Abstract: Disclosed herein is an implantable accommodative IOL system for insertion into an eye of a patient, the system comprising: an optical element and a housing including an opaque frame. The optical element comprises an optical lens having variable optical power, and the opaque frame is circumferentially disposed around a periphery of the optical element.

Abstract: Disclosed herein is an implantable accommodative IOL system for insertion into an eye of a patient, the system comprising: an optical element and a housing including an opaque frame. The optical element comprises an optical lens having variable optical power, and the opaque frame is circumferentially disposed around a periphery of the optical element.

Abstract: A system for preparing and analyzing a sample of biological material, including a test cartridge having a first housing defining a flow-through chamber, a second housing defining a central space within which the first housing is at least partially located. The first housing is rotatable relative to the second housing, and the second housing defines a plurality of circumferentially spaced chambers, one of the chambers having an inlet for receiving a sample, at least one of the chambers containing a liquid reagent, and at least one of the chambers comprising an analysis module, the chambers of the second housing each having an opening into the central space. The first housing has one or more openings into the central space so that openings can be selectively aligned with one of the openings into the chambers of the second housing by relative rotation of the first housing and second housings.

Abstract: A system for preparing and analysing a sample of biological material, including a test cartridge having a first housing defining a flow-through chamber, a second housing defining a central space within which the first housing is at least partially located. The first housing is rotatable relative to the second housing, and the second housing defines a plurality of circumferentially spaced chambers, one of the chambers having an inlet for receiving a sample, at least one of the chambers containing a liquid reagent, and at least one of the chambers comprising an analysis module, the chambers of the second housing each having an opening into the central space. The first housing has one or more openings into the central space so that openings can be selectively aligned with one of the openings into the chambers of the second housing by relative rotation of the first housing and second housings.

Abstract: Test kits for assessing male fertility include a sample holder defining an object plane, a lens, and a two dimensional light sensor defining an image plane arranged along a common linear axis. The distance between the object plane and the image plane may be no more than 50 mm, and may be no more than 30 mm. A lens aperture may have an area of 1-10 mm2. The test kit may have a housing with a maximum linear dimension of no more than 100 mm. Processing circuitry may be provided that is configured to produce a sperm count and/or sperm motility measurements by processing image data from the two-dimensional light sensor.

Abstract: A physiological monitoring system may process a physiological signal such a photoplethysmograph signal from a subject. The system may determine physiological information, such as a physiological rate, from the physiological signal. The system may use search techniques and qualification techniques to determine one or more initialization parameters. The initialization parameters may be used to calculate and qualify a physiological rate. The system may use signal conditioning to reduce noise in the physiological signal and to improve the determination of physiological information. The system may use qualification techniques to confirm determined physiological parameters. The system may also use autocorrelation techniques, cross-correlation techniques, fast start techniques, and/or reference waveforms when processing the physiological signal.

Abstract: A physiological monitoring system may process a physiological signal such a photoplethysmograph signal from a subject. The system may determine physiological information, such as a physiological rate, from the physiological signal. The system may use search techniques and qualification techniques to determine one or more initialization parameters. The initialization parameters may be used to calculate and qualify a physiological rate. The system may use signal conditioning to reduce noise in the physiological signal and to improve the determination of physiological information. The system may use qualification techniques to confirm determined physiological parameters. The system may also use autocorrelation techniques, cross-correlation techniques, fast start techniques, and/or reference waveforms when processing the physiological signal.

Abstract: A physiological monitoring system may determine physiological information, such as physiological rate information, from a physiological signal. The system may generate a lag matrix, which includes multiple segments of the physiological signal each having the same number of values. The system may generate a correlation matrix, which includes multiple correlation values, based on the lag matrix. The system may identify a peak in the correlation lag matrix, or a processed matrix derived thereof, and the corresponding lag value. The correlation matrix, or processed matrix thereof, may be rotated, averaged, or otherwise transformed by the system to identify the lag value. The system may determine physiological rate information based on the identified lag value.

Abstract: A physiological monitoring system may determine physiological information, such as physiological rate information, and other information, such as signal-to-noise information, from a physiological signal. The system may generate at least one difference signal based on the physiological signal and sort the at least one difference signal to generate at least one sorted difference signal. The system may analyze the at least one sorted difference signal to determine at least two values indicative of noise. The system may determine a value indicative of a signal-to-noise ratio based on the two or more values indicative of noise.

Abstract: A physiological monitoring system may determine physiological information, such as physiological rate information, from a physiological signal. The system may generate a correlation sequence between two segments of the physiological signal at multiple correlation lag values. The system may compare the correlation sequence to a predetermined threshold, which may vary as a function of lag. Based on the comparison, the system may determine whether the correlation sequence value exceeds the threshold, and whether the correlation sequence value corresponds to a peak. The system may identify a lag value when the correlation sequence corresponding to the lag value exceeds the threshold and corresponds to a peak. The system may determine physiological rate information based on the identified lag value.

