Mutual Information Function Assesses Autonomic Information Flow of Heart Rate Dynamics at Different Time Scales

2005 ◽  
Vol 52 (4) ◽  
pp. 584-592 ◽  
Author(s):  
D. Hoyer ◽  
B. Pompe ◽  
K.H. Chon ◽  
H. Hardraht ◽  
C. Wicher ◽  
...  
Keyword(s):  
Heart Rate ◽  
Mutual Information ◽  
Time Scales ◽  
Information Flow ◽  
Information Function ◽  
Heart Rate Dynamics ◽  
Different Time Scales ◽  
Mutual Information Function

scholarly journals Examining dependencies among different time scales in episodic memory - An experience sampling study

2019 ◽  
Author(s):  
Hyungwook Yim ◽  
Paul Garrett ◽  
Megan Baker ◽  
Vishnu Sreekumar ◽  
Simon Dennis
Keyword(s):  
Retention Interval ◽  
Mutual Information ◽  
Time Scales ◽  
Memory Retrieval ◽  
Daily Life ◽  
Ecological Validity ◽  
Experience Sampling ◽  
Recurrence Plots ◽  
Collection Phase ◽  
Different Time Scales

We re-examined whether memories of different time scales such as week, day of week, and hour of day are used independently during memory retrieval as has been previously argued (i.e., independence of scales). To overcome the limitations of previous studies, we used experience sampling technology to obtain test stimuli that have higher ecological validity and used pointwise mutual information to directly measure the degree of dependency in a formal way. Participants wore a smartphone around their neck for two weeks, which was equipped with an app that automatically collected time, images, GPS, audio and accelerometry. After a one-week retention interval, participants were presented with an image that was captured during their data collection phase, and were tested on their memory of when the event happened (i.e., week, day of week, and hour). We find that, in contrast to previous arguments, memories of different time scales were not retrieved independently. Moreover, through rendering recurrence plots of the images that the participants collected, we provide evidence the dependency may have originated from the repetitive events that the participants encountered in their daily life.

scholarly journals The Resolved Mutual Information Function as a Structural Fingerprint of Biomolecular Sequences for Interpretable Machine Learning Classifiers

Entropy ◽  
2021 ◽  
Vol 23 (10) ◽  
pp. 1357
Author(s):  
Katrin Sophie Bohnsack ◽  
Marika Kaden ◽  
Julia Abel ◽  
Sascha Saralajew ◽  
Thomas Villmann
Keyword(s):  
Machine Learning ◽  
Mutual Information ◽  
Data Sets ◽  
Sequence Classification ◽  
Information Function ◽  
Theoretical Justification ◽  
Learning Classifier ◽  
Interpretable Machine Learning ◽  
Interpretable Models ◽  
Mutual Information Function

In the present article we propose the application of variants of the mutual information function as characteristic fingerprints of biomolecular sequences for classification analysis. In particular, we consider the resolved mutual information functions based on Shannon-, Rényi-, and Tsallis-entropy. In combination with interpretable machine learning classifier models based on generalized learning vector quantization, a powerful methodology for sequence classification is achieved which allows substantial knowledge extraction in addition to the high classification ability due to the model-inherent robustness. Any potential (slightly) inferior performance of the used classifier is compensated by the additional knowledge provided by interpretable models. This knowledge may assist the user in the analysis and understanding of the used data and considered task. After theoretical justification of the concepts, we demonstrate the approach for various example data sets covering different areas in biomolecular sequence analysis.

GENERATING NONTRIVIAL LONG-RANGE CORRELATIONS AND 1/f SPECTRA BY REPLICATION AND MUTATION

1992 ◽  
Vol 02 (01) ◽  
pp. 137-154 ◽  
Author(s):  
WENTIAN LI
Keyword(s):  
Mutual Information ◽  
Long Range ◽  
Dynamical Process ◽  
Information Function ◽  
Protein Coding ◽  
Correlation Lengths ◽  
Noncoding Sequences ◽  
Long Range Correlations ◽  
Simple Sequence ◽  
Mutual Information Function

This paper aims at understanding the statistical features of nucleic acid sequences from the knowledge of the dynamical process that produces them. Two studies are carried out: first, mutual information function of the limiting sequences generated by simple sequence manipulation dynamics with replications and mutations are calculated numerically (sometimes analytically). It is shown that elongation and replication can easily produce long-range correlations. These long range correlations could be destroyed in various degrees by mutation in different sequence manipulation models. Second, mutual information functions for several human nucleic acids sequences are determined. It is observed that intron sequences (noncoding sequences) tend to have longer correlation lengths than exon sequences (protein-coding sequences).

scholarly journals Comparison Analysis of Different Time-Scale Heart Rate Variability Signals for Mental Workload Assessment in Human-Robot Interaction

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Shiliang Shao ◽  
Ting Wang ◽  
Yawei Li ◽  
Chunhe Song ◽  
Yihan Jiang ◽  
...  
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Time Scales ◽  
Time Scale ◽  
Mental Workload ◽  
Human Robot Interaction ◽  
Machine Learning Algorithms ◽  
Robot Interaction ◽  
Ecg Signals ◽  
Different Time Scales

Excessive mental workload affects human health and may lead to accidents. This study is motivated by the need to assess mental workload in the process of human-robot interaction, in particular, when the robot performs a dangerous task. In this study, the use of heart rate variability (HRV) signals with different time scales in mental workload assessment was analyzed. A humanoid dual-arm robot that can perform dangerous work was used as a human-robot interaction object. Electrocardiogram (ECG) signals of six subjects were collected in two states: during the task and in a relaxed state. Multiple time-scale (1, 3, and 5 min) HRV signals were extracted from ECG signals. Then, we extracted the same linear and nonlinear features from the HRV signals at different time scales. The performance of machine learning algorithms using the different time-scale HRV signals obtained during the human-robot interaction was evaluated. The results show that for the per-subject case with a 3 min HRV signal length, the K -nearest neighbor classifier achieved the best mental workload classification performance. For the cross-subject case with a 5 min time-scale signal length, the gentle boost classifier achieved the best mental workload classification accuracy. This study provides a novel research idea for using HRV signals to measure mental workload during human-robot interaction.

Robustness Analysis of QIM Watermarking against Additional Noise

2011 ◽  
Vol 225-226 ◽  
pp. 601-604
Author(s):  
Gao Rong Zeng ◽  
Jian Ming Liu ◽  
Ai Wen Jiang
Keyword(s):  
Mutual Information ◽  
Bit Error Rate ◽  
Gaussian Noise ◽  
Error Probability ◽  
Robustness Analysis ◽  
Information Function ◽  
Uniform Noise ◽  
Additional Noise ◽  
Information Method ◽  
Mutual Information Function

A mutual information function was defined as a criterion measuring the robustness of watermarking algorithm. Considering QIM scheme, error probability of watermarking can be calculated to validate the measurement of mutual information function. By mean of numerical computation, mutual information under Gaussian noise and uniform noise is calculated with change of noise standard deviation. In the experiment, an audio section is selected as the host and their third lever wavelet detail coefficients are quantified according to watermark bit series. Experiment results show that statistic Bit Error Rate (BER) is matched with evaluation conclusion of mutual information method when step is on the small side. Mutual information function can be selected as a cost function to evaluate the robustness of watermarking algorithm, and predict the BER.

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