1
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Wang Z, Juhasz Z. GPU Implementation of the Improved CEEMDAN Algorithm for Fast and Efficient EEG Time-Frequency Analysis. SENSORS (BASEL, SWITZERLAND) 2023; 23:8654. [PMID: 37896747 PMCID: PMC10611056 DOI: 10.3390/s23208654] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Revised: 10/09/2023] [Accepted: 10/20/2023] [Indexed: 10/29/2023]
Abstract
Time-frequency analysis of EEG data is a key step in exploring the internal activities of the human brain. Studying oscillations is an important part of the analysis, as they are thought to provide the underlying mechanism for communication between neural assemblies. Traditional methods of analysis, such as Short-Time FFT and Wavelet Transforms, are not ideal for this task due to the time-frequency uncertainty principle and their reliance on predefined basis functions. Empirical Mode Decomposition and its variants are more suited to this task as they are able to extract the instantaneous frequency and phase information but are too time consuming for practical use. Our aim was to design and develop a massively parallel and performance-optimized GPU implementation of the Improved Complete Ensemble EMD with the Adaptive Noise (CEEMDAN) algorithm that significantly reduces the computational time (from hours to seconds) of such analysis. The resulting GPU program, which is publicly available, was validated against a MATLAB reference implementation and reached over a 260× speedup for actual EEG measurement data, and provided predicted speedups in the range of 3000-8300× for longer measurements when sufficient memory was available. The significance of our research is that this implementation can enable researchers to perform EMD-based EEG analysis routinely, even for high-density EEG measurements. The program is suitable for execution on desktop, cloud, and supercomputer systems and can be the starting point for future large-scale multi-GPU implementations.
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Affiliation(s)
| | - Zoltan Juhasz
- Department of Electrical Engineering and Information Systems, University of Pannonia, 8200 Veszprem, Hungary;
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2
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Teng CL, Zhang YY, Wang W, Luo YY, Wang G, Xu J. A Novel Method Based on Combination of Independent Component Analysis and Ensemble Empirical Mode Decomposition for Removing Electrooculogram Artifacts From Multichannel Electroencephalogram Signals. Front Neurosci 2021; 15:729403. [PMID: 34707475 PMCID: PMC8542780 DOI: 10.3389/fnins.2021.729403] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2021] [Accepted: 09/01/2021] [Indexed: 12/03/2022] Open
Abstract
Electrooculogram (EOG) is one of common artifacts in recorded electroencephalogram (EEG) signals. Many existing methods including independent component analysis (ICA) and wavelet transform were applied to eliminate EOG artifacts but ignored the possible impact of the nature of EEG signal. Therefore, the removal of EOG artifacts still faces a major challenge in EEG research. In this paper, the ensemble empirical mode decomposition (EEMD) and ICA algorithms were combined to propose a novel EEMD-based ICA method (EICA) for removing EOG artifacts from multichannel EEG signals. First, the ICA method was used to decompose original EEG signals into multiple independent components (ICs), and the EOG-related ICs were automatically identified through the kurtosis method. Then, by performing the EEMD algorithm on EOG-related ICs, the intrinsic mode functions (IMFs) linked to EOG were discriminated and eliminated. Finally, artifact-free IMFs were projected to obtain the ICs without EOG artifacts, and the clean EEG signals were ultimately reconstructed by the inversion of ICA. Both EOGs correction from simulated EEG signals and real EEG data were studied, which verified that the proposed method could achieve an improved performance in EOG artifacts rejection. By comparing with other existing approaches, the EICA obtained the optimal performance with the highest increase in signal-to-noise ratio and decrease in root mean square error and correlation coefficient after EOG artifacts removal, which demonstrated that the proposed method could more effectively eliminate blink artifacts from multichannel EEG signals with less error influence. This study provided a novel promising method to eliminate EOG artifacts with high performance, which is of great importance for EEG signals processing and analysis.
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Affiliation(s)
- Chao-Lin Teng
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China.,The Key Laboratory of Neuro-informatics and Rehabilitation Engineering of Ministry of Civil Affairs, Xi'an, China.,National Engineering Research Center for Healthcare Devices, Guangzhou, China
| | - Yi-Yang Zhang
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China.,The Key Laboratory of Neuro-informatics and Rehabilitation Engineering of Ministry of Civil Affairs, Xi'an, China.,National Engineering Research Center for Healthcare Devices, Guangzhou, China
| | - Wei Wang
- Department of Psychiatry, The First Affiliated Hospital, Xi'an Jiaotong University, Xi'an, China
| | - Yuan-Yuan Luo
- Department of Psychology, Xi'an Mental Health Center, Xi'an, China
| | - Gang Wang
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China.,The Key Laboratory of Neuro-informatics and Rehabilitation Engineering of Ministry of Civil Affairs, Xi'an, China.,National Engineering Research Center for Healthcare Devices, Guangzhou, China
| | - Jin Xu
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China.,The Key Laboratory of Neuro-informatics and Rehabilitation Engineering of Ministry of Civil Affairs, Xi'an, China.,National Engineering Research Center for Healthcare Devices, Guangzhou, China
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3
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Monitoring the level of hypnosis using a hierarchical SVM system. J Clin Monit Comput 2020; 34:331-338. [PMID: 30982945 DOI: 10.1007/s10877-019-00311-1] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2018] [Accepted: 04/04/2019] [Indexed: 10/27/2022]
Abstract
Monitoring level of hypnosis is a major ongoing challenge for anesthetists to reduce anesthetic drug consumption, avoiding intraoperative awareness and prolonged recovery. This paper proposes a novel automated method for accurate assessing of the level of hypnosis with sevoflurane in 17 patients using the electroencephalogram signal. In this method, a set of distinctive features and a hierarchical classification structure based on support vector machine (SVM) methods, is proposed to discriminate the four levels of anesthesia (awake, light, general and deep states). The first stage of the hierarchical SVM structure identifies the awake state by extracting Shannon Permutation Entropy, Detrended Fluctuation Analysis and frequency features. Then deep state is identified by extracting the sample entropy feature; and finally light and general states are identified by extracting the three mentioned features of the first step. The accuracy of the proposed method of analyzing the brain activity during anesthesia is 94.11%; which was better than previous studies and also a commercial monitoring system (Response Entropy Index).
