Multiscale Mutual Mode Entropy Based Coupling Analysis of Alpha Rhythm Electroencephalogram

2014 ◽  
Vol 574 ◽  
pp. 718-722
Author(s):  
Ning Ji ◽  
Jun Tan ◽  
An Shan Pei ◽  
Jia Fei Dai ◽  
Jun Wang
Keyword(s):  
Time Series ◽  
Clinical Evaluation ◽  
Brain Function ◽  
Alpha Rhythm ◽  
Physiological Status ◽  
Noise Resistance ◽  
Middle Aged ◽  
Coupling Analysis ◽  
Scale Change ◽  
The Difference

This paper presents the Multiscale Mutual Mode Entropy algorithm to quantify the coupling degree between two alpha rhythm EEG time series which are simultaneously acquired. The results show that in the process of scale change, the young and middle-aged differ from each other in terms of the coupling degree of alpha rhythm EEG and the difference grow clear gradually. So the Multiscale Mutual Mode Entropy can be used to analyze the coupling information of time series under different physiological status, and it also has good noise resistance. Besides, as an indicator of measuring brain function, in the future it can also come to the aid of clinical evaluation of brain function.

Coupling Analysis of Beta Rhythm Electroencephalogram Based on the Multiscale Mutual Mode Entropy

2014 ◽  
Vol 595 ◽  
pp. 295-300
Author(s):  
Ning Ji ◽  
Jun Tan ◽  
An Shan Pei ◽  
Jia Fei Dai ◽  
Jun Wang
Keyword(s):  
Time Series ◽  
Clinical Evaluation ◽  
Brain Function ◽  
Physiological Status ◽  
Middle Aged ◽  
Coupling Analysis ◽  
Beta Rhythm ◽  
Scale Change ◽  
The Future ◽  
The Difference

This paper presents the Multiscale Mutual Mode Entropy algorithm to quantify the coupling degree between two beta rhythm EEG time series which are simultaneously acquired. The results show that in the process of scale change, the young and middle-aged differ from each other in terms of the coupling degree of beta rhythm EEG and the difference grow clear gradually from 4th scale. So the Multiscale Mutual Mode Entropy can be used to analyze the coupling information of time series under different physiological status. Besides, as an indicator of measuring brain function, in the future it can also come to the aid of clinical evaluation of brain function.

Coupling Analysis of Electroencephalogram Based on the Multiscale Mutual Mode Entropy

2014 ◽  
Vol 529 ◽  
pp. 670-674
Author(s):  
Ning Ji ◽  
Jun Tan ◽  
An Shan Pei ◽  
Jia Fei Dai ◽  
Jun Wang
Keyword(s):  
Time Series ◽  
Electrical Signal ◽  
Noise Resistance ◽  
Middle Aged ◽  
Coupling Analysis ◽  
Aged People ◽  
Scale Change ◽  
Change Trend

This paper presents the Multiscale Mutual Mode Entropy algorithm to quantify the coupling degree between two simultaneous acquisitions of EEG time series on different scales. It discusses the young and middle-aged people’s characteristics of brain electrical signal based on the algorithm. The results show that they both have the similar change trend of entropy value and middle-aged people have higher entropy value than the young from 6th scale gradually. Mutual Mode Entropy also has good noise resistance in the process of scale change and can distinguish the coupling difference of EEG between the two types of people.

Coupling Analysis of Electroencephalogram Based on Mutual Mode Entropy

2014 ◽  
Vol 884-885 ◽  
pp. 415-418 ◽  
Author(s):  
Ning Ji ◽  
Jia Fei Dai ◽  
Jun Wang
Keyword(s):  
Time Series ◽  
Hypothesis Testing ◽  
Clinical Evaluation ◽  
Brain Function ◽  
Brain State ◽  
Coupling Analysis ◽  
Beta Rhythm ◽  
Simultaneous Acquisition ◽  
Parameter Measuring ◽  
Degree Of Coupling

This paper presents a mutual mode entropy algorithm to quantify the degree of coupling between two simultaneous acquisition of beta rhythm EEG time series. Applying the algorithm to calculate beta rhythm EEG and hypothesis testing, the results show that coupling degree was significantly different between different physiological states which indicating that mutual mode entropy can be used to analyze the coupling between the time series. The mutual mode entropy also can be parameter measuring brain state which can be applied to assist future clinical evaluation of brain function.

