An artificial intelligence self-learning-based static electrocardiography analysis method and apparatus, the method comprising data preprocessing, heartbeat detection, heartbeat classification based on a depth learning method, heartbeat verification, heartbeat waveform feature detection, measurement and analysis of electrocardiography events, and finally automatic output of reporting data, realizing an automated static electrocardiograph analysis method having a complete and rapid flow. The static electrocardiography analysis method can also record modification information of an automatic analysis result, collect modified data, and feed same back to the depth learning model to continue training, thereby continuously making improvements and improving the accuracy of the automatic analysis method.

A monitoring device configured to be attached to a subject includes a photoplethysmography (PPG) sensor configured to measure physiological information from the subject, and at least one processor configured to process signals from the PPG sensor to determine heart rate and RR-interval (RRi) for the subject, and to determine a heart rate pattern for the subject over a period of time. The at least one processor is configured to change a sampling frequency of the PPG sensor for determining RRi in response to the determined heart rate pattern. The at least one processor is configured to reduce the sampling frequency of the PPG sensor in response to determining a pattern of heart rate below a threshold.

The subject matter disclosed herein provides methods for presenting glucose level data. Glucose data for a patient may be received. A current glucose level and a rate of change of the current glucose level may be determined based on the received glucose data. A first interface may be displayed on a screen of a device. The first interface may include a unitary icon. The unitary icon may display the current glucose level and a visualization of the rate of change. Related apparatus, systems, techniques, and articles are also described.

A method and system for assessing the effectiveness of a treatment regimen. In a preferred embodiment, baseline magnetic resonance imaging signals of a diseased portion of a subject's anatomy are acquired and image slices associated with diseased tissue or a selected region of interest are identified. The identified image slices are then stored and used in acquiring subsequent magnetic resonance imaging signals. The baseline and subsequently acquired magnetic resonance imaging signals are then processed to determine changes in location, size or other factors that provide an indication of whether treatment is effective. In another aspect, baseline and subsequent magnetic resonance parameters are also acquired and compared as part of determining the effective of treatment.

Provided are a method and system for individualized prediction of mental illness on the basis of brain function map monkey-human cross-species migration, and a method for determining a mental illness prediction model. The method for determining a mental illness prediction model comprises the steps: (a) data acquisition; (b) preprocessing; (c) brain region selection; (d) feature construction; (e) feature screening; and (f) modeling prediction. The method and system are non-invasive and have high accuracy, high sensitivity, good specificity, and are convenient to popularize.

A wearable body fat combustion measurement device includes a housing including a first chamber with an opening, a fixing means attached to the housing and configured to bring the housing into close contact with a user's skin in a state in which the opening of the first chamber faces the user's skin, a measurement sensor disposed inside the first chamber and configured to generate an electrical signal according to an amount of acetone contained in a gas evaporated from sweat discharged from the user's skin and introduced into the first chamber through the opening, a reference sensor disposed outside the first chamber and configured to generate an electrical signal according to a concentration of a gas component contained in an ambient air and detectable by the measurement sensor, and a signal processor configured to process the electrical signals received from the measurement sensor and the reference sensor.

An imaging connector includes a proximal side and a distal side. The proximal side includes a light input opening and an image output opening. The distal side includes a light output opening and an image input opening. The imaging connector is operable to (i) transmit light from the light input opening to the light output opening, and (ii) transmit an image from the image input opening to the image output opening.

Example methods, apparatus, systems, and articles of manufacture for determining intensity of a biological response to a presentation are disclosed. An example apparatus includes at least one memory and processor circuitry to execute instructions to identify trough-to-peak instances in first galvanic skin response (GSR) data for a first subject, the first GSR data collected from the first subject during exposure of the first subject to media content during a period of time; assign trough-to-peak scores to corresponding ones of the trough-to-peak instances; associate the respective ones of the trough-to-peak scores with a time corresponding to the trough of each trough-to-peak instances; assign window scores to corresponding ones of windows in the period of time based on the highest trough-to-peak score occurring within the corresponding windows to generate a first GSR intensity profile; and identify an element of the media content for modification based on the first GSR intensity profile.

A method includes receiving during a first time interval associated with a path of motion of a dynamic body, image data associated with a plurality of images of the dynamic body. The plurality of images include an indication of a position of a first marker coupled to a garment at a first location, and a position of a second marker coupled to the garment at a second location. The garment is coupled to the dynamic body. During a second time interval, an image from the plurality of images is automatically identified that includes a position of the first marker that is substantially the same as a position of a first localization element relative to the dynamic body and a position of the second marker that is substantially the same as a position of the second localization element relative to the dynamic body.

Certain aspects relate to apparatuses and techniques for non-invasive optical imaging that acquires a plurality of images corresponding to both different times and different frequencies. Additionally, alternatives described herein are used with a variety of tissue classification applications, including assessing the presence and severity of tissue conditions, such as burns and other wounds.