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4
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A Novel GPU-Based Acceleration Algorithm for a Longwave Radiative Transfer Model. APPLIED SCIENCES-BASEL 2020. [DOI: 10.3390/app10020649] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Graphics processing unit (GPU)-based computing for climate system models is a longstanding research area of interest. The rapid radiative transfer model for general circulation models (RRTMG), a popular atmospheric radiative transfer model, can calculate atmospheric radiative fluxes and heating rates. However, the RRTMG has a high calculation time, so it is urgent to study its GPU-based efficient acceleration algorithm to enable large-scale and long-term climatic simulations. To improve the calculative efficiency of radiation transfer, this paper proposes a GPU-based acceleration algorithm for the RRTMG longwave radiation scheme (RRTMG_LW). The algorithm concept is accelerating the RRTMG_LW in the g- p o i n t dimension. After implementing the algorithm in CUDA Fortran, the G-RRTMG_LW was developed. The experimental results indicated that the algorithm was effective. In the case without I/O transfer, the G-RRTMG_LW on one K40 GPU obtained a speedup of 30.98× over the baseline performance on one single Intel Xeon E5-2680 CPU core. When compared to its counterpart running on 10 CPU cores of an Intel Xeon E5-2680 v2, the G-RRTMG_LW on one K20 GPU in the case without I/O transfer achieved a speedup of 2.35×.
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5
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Anesthesia Assessment Based on ICA Permutation Entropy Analysis of Two-Channel EEG Signals. Brain Inform 2019. [DOI: 10.1007/978-3-030-37078-7_24] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022] Open
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6
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Hou F, Yu Z, Peng CK, Yang A, Wu C, Ma Y. Complexity of Wake Electroencephalography Correlates With Slow Wave Activity After Sleep Onset. Front Neurosci 2018; 12:809. [PMID: 30483046 PMCID: PMC6243118 DOI: 10.3389/fnins.2018.00809] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2018] [Accepted: 10/17/2018] [Indexed: 11/24/2022] Open
Abstract
Sleep electroencephalography (EEG) provides an opportunity to study sleep scientifically, whose chaotic, dynamic, complex, and dissipative nature implies that non-linear approaches could uncover some mechanism of sleep. Based on well-established complexity theories, one hypothesis in sleep medicine is that lower complexity of brain waves at pre-sleep state can facilitate sleep initiation and further improve sleep quality. However, this has never been studied with solid data. In this study, EEG collected from healthy subjects was used to investigate the association between pre-sleep EEG complexity and sleep quality. Multiscale entropy analysis (MSE) was applied to pre-sleep EEG signals recorded immediately after light-off (while subjects were awake) for measuring the complexities of brain dynamics by a proposed index, CI1−30. Slow wave activity (SWA) in sleep, which is commonly used as an indicator of sleep depth or sleep intensity, was quantified based on two methods, traditional Fast Fourier transform (FFT) and ensemble empirical mode decomposition (EEMD). The associations between wake EEG complexity, sleep latency, and SWA in sleep were evaluated. Our results demonstrated that lower complexity before sleep onset is associated with decreased sleep latency, indicating a potential facilitating role of reduced pre-sleep complexity in the wake-sleep transition. In addition, the proposed EEMD-based method revealed an association between wake complexity and quantified SWA in the beginning of sleep (90 min after sleep onset). Complexity metric could thus be considered as a potential indicator for sleep interventions, and further studies are encouraged to examine the application of EEG complexity before sleep onset in populations with difficulty in sleep initiation. Further studies may also examine the mechanisms of the causal relationships between pre-sleep brain complexity and SWA, or conduct comparisons between normal and pathological conditions.
