A Strategy for Classification of “Vaginal vs. Cesarean Section” Delivery: Bivariate Empirical Mode Decomposition of Cardiotocographic Recordings

The chapter presents a new approach of computer aided diagnosis of focal electroencephalogram (EEG) signals by applying bivariate empirical mode decomposition (BEMD). Firstly, the focal and non-focal EEG signals are decomposed using the BEMD, which results in intrinsic mode functions (IMFs) corresponding to each signal. Secondly, bivariate bandwidths namely, amplitude bandwidth, precession bandwidth, and deformation bandwidth are computed for each obtained IMF. Interquartile range (IQR) values of bivariate bandwidths of IMFs are employed as the features for classification. In order to perform classification least squares support vector machine (LS-SVM) is used. The results of the experiment suggest that the computed bivariate bandwidths are significantly useful to discriminate focal EEG signals. The resultant classification accuracy obtained using proposed methodology, applied on the Bern-Barcelona EEG database, is 84.01%. The obtained results are encouraging and the proposed methodology can be helpful for identification of epileptogenic focus.

Empirical Mode Decomposition Algorithms for Classification of Single-Channel EEG Manifesting McGurk Effect

Intelligent Human Computer Interaction - Lecture Notes in Computer Science ◽

10.1007/978-3-030-44689-5_5 ◽

2020 ◽

pp. 49-60

Author(s):

Arup Kumar Pal ◽

Dipanjan Roy ◽

G. Vinodh Kumar ◽

Bipra Chatterjee ◽

L. N. Sharma ◽

...

Keyword(s):

Empirical Mode Decomposition ◽

Single Channel ◽

Mcgurk Effect ◽

Decomposition Algorithms ◽

Mode Decomposition

Use of Empirical Mode Decomposition for Classification of MRCP Based Task Parameters

Intelligent Data Engineering and Automated Learning – IDEAL 2014 - Lecture Notes in Computer Science ◽

10.1007/978-3-319-10840-7_10 ◽

2014 ◽

pp. 77-84

Author(s):

Ali Hassan ◽

Hassan Akhtar ◽

Muhammad Junaid Khan ◽

Farhan Riaz ◽

Faiza Hassan ◽

...

Keyword(s):

Empirical Mode Decomposition ◽

Mode Decomposition ◽

Task Parameters

Automatic Classification of Life-Threatening Cardiac Arrhythmias Using Empirical Mode Decomposition

Lecture Notes in Networks and Systems - Advances in Intelligent Computing and Communication ◽

10.1007/978-981-16-0695-3_45 ◽

2021 ◽

pp. 473-481

Author(s):

Monalisa Mohanty ◽

Santanu Sahoo ◽

Asit Subudhi

Keyword(s):

Empirical Mode Decomposition ◽

Cardiac Arrhythmias ◽

Automatic Classification ◽

Mode Decomposition ◽

Life Threatening

Detection and Classification of Transmission Line Faults Using Empirical Mode Decomposition and Rule Based Decision Tree Based Algorithm

2018 IEEE 8th Power India International Conference (PIICON) ◽

10.1109/poweri.2018.8704372 ◽

2018 ◽

Cited By ~ 2

Author(s):

Balvinder Singh ◽

Om Prakash Mahela ◽

Tanuj Manglani

Keyword(s):

Decision Tree ◽

Transmission Line ◽

Empirical Mode Decomposition ◽

Rule Based ◽

Mode Decomposition

Application of Empirical Mode Decomposition and Support Vector Machine for the Classification of Arc Fault in Distribution Line

Lecture Notes in Electrical Engineering - Advances in Electronics, Communication and Computing ◽

10.1007/978-981-15-8752-8_39 ◽

2021 ◽

pp. 385-393

Author(s):

Himadri Lala ◽

Subrata Karmakar

Keyword(s):

Support Vector Machine ◽

Empirical Mode Decomposition ◽

Support Vector ◽

Mode Decomposition ◽

Distribution Line

Univariate and Bivariate Empirical Mode Decomposition for Postural Stability Analysis

EURASIP Journal on Advances in Signal Processing ◽

10.1155/2008/657391 ◽

2008 ◽

Vol 2008 (1) ◽

Cited By ~ 14

Author(s):

Hassan Amoud ◽

Hichem Snoussi ◽

David Hewson ◽

Jacques Duchêne

Keyword(s):

Stability Analysis ◽

Empirical Mode Decomposition ◽

Postural Stability ◽

Mode Decomposition ◽

Bivariate Empirical Mode Decomposition

An empirical mode decomposition based filtering method for classification of motor-imagery EEG signals for enhancing brain-computer interface

2015 International Joint Conference on Neural Networks (IJCNN) ◽

10.1109/ijcnn.2015.7280754 ◽

2015 ◽

Cited By ~ 17

Author(s):

Pramod Gaur ◽

Ram Bilas Pachori ◽

Hui Wang ◽

Girijesh Prasad

Keyword(s):

Empirical Mode Decomposition ◽

Motor Imagery ◽

Brain Computer Interface ◽

Computer Interface ◽

Eeg Signals ◽

Filtering Method ◽

Mode Decomposition

Bivariate empirical mode decomposition of grinding chatter signals

2016 11th International Conference on Reliability, Maintainability and Safety (ICRMS) ◽

10.1109/icrms.2016.8050145 ◽

2016 ◽

Author(s):

Jianyang Shen ◽

Huanguo Chen ◽

Yongyu Yi ◽

Jianwei Wu ◽

Yajie Li ◽

...

Keyword(s):

Empirical Mode Decomposition ◽

Grinding Chatter ◽

Mode Decomposition ◽

Bivariate Empirical Mode Decomposition

FEATURES EXTRACTION METHOD FOR BRAIN-MACHINE COMMUNICATION BASED ON THE EMPIRICAL MODE DECOMPOSITION

Biomedical Engineering Applications Basis and Communications ◽

10.4015/s1016237213500580 ◽

2013 ◽

Vol 25 (06) ◽

pp. 1350058 ◽

Cited By ~ 2

Author(s):

Pablo F. Diez ◽

Vicente A. Mut ◽

Eric Laciar ◽

Abel Torres ◽

Enrique M. Avila Perona

Keyword(s):

Empirical Mode Decomposition ◽

Communication System ◽

Brain Activity ◽

Features Extraction ◽

Eeg Signals ◽

Linear Discriminant ◽

Mode Decomposition ◽

Machine Communication ◽

Mental Tasks

A brain-machine interface (BMI) is a communication system that translates human brain activity into commands, and then these commands are conveyed to a machine or a computer. It is proposes a technique for features extraction from electroencephalographic (EEG) signals and afterward, their classification on different mental tasks. The empirical mode decomposition (EMD) is a method capable of processing non-stationary and nonlinear signals, as the EEG. The EMD was applied on EEG signals of seven subjects performing five mental tasks. Six features were computed, namely, root mean square (RMS), variance, Shannon entropy, Lempel–Ziv complexity value, and central and maximum frequencies. In order to reduce the dimensionality of the feature vector, the Wilks' lambda (WL) parameter was used for the selection of the most important variables. The classification of mental tasks was performed using linear discriminant analysis (LDA) and neural networks (NN). Using this method, the average classification over all subjects in database is 91 ± 5% and 87 ± 5% using LDA and NN, respectively. Bit rate was ranging from 0.24 bits/trial up to 0.84 bits/trial. The proposed method allows achieving higher performances in the classification of mental tasks than other traditional methods using the same database. This represents an improvement in the brain-machine communication system.