Article
Engineering, Multidisciplinary
Jinde Zheng, Miaoxian Su, Wanming Ying, Jinyu Tong, Ziwei Pan
Summary: The study introduces the improved Uniform Phase Empirical Mode Decomposition (IUPEMD) method, which enhances the accuracy and performance of signal decomposition by adaptively selecting the amplitude of the sinusoidal wave and choosing the optimal result based on orthogonality index.
Article
Business, Finance
Kunliang Xu, Weiqing Wang
Summary: A reliable crude oil price forecast is crucial for market pricing. This study incorporates a rolling window into two prevalent EEMD-based modeling paradigms to improve accuracy. The results show that EEMD plays a weak role in improving crude oil price forecasts when only the in-sample set is preprocessed, but the rolling EEMD-denoising model has an advantage for long-term forecasting.
INTERNATIONAL REVIEW OF FINANCIAL ANALYSIS
(2023)
Article
Chemistry, Multidisciplinary
Manuel A. A. Centeno-Bautista, Angel H. H. Rangel-Rodriguez, Andrea V. V. Perez-Sanchez, Juan P. P. Amezquita-Sanchez, David Granados-Lieberman, Martin Valtierra-Rodriguez
Summary: Sudden cardiac death is a significant global health problem, accounting for 15-20% of global deaths. A research proposes a methodology combining complete ensemble empirical mode decomposition (CEEMD) and convolutional neural network (CNN) to predict SCD events 30 minutes in advance with 97.5% accuracy. The study compares the results with ensemble empirical mode decomposition (EEMD) and empirical mode decomposition (EMD) methods.
APPLIED SCIENCES-BASEL
(2023)
Article
Clinical Neurology
Emmanuelle Wilhelm, Caroline Quoilin, Gerard Derosiere, Susana Paco, Anne Jeanjean, Julie Duque
Summary: In Parkinson's disease, the lack of preparatory suppression in the primary motor cortex is associated with motor slowness and is related to disease duration and motor impairment. This finding suggests a potential marker for assessing motor preparation and impairment in Parkinson's disease.
MOVEMENT DISORDERS
(2022)
Article
Engineering, Electrical & Electronic
Yaru Yue, Chengdong Chen, Xiaoyuan Wu, Xiaoguang Zhou
Summary: This article proposes an effective method for denoising ECG signals, which combines the ensemble empirical mode decomposition (EEMD), empirical mode decomposition (EMD), and wavelet packet (WP) techniques. The ECG signal is decomposed using EEMD, and then the highest frequency component is decomposed using EMD for a second time, and the high frequency components obtained from the second decomposition are decomposed and reconstructed using WP for a third time. The processed signal components are then fused to obtain the denoised ECG signal. Various evaluation metrics such as signal-to-noise ratio (SNR), mean square error (MSE), root mean square error (RMSE), and normalized cross correlation coefficient (R) are used to assess the noise reduction algorithm.
IET SIGNAL PROCESSING
(2023)
Article
Engineering, Mechanical
Atik Faysal, Wai Keng Ngui, M. H. Lim
Summary: The proposed NEEEMD method aims to further reduce white noise and select sensitive mode functions to enhance fault-related impulses through a combination of time and frequency domain characteristics. The application of MOMEDA filter improves fault diagnosis accuracy by identifying more fault characteristic impulses.
JOURNAL OF VIBRATION ENGINEERING & TECHNOLOGIES
(2021)
Article
Mathematical & Computational Biology
Ravichandra Madanu, Farhan Rahman, Maysam F. Abbod, Shou-Zen Fan, Jiann-Shing Shieh
Summary: A survey found that 47% of surgical complication mortalities are due to anesthetic overdose, highlighting the necessity for regulating anesthesia levels. Deep learning methods have been utilized for predicting patient anesthesia depth using EEG signals, with CNN being a popular algorithm. Various decomposition methods were used in this study to extract features from EEG signals, demonstrating potential for further research in visual mapping of DOA using EEG signals and DL methods.