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Affiliation(s)
- Fengzhen Hou
- Key Laboratory of Biomedical Functional Materials, School of Science, China Pharmaceutical University, Nanjing, China
| | - Zhinan Yu
- Key Laboratory of Biomedical Functional Materials, School of Science, China Pharmaceutical University, Nanjing, China
| | - Chung-Kang Peng
- Division of Interdisciplinary Medicine and Biotechnology, Department of Medicine, Beth Israel Deaconess Medical Center/Harvard Medical School, Boston, MA, United States
| | - Albert Yang
- Division of Interdisciplinary Medicine and Biotechnology, Department of Medicine, Beth Israel Deaconess Medical Center/Harvard Medical School, Boston, MA, United States
| | - Chunyong Wu
- Key Laboratory of Drug Quality Control and Pharmacovigilance, Ministry of Education, China Pharmaceutical University, Nanjing, China.,Department of Pharmaceutical Analysis, China Pharmaceutical University, Nanjing, China
| | - Yan Ma
- Division of Interdisciplinary Medicine and Biotechnology, Department of Medicine, Beth Israel Deaconess Medical Center/Harvard Medical School, Boston, MA, United States
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7
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Muñoz-Gutiérrez PA, Giraldo E, Bueno-López M, Molinas M. Localization of Active Brain Sources From EEG Signals Using Empirical Mode Decomposition: A Comparative Study. Front Integr Neurosci 2018; 12:55. [PMID: 30450041 PMCID: PMC6224487 DOI: 10.3389/fnint.2018.00055] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2018] [Accepted: 10/16/2018] [Indexed: 11/21/2022] Open
Abstract
The localization of active brain sources from Electroencephalogram (EEG) is a useful method in clinical applications, such as the study of localized epilepsy, evoked-related-potentials, and attention deficit/hyperactivity disorder. The distributed-source model is a common method to estimate neural activity in the brain. The location and amplitude of each active source are estimated by solving the inverse problem by regularization or using Bayesian methods with spatio-temporal constraints. Frequency and spatio-temporal constraints improve the quality of the reconstructed neural activity. However, separation into frequency bands is beneficial when the relevant information is in specific sub-bands. We improved frequency-band identification and preserved good temporal resolution using EEG pre-processing techniques with good frequency band separation and temporal resolution properties. The identified frequency bands were included as constraints in the solution of the inverse problem by decomposing the EEG signals into frequency bands through various methods that offer good frequency and temporal resolution, such as empirical mode decomposition (EMD) and wavelet transform (WT). We present a comparative analysis of the accuracy of brain-source reconstruction using these techniques. The accuracy of the spatial reconstruction was assessed using the Wasserstein metric for real and simulated signals. We approached the mode-mixing problem, inherent to EMD, by exploring three variants of EMD: masking EMD, Ensemble-EMD (EEMD), and multivariate EMD (MEMD). The results of the spatio-temporal brain source reconstruction using these techniques show that masking EMD and MEMD can largely mitigate the mode-mixing problem and achieve a good spatio-temporal reconstruction of the active sources. Masking EMD and EEMD achieved better reconstruction than standard EMD, Multiple Sparse Priors, or wavelet packet decomposition when EMD was used as a pre-processing tool for the spatial reconstruction (averaged over time) of the brain sources. The spatial resolution obtained using all three EMD variants was substantially better than the use of EMD alone, as the mode-mixing problem was mitigated, particularly with masking EMD and EEMD. These findings encourage further exploration into the use of EMD-based pre-processing, the mode-mixing problem, and its impact on the accuracy of brain source activity reconstruction.
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Affiliation(s)
- Pablo Andrés Muñoz-Gutiérrez
- Electronic Instrumentation Technology, Universidad del Quindío, Armenia, Colombia.,Department of Electrical Engineering, Universidad Tecnológica de Pereira, Pereira, Colombia
| | - Eduardo Giraldo
- Department of Electrical Engineering, Universidad Tecnológica de Pereira, Pereira, Colombia
| | | | - Marta Molinas
- Department of Engineering Cybernetics, Norwegian University of Science and Technology, Trondheim, Norway
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8
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Haghighi SJ, Komeili M, Hatzinakos D, Beheiry HE. 40-Hz ASSR for Measuring Depth of Anaesthesia During Induction Phase. IEEE J Biomed Health Inform 2018; 22:1871-1882. [DOI: 10.1109/jbhi.2017.2778140] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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9
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Design and Evaluation of a Real Time Physiological Signals Acquisition System Implemented in Multi-Operating Rooms for Anesthesia. J Med Syst 2018; 42:148. [PMID: 29961144 DOI: 10.1007/s10916-018-0999-1] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2017] [Accepted: 06/21/2018] [Indexed: 10/28/2022]
Abstract
With critical importance of medical healthcare, there exist urgent needs for in-depth medical studies that can access and analyze specific physiological signals to provide theoretical support for practical clinical care. As a consequence, obtaining the valuable medical data with minimal cost and impacts on hospital work comes as the first concern of researchers. Anesthesia plays a widely recognized role in surgeries, which attracts people to undertake relevant research. In this paper, a real-time physiological medical signal data acquisition system (PMSDA) for the multi-operating room applications is proposed with high universality of the hospital practical settings and research requirements. By utilizing a wireless communication approach, it provides an easily accessible network platform for collection of physiological medical signals such as photoplethysmogram (PPG), electrocardiograph (ECG) and electroencephalogram (EEG) during the surgery. In addition, the raw data is stored on a server for safe backup and further analysis of depth of anesthesia (DoA). Results show that the PMSDA exhibits robust, high quality performance and efficiently reduces costs compared to previously manual methods and allows seamless integration into hospital environment, independent of its routine work. Overall, it provides a pragmatic and flexible surgery-data acquisition system model with low impact and resource cost applicable to research in critical and practical medical circumstances.
