Disclosed herein is a non-transitory computer-readable media having computer-executable instructions thereon. When executed by the processor of a mobile computing device, the computer-executable instructions cause the processor to wirelessly communicatively couple the mobile computing device to a GI sensor (optionally by displaying instructions on a pairing screen), display a sensor placement screen that instructs the user to place the GI sensor on a body of a patient, prompts the user to administer a GI agent into the body of the patient; transmit a signal from the mobile computing device to the GI sensor to cause the GI sensor to initiate collection of light absorbance data for calculating GI function of the patient; receive light absorbance data from the GI sensor, and store the received light absorbance data.

Brain stimulation techniques are disclosed, utilizing a brain state monitoring and stimulating component having at least one processor and memory, and a digital signal processing unit configured and operable to predict at least one brain state about to occur in a brain of a treated subject based on data or signals indicative of at least one brain state of the treated subject. The digital signal processing unit can be configured to generate based on the predicted at least one brain state stimulation data for adjusting, regulating or triggering, brain stimulation signals applied to the brain of the treated subject. The at least one processor, memory, and digital signal processing unit are embedded in some embodiments in a single hardware device.

Systems, methods, devices, and other techniques for estimating the age and sex of a person through analysis of an electrocardiogram (ECG) recording for the person. Some aspects include recording an ECG of a person, processing data representing the ECG with an age-estimation neural network to generate an estimated age of the person, and outputting an indication of the estimated age of the person. Other aspects include processing the ECG with a sex prediction neural network to generate a predicted sex of the person and outputting an indication of the predicted sex of the person.

The present invention provides a system and method for diagnosis and treatment of hair and scalp. The system comprises an electronic user interface, a plurality of data blocks configured to collect hair and scalp related data from a plurality of IOT devices, a processor coupled to an AI engine configured to process the data for identifying a problem and recommending a treatment, and a controller encoded with instructions to process a plurality of data models wherein the AI engine determines identifiers in the data based on at least one of the plurality of data models for analyzing condition of hair and scalp to recommend the treatment to a user on the interface.

A blood pressure level change detection apparatus includes a change point detection unit and a level change/return determination unit. The change point detection unit detects a first change point as a change point in time-series data of blood pressure. A level change/return determination unit acquires a first average blood pressure level and a second average blood pressure level for time-series data of blood pressure. When a difference between the first average blood pressure level and the second average blood pressure level is greater than or equal to a predetermined level threshold value, the level change/return determination unit determines that a blood pressure level change has occurred at the first change point.

A system for measuring pressure of an eye includes an excitation source for producing a travelling air vortex ring and for directing the travelling air vortex ring to the eye, a detector for detecting an interaction between the travelling air vortex ring and a surface of the eye, and a processing device for determining an estimate of the pressure of the eye based on the detected interaction between the travelling air vortex ring and the surface of the eye. The travelling air vortex ring is produced by directing an air pressure pulse into a flow guide, and the air pressure pulse is generated with an electric spark or otherwise so that no swinging mass, such as a piston, is needed. This is advantageous especially in a case of a handheld device because a swinging mass would tend to adversely move the handheld device during a measurement.

The present invention relates to a method of analysing cardiac data relating to a patient, comprising: providing cardiac data relating to the patient—optionally by using a means for providing physiological data (20); determining one or more properties of the data, wherein the or each property is determined over a particular context length, the context length being selected based on the or each property—optionally using an analysis module (24); comparing the or each property against a respective predetermined threshold value, thereby to indicate a probability of the patient experiencing a cardiac event—optionally using a means for providing an output (26); and providing an output based on the comparison. A system and apparatus corresponding to this method is also disclosed.

Systems and a computer implemented method for classifying a brain status of a subject, from a neural activity response of the subject to an induced TMS stimulation; the method comprising: constructing a machine learning classifier (MLC) configured to classify a subjects brain status; training the MLC using a training set, the training set comprising pairs of training output-classification vectors and their corresponding training input vectors, all extracted from a database of subjects with known brain status classifications; and applying the trained MLC on an input vector comprising features extracted from a tested-subjects brain neural activity response to the induced TMS stimulation, to obtain an output classification vector for the tested-subjects brain status.

Wearable data acquisition devices including sensors for detecting physiological signals are used to detect and measure various parameters pertaining to the health of the wearer. Through the use of algorithms, the data detected and measured by said sensors are processed to make inferences used to monitor the health of the wearer. An signal to noise ratio (SNR) calculation is used as a component of the sensitivity analysis of an algorithm. For example, a sleep algorithm will select a block of data, the SNR calculation will be done, with the average quality of the signal as result. The value associated with said signal quality may then be subjected to sensitivity analysis, which will yield a sleep accuracy value which, in turn, is indicative of the minimum signal quality which is required.

The present invention provides a neurological disorders decision support system that can assist an examiner to diagnose an examinee. The neurological disorders decision support system includes a user module, a screening module, an intelligent calculation module and a diagnosis module. The user module sends an inquiry to the examinee, receives a response message from the examinee, and retrieves a physiological characteristic signal of the examinee. The screening module executes a neurological examination application program to indicate to the examinee to obtain physiological characteristic signals. The screening module outputs response messages and physiological characteristic signals for the intelligent calculation module to execute an algorithm to generate an analysis report. The analysis report assists the examiner for diagnosis, and sends a diagnosis notification to the user module through the diagnosis module. The invention also provides a neurological disorders decision support method.