New feature extraction methods in the time-frequency plane for Chinese tone analysis

Machine learning algorithms can learn mechanisms of antimicrobial resistance from the data of DNA sequence without any a priori information. Interpreting a trained machine learning algorithm can be exploited for validating the model and obtaining new information about resistance mechanisms. Different feature extraction methods, such as SNP calling and counting nucleotide k-mers have been proposed for presenting DNA sequences to the model. However, there are trade-offs between interpretability, computational complexity and accuracy for different feature extraction methods. In this study, we have proposed a new feature extraction method, counting amino acid k-mers or oligopeptides, which provides easier model interpretation compared to counting nucleotide k-mers and reaches the same or even better accuracy in comparison with different methods. Additionally, we have trained machine learning algorithms using different feature extraction methods and compared the results in terms of accuracy, model interpretability and computational complexity. We have built a new feature selection pipeline for extraction of important features so that new AMR determinants can be discovered by analyzing these features. This pipeline allows the construction of models that only use a small number of features and can predict resistance accurately.

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Intelligent Fault Diagnosis Method Using Acoustic Emission Signals for Bearings under Complex Working Conditions

Applied Sciences ◽

10.3390/app10207068 ◽

2020 ◽

Vol 10 (20) ◽

pp. 7068

Author(s):

Minh Tuan Pham ◽

Jong-Myon Kim ◽

Cheol Hong Kim

Keyword(s):

Acoustic Emission ◽

Feature Extraction ◽

Deep Learning ◽

Fault Diagnosis ◽

Extraction Methods ◽

High Accuracy ◽

Time Frequency ◽

Bearing Fault ◽

Bearing Fault Diagnosis ◽

Ae Signals

Recent convolutional neural network (CNN) models in image processing can be used as feature-extraction methods to achieve high accuracy as well as automatic processing in bearing fault diagnosis. The combination of deep learning methods with appropriate signal representation techniques has proven its efficiency compared with traditional algorithms. Vital electrical machines require a strict monitoring system, and the accuracy of these machines’ monitoring systems takes precedence over any other factors. In this paper, we propose a new method for diagnosing bearing faults under variable shaft speeds using acoustic emission (AE) signals. Our proposed method predicts not only bearing fault types but also the degradation level of bearings. In the proposed technique, AE signals acquired from bearings are represented by spectrograms to obtain as much information as possible in the time–frequency domain. Feature extraction and classification processes are performed by deep learning using EfficientNet and a stochastic line-search optimizer. According to our various experiments, the proposed method can provide high accuracy and robustness under noisy environments compared with existing AE-based bearing fault diagnosis methods.

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The CSP-Based New Features Plus Non-Convex Log Sparse Feature Selection for Motor Imagery EEG Classification

Sensors ◽

10.3390/s20174749 ◽

2020 ◽

Vol 20 (17) ◽

pp. 4749

Author(s):

Shaorong Zhang ◽

Zhibin Zhu ◽

Benxin Zhang ◽

Bao Feng ◽

Tianyou Yu ◽

...

Keyword(s):

Feature Extraction ◽

Feature Selection ◽

Motor Imagery ◽

Classification Accuracy ◽

Feature Selection Method ◽

Extraction Methods ◽

Extraction Time ◽

Classification Model ◽

Discrete Wavelet ◽

New Feature

The common spatial pattern (CSP) is a very effective feature extraction method in motor imagery based brain computer interface (BCI), but its performance depends on the selection of the optimal frequency band. Although a lot of research works have been proposed to improve CSP, most of these works have the problems of large computation costs and long feature extraction time. To this end, three new feature extraction methods based on CSP and a new feature selection method based on non-convex log regularization are proposed in this paper. Firstly, EEG signals are spatially filtered by CSP, and then three new feature extraction methods are proposed. We called them CSP-wavelet, CSP-WPD and CSP-FB, respectively. For CSP-Wavelet and CSP-WPD, the discrete wavelet transform (DWT) or wavelet packet decomposition (WPD) is used to decompose the spatially filtered signals, and then the energy and standard deviation of the wavelet coefficients are extracted as features. For CSP-FB, the spatially filtered signals are filtered into multiple bands by a filter bank (FB), and then the logarithm of variances of each band are extracted as features. Secondly, a sparse optimization method regularized with a non-convex log function is proposed for the feature selection, which we called LOG, and an optimization algorithm for LOG is given. Finally, ensemble learning is used for secondary feature selection and classification model construction. Combing feature extraction and feature selection methods, a total of three new EEG decoding methods are obtained, namely CSP-Wavelet+LOG, CSP-WPD+LOG, and CSP-FB+LOG. Four public motor imagery datasets are used to verify the performance of the proposed methods. Compared to existing methods, the proposed methods achieved the highest average classification accuracy of 88.86, 83.40, 81.53, and 80.83 in datasets 1–4, respectively. The feature extraction time of CSP-FB is the shortest. The experimental results show that the proposed methods can effectively improve the classification accuracy and reduce the feature extraction time. With comprehensive consideration of classification accuracy and feature extraction time, CSP-FB+LOG has the best performance and can be used for the real-time BCI system.