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10
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A Parallel and Optimization Approach for Land-Surface Temperature Retrieval on a Windows-Based PC Cluster. SUSTAINABILITY 2018. [DOI: 10.3390/su10030621] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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11
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Research on the Parallelization of the DBSCAN Clustering Algorithm for Spatial Data Mining Based on the Spark Platform. REMOTE SENSING 2017. [DOI: 10.3390/rs9121301] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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12
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Shalbaf A, Saffar M, Sleigh JW, Shalbaf R. Monitoring the Depth of Anesthesia Using a New Adaptive Neurofuzzy System. IEEE J Biomed Health Inform 2017; 22:671-677. [PMID: 28574372 DOI: 10.1109/jbhi.2017.2709841] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Accurate and noninvasive monitoring of the depth of anesthesia (DoA) is highly desirable. Since the anesthetic drugs act mainly on the central nervous system, the analysis of brain activity using electroencephalogram (EEG) is very useful. This paper proposes a novel automated method for assessing the DoA using EEG. First, 11 features including spectral, fractal, and entropy are extracted from EEG signal and then, by applying an algorithm according to exhaustive search of all subsets of features, a combination of the best features (Beta-index, sample entropy, shannon permutation entropy, and detrended fluctuation analysis) is selected. Accordingly, we feed these extracted features to a new neurofuzzy classification algorithm, adaptive neurofuzzy inference system with linguistic hedges (ANFIS-LH). This structure can successfully model systems with nonlinear relationships between input and output, and also classify overlapped classes accurately. ANFIS-LH, which is based on modified classical fuzzy rules, reduces the effects of the insignificant features in input space, which causes overlapping and modifies the output layer structure. The presented method classifies EEG data into awake, light, general, and deep states during anesthesia with sevoflurane in 17 patients. Its accuracy is 92% compared to a commercial monitoring system (response entropy index) successfully. Moreover, this method reaches the classification accuracy of 93% to categorize EEG signal to awake and general anesthesia states by another database of propofol and volatile anesthesia in 50 patients. To sum up, this method is potentially applicable to a new real-time monitoring system to help the anesthesiologist with continuous assessment of DoA quickly and accurately.
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13
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Chen YC, Hsiao TC. Instantaneous phase difference analysis between thoracic and abdominal movement signals based on complementary ensemble empirical mode decomposition. Biomed Eng Online 2016; 15:112. [PMID: 27716248 PMCID: PMC5053353 DOI: 10.1186/s12938-016-0233-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2016] [Accepted: 09/28/2016] [Indexed: 11/23/2022] Open
Abstract
BACKGROUND Thoracoabdominal asynchrony is often adopted to discriminate respiratory diseases in clinics. Conventionally, Lissajous figure analysis is the most frequently used estimation of the phase difference in thoracoabdominal asynchrony. However, the temporal resolution of the produced results is low and the estimation error increases when the signals are not sinusoidal. Other previous studies have reported time-domain procedures with the use of band-pass filters for phase-angle estimation. Nevertheless, the band-pass filters need calibration for phase delay elimination. METHODS To improve the estimation, we propose a novel method (named as instantaneous phase difference) that is based on complementary ensemble empirical mode decomposition for estimating the instantaneous phase relation between measured thoracic wall movement and abdominal wall movement. To validate the proposed method, experiments on simulated time series and human-subject respiratory data with two breathing types (i.e., thoracic breathing and abdominal breathing) were conducted. Latest version of Lissajous figure analysis and automatic phase estimation procedure were compared. RESULTS The simulation results show that the standard deviations of the proposed method were lower than those of two other conventional methods. The proposed method performed more accurately than the two conventional methods. For the human-subject respiratory data, the results of the proposed method are in line with those in the literature, and the correlation analysis result reveals that they were positively correlated with the results generated by the two conventional methods. Furthermore, the standard deviation of the proposed method was also the smallest. CONCLUSIONS To summarize, this study proposes a novel method for estimating instantaneous phase differences. According to the findings from both the simulation and human-subject data, our approach was demonstrated to be effective. The method offers the following advantages: (1) improves the temporal resolution, (2) does not introduce a phase delay, (3) works with non-sinusoidal signals, (4) provides quantitative phase estimation without estimating the embedded frequency of breathing signals, and (5) works without calibrated measurements. The results demonstrate a higher temporal resolution of the phase difference estimation for the evaluation of thoracoabdominal asynchrony.
