A heart treatment system is disclosed capable of diagnosing one or more critical regions of interest for a biological rhythm disorder by sensing signals from biological tissue. If a critical region is not present at the current location of sensed signals, the system is capable of indicating a guidance direction in which to navigate to reach one or more critical regions. Ablation energy is delivered to treat said region of interest. Signals are again sensed and analyzed to assess the impact of treatment. This process is repeated until all critical regions of interest are treated. In some embodiments, all functionality is provided by a single sensing and treating catheter with display device and analytical software.

Brain computer interface BCI comprising an input adapted to be connected to at least one electroencephalography EEG sensor to receive EEG signals, the BCI further comprising a processor running an associative model trained to simulate electrocorticography ECoG signal features from EEG signals received via the input, the BCI comprising an output to transmit the simulated ECoG signal features.

Methods and systems for imaging and analysis are described. Accurate pressure ulcer measurement is critical in assessing the effectiveness of treatment. However, the traditional measuring process is subjective. Each health care provider may measure the same wound differently, especially related to the depth of the wound. Even the same health care provider may obtain inconsistent measurements when measuring the same wound at different times. Also, the measuring process requires frequent contact with the wound, which increases risk of contamination or infection and can be uncomfortable for the patient. The present application describes a new automatic pressure ulcer monitoring system (PrUMS), which uses a tablet connected to a 3D scanner, to provide an objective, consistent, non-contact measurement method. The present disclosure combines color segmentation on 2D images and 3D surface gradients to automatically segment the wound region for advanced wound measurements.

An infrared image sequence-based sleep quality evaluation system and method. The method comprises: obtaining a plurality of respiratory infrared image sequences to be evaluated, one respiratory infrared image sequence comprising a plurality of respiratory infrared image frames to be evaluated; performing sleep quality evaluation on each respiratory infrared image sequence in the plurality of respiratory infrared image sequences by means of a classifier to obtain a sleep quality evaluation result corresponding to each respiratory infrared image sequence; and counting the number of different sleep quality evaluation results according to the sleep quality evaluation results respectively corresponding to the plurality of respiratory infrared image sequences, and determining the sleep quality evaluation result with the largest number as a sleep quality evaluation result of a user. Contactless sleep monitoring can be carried out on a user, monitoring costs are reduced at the same time, and evaluation accuracy of sleep quality is improved.

The present invention relates to non-invasive method and system for characterising and certifying cognitive activities by detecting gaseous substances emitted by an organism, by means of the respiration, perspiration, and/or secretion, and changes measureable by sensors during said cognitive activities. Substance detection makes it possible to characterise the olfactory signal in order to determine and certify whether or not a cognitive activity has occurred and to classify said signals into different categories of cognitive activities.

A procedure can be assisted by a processor system, such as a computer system. A trajectory can be used to identify a selected trajectory or path of an instrument to reach a tumor within a brain of a subject, reach a selected portion of the anatomy (e.g. sub-thalamic nucleus (STN) or spinal cord), or other appropriate target. The planning algorithm can include both inputted data and learned rankings or ratings related to selected trajectories. The planning algorithm can used the learned ratings to rate and later determined trajectories.

A bed-leaving prediction device (server device) (10) is connected through a digital communication network (60) to: a portable information processing terminal (40) of care staff; environmental sensors (32 to 34) for detecting environment values such as temperature in a room; a human sensor (31); and a bed sensor (35). A bed-leaving prediction processing section (115) calculates a bed-leaving prediction value indicative of a degree of possibility that a care recipient leaves a sleeping furniture after a second time interval has expired since a current time point based on a plurality of environment values detected in a time period between the current time point and a time point before expiration of a first time interval, outputs of the human sensor, and outputs of the bed sensor. A bed-leaving notification processing section (117) compares the bed-leaving prediction value with a threshold value, and transmits, to the portable information processing terminal, a bed-leaving notification indicating that the care recipient leaves the sleeping furniture after the second time interval expires when the bed-leaving prediction value exceeds the threshold value.

A method of, and system for, predicting conductive hearing loss risk in a person. The method includes utilizing, by using a processor, at least the following as inputs to a prediction model: (A) at least one first air conduction value for the person, and (B) any one of (a) a second air conduction value in noise for the person for in-phase binaural stimuli, or (b) a third air conduction value in noise for the person for antiphasic binaural stimuli. The method further includes predicting, by using the processor and an output of the prediction model, whether the person has a risk of conductive hearing loss. The method may be implemented without the need for bone conduction audiometry or any other clinical test to determine conductive hearing loss. The prediction model may be a logistic regression model.

According to an embodiment, a method of processing acceleration information by a wearable device is provided, the method including: acquiring the acceleration information indicating acceleration according to movement of the wearable device; acquiring a plurality of acceleration characteristics including a plurality of characteristics of a power spectrum of the acceleration and a range of the acceleration on the basis of the acceleration information; determining characteristic values according to two characteristics determined according to a type of a disease to be monitored among the plurality of acceleration characteristics; and providing an alarm signal in response to the characteristic value falling within an abnormality range.

A method and a system are provided for taking biomarker measurements of patients who have ADHD. Mathematical analysis (e.g., pattern recognition, machine learning and AI algorithms) of the biomarker measurements is used to create a unique personal prediction model and data set for an individual patient. The unique personal data set is used to diagnose and monitor a particular problem of the individual patient associated with ADHD, or to recommend a treatment for a particular problem of the individual patient associated with ADHD, or to predict an outcome of a treatment for a particular problem of the individual patient associated with ADHD.