Activities and abnormalities in activities are detected by: (1) receiving data corresponding to measurements of an activity occurring during a time interval; (2) determining a plurality of primitives associated with the data, wherein each of the plurality of primitives represents a characteristic pattern in a portion of the time interval; (3) derive an activity structure relating a first subset of the plurality of primitives that are correlated in time; and (4) based on the activity structure, classify a second subset of the plurality of primitives as an abnormal instance of the bodily activity.

In some embodiments, the present invention provides an exemplary inventive system that includes: an apparatus to record: individual's brain electrical activity, a physiological parameter of the individual, and iii) an environmental parameter; a computer processor configured to perform: obtaining a recording of the electrical signal data; projecting the obtained recording of electrical signal data onto a pre-determined ordering of a denoised optimal set wavelet packet atoms to obtain a set of projections; normalizing the particular set of projections of the individual using a pre-determined set of normalization factors to form a set of normalized projections; determining a personalized mental state of the individual by assigning a brain state; determining a relationship between: the physiological parameter, the environmental parameter, and the personalized mental state; generating an output, including: a visual indication, representative of the personalized mental state, and) a feedback output configured to affect the personalized mental state of the individual.

In some embodiments, the present invention provides an exemplary inventive system that includes: an apparatus to record: individual's brain electrical activity, a physiological parameter of the individual, and iii) an environmental parameter; a computer processor configured to perform: obtaining a recording of the electrical signal data; projecting the obtained recording of electrical signal data onto a pre-determined ordering of a denoised optimal set wavelet packet atoms to obtain a set of projections; normalizing the particular set of projections of the individual using a pre-determined set of normalization factors to form a set of normalized projections; determining a personalized mental state of the individual by assigning a brain state; determining a relationship between: the physiological parameter, the environmental parameter, and the personalized mental state; generating an output, including: a visual indication, representative of the personalized mental state, and) a feedback output configured to affect the personalized mental state of the individual.

A monitoring device suitable for attachment to a surface of a subject, the device having a data collector and a processor. The data collector includes a flexible foil attached to a less flexible socket, where the foil forms a dermal side surface of the data collector for adhesion to a skin surface of a subject to be monitored. To enable communication of electrical signals between the data collector and the processor, the data collector includes a distribution structure formed as a pattern of an electrically conductive material on an outer surface of a foldable sheet. The foldable sheet forms a layer in the flexible foil and having an interface portion which is folded into an aperture in the socket to form a coupling inside the cavity for electrical communication with a matching coupling of the processor when the processor is received in the cavity.

An electronic device comprising: a sensor; a memory; and a first processor, wherein the first processor is configured to: receive first data of a user of the electronic device sensed at preconfigured intervals via the sensor; identify second data comprising location information of the user or information input by the user; compare the first data and the second data with threshold values pre-stored in the memory to generate a result; detect a current health state of the user based on the result; output recommended guide information based on the detected current health state; and re-detect the current health state of the user based on changed first data received at the preconfigured intervals and the second data.

A method of modulating a brain activity of a mammal is achieved by subjecting the mammal to repetitive transcranial magnetic stimulation (rTMS) with an rTMS apparatus at variable pulse intervals for a time sufficient to modulate said brain activity. Improvement in a physiological condition or a clinical condition is achieved. Conditions to be treated include but are not limited to PTSD, autism spectrum disorder and Alzheimer's disease. Wavelet transform analysis is used to determine the variable pulse intervals employed.

A method of distinguishing a non-epileptic seizure from an epileptic seizure in a patient, comprising: detecting a seizure in a patient based on at least one first body signal of the patient selected from an autonomic signal, a neurologic signal, a metabolic signal, an endocrine signal, and a tissue stress marker signal; analyzing at least one second body signal of the patient selected from an autonomic signal, a neurologic signal, a metabolic signal, an endocrine signal, and a tissue stress marker signal; determining, based on the analyzing, at least a first classification index comprising at least one of an epileptic seizure index and a non-epileptic seizure index; and classifying the seizure as one of a non-epileptic seizure or an epileptic seizure based on the at least a first classification index. A medical device system capable of implementing the method. A computer-readable device for storing data that, when executed, perform the method.

A brain disease diagnosis assistance system acquires a plurality of learning data items, each including brain wave feature data including feature amounts of brain waves extracted from the brain waves and disease information attached to the brain wave feature data, the disease information indicating a state of a brain disease corresponding to the brain wave feature data and classifies the plurality of acquired learning data into a plurality of clusters. A classifier for classifying learning data into portions corresponding respectively to types of disease information is generated based on disease information attached to learning data in each classified cluster. Then, brain wave feature data of a subject is acquired and a cluster to which the brain wave feature data is classified is specified and one of a plurality of brain diseases, which corresponds to the brain wave feature data of the subject, is determined through the generated classifier.

A system for measuring and monitoring physiologically relevant motion of a subject includes at least a motion sensor to measure movement of the subject and produce a series of movement data representing the movement of the subject over a period of time. The system also includes at least a biometric sensor to simultaneously measure biometrics of the subject and produce a series of biometric values of the subject over the period of time. The system is configured to determine a noise-to-signal ratio for the series of movement data as a function of biometric intervals in the series of biometric values and identify at least a portion of the series of movement data as corresponding to a physiologically relevant biorhythm. The system can be used to diagnose and monitor a disease or disorder, including a neurological disorder or a traumatic brain injury.

A method, comprising receiving a time series of patient body signal, determining first and second sliding time windows for the time series; applying an autoregression algorithm, comprising: applying an autoregression analysis to each of the first and second windows, yielding autoregression coefficients and a residual variance for each window; estimating a parameter vector for each window based on the autoregression coefficients and residual variances; and determining a difference between the parameter vectors; and determining seizure onset and seizure termination based on the difference between the parameter vectors. A non-transitory computer readable program storage unit encoded with instructions that, when executed by a computer, perform the method.