An alert is used to inform a user that their blood glucose level has dropped below a threshold (e.g., the user is hypoglycemic) or has increased above a threshold (e.g., the user is hyperglycemic). There is a hierarchy of alerts from lowest priority to highest priority. The alert is communicated by a user device (e.g., a mobile device), which is, for example, a smartphone, smart watch, home automation device, or the like. The alert is modified in order to increase the likelihood that the user receives or acknowledges the alert within a minimal amount of time. An intensity level (e.g., a volume) of an alert is modified based on, for example, whether the user has acknowledged a previous alert. A modality or a sensory channel of an alert is changed if an initial alert does fails to elicit a response from the user.

A method of reversing hearing loss using a therapy stimulator machine that includes a first and second handheld probe electrode; using the hand held probe electrodes with water to apply pressure to locations and at the same time wiggle the probe electrodes around the patient's ear to stimulate hearing loss reversal. Simultaneously an isolated sound source is applied to the side of the ear under treatment so patient would be able to appreciate the improvement in the hearing capability instantly.

An exemplary system includes an electro-acoustic stimulation (“EAS”) sound processor, a cochlear implant communicatively coupled to the EAS sound processor, an electrode array communicatively coupled to the cochlear implant, and a receiver communicatively coupled to the EAS sound processor and configured to be in communication with an ear of a patient. The EAS sound processor 1) directs, while in a self-fitting mode, the receiver to apply acoustic stimulation to the patient, 2) records, using at least one electrode included in the electrode array, an evoked response that occurs in response to the acoustic stimulation, 3) compares the evoked response to a baseline evoked response recorded by the EAS sound processor prior to recording the evoked response, and 4) performs a predetermined action based on the comparison between the evoked response and the baseline evoked response. Corresponding systems and methods are also disclosed.

The present disclosure relates to methods for evaluating the sound quality of a digital engineering process by, in part, measuring the frequency following response (FFR) of the human auditory system elicited by identical auditory stimuli (e.g., a musical interval) encoded with variations of a digital signal processing technique (e.g., various sampling rates). Once measured, the FFR may be analyzed to determine the comparative effect of each digital signal processing technique on a human subject's ability to process complex stimuli presented by the digital engineering process.

The present invention relates to an AI (Artificial Intelligence) based device for providing brain information comprising: a brain signal measuring portion that collects a signal related to user's brain; a brain signal stimulating portion that stimulates the user's brain for an operation of collecting a signal of the brain signal measuring portion; and a diagnosing portion that determines the user's brain state on the basis of the collected signal.

A hearing test system comprises: a control device for controlling a hearing aid comprising a communication interface arranged to allow two-way communication and software for executing predefined sequences of sounds saved in a memory module of the device in response to an instruction from a control station equipped with remote control software; at least one hearing aid comprising a communication interface arranged to allow two-way communication with the control device, the device comprising a software layer for communication with the hearing aid and being arranged to provide a gateway between the control station and the hearing aid; and means for the sound insulation of the hearing aid.

Systems and methods are provided for evaluating hearing health of a given user. An input audio signal is transformed into the frequency domain and a first and second hearing profile are applied to the input audio sample. The first hearing profile represents a healthy hearing standard and the second hearing profile is the given user's hearing profile. Using the hearing profiles, first and second perceptually relevant information (PRI) values are generated for the input audio sample. The first and second PRI values are analyzed against each other to generate a PRI index value for the given user, where the PRI index value is a hearing health index value for the given user. The given user's hearing profile may additionally be applied to differently processed audio samples to evaluate the amount of perceptual rescue offered by various digital signal processing algorithms.

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.

The present disclosure relates to communication devices. Such devices may comprise input for receiving sound signal to be processed and presented to a user, and output for outputting the processed signal to a user perceivable as sound. Such processing may be performed by use of a processor for processing the sound signal in dependence of a setting or a set of setting to compensate a hearing loss profile. Further, the communication device may comprise a bio-signal acquisition and amplifier component in communication with a user interface for providing the bio-signals as input to the user interface, the user interface controlling the setting or set of setting for operation of the communication device.

A fully autonomous system is used to diagnose an ear infection in a patient. For example, a processor receives patient data about a patient, the patient data comprising at least one of: patient history from medical records for the patient, one or more vitals measurements of the patient, and answers from the patient about the patient's condition. The processor receives a set of biomarker features extracted from measurement data taken from an ear of the patient. The processor synthesizes the patient data and the biomarker features into input data, and applies the synthesized input data to a trained diagnostic model, the diagnostic model comprising a machine learning model configured to output a probability-based diagnosis of an ear infection from the synthesized input data. The processor outputs the determined diagnosis from the diagnostic model. A service may then determine a therapy for the patient based on the determined diagnosis.