The Experts below are selected from a list of 69 Experts worldwide ranked by ideXlab platform

Craig S. Hamilton - One of the best experts on this subject based on the ideXlab platform.

  • Acoustic Pharyngometry: A Substitute for Drug-Induced Sleep Endoscopy?
    Otolaryngology–Head and Neck Surgery, 2012
    Co-Authors: Michael Friedman, Christian G. Samuelson, Craig S. Hamilton
    Abstract:

    Objective: Upper airway collapse measured using supine acoustic pharyngometry at respiratory residual volume (RV) has previously been correlated with obstructive sleep apnea-hypopnea syndrome (OSAHS) severity. We aim to assess the agreement between sites of maximal upper airway obstruction measured by supine acoustic pharyngometry and drug-induced sleep-endoscopy (DISE) in snoring/OSAHS patients.Method: In this case series, 50 consecutive patients with known snoring/OSAHS underwent in-office supine acoustic pharyngometry and DISE. Pharyngometric measurements at respiratory tidal volume were compared against the Standard Normal Curve to establish airway landmarks. Sites of minimal cross-sectional-area at respiratory RV were compared with sites of maximal obstruction identified through DISE.Results: Fifty patients (68% male, 32% female, age 47.3 ± 13.7, mean AHI 37.0 ± 26.8) were evaluated. All endoscopic assessments were performed by a single investigator (M.F.). Regions of maximal upper airway collapse pe...

Dimitar Kazakov - One of the best experts on this subject based on the ideXlab platform.

  • SAX Discretization Does Not Guarantee Equiprobable Symbols
    IEEE Transactions on Knowledge and Data Engineering, 2015
    Co-Authors: Matthew Butler, Dimitar Kazakov
    Abstract:

    In time series analysis research, there is a strong interest in discrete representations of real valued data streams. One approach still considered state-of-the-art is the Symbolic Aggregate Approximation (SAX) algorithm. The interest of this paper concerns the SAX assumption of data being highly Gaussian and the use of the Standard Normal Curve to choose partitions to discretize the data. The SAX approach chooses partitions on the Standard Normal Curve that would produce an equal probability for each symbol. This procedure is generally valid as a time series is Normalized to have $\mu = 0$ and $\sigma = 1$ . However, there exists a caveat to this assumption of equi-probability due to the intermediate step of Piecewise Aggregate Approximation (PAA). We show in this paper that when PAA is applied, the distribution of the data is altered, resulting in a shrinking Standard deviation that is proportional to the number of points used to create a segment of the PAA representation and the degree of auto-correlation within the series. Data that exhibits statistically significant auto-correlation is less affected by this shrinking distribution. As the Standard deviation of the data contracts, the mean remains the same, however the distribution is no longer Standard Normal and therefore the partitions based on the Standard Normal Curve are no longer valid for the assumption of equal probability.

  • Creating a level playing field for all symbols in a discretization
    arXiv: Data Structures and Algorithms, 2012
    Co-Authors: Matthew Butler, Dimitar Kazakov
    Abstract:

    In time series analysis research there is a strong interest in discrete representations of real valued data streams. One approach that emerged over a decade ago and is still considered state-of-the-art is the Symbolic Aggregate Approximation algorithm. This discretization algorithm was the first symbolic approach that mapped a real-valued time series to a symbolic representation that was guaranteed to lower-bound Euclidean distance. The interest of this paper concerns the SAX assumption of data being highly Gaussian and the use of the Standard Normal Curve to choose partitions to discretize the data. Though not necessarily, but generally, and certainly in its canonical form, the SAX approach chooses partitions on the Standard Normal Curve that would produce an equal probability for each symbol in a finite alphabet to occur. This procedure is generally valid as a time series is Normalized before the rest of the SAX algorithm is applied. However there exists a caveat to this assumption of equi-probability due to the intermediate step of Piecewise Aggregate Approximation (PAA). What we will show in this paper is that when PAA is applied the distribution of the data is indeed altered, resulting in a shrinking Standard deviation that is proportional to the number of points used to create a segment of the PAA representation and the degree of auto-correlation within the series. Data that exhibits statistically significant auto-correlation is less affected by this shrinking distribution. As the Standard deviation of the data contracts, the mean remains the same, however the distribution is no longer Standard Normal and therefore the partitions based on the Standard Normal Curve are no longer valid for the assumption of equal probability.

Michael Friedman - One of the best experts on this subject based on the ideXlab platform.