SCCMI Algorithm for Coupling Analysis of Multi-Lead Epileptic EEG Signals

2013 ◽  
Vol 373-375 ◽  
pp. 681-684
Author(s):  
Chao Cui ◽  
Ting Ting Han ◽  
Meng Qiu Gao ◽  
Wei Lu ◽  
Chao Zou ◽  
...  
Keyword(s):  
Time Series ◽  
Mutual Information ◽  
Large Scale ◽  
Time Sequence ◽  
Eeg Signals ◽  
Coupling Analysis ◽  
Noise Effect ◽  
Code Algorithm ◽  
Symbolic Code ◽  
The Difference

Symbolic-code condition mutual information (SCCMI) algorithm is proposed,which can detect coupling between several systems.SCCMI combines condition mutual information with symbolic-code algorithm. Condition mutual information entropy is used to finding coupling degree between time series .The meaning of symbolic-code algorithm is to retention large scale information of time sequence, whats more ,reduce noise effect. SCCMI algorithm is used to analyze difference of coupling between epileptic EEG signals and normal ones .Hypothesis testing was done by SPSS.It turns out that the difference between epileptic EEG signals and normal ones is significant.SCCMI algorithm is proved to be effective. And coupling degree can be used as a parameter to measure if brain is healthy.

EVALUASI PROYEKSI DEMAND BARANG DAN PENUMPANG PELABUHAN DI PROVINSI MALUKU

2019 ◽  
Vol 19 (2) ◽  
pp. 101-110
Author(s):  
Adrian Firdaus ◽  
M. Dwi Yoga Sutanto ◽  
Rajin Sihombing ◽  
M. Weldy Hermawan
Keyword(s):  
Time Series ◽  
Projection Method ◽  
Development Plan ◽  
Master Plan ◽  
Port Development ◽  
Loading And Unloading ◽  
The Difference ◽  
Existing Data

Abstract Every port in Indonesia must have a Port Master Plan that contains an integrated port development plan. This study discusses one important aspect in the preparation of the Port Master Plan, namely the projected movement of goods and passengers, which can be used as a reference in determining the need for facilities at each stage of port development. The case study was conducted at a port located in a district in Maluku Province and aims to evaluate the analysis of projected demand for goods and passengers occurring at the port. The projection method used is time series and econometric projection. The projection results are then compared with the existing data in 2018. The results of this study show that the econometric projection gives adequate results in predicting loading and unloading activities as well as the number of passenger arrival and departure in 2018. This is indicated by the difference in the percentage of projection results towards the existing data, which is smaller than 10%. Whereas for loading and unloading activities, time series projections with logarithmic trends give better results than econometric projections. Keywords: port, port master plan, port development, unloading activities  Abstrak Setiap pelabuhan di Indonesia harus memiliki sebuah Rencana Induk Pelabuhan yang memuat rencana pengem-bangan pelabuhan secara terpadu. Studi ini membahas salah satu aspek penting dalam penyusunan Rencana Induk Pelabuhan, yaitu proyeksi pergerakan barang dan penumpang, yang dapat dipakai sebagai acuan dalam penentuan kebutuhan fasilitas di setiap tahap pengembangan pelabuhan. Studi kasus dilakukan pada sebuah pelabuhan yang terletak di sebuah kabupaten di Provinsi Maluku dan bertujuan untuk melakukan evaluasi ter-hadap analisis proyeksi demand barang dan penumpang yang terjadi di pelabuhan tersebut. Metode proyeksi yang dipakai adalah proyeksi deret waktu dan ekonometrik. Hasil proyeksi selanjutnya dibandingkan dengan data eksisting tahun 2018. Hasil studi ini menunjukkan bahwa proyeksi ekonometrik memberikan hasil yang cukup baik dalam memprediksi aktivitas bongkar barang serta jumlah penumpang naik dan turun di tahun 2018. Hal ini diindikasikan dengan selisih persentase hasil proyeksi terhadap data eksisting yang lebih kecil dari 10%. Sedangkan untuk aktivitas muat barang, proyeksi deret waktu dengan tren logaritmik memberikan hasil yang lebih baik daripada proyeksi ekonometrik. Kata-kata kunci: pelabuhan, rencana induk pelabuhan, pengembangan pelauhan, aktivitas bongkar barang

