Seizure onset detection based on frequency domain metric of empirical mode decomposition

2018 ◽  
Vol 12 (8) ◽  
pp. 1489-1496 ◽  
Author(s):  
Ahmet Mert ◽  
Aydin Akan
Keyword(s):  
Frequency Domain ◽  
Empirical Mode Decomposition ◽  
Onset Detection ◽  
Seizure Onset ◽  
Mode Decomposition

Identification of tooth fault in a gearbox based on cyclostationarity and empirical mode decomposition

2017 ◽  
Vol 17 (3) ◽  
pp. 494-513 ◽  
Author(s):  
Jong-Sik Kim ◽  
Sang-Kwon Lee
Keyword(s):  
Signal Processing ◽  
Frequency Domain ◽  
Empirical Mode Decomposition ◽  
Health Monitoring ◽  
Rotating Machinery ◽  
Intrinsic Mode Functions ◽  
Simulated Signal ◽  
Mode Decomposition ◽  
Signal Processing Methods ◽  
The Impact

In the previous work, the cyclostationarity process, which is one of signal processing methods, has been used in health monitoring of the rotating machinery because of the superior detecting property of hidden periodicity. However, it is often difficult to acquire the information about the hidden periodicity due to the fault of the rotating machinery when the impact signal is low. Therefore, a certain preprocessing tool to extract the information about the impact signal due to the fault is required. This article presents the new detection process of tooth faults in a gearbox system based on the empirical mode decomposition algorithm which adaptively decomposes the signal into a set of intrinsic mode functions and the cyclostationarity process which identifies the hidden periodicity clearly in bi-frequency domain. The proposed method was demonstrated with a simulated signal and was applied to the detection of four types of conditions of tooth fault successfully.

scholarly journals Collaborative Sleep Electroencephalogram Data Analysis Based on Improved Empirical Mode Decomposition and Clustering Algorithm

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Xiangwei Zheng ◽  
Xiaochun Yin ◽  
Xuexiao Shao ◽  
Yalin Li ◽  
Xiaomei Yu
Keyword(s):  
Frequency Domain ◽  
Empirical Mode Decomposition ◽  
Clustering Algorithm ◽  
Sleep Stage ◽  
Sleep Stages ◽  
Sleep Staging ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Stage Classification ◽  
Sleep Stage Classification

Sleep-related diseases seriously affect the life quality of patients. Sleep stage classification (or sleep staging), which studies the human sleep process and classifies the sleep stages, is an important reference to the diagnosis and study of sleep disorders. Many scholars have conducted a series of sleep staging studies, but the correlation between different sleep stages and the accuracy of classification still needs to be improved. Therefore, this paper proposes an automatic sleep stage classification based on EEG. By constructing an improved empirical mode decomposition and K-means experimental model, the concept of “frequency-domain correlation coefficient” is defined. In the process of feature extraction, the feature vector with the best correlation in the time-frequency domain is selected. Extraction and classification of EEG features are realized based on the K-means clustering algorithm. Experimental results demonstrate that the classification accuracy is significantly improved, and our proposed algorithm has a positive impact on sleep staging compared with other algorithms.

Denoising signals for photoacoustic imaging in frequency domain based on empirical mode decomposition

Optik ◽  
2018 ◽  
Vol 160 ◽  
pp. 402-414 ◽  
Author(s):  
Qinglin Kong ◽  
Qian Song ◽  
Yan Hai ◽  
Rui Gong ◽  
Jietao Liu ◽  
...  
Keyword(s):  
Frequency Domain ◽  
Empirical Mode Decomposition ◽  
Photoacoustic Imaging ◽  
Mode Decomposition

An Application of the Frequency-Domain Experience Mode Decomposition to Enhance Deconvolution Results

2013 ◽  
Vol 397-400 ◽  
pp. 2120-2123
Author(s):  
Ya Nan Zhang ◽  
Yong Shou Dai ◽  
Jin Jie Ding ◽  
Man Man Zhang ◽  
Rong Rong Wang
Keyword(s):  
Reflection Coefficient ◽  
Frequency Domain ◽  
Empirical Mode Decomposition ◽  
Amplitude Spectrum ◽  
Seismic Section ◽  
Mode Decomposition ◽  
The Time Domain ◽  
Non Stationary Signal ◽  
Different Characteristics ◽  
Emd Method