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Affiliation(s)
- Ya-Chen Chen
- Institute of Computer Science and Engineering, National Chiao Tung University, Hsinchu, 30010 Taiwan
| | - Tzu-Chien Hsiao
- Department of Computer Science, National Chiao Tung University, Hsinchu, 30010 Taiwan
- Institute of Biomedical Engineering, National Chiao Tung University, Hsinchu, 30010 Taiwan
- Biomedical Electronics Translational Research Center and Biomimetic Systems Research Center, National Chiao Tung University, Hsinchu, 30010 Taiwan
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14
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Li T, Wen P. Depth of anaesthesia assessment based on adult electroencephalograph beta frequency band. AUSTRALASIAN PHYSICAL & ENGINEERING SCIENCES IN MEDICINE 2016; 39:773-81. [DOI: 10.1007/s13246-016-0459-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/08/2015] [Accepted: 06/12/2016] [Indexed: 11/29/2022]
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15
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OpenCL Implementation of a Parallel Universal Kriging Algorithm for Massive Spatial Data Interpolation on Heterogeneous Systems. ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION 2016. [DOI: 10.3390/ijgi5060096] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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16
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Chang CF, Liang WK, Lai CL, Hung DL, Juan CH. Theta Oscillation Reveals the Temporal Involvement of Different Attentional Networks in Contingent Reorienting. Front Hum Neurosci 2016; 10:264. [PMID: 27375459 PMCID: PMC4891329 DOI: 10.3389/fnhum.2016.00264] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2015] [Accepted: 05/19/2016] [Indexed: 11/25/2022] Open
Abstract
In the visual world, rapidly reorienting to relevant objects outside the focus of attention is vital for survival. This ability from the interaction between goal-directed and stimulus-driven attentional control is termed contingent reorienting. Neuroimaging studies have demonstrated activations of the ventral and dorsal attentional networks (DANs) which exhibit right hemisphere dominance, but the temporal dynamics of the attentional networks still remain unclear. The present study used event-related potential (ERP) to index the locus of spatial attention and Hilbert-Huang transform (HHT) to acquire the time-frequency information during contingent reorienting. The ERP results showed contingent reorienting induced significant N2pc on both hemispheres. In contrast, our time-frequency analysis found further that, unlike the N2pc, theta oscillation during contingent reorienting differed between hemispheres and experimental sessions. The inter-trial coherence (ITC) of the theta oscillation demonstrated that the two sides of the attentional networks became phase-locked to contingent reorienting at different stages. The left attentional networks were associated with contingent reorienting in the first experimental session whereas the bilateral attentional networks play a more important role in this process in the subsequent session. This phase-locked information suggests a dynamic temporal evolution of the involvement of different attentional networks in contingent reorienting and a potential role of the left ventral network in the first session.
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Affiliation(s)
- Chi-Fu Chang
- Institute of Cognitive Neuroscience, National Central University Taoyuan City, Taiwan
| | - Wei-Kuang Liang
- Institute of Cognitive Neuroscience, National Central University Taoyuan City, Taiwan
| | - Chiou-Lian Lai
- Department of Neurology, Kaohsiung Medical University Kaohsiung City, Taiwan
| | - Daisy L Hung
- Institute of Cognitive Neuroscience, National Central University Taoyuan City, Taiwan
| | - Chi-Hung Juan
- Institute of Cognitive Neuroscience, National Central University Taoyuan City, Taiwan
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17
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Zeng K, Chen D, Ouyang G, Wang L, Liu X, Li X. An EEMD-ICA Approach to Enhancing Artifact Rejection for Noisy Multivariate Neural Data. IEEE Trans Neural Syst Rehabil Eng 2016; 24:630-8. [DOI: 10.1109/tnsre.2015.2496334] [Citation(s) in RCA: 49] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
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18
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Gu Y, Han J, Liang Z, Yan J, Li Z, Li X. Empirical mode decomposition-based motion artifact correction method for functional near-infrared spectroscopy. JOURNAL OF BIOMEDICAL OPTICS 2016; 21:15002. [PMID: 26747474 DOI: 10.1117/1.jbo.21.1.015002] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/09/2015] [Accepted: 11/30/2015] [Indexed: 06/05/2023]
Affiliation(s)
- Yue Gu
- Yanshan University, Institute of Electrical Engineering, No. 438, Hebei Street, Haigang District, Qinhuangdao 066004, China
| | - Junxia Han
- Beijing Normal University, State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, No. 19, Xinjiekou Wai Street, Haidian District, Beijing 100875, ChinacBeijing Normal University, Center for Collaboration a
| | - Zhenhu Liang
- Yanshan University, Institute of Electrical Engineering, No. 438, Hebei Street, Haigang District, Qinhuangdao 066004, China
| | - Jiaqing Yan
- Yanshan University, Institute of Electrical Engineering, No. 438, Hebei Street, Haigang District, Qinhuangdao 066004, China
| | - Zheng Li
- Beijing Normal University, State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, No. 19, Xinjiekou Wai Street, Haidian District, Beijing 100875, ChinacBeijing Normal University, Center for Collaboration a
| | - Xiaoli Li
- Beijing Normal University, State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, No. 19, Xinjiekou Wai Street, Haidian District, Beijing 100875, ChinacBeijing Normal University, Center for Collaboration a