279 WACSAW: A Statistical and Individually Adaptive Method to Determine Sleep and Wakefulness from Activity

SLEEP ◽  
2021 ◽  
Vol 44 (Supplement_2) ◽  
pp. A111-A112
Author(s):  
Austin Vandegriffe ◽  
V A Samaranayake ◽  
Matthew Thimgan
Keyword(s):  
Time Series ◽  
Optimal Transport ◽  
Activity Patterns ◽  
Adaptive Method ◽  
Wasserstein Distance ◽  
Activity Data ◽  
Commercial Grade ◽  
Movement Data ◽  
The Difference ◽  
Levene Test

Abstract Introduction Technological innovations have broadened the type and amount of activity data that can be captured in the home and under normal living conditions. Yet, converting naturalistic activity patterns into sleep and wakefulness states has remained a challenge. Despite the successes of current algorithms, they do not fill all actigraphy needs. We have developed a novel statistical approach to determine sleep and wakefulness times, called the Wasserstein Algorithm for Classifying Sleep and Wakefulness (WACSAW), and validated the algorithm in a small cohort of healthy participants. Methods WACSAW functional routines: 1) Conversion of the triaxial movement data into a univariate time series; 2) Construction of a Wasserstein weighted sum (WSS) time series by measuring the Wasserstein distance between equidistant distributions of movement data before and after the time-point of interest; 3) Segmenting the time series by identifying changepoints based on the behavior of the WSS series; 4) Merging segments deemed similar by the Levene test; 5) Comparing segments by optimal transport methodology to determine the difference from a flat, invariant distribution at zero. The resulting histogram can be used to determine sleep and wakefulness parameters around a threshold determined for each individual based on histogram properties. To validate the algorithm, participants wore the GENEActiv and a commercial grade actigraphy watch for 48 hours. The accuracy of WACSAW was compared to a detailed activity log and benchmarked against the results of the output from commercial wrist actigraph. Results WACSAW performed with an average accuracy, sensitivity, and specificity of >95% compared to detailed activity logs in 10 healthy-sleeping individuals of mixed sexes and ages. We then compared WACSAW’s performance against a common wrist-worn, commercial sleep monitor. WACSAW outperformed the commercial grade system in each participant compared to activity logs and the variability between subjects was cut substantially. Conclusion The performance of WACSAW demonstrates good results in a small test cohort. In addition, WACSAW is 1) open-source, 2) individually adaptive, 3) indicates individual reliability, 4) based on the activity data stream, and 5) requires little human intervention. WACSAW is worthy of validating against polysomnography and in patients with sleep disorders to determine its overall effectiveness. Support (if any):

scholarly journals Alpha EEG Frontal Asymmetries during Audiovisual Perception in Cochlear Implant Users

2015 ◽  
Vol 54 (06) ◽  
pp. 500-504 ◽  
Author(s):  
A. G. Maglione ◽  
A. Scorpecci ◽  
P. Malerba ◽  
P. Marsella ◽  
S. Giannantonio ◽  
...  
Keyword(s):  
Cochlear Implant ◽  
Alpha Rhythm ◽  
Normal Hearing ◽  
Experimental Conditions ◽  
Audiovisual Perception ◽  
Audiovisual Stimuli ◽  
The Difference ◽  
Eeg Alpha ◽  
The One ◽  
Hearing Children

