Journal: PeerJ

Article Title: Classifying acoustic signals into phoneme categories: average and dyslexic readers make use of complex dynamical patterns and multifractal scaling properties of the speech signal

doi: 10.7717/peerj.837

Figure Lengend Snippet: The profile of stimulus 1 is divided into nonoverlapping segments v of size s ; the signal is detrended and the mean RMS deviation of the RMS variation in each segment is calculated; this is repeated for different scales and results in the fluctuation function: F 2 ( s , v ). The slope of the best fitting line through these points is an estimate of the Hurst exponent. See text for details.

Article Snippet: The Multifractal spectrum was obtained by Multifractal Detrended Fluctuation Analysis based on the entire stimulus signal, using Matlab code by .

Techniques:

Journal: PeerJ

Article Title: Classifying acoustic signals into phoneme categories: average and dyslexic readers make use of complex dynamical patterns and multifractal scaling properties of the speech signal

doi: 10.7717/peerj.837

Figure Lengend Snippet: Multifractal Detrended Fluctuation Analysis of the 40 stimuli.

Article Snippet: The Multifractal spectrum was obtained by Multifractal Detrended Fluctuation Analysis based on the entire stimulus signal, using Matlab code by .

Techniques:

Journal: PeerJ

Article Title: Classifying acoustic signals into phoneme categories: average and dyslexic readers make use of complex dynamical patterns and multifractal scaling properties of the speech signal

doi: 10.7717/peerj.837

Figure Lengend Snippet: Strong inference. A summary of the hypotheses competing to explain which features of the acoustic signal are used in speech perception.

Article Snippet: The Multifractal spectrum was obtained by Multifractal Detrended Fluctuation Analysis based on the entire stimulus signal, using Matlab code by .

Techniques:

Journal: Journal of Diabetes Science and Technology

Article Title: Complexity of Continuous Glucose Monitoring Data in Critically Ill Patients: Continuous Glucose Monitoring Devices, Sensor Locations, and Detrended Fluctuation Analysis Methods

doi:

Figure Lengend Snippet: Example of multifractal spectrum that is produced from multifractal DFA. Note the monofractal signal produces a very narrow spectrum, indicating monofractal scaling is present and monofractal DFA is sufficient to characterize the scaling and correlation properties of the signal.

Article Snippet: This study uses both monofractal DFA and multifractal DFA implemented in MATLAB (Mathworks, Natick, MA) based on the descriptions provided by Ihlen 32 and Kantelhardt and coauthors.

Techniques: Produced

Journal: Journal of Diabetes Science and Technology

Article Title: Complexity of Continuous Glucose Monitoring Data in Critically Ill Patients: Continuous Glucose Monitoring Devices, Sensor Locations, and Detrended Fluctuation Analysis Methods

doi:

Figure Lengend Snippet: Results from Monofractal Detrended Fluctuation Analysis of Sensor Glucose and ISIG Data Over Cohort

Article Snippet: This study uses both monofractal DFA and multifractal DFA implemented in MATLAB (Mathworks, Natick, MA) based on the descriptions provided by Ihlen 32 and Kantelhardt and coauthors.

Techniques:

Journal: Journal of Diabetes Science and Technology

Article Title: Complexity of Continuous Glucose Monitoring Data in Critically Ill Patients: Continuous Glucose Monitoring Devices, Sensor Locations, and Detrended Fluctuation Analysis Methods

doi:

Figure Lengend Snippet: Multifractal spectrums comparing CGM device types and sensor locations. The plots on the left were created using SG data, and the plots on the right were created using ISIG data.

Article Snippet: This study uses both monofractal DFA and multifractal DFA implemented in MATLAB (Mathworks, Natick, MA) based on the descriptions provided by Ihlen 32 and Kantelhardt and coauthors.

Techniques:

Journal: Journal of Diabetes Science and Technology

Article Title: Complexity of Continuous Glucose Monitoring Data in Critically Ill Patients: Continuous Glucose Monitoring Devices, Sensor Locations, and Detrended Fluctuation Analysis Methods

doi:

Figure Lengend Snippet: Multifractal spectrum comparison for data sets that had the same scaling exponent from monofractal DFA.

Article Snippet: This study uses both monofractal DFA and multifractal DFA implemented in MATLAB (Mathworks, Natick, MA) based on the descriptions provided by Ihlen 32 and Kantelhardt and coauthors.

Techniques: Comparison

Journal: Journal of Diabetes Science and Technology

Article Title: Complexity of Continuous Glucose Monitoring Data in Critically Ill Patients: Continuous Glucose Monitoring Devices, Sensor Locations, and Detrended Fluctuation Analysis Methods

doi:

Figure Lengend Snippet: (A) This example shows good agreement between SG data for each of the three CGM devices, but the multifractal spectrums for each data set are quite different. (B) This example shows average agreement between SG data for two CGM devices, but the multifractal spectrums for each data set overlap.

Article Snippet: This study uses both monofractal DFA and multifractal DFA implemented in MATLAB (Mathworks, Natick, MA) based on the descriptions provided by Ihlen 32 and Kantelhardt and coauthors.

Techniques: