Embodiments of the present invention may provide techniques that provide improved detection of catecholamines, such as dopamine. For example, in an embodiment, a method for catecholamine sensing may comprise outputting a signal responsive to a level of at least one catecholamine in neural tissue from a catecholamine sensor, analyzing the signal responsive to a catecholamine level in the neural tissue using circuitry connected to the catecholamine sensor, the circuitry comprising at least one computing device comprising a processor, memory accessible by the processor, and program instructions stored in the memory and executable by the processor, generating, using the circuitry, data representing the catecholamine level in the neural tissue, and transmitting the generated data representing the catecholamine level in the neural tissue using communication circuitry.

Described are methods, devices, and systems for a novel, inexpensive, easy to use therapy for treatment of anxiety. Described are methods and devices to treat anxiety that involves no medication. Methods and devices described herein use alternating magnetic fields to gently tune the brain and affect symptoms of anxiety.

Techniques for determining treatment of a subject with IS based on EEG-derived biomarkers are provided. According to some aspects, a method of adapting treatment of a subject having infantile spasms (IS) is provided, the method comprising obtaining electroencephalogram (EEG) data of the subject, determining a measure of delta power of the EEG data and/or a measure of spike frequency of the EEG data, and determining subsequent treatment of the infantile spasms of the subject based at least in part on the determined measure of delta power of the EEG data and/or measure of spike frequency of the EEG data.

Methods, systems, and devices are disclosed for an efficient hardware architecture to implement gradient boosted trees for detecting biological conditions. For example, a method of detecting a biological condition includes receiving, by a device, a plurality of physiological signals from a plurality of input channels of the device, selecting, based on a trained prediction model, one or more input channels from the plurality of input channels, converting the one or more physiological signals received from the one or more input channels to one or more digital physiological signals, identifying, by using the plurality of gradient boosted decision trees, the selected characteristic in the one or more digital physiological signals, and determining a presence of a physiological condition based on an addition of the output values obtained from the plurality of gradient boosted decision trees.

Neurological abnormalities are often discovered through observation by health care providers, and/or parent report. Many neurodevelopmental disorders such as ASD are purely identified through behavioral analysis, and cannot be screened for using a biomarker or quantitative stimulus-response test. Current screening tools contain subjective components based on parent report and clinician observation, vary in consistency of use across providers, and demands resources, knowledge, and access to skilled expertise. As a result, the only tests used today require lengthy and subjective behavioral analysis and often, miss or misidentify neurodevelopmental disorders contributing to a delayed diagnosis. The technology disclosed herein allow for a solution to this systemic problem.

A method and user device for providing a unified Parkinson's disease rating scale (UPDRS) value by a user device includes generating, by a sensor of the user device, sensor data based on a user of the user device manipulating the user device. The UPDRS value is determined based on the sensor data and a deep neural network (DNN) model. The UPDRS value is provided to permit an evaluation of the user based on the UPDRS value.

Systems and methods for identifying abnormal movements or tremors in one or more human subjects. Kinetic and/or electromyographic sensors are employed in detection hardware to detect voluntary and involuntary movements. The data collected from such voluntary and involuntary movement detection can be further processed and compared to baseline data to identify and distinguish abnormal movements from normal movements. The identification of abnormal movements may indicate a neurodegenerative disorder.

Systems and methods for machine learning of voice and other attributes are provided. The system receives input data, isolates predetermined sounds from isolated speech of a speaker of interest, summarizes the features to generate variables that describe the speaker, and generates a predictive model for detecting a desired feature of a person Also provided are systems and methods for detecting one or more attributes of a speaker based on analysis of audio samples or other types of digitally-stored information (e.g, videos, photos, etc.).

Systems and methods for machine learning of voice and other attributes are provided. The system receives input data, isolates predetermined sounds from isolated speech of a speaker of interest, summarizes the features to generate variables that describe the speaker, and generates a predictive model for detecting a desired feature of a person Also provided are systems and methods for detecting one or more attributes of a speaker based on analysis of audio samples or other types of digitally-stored information (e.g, videos, photos, etc.).

Monitoring brain neural activity comprises repeatedly applying electrical stimuli to evoke neural responses in the brain. Neural responses evoked by the stimuli are recorded. The recorded neural responses are assessed for changed characteristics over time, to monitor a time-varying effect on the recorded neural responses of local field potentials arising from a source other than the electrical stimuli.