SummaryObjectives: The aim of the present study is to investigate the variations of the electroencephalographic (EEG) alpha rhythm in order to measure the appreciation of bilateral and unilateral young cochlear implant users during the observation of a musical cartoon. The cartoon has been modified for the generation of three experimental conditions: one with the original audio, another one with a distorted sound and, finally, a mute version.Methods: The EEG data have been recorded during the observation of the cartoons in the three experimental conditions. The frontal alpha EEG imbalance has been calculated as a measure of motivation and pleasantness to be compared across experimental populations and conditions.Results: The EEG frontal imbalance of the alpha rhythm showed significant variations during the perception of the different cartoons. In particular, the pattern of activation of normal-hearing children is very similar to the one elicited by the bilateral implanted patients. On the other hand, results related to the unilateral subjects do not present significant variations of the imbalance index across the three cartoons.Conclusion: The presented results suggest that the unilateral patients could not appreciate the difference in the audio format as well as bilaterally implanted and normal hearing subjects. The frontal alpha EEG imbalance is a useful tool to detect the differences in the appreciation of audiovisual stimuli in cochlear implant patients.

Data Fusion of Total Solar Irradiance Composite Time Series Using 40 years of Satellite Measurements: First Results 

2021 ◽  
Author(s):  
Jean-Philippe Montillet ◽  
Wolfgang Finsterle ◽  
Werner Schmutz ◽  
Margit Haberreiter ◽  
Rok Sikonja
Keyword(s):  
Time Series ◽  
Data Fusion ◽  
Solar Irradiance ◽  
Total Solar Irradiance ◽  
Maunder Minimum ◽  
The Sun ◽  
Climate Reconstructions ◽  
The Earth ◽  
First Results ◽  
The Difference

<p><span>Since the late 70’s, successive satellite missions have been monitoring the sun’s activity, recording total solar irradiance observations. These measurements are important to estimate the Earth’s energy imbalance, </span><span>i.e. the difference of energy absorbed and emitted by our planet. Climate modelers need the solar forcing time series in their models in order to study the influence of the Sun on the Earth’s climate. With this amount of TSI data, solar irradiance reconstruction models  can be better validated which can also improve studies looking at past climate reconstructions (e.g., Maunder minimum). V</span><span>arious algorithms have been proposed in the last decade to merge the various TSI measurements over the 40 years of recording period. We have developed a new statistical algorithm based on data fusion.  The stochastic noise processes of the measurements are modeled via a dual kernel including white and coloured noise.  We show our first results and compare it with previous releases (PMOD,ACRIM, ... ). </span></p>

Failure time prognosis in manufacturing process using multi-dislocated time series convolutional neural network

2020 ◽  
pp. 095440892097414
Author(s):  
Bohao Li ◽  
Liping Zhao ◽  
Yiyong Yao
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Convolutional Neural Network ◽  
Manufacturing Process ◽  
Failure Time ◽  
Great Difficulty ◽  
Milling Process ◽  
Machining Process ◽  
Massive Data ◽  
The Difference

Failure time prognosis in manufacturing process plays a crucial role in guaranteeing manufacturing safety and reducing maintenance loss. However, most current prognosis methods face great difficulty when handling massive data collected from manufacturing process. Convolutional neural network (CNN) provides an effective way to extract features with massive data. Due to the difference between images and multisensory signals, CNN is not suitable for machining process. Inspired by the idea of CNN, a novel prognosis framework is proposed based on the characteristics of multisensory signals, which is called multi-dislocated time series convolutional neural network (MDTSCNN). The proposed MDTSCNN is composed of multi-dislocate layer, convolutional layer, pooling layer and fully connected layer. By adding a multi-dislocate layer, this model can learn the relationship between different signals and different intervals in periodic multisensory signals. The effectiveness of proposed method is validated by a milling process. Compared to other prognosis method, the proposed MDTSCNN shows enhanced performances in prediction accuracy.

Testing the Difference between Two Independent Time Series Models

2017 ◽  
Vol 41 (3) ◽  
pp. 665-669 ◽  
Author(s):  
Mohammad Reza Mahmoudi ◽  
Mohsen Maleki ◽  
Abbas Pak
Keyword(s):  
Time Series ◽  
Time Series Models ◽  
The Difference
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