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19
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Liang Z, Wang Y, Sun X, Li D, Voss LJ, Sleigh JW, Hagihira S, Li X. EEG entropy measures in anesthesia. Front Comput Neurosci 2015; 9:16. [PMID: 25741277 PMCID: PMC4332344 DOI: 10.3389/fncom.2015.00016] [Citation(s) in RCA: 140] [Impact Index Per Article: 15.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2014] [Accepted: 01/28/2015] [Indexed: 11/13/2022] Open
Abstract
HIGHLIGHTS ► Twelve entropy indices were systematically compared in monitoring depth of anesthesia and detecting burst suppression.► Renyi permutation entropy performed best in tracking EEG changes associated with different anesthesia states.► Approximate Entropy and Sample Entropy performed best in detecting burst suppression. OBJECTIVE Entropy algorithms have been widely used in analyzing EEG signals during anesthesia. However, a systematic comparison of these entropy algorithms in assessing anesthesia drugs' effect is lacking. In this study, we compare the capability of 12 entropy indices for monitoring depth of anesthesia (DoA) and detecting the burst suppression pattern (BSP), in anesthesia induced by GABAergic agents. METHODS Twelve indices were investigated, namely Response Entropy (RE) and State entropy (SE), three wavelet entropy (WE) measures [Shannon WE (SWE), Tsallis WE (TWE), and Renyi WE (RWE)], Hilbert-Huang spectral entropy (HHSE), approximate entropy (ApEn), sample entropy (SampEn), Fuzzy entropy, and three permutation entropy (PE) measures [Shannon PE (SPE), Tsallis PE (TPE) and Renyi PE (RPE)]. Two EEG data sets from sevoflurane-induced and isoflurane-induced anesthesia respectively were selected to assess the capability of each entropy index in DoA monitoring and BSP detection. To validate the effectiveness of these entropy algorithms, pharmacokinetic/pharmacodynamic (PK/PD) modeling and prediction probability (Pk) analysis were applied. The multifractal detrended fluctuation analysis (MDFA) as a non-entropy measure was compared. RESULTS All the entropy and MDFA indices could track the changes in EEG pattern during different anesthesia states. Three PE measures outperformed the other entropy indices, with less baseline variability, higher coefficient of determination (R (2)) and prediction probability, and RPE performed best; ApEn and SampEn discriminated BSP best. Additionally, these entropy measures showed an advantage in computation efficiency compared with MDFA. CONCLUSION Each entropy index has its advantages and disadvantages in estimating DoA. Overall, it is suggested that the RPE index was a superior measure. Investigating the advantages and disadvantages of these entropy indices could help improve current clinical indices for monitoring DoA.
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Affiliation(s)
- Zhenhu Liang
- Institute of Electrical Engineering, Yanshan University Qinhuangdao, China
| | - Yinghua Wang
- State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University Beijing, China ; Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal University Beijing, China
| | - Xue Sun
- Institute of Electrical Engineering, Yanshan University Qinhuangdao, China
| | - Duan Li
- Institute of Information Science and Engineering, Yanshan University Qinhuangdao, China
| | - Logan J Voss
- Department of Anesthesia, Waikato Hospital Hamilton, New Zealand
| | - Jamie W Sleigh
- Department of Anesthesia, Waikato Hospital Hamilton, New Zealand
| | - Satoshi Hagihira
- Department of Anesthesiology, Osaka University Graduate School of Medicine Osaka, Japan
| | - Xiaoli Li
- State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University Beijing, China ; Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal University Beijing, China
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Chai R, Ling SH, Hunter GP, Tran Y, Nguyen HT. Brain–Computer Interface Classifier for Wheelchair Commands Using Neural Network With Fuzzy Particle Swarm Optimization. IEEE J Biomed Health Inform 2014; 18:1614-24. [DOI: 10.1109/jbhi.2013.2295006] [Citation(s) in RCA: 54] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
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Han M, Ge S, Wang M, Hong X, Han J. A novel dynamic update framework for epileptic seizure prediction. BIOMED RESEARCH INTERNATIONAL 2014; 2014:957427. [PMID: 25050381 PMCID: PMC4090468 DOI: 10.1155/2014/957427] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/18/2014] [Revised: 05/19/2014] [Accepted: 06/02/2014] [Indexed: 12/02/2022]
Abstract
Epileptic seizure prediction is a difficult problem in clinical applications, and it has the potential to significantly improve the patients' daily lives whose seizures cannot be controlled by either drugs or surgery. However, most current studies of epileptic seizure prediction focus on high sensitivity and low false-positive rate only and lack the flexibility for a variety of epileptic seizures and patients' physical conditions. Therefore, a novel dynamic update framework for epileptic seizure prediction is proposed in this paper. In this framework, two basic sample pools are constructed and updated dynamically. Furthermore, the prediction model can be updated to be the most appropriate one for the prediction of seizures' arrival. Mahalanobis distance is introduced in this part to solve the problem of side information, measuring the distance between two data sets. In addition, a multichannel feature extraction method based on Hilbert-Huang transform and extreme learning machine is utilized to extract the features of a patient's preseizure state against the normal state. At last, a dynamic update epileptic seizure prediction system is built up. Simulations on Freiburg database show that the proposed system has a better performance than the one without update. The research of this paper is significantly helpful for clinical applications, especially for the exploitation of online portable devices.