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Methods of EEG Signal Features Extraction Using Linear Analysis in Frequency and Time-Frequency Domains

ISRN Neuroscience ◽

10.1155/2014/730218 ◽

2014 ◽

Vol 2014 ◽

pp. 1-7 ◽

Cited By ~ 113

Author(s):

Amjed S. Al-Fahoum ◽

Ausilah A. Al-Fraihat

Keyword(s):

Feature Extraction ◽

Extraction Methods ◽

Eeg Signal ◽

Property A ◽

Frequency Distributions ◽

Time Frequency ◽

Signal Features ◽

Frequency Domains ◽

Auto Regressive ◽

The Cost

Technically, a feature represents a distinguishing property, a recognizable measurement, and a functional component obtained from a section of a pattern. Extracted features are meant to minimize the loss of important information embedded in the signal. In addition, they also simplify the amount of resources needed to describe a huge set of data accurately. This is necessary to minimize the complexity of implementation, to reduce the cost of information processing, and to cancel the potential need to compress the information. More recently, a variety of methods have been widely used to extract the features from EEG signals, among these methods are time frequency distributions (TFD), fast fourier transform (FFT), eigenvector methods (EM), wavelet transform (WT), and auto regressive method (ARM), and so on. In general, the analysis of EEG signal has been the subject of several studies, because of its ability to yield an objective mode of recording brain stimulation which is widely used in brain-computer interface researches with application in medical diagnosis and rehabilitation engineering. The purposes of this paper, therefore, shall be discussing some conventional methods of EEG feature extraction methods, comparing their performances for specific task, and finally, recommending the most suitable method for feature extraction based on performance.

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New feature extraction approach for epileptic EEG signal detection using time-frequency distributions

Medical & Biological Engineering & Computing ◽

10.1007/s11517-010-0590-5 ◽

2010 ◽

Vol 48 (4) ◽

pp. 321-330 ◽

Cited By ~ 46

Author(s):

Carlos Guerrero-Mosquera ◽

Armando Malanda Trigueros ◽

Jorge Iriarte Franco ◽

Ángel Navia-Vázquez

Keyword(s):

Feature Extraction ◽

Signal Detection ◽

Eeg Signal ◽

Frequency Distributions ◽

Time Frequency ◽

New Feature

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A New Feature Extraction Method for Bone-Conducted Life Sounds Based on F-Ratio

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.411-414.1598 ◽

2013 ◽

Vol 411-414 ◽

pp. 1598-1604

Author(s):

Ye Teng An ◽

Hong Cui Wang ◽

Song Gun Hyon ◽

Sai Chen ◽

Jian Wu Dang

Keyword(s):

Feature Extraction ◽

Recognition Task ◽

Extraction Methods ◽

Frequency Region ◽

Frequency Filter ◽

Feature Extraction Method ◽

Discriminative Feature ◽

Frequency Components ◽

Feature Based ◽

New Feature

Bone-conducted life sounds are useful for monitoring human healthy situation. Although a number of feature extraction methods were proposed for air-conducted speech, they may not meet the requirements of the recognition task for bone-conducted life sounds since there is a large difference between air-conducted speech and bone-conducted life sounds. In order to obtain features that can characterize bone-conducted signals, in this study, we first analyze the property of bone-conducted life sounds itself and compare each kind of life sounds in the frequency region. Then we adopt the methods of F-ratio and improved F-ratio separately to measure the dependences between frequency components and characteristics of life sounds. According to the result of analysis, we design a new adaptive frequency filter to extract the desired discriminative feature. The new feature is combined with the Hidden Markov Model and applied to classify different kinds of bone-conducted life sounds. The experimental results show that the error rate using the proposed feature based on State mean F-ratio is reduced by 7.2% compared with the MFCC feature.

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