A method of assessing or monitoring the normal skin sympathetic nerve activity in a living subject, the subject having a skin, comprises assessing or measuring electrodermal activity, wherein the electrodermal activity is skin conductance, galvanic skin response, electrodermal response, psychogalvanic reflex, skin conductance response, sympathetic skin response or skin conductance level. Skin conductance may be assessed by calculating skin conductance fluctuation peaks per time unit, and when the skin conductance fluctuations peaks are above a certain predefined value, the normal skin sympathetic nerve activity is defined in an analyzing window with a length of about 15 to 60 seconds, the normal skin sympathetic nerve activity is assessed as being obtained or successful. Alternatively, the skin conductance may be assessed by calculating rise time of the mean skin conductance level, the area under the skin conductance fluctuations or the size of the amplitude of the skin conductance fluctuation.

A method for variable electrostimulative behavior modification includes sensing placement of a first anatomical portion of a body in contact to a surface of a target object adapted for behavior modification with a multiplicity of electrostimulative end points coupled to an energy source. The method additionally includes delivering an electrical neurostimulus through one of the end points nearest to the placement of the first anatomical portion in proximity to the surface the electrical neurostimulus including a waveform promoting a modification of behavior associated with the placement of the first anatomical portion in proximity to the surface. Thereafter, an anomalous characteristic of the sensed placement indicating a failure to modify the behavior may be detected and a profile of the waveform of the electrical neurostimulus changes to a different waveform in response to the anomalous characteristic of the sensed placement.

A method for variable electrostimulative behavior modification includes sensing placement of a first anatomical portion of a body in contact to a surface of a target object adapted for behavior modification with a multiplicity of electrostimulative end points coupled to an energy source. The method additionally includes delivering an electrical neurostimulus through one of the end points nearest to the placement of the first anatomical portion in proximity to the surface the electrical neurostimulus including a waveform promoting a modification of behavior associated with the placement of the first anatomical portion in proximity to the surface. Thereafter, an anomalous characteristic of the sensed placement indicating a failure to modify the behavior may be detected and a profile of the waveform of the electrical neurostimulus changes to a different waveform in response to the anomalous characteristic of the sensed placement.

An apparatus for providing information about a user's brain resources is provided. The apparatus includes at least sensor interface circuitry and processing circuitry coupled to the sensor interface circuitry. In a calibration mode, the sensor interface circuitry is configured to receive first sensor data from an electroencephalography sensor. The first sensor data are indicative of an electroencephalogram of the user. Further, the sensor interface circuitry is configured to receive second sensor data from a physiological sensor in the calibration mode. The second sensor data are indicative of a physiological property of the user. In the calibration mode, the processing circuitry is configured to train a brain-physiological model for the user based on the first sensor data and the second sensor data.

A system and method are provided for monitoring subject behavior during sleep onset. In some aspects, a system includes one or more sensors configured to acquire behavioral data from a subject using input provided during sleep onset. The system also includes a processor programmed to at least assemble a time-series of behavioral responses using the behavioral data acquired using the one or more sensors, and estimate an instantaneous probability of response using the time-series of behavioral responses. The processor is also programmed to generate a statistical model of wakefulness using the instantaneous probability of response, and estimate, using the model, a probability indicative of a degree to which the subject is awake at each point in time during the sleep onset process. The processor is further configured to generate a report indicative of sleep onset in the subject. The system also includes an output for displaying the report.

A life management system receives data from a client device worn by a user, the data comprising biotelemetry data and activity data collected about a user wearing the client device. The life management system generates snapshot information using information from a group consisting of: the biotelemetry data, activity data, social data associated with the user, and user profile information associated with the user. The life management system generates a recommendation using portions of the snapshot information, and updates the snapshot information with the recommendation. The life management system executes a recommendation associated with the snapshot information in accordance with the user controls associated with the user.

A method of predicting a healthcare event includes: receiving via an input device, classifying personal information for each of a plurality of persons; collecting measurements of at least one health indicator during a predefined learning period; creating a personal physiological pattern profile, based on the collected data; associating each of the plurality of persons to a physiological cluster based on each person's personal physiological pattern profile and based on the classifying personal information of each of the plurality of persons; creating, for each physiological cluster, a health indicator deviation pattern for the healthcare event; continuously monitoring values of the health indicator of the person; and determining an occurrence probability of the healthcare event when the monitored indicators deviate from the personal physiological pattern profile. A system for predicting a healthcare event is also disclosed.

A method of predicting a healthcare event includes: receiving via an input device, classifying personal information for each of a plurality of persons; collecting measurements of at least one health indicator during a predefined learning period; creating a personal physiological pattern profile, based on the collected data; associating each of the plurality of persons to a physiological cluster based on each person's personal physiological pattern profile and based on the classifying personal information of each of the plurality of persons; creating, for each physiological cluster, a health indicator deviation pattern for the healthcare event; continuously monitoring values of the health indicator of the person; and determining an occurrence probability of the healthcare event when the monitored indicators deviate from the personal physiological pattern profile. A system for predicting a healthcare event is also disclosed.

One or more embodiments described herein relate to predicting, using adaptive artificial intelligence techniques, typical and aberrant physiological reactions of a patient to psychiatric counseling. Treatment plans can be determined and calculated based on previously-gathered demographic and/or biometric data, and/or modifications to treatment plans can be determined and/or implemented based on emergent recognition of reaction types, such as reclassifying reactions that would previously have been deemed typical as aberrant (or vice versa).

Disclosed are methods and medical device systems for automated delivery of therapies for pain and determination of need for and safety of treatment. In one embodiment, such a medical device system may comprise a sensor configured to sense at least one body signal from a patient; and a medical device configured to receive a first sensed body signal from the sensor; determine a patient pain index based at least in part on said first sensed body signal; determine whether said patient pain index is above at least a first pain index threshold; determine a safety index based at least in part on a second sensed body signal; select a pain treatment regimen based on at least one of said safety index and or a determination that said pain index is above said first pain index threshold; and deliver said pain treatment regimen.