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Affiliation(s)
- Min Han
- Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian 116024, China
| | - Sunan Ge
- Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian 116024, China
| | - Minghui Wang
- Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian 116024, China
| | - Xiaojun Hong
- Department of Neurology, First Affiliated Hospital of Dalian Medical University, Dalian 116011, China
| | - Jie Han
- Department of Neurology, First Affiliated Hospital of Dalian Medical University, Dalian 116011, China
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Li TN, Li Y. Depth of anaesthesia monitors and the latest algorithms. ASIAN PAC J TROP MED 2014; 7:429-37. [DOI: 10.1016/s1995-7645(14)60070-5] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2014] [Revised: 03/15/2014] [Accepted: 04/15/2014] [Indexed: 10/25/2022] Open
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Ensemble Empirical Mode Decomposition Parameters Optimization for Spectral Distance Measurement in Hyperspectral Remote Sensing Data. REMOTE SENSING 2014. [DOI: 10.3390/rs6032069] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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EOG artifact correction from EEG recording using stationary subspace analysis and empirical mode decomposition. SENSORS 2013; 13:14839-59. [PMID: 24189330 PMCID: PMC3871096 DOI: 10.3390/s131114839] [Citation(s) in RCA: 60] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/18/2013] [Revised: 10/21/2013] [Accepted: 10/29/2013] [Indexed: 11/16/2022]
Abstract
Ocular contamination of EEG data is an important and very common problem in the diagnosis of neurobiological events. An effective approach is proposed in this paper to remove ocular artifacts from the raw EEG recording. First, it conducts the blind source separation on the raw EEG recording by the stationary subspace analysis, which can concentrate artifacts in fewer components than the representative blind source separation methods. Next, to recover the neural information that has leaked into the artifactual components, the adaptive signal decomposition technique EMD is applied to denoise the components. Finally, the artifact-only components are projected back to be subtracted from EEG signals to get the clean EEG data. The experimental results on both the artificially contaminated EEG data and publicly available real EEG data have demonstrated the effectiveness of the proposed method, in particular for the cases where limited number of electrodes are used for the recording, as well as when the artifact contaminated signal is highly non-stationary and the underlying sources cannot be assumed to be independent or uncorrelated.
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Analysis of EEG via Multivariate Empirical Mode Decomposition for Depth of Anesthesia Based on Sample Entropy. ENTROPY 2013. [DOI: 10.3390/e15093458] [Citation(s) in RCA: 33] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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Huang CW, Sue PD, Abbod MF, Jiang BC, Shieh JS. Measuring center of pressure signals to quantify human balance using multivariate multiscale entropy by designing a force platform. SENSORS 2013; 13:10151-66. [PMID: 23966184 PMCID: PMC3812597 DOI: 10.3390/s130810151] [Citation(s) in RCA: 31] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/03/2013] [Revised: 07/27/2013] [Accepted: 08/01/2013] [Indexed: 11/17/2022]
Abstract
To assess the improvement of human body balance, a low cost and portable measuring device of center of pressure (COP), known as center of pressure and complexity monitoring system (CPCMS), has been developed for data logging and analysis. In order to prove that the system can estimate the different magnitude of different sways in comparison with the commercial Advanced Mechanical Technology Incorporation (AMTI) system, four sway tests have been developed (i.e., eyes open, eyes closed, eyes open with water pad, and eyes closed with water pad) to produce different sway displacements. Firstly, static and dynamic tests were conducted to investigate the feasibility of the system. Then, correlation tests of the CPCMS and AMTI systems have been compared with four sway tests. The results are within the acceptable range. Furthermore, multivariate empirical mode decomposition (MEMD) and enhanced multivariate multiscale entropy (MMSE) analysis methods have been used to analyze COP data reported by the CPCMS and compare it with the AMTI system. The improvements of the CPCMS are 35% to 70% (open eyes test) and 60% to 70% (eyes closed test) with and without water pad. The AMTI system has shown an improvement of 40% to 80% (open eyes test) and 65% to 75% (closed eyes test). The results indicate that the CPCMS system can achieve similar results to the commercial product so it can determine the balance.
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Affiliation(s)
- Cheng-Wei Huang
- Department of Mechanical Engineering, Yuan Ze University, Chung-Li 32003, Taiwan; E-Mails: (C.-W.H.); (P.-D.S.)
| | - Pei-Der Sue
- Department of Mechanical Engineering, Yuan Ze University, Chung-Li 32003, Taiwan; E-Mails: (C.-W.H.); (P.-D.S.)
| | - Maysam F. Abbod
- School of Engineering and Design, Brunel University, London UB8 3PH, UK; E-Mail:
| | - Bernard C. Jiang
- Department of Industrial Management, National Taiwan University of Science and Technology, Taipei 106, Taiwan; E-Mail:
- Center for Dynamical Biomarkers and Translational Medicine, National Central University, Chung-Li 32001, Taiwan
| | - Jiann-Shing Shieh
- Department of Mechanical Engineering, Yuan Ze University, Chung-Li 32003, Taiwan; E-Mails: (C.-W.H.); (P.-D.S.)
