The mammalian brain exists in a number of attractors. In order to characterize these attractors we have collected the time series data from the EEG recording of rat models. The time series was obtained by recording of the frontoparietal, occipital and temporal regions of the rat brain. Significant changes have
been observed in the dimensionalities of these brain attractors between the normal state, focal ischemic
state and the drug induced state. Thus, these three states were characterized by unique lyapunov exponents,
correlation dimensions and embedding dimensions. The inverse of the lyapunov exponent gave us the long
term coherence of the rat brain and was found to differ for the three states. The autocorrelation function
measured the mean similarity of the EEG signal with itself after a time t. The degree of decay was high indicating that there was maximum correlation in the time series. Thus, the autocorrelation functions clearly indicate the effect of focal cerebral ischemia and drugs induced on the rat brain.
Review:Wavelet transform based electroencephalogram methodsijtsrd
In this paper, EEG signals are the signatures of neural activities. There have been many algorithms developed so far for processing EEG signals. The analysis of brain waves plays an important role in diagnosis of different brain disorders. Brain is made up of billions of brain cells called neurons, which use electricity to communicate with each other. The combination of millions of neurons sending signals at once produces an enormous amount of electrical activity in the brain, which can be detected using sensitive medical equipment such as an EEG which measures electrical levels over areas of the scalp. The electroencephalogram (EEG) recording is a useful tool for studying the functional state of the brain and for diagnosing certain disorders. The combination of electrical activity of the brain is commonly called a Brainwave pattern because of its wave-like nature. EEG signals are low voltage signals that are contaminated by various types of noises that are also called as artifacts. Statistical method for removing artifacts from EEG recordings through wavelet transform without considering SNR calculation is proposed Miss. N. R. Patil | Prof. S. N. Patil"Review:Wavelet transform based electroencephalogram methods" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-3 , April 2018, URL: http://www.ijtsrd.com/papers/ijtsrd11542.pdf http://www.ijtsrd.com/engineering/bio-mechanicaland-biomedical-engineering/11542/reviewwavelet-transform-based-electroencephalogram-methods/miss-n-r-patil
International Journal of Computational Engineering Research(IJCER)ijceronline
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
Analysis of EEG data Using ICA and Algorithm Development for Energy Comparisonijsrd.com
This Electroencephalogram (EEG) signal analysis very useful in clinical research and brain computer interface application. EEG signal (brain wave) recordings are highly susceptible from artifacts which are originated from the non-cerebral origin of the brain. EEG detection and rejection of artifacts are necessary for acquiring correct information from EEG signal. Emotiv, Epoc headset can record 16 channels from the scalp of the electrode. EEGLAB allows analysis of EEG signal through Event related potential (ERP) analysis, Independent component analysis (ICA), and time/frequency analysis. Independent component analysis (ICA) may be suitable method for detecting artifacts. We analyzed EEG data which are recorded using emotiv epoc in a different situation for a single person. EEG data are preprocessed by EEGLAB and decomposes the data by the ICA. Using statistical method, analyzed the all the dataset and finding the relationship among the dataset. T- Test shows that EEG pattern is unique in a person. EEG data is divided into different frequency band to find the relationship between the dataset. Also develop the algorithm for calculating energy of dataset for each channel. Comparing the energy for each dataset and each channel to find the maximum and minimum value of energy. In higher frequency range (13-100 Hz) dataset D (meditation) contains maximum value of energy for most channels among all datasets.
A Novel Approach For Detection of Neurological Disorders through Electrical P...IJECEIAES
This paper talks about the phenomenon of recurrence and using this concept it proposes a novel and a very simple and user friendly method to diagnose the neurological disorders by using the EEG signals.The mathematical concept of recurrence forms the basis for the detection of neurological disorders,and the tool used is MATLAB. Using MATLAB, an algorithm is designed which uses EEG signals as the input and uses the synchronizing patterns of EEG signals to determine various neurological disorders through graphs and recurrence plots
Review:Wavelet transform based electroencephalogram methodsijtsrd
In this paper, EEG signals are the signatures of neural activities. There have been many algorithms developed so far for processing EEG signals. The analysis of brain waves plays an important role in diagnosis of different brain disorders. Brain is made up of billions of brain cells called neurons, which use electricity to communicate with each other. The combination of millions of neurons sending signals at once produces an enormous amount of electrical activity in the brain, which can be detected using sensitive medical equipment such as an EEG which measures electrical levels over areas of the scalp. The electroencephalogram (EEG) recording is a useful tool for studying the functional state of the brain and for diagnosing certain disorders. The combination of electrical activity of the brain is commonly called a Brainwave pattern because of its wave-like nature. EEG signals are low voltage signals that are contaminated by various types of noises that are also called as artifacts. Statistical method for removing artifacts from EEG recordings through wavelet transform without considering SNR calculation is proposed Miss. N. R. Patil | Prof. S. N. Patil"Review:Wavelet transform based electroencephalogram methods" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-3 , April 2018, URL: http://www.ijtsrd.com/papers/ijtsrd11542.pdf http://www.ijtsrd.com/engineering/bio-mechanicaland-biomedical-engineering/11542/reviewwavelet-transform-based-electroencephalogram-methods/miss-n-r-patil