  • Acoustic Pharyngometry: A Substitute for Drug-Induced Sleep Endoscopy?
    Otolaryngology–Head and Neck Surgery, 2012
    Co-Authors: Michael Friedman, Christian G. Samuelson, Craig S. Hamilton
    Abstract:

    Objective: Upper airway collapse measured using supine acoustic pharyngometry at respiratory residual volume (RV) has previously been correlated with obstructive sleep apnea-hypopnea syndrome (OSAHS) severity. We aim to assess the agreement between sites of maximal upper airway obstruction measured by supine acoustic pharyngometry and drug-induced sleep-endoscopy (DISE) in snoring/OSAHS patients.Method: In this case series, 50 consecutive patients with known snoring/OSAHS underwent in-office supine acoustic pharyngometry and DISE. Pharyngometric measurements at respiratory tidal volume were compared against the Standard Normal Curve to establish airway landmarks. Sites of minimal cross-sectional-area at respiratory RV were compared with sites of maximal obstruction identified through DISE.Results: Fifty patients (68% male, 32% female, age 47.3 ± 13.7, mean AHI 37.0 ± 26.8) were evaluated. All endoscopic assessments were performed by a single investigator (M.F.). Regions of maximal upper airway collapse pe...

Matthew Butler - One of the best experts on this subject based on the ideXlab platform.

  • SAX Discretization Does Not Guarantee Equiprobable Symbols
    IEEE Transactions on Knowledge and Data Engineering, 2015
    Co-Authors: Matthew Butler, Dimitar Kazakov
    Abstract:

    In time series analysis research, there is a strong interest in discrete representations of real valued data streams. One approach still considered state-of-the-art is the Symbolic Aggregate Approximation (SAX) algorithm. The interest of this paper concerns the SAX assumption of data being highly Gaussian and the use of the Standard Normal Curve to choose partitions to discretize the data. The SAX approach chooses partitions on the Standard Normal Curve that would produce an equal probability for each symbol. This procedure is generally valid as a time series is Normalized to have $\mu = 0$ and $\sigma = 1$ . However, there exists a caveat to this assumption of equi-probability due to the intermediate step of Piecewise Aggregate Approximation (PAA). We show in this paper that when PAA is applied, the distribution of the data is altered, resulting in a shrinking Standard deviation that is proportional to the number of points used to create a segment of the PAA representation and the degree of auto-correlation within the series. Data that exhibits statistically significant auto-correlation is less affected by this shrinking distribution. As the Standard deviation of the data contracts, the mean remains the same, however the distribution is no longer Standard Normal and therefore the partitions based on the Standard Normal Curve are no longer valid for the assumption of equal probability.

  • Creating a level playing field for all symbols in a discretization
    arXiv: Data Structures and Algorithms, 2012
    Co-Authors: Matthew Butler, Dimitar Kazakov
    Abstract:

    In time series analysis research there is a strong interest in discrete representations of real valued data streams. One approach that emerged over a decade ago and is still considered state-of-the-art is the Symbolic Aggregate Approximation algorithm. This discretization algorithm was the first symbolic approach that mapped a real-valued time series to a symbolic representation that was guaranteed to lower-bound Euclidean distance. The interest of this paper concerns the SAX assumption of data being highly Gaussian and the use of the Standard Normal Curve to choose partitions to discretize the data. Though not necessarily, but generally, and certainly in its canonical form, the SAX approach chooses partitions on the Standard Normal Curve that would produce an equal probability for each symbol in a finite alphabet to occur. This procedure is generally valid as a time series is Normalized before the rest of the SAX algorithm is applied. However there exists a caveat to this assumption of equi-probability due to the intermediate step of Piecewise Aggregate Approximation (PAA). What we will show in this paper is that when PAA is applied the distribution of the data is indeed altered, resulting in a shrinking Standard deviation that is proportional to the number of points used to create a segment of the PAA representation and the degree of auto-correlation within the series. Data that exhibits statistically significant auto-correlation is less affected by this shrinking distribution. As the Standard deviation of the data contracts, the mean remains the same, however the distribution is no longer Standard Normal and therefore the partitions based on the Standard Normal Curve are no longer valid for the assumption of equal probability.

Christian G. Samuelson - One of the best experts on this subject based on the ideXlab platform.

  • Acoustic Pharyngometry: A Substitute for Drug-Induced Sleep Endoscopy?
    Otolaryngology–Head and Neck Surgery, 2012
    Co-Authors: Michael Friedman, Christian G. Samuelson, Craig S. Hamilton
    Abstract:

    Objective: Upper airway collapse measured using supine acoustic pharyngometry at respiratory residual volume (RV) has previously been correlated with obstructive sleep apnea-hypopnea syndrome (OSAHS) severity. We aim to assess the agreement between sites of maximal upper airway obstruction measured by supine acoustic pharyngometry and drug-induced sleep-endoscopy (DISE) in snoring/OSAHS patients.Method: In this case series, 50 consecutive patients with known snoring/OSAHS underwent in-office supine acoustic pharyngometry and DISE. Pharyngometric measurements at respiratory tidal volume were compared against the Standard Normal Curve to establish airway landmarks. Sites of minimal cross-sectional-area at respiratory RV were compared with sites of maximal obstruction identified through DISE.Results: Fifty patients (68% male, 32% female, age 47.3 ± 13.7, mean AHI 37.0 ± 26.8) were evaluated. All endoscopic assessments were performed by a single investigator (M.F.). Regions of maximal upper airway collapse pe...