- Center for Dynamical Biomarkers and Translational Medicine, National Central University, Chung-Li 32001, Taiwan
- Author to whom correspondence should be addressed; E-Mail: ; Tel.: +886-3-463-8800 (ext. 2470); Fax: +886-3-455-8013
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Chen D, Li X, Cui D, Wang L, Lu D. Global Synchronization Measurement of Multivariate Neural Signals with Massively Parallel Nonlinear Interdependence Analysis. IEEE Trans Neural Syst Rehabil Eng 2013; 22:33-43. [PMID: 23674459 DOI: 10.1109/tnsre.2013.2258939] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
The estimation of synchronization amongst multiple brain regions is a critical issue in understanding brain functions. There is a lack of an appropriate approach which is capable of 1) measuring the direction and strength of synchronization of activities of multiple brain regions, and 2) adapting to the quickly increasing sizes and scales of neural signals. Nonlinear Interdependence (NLI) analysis is an effective method for measuring synchronization direction and strength of bivariate neural signal. However, the method currently does not directly apply in handling multivariate signal. Its application in practice has also long been largely hampered by the ultra-high complexity of NLI algorithms. Aiming at these problems, this study 1) extends the conventional NLI to quantify the global synchronization of multivariate neural signals, and 2) develops a parallelized NLI method with general-purpose computing on the graphics processing unit (GPGPU), namely, G-NLI. The approach performs synchronization measurement in a massively parallel manner. The G-NLI has improved the runtime performance by more than 1000 times comparing to the original sequential NLI. Meanwhile, the G-NLI was employed to analyze 10-channel local field potential (LFP) recordings from a patient suffering from temporal lobe epilepsy. The results demonstrate that the proposed G-NLI method can support real-time global synchronization measurement and it could be successful in localization of epileptic focus.
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Nguyen-Ky T, Wen PP, Li Y. Consciousness and depth of anesthesia assessment based on Bayesian analysis of EEG signals. IEEE Trans Biomed Eng 2013; 60:1488-98. [PMID: 23314762 DOI: 10.1109/tbme.2012.2236649] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
This study applies Bayesian techniques to analyze EEG signals for the assessment of the consciousness and depth of anesthesia (DoA). This method takes the limiting large-sample normal distribution as posterior inferences to implement the Bayesian paradigm. The maximum a posterior (MAP) is applied to denoise the wavelet coefficients based on a shrinkage function. When the anesthesia states change from awake to light, moderate, and deep anesthesia, the MAP values increase gradually. Based on these changes, a new function B(DoA) is designed to assess the DoA. The new proposed method is evaluated using anesthetized EEG recordings and BIS data from 25 patients. The Bland-Alman plot is used to verify the agreement of B(DoA) and the popular BIS index. A correlation between B(DoA) and BIS was measured using prediction probability P(K). In order to estimate the accuracy of DoA, the effect of sample n and variance τ on the maximum posterior probability is studied. The results show that the new index accurately estimates the patient's hypnotic states. Compared with the BIS index in some cases, the B(DoA) index can estimate the patient's hypnotic state in the case of poor signal quality.
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Affiliation(s)
- Tai Nguyen-Ky
- Faculty of Engineering and Surveying, Centre for Systems Biology, University of Southern Queensland, Toowoomba, Qld 4350, Australia.
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Multivariate Multiscale Entropy Applied to Center of Pressure Signals Analysis: An Effect of Vibration Stimulation of Shoes. ENTROPY 2012. [DOI: 10.3390/e14112157] [Citation(s) in RCA: 34] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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31
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Lin CF, Zhu JD. Hilbert-Huang transformation-based time-frequency analysis methods in biomedical signal applications. Proc Inst Mech Eng H 2012; 226:208-16. [PMID: 22558835 DOI: 10.1177/0954411911434246] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Hilbert-Huang transformation, wavelet transformation, and Fourier transformation are the principal time-frequency analysis methods. These transformations can be used to discuss the frequency characteristics of linear and stationary signals, the time-frequency features of linear and non-stationary signals, the time-frequency features of non-linear and non-stationary signals, respectively. The Hilbert-Huang transformation is a combination of empirical mode decomposition and Hilbert spectral analysis. The empirical mode decomposition uses the characteristics of signals to adaptively decompose them to several intrinsic mode functions. Hilbert transforms are then used to transform the intrinsic mode functions into instantaneous frequencies, to obtain the signal's time-frequency-energy distributions and features. Hilbert-Huang transformation-based time-frequency analysis can be applied to natural physical signals such as earthquake waves, winds, ocean acoustic signals, mechanical diagnosis signals, and biomedical signals. In previous studies, we examined Hilbert-Huang transformation-based time-frequency analysis of the electroencephalogram FPI signals of clinical alcoholics, and 'sharp I' wave-based Hilbert-Huang transformation time-frequency features. In this paper, we discuss the application of Hilbert-Huang transformation-based time-frequency analysis to biomedical signals, such as electroencephalogram, electrocardiogram signals, electrogastrogram recordings, and speech signals.
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Affiliation(s)
- Chin-Feng Lin
- Department of Electrical Engineering, National Taiwan Ocean University, Taiwan, Republic of China.
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Li D, Li X, Hagihira S, Sleigh JW. The effect of isoflurane anesthesia on the electroencephalogram assessed by harmonic wavelet bicoherence-based indices. J Neural Eng 2011; 8:056011. [DOI: 10.1088/1741-2560/8/5/056011] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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Park C, Looney D, Kidmose P, Ungstrup M, Mandic DP. Time-Frequency Analysis of EEG Asymmetry Using Bivariate Empirical Mode Decomposition. IEEE Trans Neural Syst Rehabil Eng 2011; 19:366-73. [DOI: 10.1109/tnsre.2011.2116805] [Citation(s) in RCA: 65] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
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