International Journal of Computational Engineering Research(IJCER)ijceronline
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
Analysis of EEG data Using ICA and Algorithm Development for Energy Comparisonijsrd.com
This Electroencephalogram (EEG) signal analysis very useful in clinical research and brain computer interface application. EEG signal (brain wave) recordings are highly susceptible from artifacts which are originated from the non-cerebral origin of the brain. EEG detection and rejection of artifacts are necessary for acquiring correct information from EEG signal. Emotiv, Epoc headset can record 16 channels from the scalp of the electrode. EEGLAB allows analysis of EEG signal through Event related potential (ERP) analysis, Independent component analysis (ICA), and time/frequency analysis. Independent component analysis (ICA) may be suitable method for detecting artifacts. We analyzed EEG data which are recorded using emotiv epoc in a different situation for a single person. EEG data are preprocessed by EEGLAB and decomposes the data by the ICA. Using statistical method, analyzed the all the dataset and finding the relationship among the dataset. T- Test shows that EEG pattern is unique in a person. EEG data is divided into different frequency band to find the relationship between the dataset. Also develop the algorithm for calculating energy of dataset for each channel. Comparing the energy for each dataset and each channel to find the maximum and minimum value of energy. In higher frequency range (13-100 Hz) dataset D (meditation) contains maximum value of energy for most channels among all datasets.
A Novel Approach For Detection of Neurological Disorders through Electrical P...IJECEIAES
This paper talks about the phenomenon of recurrence and using this concept it proposes a novel and a very simple and user friendly method to diagnose the neurological disorders by using the EEG signals.The mathematical concept of recurrence forms the basis for the detection of neurological disorders,and the tool used is MATLAB. Using MATLAB, an algorithm is designed which uses EEG signals as the input and uses the synchronizing patterns of EEG signals to determine various neurological disorders through graphs and recurrence plots
Classification of EEG Signals for Brain-Computer InterfaceAzoft
This e-book gives you a sneak peak into how the classification of right hand movements via EEG could contribute to the development of a brain-computer interface. The Azoft R&D department, along with Sergey Alyamkin and Expasoft provide detailed data from research done for the "Grasp-and-Lift EEG Detection" competition organized by Kaggle. You’ll learn why the deep learning algorithms can be effective in various types of signal classifications and how to apply convolutional neural networks for a specific task such as identifying hand motions from EEG recordings.
See more details on our website: http://rnd.azoft.com/classification-eeg-signals-brain-computer-interface/
5. detection and separation of eeg artifacts using wavelet transform nov 11, ...IAESIJEECS
Bio-medical signal processing is one of the most important techniques of multichannel sensor network and it has a substantial concentration in medical application. However, the real-time and recorded signals in multisensory instruments contains different and huge amount of noise, and great work has been completed in developing most favorable structures for estimating the signal source from the noisy signal in multichannel observations. Methods have been developed to obtain the optimal linear estimation of the output signal through the Wide-Sense-Stationary (WSS) process with the help of time-invariant filters. In this process, the input signal and the noise signal are assumed to achieve the linear output signal. During the process, the non-stationary signals arise in the bio-medical signal processing in addition to it there is no effective structure to deal with them. Wavelets transform has been proved to be the efficient tool for handling the non-stationary signals, but wavelet provide any possible way to approach multichannel signal processing. Based on the basic structure of linear estimation of non-stationary multichannel data and statistical models of spatial signal coherence acquire through the wavelet transform in multichannel estimation. The above methods can be used for Electroencephalography (EEG) signal denoising through the original signal and then implement the noise reduction technique to evaluate their performance such as SNR, MSE and computation time.
MHEALTH APPLICATIONS DEVELOPED BY THE MINISTRY OF HEALTH FOR PUBLIC USERS INK...hiij
mHealth applications have shown promise in supporting the delivery of health services in peoples’ daily life. Recently, the Ministry of Health in the Kingdom of Saudi Arabia (MOH) has launched several mHealth applications to develop work mechanisms. Our study aimed to identify and understand the design of mHealth apps by classifying their persuasive features using the Persuasive Systems Design (PSD) model and expert evaluation method. This paper presents the distinct persuasive features applied in recent applications launched by MOH for public users called “Sehha & Mawid” Apps. The results revealed the extensive use of persuasive features; particularly features related to credibility support, dialogue support and primary task support respectively. The implementation and design of social support features were found to be poor; this could be due to the nature of the apps or lack of knowledge from the developers’
perspectives. The findings suggest some features that may improve the persuasion for the evaluated apps.
Feature Extraction Techniques and Classification Algorithms for EEG Signals t...Editor IJCATR
EEG (Electroencephalogram) signal is a neuro signal which is generated due the different electrical activities in the brain.
