Abstract: A display device configured to receive raw values, calculate smoothed values, and display the smoothed values only if the smoothed values satisfy a consistency check. In some embodiments, the consistency check may be satisfied if none of the smoothed values (i) corresponds to a raw value that triggered a first or second alarm and (ii) would not also trigger the first or second alarm. In some embodiments, if the smoothed values do not satisfy the consistency check, the display device may re-calculate the smoothed values such that the consistency check is satisfied. The display device may display the smoothed values in place of corresponding raw values in a historical trend graph.

Abstract: A display device configured to receive raw values, calculate smoothed values, and display the smoothed values only if the smoothed values satisfy a consistency check. In some embodiments, the consistency check may be satisfied if none of the smoothed values (i) corresponds to a raw value that triggered a first or second alarm and (ii) would not also trigger the first or second alarm. In some embodiments, if the smoothed values do not satisfy the consistency check, the display device may re-calculate the smoothed values such that the consistency check is satisfied. The display device may display the smoothed values in place of corresponding raw values in a historical trend graph.

Abstract: Systems, methods, and apparatuses for reducing error between calculated analyte levels and impractical analyte measurements using practical analyte measurements as reference measurements for calibration. Reducing the error may include converting a practical reference analyte measurement into an estimated impractical analyte level, updating a conversion function using the estimated impractical analyte level, and using the updated conversion function to calculate an analyte level. In some aspects, the practical reference analyte measurement may be a capillary blood analyte measurement, and the estimated impractical analyte level may be an estimated venous blood analyte level. In some aspects, the updated conversion function may minimize the error between analyte levels calculated using the updated conversion function and estimated venous blood analyte levels.

Abstract: Methods, systems, and apparatuses for dynamic modification of calibration frequency. Dynamic modification of calibration frequency may include one or more of: receiving sensor data conveyed by an analyte sensor comprising an analyte indicator, using the sensor data to calculate one or more analyte levels, and receiving one or more reference analyte level measurements. Dynamic modification of calibration frequency may include using the sensor data, the one or more calculated analyte levels, and/or the one or more reference analyte level measurements to calculate a degradation rate of the analyte indicator of the analyte sensor. Dynamic modification of calibration frequency may include setting a dynamic calibration frequency based on the calculated degradation rate.

Abstract: A display device configured to receive raw values, calculate smoothed values, and display the smoothed values only if the smoothed values satisfy a consistency check. In some embodiments, the consistency check may be satisfied if none of the smoothed values (i) corresponds to a raw value that triggered a first or second alarm and (ii) would not also trigger the first or second alarm. In some embodiments, if the smoothed values do not satisfy the consistency check, the display device may re-calculate the smoothed values such that the consistency check is satisfied. The display device may display the smoothed values in place of corresponding raw values in a historical trend graph.

Abstract: Methods and systems for assessing brain activity and collecting information related to a subject's condition and brain electrical activity are provided. The methods and systems include collecting and transferring data related to the neurological state of a subject. The systems and methods further include performing a first assessment of the collected data using an assessment device and performing a second assessment of the collected data using a computer. The methods further include comparing the first assessment and the second assessment and recording whether the first assessment and second assessment are consistent.

Abstract: A method of building classifiers for neurological assessment is described. The method comprises the steps of extracting quantitative features from a plurality of clinical features, and selecting a subset of features from the extracted pool of features to construct binary classifiers. A device for performing point-of-care neurological assessment using clinical features is also described.

Abstract: A method of building classifiers for neurological assessment is described. The method comprises the steps of extracting quantitative features from a plurality of clinical features, and selecting a subset of features from the extracted pool of features to construct binary classifiers. A device for performing point-of-care neurological assessment using clinical features is also described.

Abstract: A method of building classifiers for neurological assessment is described. The method comprises the steps of extracting quantitative features from a plurality of clinical features, and selecting a subset of features from the extracted pool of features to construct binary classifiers. A device for performing point-of-care neurological assessment using clinical features is also described.

Abstract: A method of building classifiers for neurological assessment is described. The method comprises the steps of extracting quantitative features from a plurality of clinical features, and selecting a subset of features from the extracted pool of features to construct binary classifiers. A device for performing point-of-care neurological assessment using clinical features is also described.

Abstract: A method of building binary classifiers for classification of brain electrical activity data into one or more neurological classes is described. The method comprises the steps of extracting quantitative features from the brain electrical activity data, and reducing the pool of extracted features into a computationally manageable and statistically relevant set of features which can then be used for designing one or more classifiers.