The present disclosure describes an electrode patch, and a method, a measurement arrangement, and a detection system utilizing the electrode patch for detecting an indicator of Parkinson's disease in a person from a muscle in a limb of the person. The electrode patch comprises two measurement electrodes, wherein a distance between centres of the measurement electrodes is above 2 cm and less than 4 cm, and a reference electrode positioned such that a lateral distance of from a centre of the reference electrode from to an axis passing through the centres of the measurement electrodes is at least he distance between the measurement electrodes.

The present disclosure relates to systems and methods for characterizing a behavior change of a process. A behavior model that can include a set of behavior parameters can be generated based on behavior data characterizing a prior behavior change of a process. A stimulus parameter for a performance test can be determined based on the set of behavior parameters. An application of the performance test to the process can be controlled based on the stimulus parameter to provide a measure of behavior change of the process. Response data characterizing one or more responses associated with the process during the performance test can be received. The set of behavior parameters can be updated based on the response data to update the behavior model characterizing the behavior change of the process. In some examples, the behavior model can be evaluated to improve or affect a future behavior performance of the process.

A technical solution is described for implementing a computer-executed signal processing algorithm to search for time domain segments of a recorded electroencephalogram (EEG) that are highly correlated, either positively or negatively, to one or more, individual, synthetically generated, single-period single-frequency (SPSF) sinusoids. The SPSFs are motivated by the combined concepts of individual Striatal Beat Frequencies (SBF) used to model cortical neuron activity, Frequency Domain Reflectometry used to study Voltage Standing Wave Ratios (VSWR), Geophysics Seismograms, and ghosting effects of multipath passing through periodic sinusoids. This computationally intense approach is only recently realizable through the advent of high performance computing. The SPSF approach, since it is not constrained to the error-laden one-window-fits-all approach of the Time-Frequency Spectrogram, offer's a more detailed basis to assess, and truer visualization of, the health of brain's electrical activities. This approach is a push-back against the Uncertainty Principal.

In some embodiments, the systems and methods of the disclosure can efficiently and accurately classify neurodegenerative disorder(s) and/or movement disorder(s) of a subject (e.g., a patient) using at least quantitative features associated with one or more regions of interest determined from one or more sets of image data of the subjects brain. The method may include processing one or more sets of MRI image data of the subjects brain to extract one or more quantitative features for one or more regions. The one or more quantitative features may include a first quantitative and a second quantitative feature. The method may further include classifying at least the one or more quantitative features into one or more classes associated with neurodegenerative dementia disorder, neurodegenerative movement disorder, non-neurodegenerative movement disorder and/or heathy control. The method may include generating a report including a classification of at least the one or more quantitative features.

A system, method, and non-transitory computer readable medium for discriminating between patients with neurodegenerative disease and healthy patients. The method includes obtaining a first plurality of voice signals from known healthy humans and known neurogenerative diseases humans, extracting long-term acoustic features of the first plurality of voice signals, extracting Mel frequency coefficients (MFCCs) from the first plurality of voice signals, creating a set A of short-term acoustic features based on the MFCCs, performing a backward stepwise selection to create a set B of long-term acoustic features and a set C, where set C includes the features of set B combined with the features of set A, creating a random forest classification model, obtaining a second plurality of voice signals from humans of undetermined health status, and applying the second plurality of voice signals against the random forest classification model to determine which patients are neurodegenerative diseased patients.

Example systems, methods, and apparatus are provided for using data collected from the responses of an individual with the computerized tasks of a cognitive platform to derive performance metrics as an indicator of cognitive abilities, and applying predictive models to generate an indication of a neurodegenerative condition. The example systems, methods, and apparatus also can be configured to adapt the computerized tasks to enhance the individual's cognitive abilities, and for using data collected from the responses of an individual with the adapted computerized tasks to derive performance metrics and applying predictive models to generate the indication of neurodegenerative condition.

In some examples, a system may include a plurality of electrodes, electrical stimulation circuitry, and a controller. The controller may be configured to select one or more parameters of therapy to be delivered to a brain of a patient and to control the electrical stimulation circuitry to deliver the therapy to the brain of the patient based on the selected parameters and via a first one or more electrodes of the plurality of electrodes. The parameters may be defined based on a first plurality of electrical signals sensed at a plurality of different positions within the brain of the patient when electrical stimulation is not delivered at each of the positions and a second plurality of electrical signals sensed at each of the plurality of different positions within the brain of the patient in response to electrical stimulation delivered at each of the positions at a plurality of different intensities.

The server is a learning apparatus including a data acquisition unit and a learning unit. The data acquisition unit acquires profile data and a selected assistance level as learning data. The profile data indicates a profile related to a trainee before executing rehabilitation regarding rehabilitation executed using a walking training apparatus as a rehabilitation support system. The selected assistance level is an assistance level selected at the time of executing the rehabilitation. The learning unit learns to determine a recommended assistance level recommended to be selected when the trainee uses the rehabilitation support system based on the learning data. Further, the learning unit generates a trained model that receives the profile data and outputs the recommended assistance level based on the learning.

A system and method for evaluation, detection, conditioning, and treatment of neurological functioning and conditions which uses data obtained while a person is engaged in simultaneously in a range of primary physical tasks combined with defined types of associative activity, such as listening, reading, speaking, mathematics, logic puzzles, navigation of a virtual environment, recall of past stimuli, etc. The data from the physical and associative activities are combined to generate a composite functioning score visualization indicating the relative functioning of areas aspects of neurological functioning; including those in which deficiencies may be present, which are early indicators of possible neurological conditions. Through algorithmic recommendations combined with expert and user input, a conditioning regimen targeting neurological aspects of interest paired with periodic testing allows the user to track their progress in these areas over time.

The present invention relates to systems adapted for remotely and intelligently tuning movement disorder of therapy systems. The present invention still further provides systems adapted for quantifying movement disorders for the treatment of patients who exhibit symptoms of such movement disorders including, but not limited to, Parkinson's disease and Parkinsonism, Dystonia, Chorea, and Huntington's disease, Ataxia, Tremor and Essential Tremor, Tourette syndrome, stroke, and the like. The present invention yet further relates to systems adapted for remotely and intelligently or automatically tuning a therapy device using objective quantified movement disorder symptom data to determine the therapy setting or parameters to be transmitted and provided to the subject via his or her therapy device. The systems of the present invention also are adapted to provide treatment and tuning intelligently, automatically and remotely, allowing for home monitoring of subjects.