This disclosure relates generally to a Parkinson's disease detection system. Parkinson's disease is a neuro-degenerative disorder affecting motor and cognitive functions of subjects. Since symptom manifestation is limited in Parkinson's disease, identifying Parkinson's disease in the early stage is a challenging task. The present disclosure overcomes the limitations of the conventional methods for detecting Parkinson's disease by utilizing a graph theory approach. Here, each pressure sensor attached to an insole corresponding to a plurality of pressure points associated with a foot of the subject is considered as a node of a connectivity graph. The foot dynamics analysis is performed based on a metric known as mediolateral stability index and the mediolateral stability index is calculated by utilizing a betweenness centrality associated with each node of the connectivity graph. Further, the mediolateral stability index is compared with standard values to detect the intensity of the Parkinson's disease.

Systems and methods for localizing an upper eyelid in an image of a subject are provided. An image of an eye of the subject is obtained in electronic format. The image is inputted into a trained neural network comprising at least 10,000 parameters, thereby obtaining a set of coordinates for an upper eyelid in the image. This obtaining and inputting can be repeated over the course of a non-zero duration thereby obtaining a corresponding set of coordinates for the upper eyelid in each image in a plurality of images. Each corresponding set of coordinates for the upper eyelid from each image in the plurality of images can be used to determine whether the subject is afflicted with a neurological condition.

The method of the present invention quantifies the severity of a subject's neurodegenerative disorder. The subject answers a questionnaire which results in a patient-reported outcome dataset. Benchmark tests are carried out by the subject performing one or more tasks resulting in a task result dataset. Continuous sensors collect data resulting in a sensor dataset. Short assessment tests of the subject are conducted resulting in a short assessment dataset. The patient-reported outcome dataset, task result dataset, sensor dataset, and short assessment dataset are aggregated into an output dataset that includes non-motor outcome measures and motor outcome measures. A single score is generated that quantifies the severity of a neurodegenerative disorder of the subject based on the output dataset.

Systems and method may be used for interfacing with a patient. Systems may include a plurality of electrodes in electrical communication with a processor. The processor may be configured to receive sense signals from electrodes and to determine the reliability of the received signal. A test tone signal comprising a test tone frequency may be applied, and the magnitude of the test tone frequency may be analyzed in the received signal. If it is determined that the magnitude of the test tone frequency is below a threshold, the system may take action, such as lowering the gain on an amplifier. Stimulation signals may be applied to the patient at a stimulation frequency simultaneously with one or both of receiving sense signals and providing the test tone signal.

A stimulation system includes an energy source, an electronics unit with a controller, and an actuator that is coupled with the electronics unit and/or the energy source. The actuator emits electromagnetic waves for stimulation of genetically manipulated tissue. The electronics unit is disposed in a housing. The stimulation system is configured for at least temporary implantation in a human or animal body. The controller controls the stimulation of tissue in the body by way of the electromagnetic waves emitted by the actuator. A selector of the stimulation system selects the area of the said tissue for stimulation. The selector includes a masking device for masking certain areas of the tissue, so that an intensity of the stimulation for the masked areas is reduced or equal to zero.

The present invention is directed to a method for determining a paroxysmal slow waves event (PSWE) so as to determine blood-brain barrier dysfunction (BBBD) or increased risk of developing a neurological disease or disorder in a subject.

Systems and methods are provided for evaluating a patient for a neurological movement disorder. A three-dimensional medical image of a brain of the patient is captured and provided to an artificial neural network having at least one convolutional layer to provide a set of output values. The set of output values is provided to a machine learning model to provide a clinical parameter representing one of a presence of the neurological movement disorder in the patient and a response of the patient to a specific treatment for the neurological movement disorder.

Embodiments of the instant invention relate to applying motor learning to promote remyelination following demyelination in a subject having a condition or disease. In certain embodiments, applying motor learning alone or in combination with vagus nerve stimulation (VNS) induces the production of new and preserves surviving oligodendrocytes. In accordance with certain embodiments of the disclosure, motor learning, when properly timed, enhances oligodendrogenesis after injury and recruits mature oligodendrocytes to participate in remyelination through the generation of new myelin sheaths. In other aspects of the disclosure, VNS paired with motor learning enhances remyelination following demyelination.

In one embodiment, a method for detecting tremors includes generating electromagnetic fields proximate to an individual's body part with a circuit to generate an eddy current density on a surface of the body part, receiving magnetic fields generated by the eddy current with the circuit that change a resonant frequency of the circuit, sensing the resonant frequency as it changes over time, and determining a movement frequency of the body part from the resonant frequency to quantify tremors in the body part.

The disclosure provides a theseometer or proprioceptometer for objectively quantifying the proprioceptive performance of a subject such as a human. The disclosed theseometer is a device comprising a clear, rigid material or screen having or exhibiting a distinguishable target embraced by a series of concentric rings, a digital camera with a lens concentric to the target, a base unit comprising an electronic processor and memory for analyzing data and, optionally, a wheeled base to provide mobility and portability.