To improve the resolution of the seismic section after deconvolution, a method based on frequency-domain experience mode decomposition was proposed. Empirical mode decomposition (EMD) method is usually used to analyze the time domain non-stationary signal, in order to better recover original reflection coefficient sequence, empirical mode decomposition was implemented for frequency-domain amplitude spectrum. Through the different characteristics between the equivalent filter amplitude after deconvolution and reflection coefficient sequence amplitude in frequency-domain, the real reflection coefficient sequence was recovered. Simulation results indicate that the method is effective and feasible.

scholarly journals ANALYSIS OF THE PRECIPITATION CLIMATE SIGNAL USING EMPIRICAL MODE DECOMPOSITION (EMD) OVER THE CASPIAN CATCHMENT AREA

2019 ◽  
Vol XLII-4/W18 ◽  
pp. 923-929
Author(s):  
F. Sabzehee ◽  
V. Nafisi ◽  
S. Iran Pour ◽  
B. D. Vishwakarma
Keyword(s):  
Time Series ◽  
Wavelet Transform ◽  
Frequency Domain ◽  
Empirical Mode Decomposition ◽  
Precipitation Time Series ◽  
Climate Signal ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Instantaneous Frequencies ◽  
Better Than

Abstract. In this paper, we employ Empirical Mode Decomposition (EMD) together with Hilbert Transform to analyze precipitation time series over the Caspian Sea catchment. Several studies have shown that EMD can extract nonlinear and non-stationary signals better than Fast Fourier Transform (FFT) and Wavelet Transform. EMD decomposes the time series into a finite number of Intrinsic Mode Functions (IMFs) in the time-frequency domain, while FFT helps us operate either in the time or the frequency domain, which fuels limitations such as the inability of nonstationary signal processing and the lack of time transparency. Although Wavelet Transform is shown to be better than FFT, it fails to detect the instantaneous frequencies and needs to have prior information about characteristics of the data. On the other hand, EMD has shown that it is almost able to determine the signal characteristics with no previous assumptions to estimate the instantaneous frequencies of the signal. In this work, EMD is applied to identify the main frequencies of precipitation time series. Thereafter, a statistical procedure is used to identify the prominent IMF of the original signal.We use the correlation coefficient, Minkowski distance and variance test to extract the relevant and prominent IMFs. The results show that IMF 1–3 are the relevant components and are related to annual and biennial variations of precipitation time series over the Caspian catchment during 2003–2016, respectively.

scholarly journals A Gyro Signal Characteristics Analysis Method Based on Empirical Mode Decomposition

2016 ◽  
Vol 2016 ◽  
pp. 1-7 ◽  
Author(s):  
Qinghua Zeng ◽  
Shanshan Gu ◽  
Jianye Liu ◽  
Sheng Liu ◽  
Weina Chen
Keyword(s):  
Frequency Domain ◽  
Empirical Mode Decomposition ◽  
Three Dimensional ◽  
Allan Variance ◽  
Analysis Method ◽  
Time Frequency ◽  
Noise Characteristics ◽  
Mode Decomposition ◽  
Signal Characteristics ◽  
Characteristics Analysis

It is difficult to analyze the nonstationary gyro signal in detail for the Allan variance (AV) analysis method. A novel approach in the time-frequency domain for gyro signal characteristics analysis is proposed based on the empirical mode decomposition and Allan variance (EMDAV). The output signal of gyro is decomposed by empirical mode decomposition (EMD) first, and then the decomposed signal is analyzed by AV algorithm. Consequently, the gyro noise characteristics are demonstrated in the time-frequency domain with a three-dimensional (3D) manner. Practical data of fiber optic gyro (FOG) and MEMS gyro are processed by the AV method and the EMDAV algorithm separately. The results indicate that the details of gyro signal characteristics in different frequency bands can be described with the help of EMDAV, and the analysis dimensions are extended compared with the common AV. The proposed EMDAV, as a complementary tool of the AV, which provides a theoretical reference for the gyro signal preprocessing, is a general approach for the analysis and evaluation of gyro performance.

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