MATHEMATICAL BIOSCIENCES AND ENGINEERING
(2021)
Article
Clinical Neurology
Daniele Belvisi, Andrea Fabbrini, Maria Ilenia De Bartolo, Matteo Costanzo, Nicoletta Manzo, Giovanni Fabbrini, Giovanni Defazio, Antonella Conte, Alfredo Berardelli
Summary: The study aimed to identify the pathophysiological substrate of Parkinson's disease subtypes using neurophysiological techniques. Two clinical clusters were identified, with one having milder symptoms and the other having a combination of severe motor and nonmotor manifestations. Differences in motor system dysfunction were observed between the subtypes, while sensory function and sensorimotor integration mechanisms did not differ.
MOVEMENT DISORDERS
(2021)
Article
Neurosciences
Carla Piano, Francesco Bove, Delia Mulas, Enrico Di Stasio, Alfonso Fasano, Anna Rita Bentivoglio, Antonio Daniele, Beatrice Cioni, Paolo Calabresi, Tommaso Tufo
Summary: This study prospectively followed nine PD patients who received EMCS treatment and found that EMCS was a safe and effective option, leading to improvements in motor symptoms and quality of life, as well as reductions in motor complications and dopaminergic therapy even in the long-term follow-up.
Article
Computer Science, Information Systems
Muhammad Ali, Dost Muhammad Khan, Imran Saeed, Huda M. M. Alshanbari
Summary: The objective of this research is to extend the scope of the empirical mode decomposition (EMD) algorithm for decomposing nonlinear and non-stationary time series. The proposed extension, called Akima-EMD, utilizes both clean and noisy data sets and employs Akima spline interpolation technique for constructing upper and lower envelopes. It has shown successful extraction of noise in the form of the first IMF using synthetic and real-world time series data analysis.
Article
Computer Science, Information Systems
Kai Zhou, Zhixiang Yin, Yu Peng, Zhiliang Zeng
Summary: This study used ensemble empirical mode decomposition and temporal convolutional network to process photoplethysmography signals and obtained more accurate diastolic and systolic blood pressure measurement results.
Article
Engineering, Electrical & Electronic
Chunlian Xia, Jing Huang
Summary: This paper introduces the application of empirical mode decomposition method in inflation forecasting, which decomposes the time series into eigenmode functions with different time scales to predict inflation rate more accurately. The predicted results show that the method's predicted values are relatively close to the actual values, indicating a better prediction effect of the EEMD model.
JOURNAL OF SENSORS
(2022)
Article
Biotechnology & Applied Microbiology
Ro'ee Gilron, Simon Little, Randy Perrone, Robert Wilt, Coralie de Hemptinne, Maria S. Yaroshinsky, Caroline A. Racine, Sarah S. Wang, Jill L. Ostrem, Paul S. Larson, Doris D. Wang, Nick B. Galifianakis, Ian O. Bledsoe, Marta San Luciano, Heather E. Dawes, Gregory A. Worrell, Vaclav Kremen, David A. Borton, Timothy Denison, Philip A. Starr
Summary: The use of an implantable two-way neural interface enables wireless neural monitoring and stimulation for up to 15 months following implantation. This technological approach may have wide applicability to brain disorders treatable by invasive neuromodulation.
NATURE BIOTECHNOLOGY
(2021)
Article
Business
Kunliang Xu, Hongli Niu
Summary: This study proposes a new ensemble model based on sliding decomposition for crude oil futures price prediction. The empirical findings indicate that this model does not improve prediction accuracy compared to other models in out-of-sample data.
TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE
(2022)
Article
Physics, Multidisciplinary
Jung-Hoon Cho, Dong-Kyu Kim, Eui-Jin Kim
Summary: This study investigates the causal relationship between the spread of COVID-19 and mobility level using ensemble empirical mode decomposition and causal decomposition approach. The findings show that mobility level is highly associated with the long-term variations of COVID-19 cases, and the intrastate causal strength is influenced by median age and political orientation. Interstate causality results reveal a negative association with interstate distance and a positive association with airline traffic. Clustering analysis shows that states with higher gross domestic product and more political democracy tend to adhere more to social distancing.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2022)
Article
Mathematical & Computational Biology
Dylan Rich, Fanny Cazettes, Yunyan Wang, Jose Luis Pena, Brian J. Fischer
JOURNAL OF COMPUTATIONAL NEUROSCIENCE
(2015)
Article
Clinical Neurology
Agathe Bridoux, Xavier Drouot, Aude Sangare, Tarik Al-ani, Arnaud Brignol, Anais Charles-Nelson, Pierre Brugieres, Gaetane Gouello, Koichi Hosomi, Helene Lepetit, Stephane Palfi
Article
Clinical Neurology
Agathe Bridoux, Xavier Drouot, Aude Sangare, Tarik Al-ani, Arnaud Brignol, Anais Charles-Nelson, Pierre Brugieres, Gaetane Gouello, Koichi Hosomi, Helene Lepetit, Stephane Palfi
Article
Neurosciences
Fanny Cazettes, Brian J. Fischer, Jose L. Pena
JOURNAL OF NEUROSCIENCE
(2016)
Article
Neurosciences
Fanny Cazettes, Brian J. Fischer, Michael V. Beckert, Jose L. Pena
JOURNAL OF NEUROSCIENCE
(2018)
Article
Computer Science, Interdisciplinary Applications
Arnaud Brignol, Tarik Al-ani, Xavier Drouot
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2013)
Article
Clinical Neurology
S. S. Ayache, T. Al-ani, J. -P. Lefaucheur
NEUROPHYSIOLOGIE CLINIQUE-CLINICAL NEUROPHYSIOLOGY
(2014)
Article
Biology
Fanny Cazettes, Brian J. Fischer, Jose L. Pena
Article
Neurosciences
Jose L. Pena, Fanny Cazettes, Michael V. Beckert, Brian J. Fischer
JOURNAL OF NEUROSCIENCE
(2019)
Article
Biochemistry & Molecular Biology
Fanny Cazettes, Davide Reato, Joao P. Morais, Alfonso Renart, Zachary F. Mainen
Summary: Transient changes in pupil size under constant luminance are coupled to rapid changes in arousal state, which may be attributed to the phasic activity of the noradrenergic system. Additionally, serotonin modulation seems to affect pupil size and may be related to signaling unexpected events. The study shows a tight relationship between phasic activation of serotonin neurons and changes in pupil size.
Article
Neurosciences
Anne E. Urai, Valeria Aguillon-Rodriguez, Ines C. Laranjeira, Fanny Cazettes, Zachary F. Mainen, Anne K. Churchland
Summary: The study introduces an alternative method for motivating mice to perform specific behaviors by providing them with access to slightly sour water as a reward, which does not impact their willingness to engage in decision-making tasks.
Proceedings Paper
Engineering, Biomedical
Paschalis A. Bizopoulos, Tarik Al-Ani, Dimitrios G. Tsalikakis, Alexandros T. Tzallas, Dimitrios D. Koutsouris, Dimitrios I. Fotiadis
2013 35TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC)
(2013)
Proceedings Paper
Engineering, Electrical & Electronic
A. Gacem, N. Nadjar-Gauthier, E. Monacelli, T. Al-ani, Y. Oussar
PROCEEDINGS OF THE ASME 11TH BIENNIAL CONFERENCE ON ENGINEERING SYSTEMS DESIGN AND ANALYSIS, 2012, VOL 2
(2012)
Proceedings Paper
Engineering, Biomedical
Arnaud Brignol, Tarik Al-ani, Xavier Drouot
IEEE 12TH INTERNATIONAL CONFERENCE ON BIOINFORMATICS & BIOENGINEERING
(2012)
Proceedings Paper
Robotics
Dalila Trad, Tarik Al-ani, Eric Monacelli, Stephane Delaplace, Mohamed Jemni
2011 IEEE INTERNATIONAL CONFERENCE ON REHABILITATION ROBOTICS (ICORR)
(2011)
Article
Biochemical Research Methods
Aline Silva da Cruz, Maria Margarida Drehmer, Wagner Baetas-da-Cruz, Joao Carlos Machado
Summary: This study quantified microcirculation cerebral blood flow in a rat model of ischemic stroke using ultrasound biomicroscopy and ultrasound contrast agents. The results showed high sensitivity and specificity of this method, making it a valuable tool for preclinical studies.