Abstract: A physiological monitoring system may determine physiological information, such as physiological rate information, and other information, such as noise information, from a physiological signal. The system may generate a first difference signal based on a first segment of the physiological signal and sort the first difference signal to generate a first sorted difference signal. The system may generate a second difference signal based on a second segment of the physiological signal and sort the second difference signal to generate a second sorted difference signal. The first and second sorted difference signals may be analyzed and a value indicative of noise may be determined based on the analysis.

Abstract: A physiological monitoring system may process a physiological signal such a photoplethysmograph signal from a subject. The system may determine physiological information, such as a physiological rate, from the physiological signal. The system may use search techniques and qualification techniques to determine one or more initialization parameters. The initialization parameters may be used to calculate and qualify a physiological rate. The system may use signal conditioning to reduce noise in the physiological signal and to improve the determination of physiological information. The system may use qualification techniques to confirm determined physiological parameters. The system may also use autocorrelation techniques, cross-correlation techniques, fast start techniques, and/or reference waveforms when processing the physiological signal.

Abstract: A physiological monitoring system may determine physiological information, such as physiological rate information, from a physiological signal. The system may condition the physiological signal to assist in the determination of the physiological information. The system may generate a positive signal and a negative signal based on respective positive and negative values of the physiological signal. The system may filter the positive and negative signals, combine the filtered signals, and modify the physiological signal based on the combined signal. The physiological signal may be modified, for example, by subtracting the combined signal from the physiological signal.

Abstract: A physiological monitoring system may determine physiological information, such as physiological rate information, from a physiological signal. The system may select two segments of the physiological signal, shifted in time relative to one another by a lag value, and determine a correlation value between the two segments. The system may determine a metric based on the segments, and determine correlation information based on the correlation value and the metric. The metric may be indicative of how well the segments are correlated, providing additional information relative to the correlation value. Based on the metric, the system may modify the correlation value, determine a confidence value, or determine other information. The system may determine physiological rate information based on the correlation information.

Abstract: A physiological monitoring system may determine physiological information, such as physiological rate information, from a physiological signal. The system may receive a calculated value indicative of a period associated with a physiological rate. The system may generated a first sorted difference signal based on a segment of the physiological signal having a size corresponding to the period. The system may generate second and third sorted difference signals based on segments of the physiological signal having sizes corresponding to a fraction of the period and a multiple of the period. The system may analyze the first, second, and third sorted difference signals, and qualify or disqualify the calculated value based on the analysis.

Abstract: A physiological monitoring system may determine physiological information, such as physiological rate information, from a physiological signal. The system may receive a calculated value indicative of a period associated with a physiological rate. The system may determine a first value indicative of a baseline of the physiological signal and a second value indicative of a deviation of the physiological signal from the baseline. The first value may, for example, be a median value, an average, or a coefficient corresponding to a best fit curve of the physiological signal. The second value may be a standard deviation value, a standard error, or a root mean square value based on the physiological signal. The system may qualify or disqualify the calculated value based on the first and second values.

Abstract: A physiological monitoring system may determine physiological information, such as physiological rate information, from a physiological signal. The system may generate a correlation sequence using two segments of the physiological signal. The system may determine a first correlation lag value that corresponds to a peak in the correlation sequence, and also determine a second correlation lag value equal to a fraction of the first correlation lag value. The fraction may be, for example, one half. The system may qualify or disqualify the correlation lag value based on the correlation value at the second lag value. The system may compare the correlation value at the second lag value to a threshold, to the correlation sequence at the first lag value, or both.

Abstract: A physiological monitoring system may determine physiological information, such as physiological rate information, from a physiological signal. The system may determine a skew metric based on the physiological signal. The system may also determine a correlation lag value corresponding to a peak in a correlation sequence derived from the physiological signal. The system may qualify or disqualify the correlation lag value based on the skew metric. The system may, for example, compare the skew metric and the correlation lag value to a reference set of skew metric values and correlation lag values to determine whether to qualify or disqualify the correlation lag value.

Abstract: A physiological monitoring system may determine physiological information, such as physiological rate information, from a physiological signal. The system may receive a calculated value indicative of a physiological rate. The system may generate value pairs from a first collection of values of the physiological signal and another collection of corresponding value of the physiological signal spaced from the first collection based on the calculated value. The system may determine a best fit linear relationship based on the value pairs and determine at least one statistical metric based on the linear relationship and the value pairs. The system may qualify or disqualify the calculated value based on the at least one statistical metric.

Abstract: A physiological monitoring system may determine physiological information, such as physiological rate information, from a physiological signal. The system may determine a skew metric based on the physiological signal. The system may determine an algorithm setting based on a reference relationship between the determined skew metric and a value indicative of a physiological rate. The algorithm setting may, for example, affect the amount of filtering applied to the physiological signal.