardiogram (ECG) data; an optical sensor configured to provide optical data; and a controller configured to access each of the ECG data and the optical data, the controller configured to: isolate and normalize R-wave information from the ECG data, isolate information associated with the cardiac rhythm from the isolated and normalized R-wave information to provide pulse waves, determine temporal characteristics of the pulse waves, convert and normalize the optical data in a wavelet time-frequency plane, and calculate pulse arrival time utilizing each of the temporal characteristics of the pulse waves and the converted and normalized optical data.

In accordance with some embodiments of the disclosed subject matter, mechanisms (which can, for example, include systems, methods, and media) for low-power encoding of continuous physiological signals are provided. In some embodiments, a system comprises: a physiological sensor; and a remote monitor comprising: a battery; memory storing a k-ary tree including a root with k branches corresponding to k delta values, k nodes at a first depth below the Leads root node each having k branches corresponding to the k delta values the nodes indexed to indicate the lateral position of the node within the depth; a processor programmed to: receive a first sample value from the sensor; receive a second sample value; calculate a difference between the second first sample values; determine that the delta corresponds to a first delta of the k delta values; encode a sequence of deltas based on a depth and node index.

A Cheyne-Stokes (CS) diagnosis system classifies periods of CS-like breathing by examining a signal indicative of a respiratory parameter. For example, nasal flow data is processed to classify it as unambiguously CS breathing or nearly so and to display the classification Processing may detect and display: apnoeas, hypopnoeas, flow-limitation and snore. The signal may be split into equal length epochs and event features are extracted. Statistics are applied to these primary feature(s) to produce secondary feature(s) representing the entire epoch. Each secondary feature is grouped with other feature(s) extracted from the entire epoch rather than from the epoch events. This final group of features is the epoch pattern. The epoch pattern is classified to produce a probability for possible event classes (e.g., Cheyne-Stokes breathing, OSA, etc.). The epoch is assigned to the class with the highest probability, which may both be reported as an indication of disease state.

A system including a medical device is provided. The medical device includes at least one sensor configured to acquire first data descriptive of a patient, first memory storing a plurality of templates, and at least one processor coupled to the at least one sensor and the first memory. The at least one processor is configured to identify a first template of the plurality of templates that is similar to the first data, to determine first difference data based on the first template and the first data, and to store the first difference data in association with the first template. The system may further include the programmable device.

A system and method for applying a uniform dynamic gain over cardiac data with the aid of a digital computer is provided. A time series of a plurality of voltage values that comprises a digital representation of a raw electrocardiography (ECG) signal recorded by an ambulatory monitor recorder is obtained by an least one computer processor, the time series including segments of noise and segments of non-noise. The segments of non-noise are analyzed by the at least one computer processor and a single gain factor for all of the values in the analyzed non-noise segments is determined by the at least one computer processor based on the analysis. The single gain factor to all of the values in the non-noise segments is applied by the at least one computer processor.

Neurological Disease Mechanism Analysis for Diagnosis, Drug Screening, (Deep) Brain Stimulation Therapy design and monitoring, Stem Cell Transplantation therapy design and monitoring, Brain Machine Interface design, control, and monitoring.