JOURNAL OF NEUROSCIENCE METHODS
(2024)
Article
Biochemical Research Methods
Christina Dalla, Ivana Jaric, Pavlina Pavlidi, Georgia E. Hodes, Nikolaos Kokras, Anton Bespalov, Martien J. Kas, Thomas Steckler, Mohamed Kabbaj, Hanno Wuerbel, Jordan Marrocco, Jessica Tollkuhn, Rebecca Shansky, Debra Bangasser, Jill B. Becker, Margaret McCarthy, Chantelle Ferland-Beckham
Summary: Many funding agencies have emphasized the importance of considering sex as a biological variable in experimental design to improve the reproducibility and translational relevance of preclinical research. Omitting the female sex from experimental designs in neuroscience and pharmacology can result in biased or limited understanding of disease mechanisms. This article provides methodological considerations for incorporating sex as a biological variable in in vitro and in vivo experiments, including the influence of age and hormone levels, and proposes strategies to enhance methodological rigor and translational relevance in preclinical research.
JOURNAL OF NEUROSCIENCE METHODS
(2024)
Article
Biochemical Research Methods
Wenyu Gu, Dongxu Li, Jia-Hong Gao
Summary: We developed a precise and rapid method for positioning and labelling triaxial OPMs on a wearable magnetoencephalography (MEG) system, improving the efficiency of OPM positioning and labelling.
JOURNAL OF NEUROSCIENCE METHODS
(2024)
Article
Biochemical Research Methods
Kai Lin, Linhang Zhang, Jing Cai, Jiaqi Sun, Wenjie Cui, Guangda Liu
Summary: The article introduces an EEG feature map processing model for emotion recognition, which achieves significantly improved accuracy by fusing EEG information at different spatial scales and introducing a channel attention mechanism.
JOURNAL OF NEUROSCIENCE METHODS
(2024)
Article
Biochemical Research Methods
John E. Parker, Asier Aristieta, Aryn H. Gittis, Jonathan E. Rubin
Summary: This work presents a toolbox that implements a methodology for automated classification of neural responses based on spike train recordings. The toolbox provides a user-friendly and efficient approach to detect various types of neuronal responses that may not be identified by traditional methods.
JOURNAL OF NEUROSCIENCE METHODS
(2024)
Article
Biochemical Research Methods
Yun Liang, Ke Bo, Sreenivasan Meyyappan, Mingzhou Ding
Summary: This study compared the performance of SVM and CNN on the same datasets and found that CNN achieved consistently higher classification accuracies. The classification accuracies of SVM and CNN were generally not correlated, and the heatmaps derived from them did not overlap significantly.
JOURNAL OF NEUROSCIENCE METHODS
(2024)
Article
Biochemical Research Methods
Antonino Visalli, Maria Montefinese, Giada Viviani, Livio Finos, Antonino Vallesi, Ettore Ambrosini
Summary: This study introduces an analytical strategy that allows the use of mixed-effects models (LMM) in mass univariate analyses of EEG data. The proposed method overcomes the computational costs and shows excellent performance properties, making it increasingly important in the field of neuroscience.
JOURNAL OF NEUROSCIENCE METHODS
(2024)
Article
Biochemical Research Methods
Xavier Cano-Ferrer, Alexandra Tran -Van -Minh, Ede Rancz
Summary: This study developed a novel rotation platform for studying neural processes and spatial navigation. The platform is modular, affordable, and easy to build, and can be driven by the experimenter or animal movement. The research demonstrated the utility of the platform, which combines the benefits of head fixation and intact vestibular activity.
JOURNAL OF NEUROSCIENCE METHODS
(2024)