Different types of electrical activities correspond to different states of the brain. Every physical activity of a person is due to some
activity in the brain which in turn generates an electrical signal. These signals can be captured and processed to get the useful information
that can be used in early detection of some mental diseases. This paper focus on the usefulness of EGG signal in detecting the human
stress levels. It also includes the comparison of various preprocessing algorithms ( DCT and DWT.) and various classification algorithms
(LDA, Naive Bayes and ANN.). The paper proposes a system which will process the EEG signal and by applying the combination of
classifiers, will detect the human stress levels.
International Journal of Engineering Research and Applications (IJERA) is a team of researchers not publication services or private publications running the journals for monetary benefits, we are association of scientists and academia who focus only on supporting authors who want to publish their work. The articles published in our journal can be accessed online, all the articles will be archived for real time access.
Our journal system primarily aims to bring out the research talent and the works done by sciaentists, academia, engineers, practitioners, scholars, post graduate students of engineering and science. This journal aims to cover the scientific research in a broader sense and not publishing a niche area of research facilitating researchers from various verticals to publish their papers. It is also aimed to provide a platform for the researchers to publish in a shorter of time, enabling them to continue further All articles published are freely available to scientific researchers in the Government agencies,educators and the general public. We are taking serious efforts to promote our journal across the globe in various ways, we are sure that our journal will act as a scientific platform for all researchers to publish their works online.
A New Approach to Denoising EEG Signals - Merger of Translation Invariant Wav...CSCJournals
In this paper we present a new algorithm using a merger of Independent Component Analysis and Translation Invariant Wavelet Transform. The efficacy of this algorithm is evaluated by applying contaminated EEG signals. Its performance was compared to three fixed-point ICA algorithms (FastICA, EFICA and Pearson-ICA) using Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR), Signal to Distortion Ratio (SDR), and Amari Performance Index. Experiments reveal that our new technique is the most accurate separation method.
These are the slides that I presented at the first Brain Control Club hackathon in Paris, see http://cri-paris.org/scientific-clubs/brain-control-club/
Non invasive modalities of neurocognitive science used for brain mappingeSAT Journals
Abstract The brain plays a pivotal role in the study neurocognitive science. It is the seat of intelligence and is a complex organ which is being widely studied. Although mapping different areas of the brain has been carried a lot still remains for the study of cognition. Various non invasive modalities used for brain mapping are classified according to the measurement techniques used. Electromagnetic Technique uses two modalities for studying electromagnetic and electrical activity of the brain EEG (Electroencephalography) MEG (Magnetoencephalogrphy).Whereas hemodynamic technique uses recording of hemodynamic activity of the brain, these modalities are MRI (Magnetic Resonance Imaging).fMRI (functional Magnetic Resonance Imaging) PET (Positron Emission Tomography), SPECT (Single Photon Emission Computed Tomography) NRIS (Near Infrared Spectroscopy ). Hemodynamic techniques like MRI, fMRI, PET and SPECT provide excellent spatial resolution while electromagnetic modalities provide excellent temporal resolution .This paper explains various noninvasive modalities, their working principle and a comparative study of all the modalities. Index Terms: modalities, spatial and temporal resolution, Radionuclide, Artifacts
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Classification of EEG Signal for Epileptic Seizure DetectionusingEMD and ELMIJTET Journal
Abstract—This paper proposes the classification of EEG signal for epilepsy diagnosis. Epilepsy is a neurological disorder which occurs due to synchronous neuronal activity in brain. Empirical Mode Decomposition (EMD), Extreme Learning Machine (ELM) are the techniquedelivered in the proposed method.Input EEG signal, which is available in online as Bonn Database is decomposed into five Intrinsic Mode Functions (IMFs) using EMD.Higher Order Statistical moments such as Variance, Skewness and Kurtosis are drawn out as features from the decomposed signals. Extreme Learning Machine is used as a classifier to classify the EEG signals with the taken features, under various categories that include healthy and ictal, interictal and ictal, Non seizure and seizure, healthy, interictal and ictal. The proposed method gives 100%accuracy, 100%sensitivity in discriminating interictal and ictal, non seizure and seizure, healthy and ictal, healthy, interictal and ictal, 100% specificity in classifying healthy and ictal, interictal and ictal and 100% and 99%accuracy in case of discriminating interictal and ictal, non seizure and seizure.
HuddleUp promotes internal networking by generating warm contacts. The first step and the hardest is to approach someone you don't know. Breakdown silos and build a social culture.
Connect - Catch Up - Collaborate
Classification of EEG Signals for Brain-Computer InterfaceAzoft
This e-book gives you a sneak peak into how the classification of right hand movements via EEG could contribute to the development of a brain-computer interface. The Azoft R&D department, along with Sergey Alyamkin and Expasoft provide detailed data from research done for the "Grasp-and-Lift EEG Detection" competition organized by Kaggle. You’ll learn why the deep learning algorithms can be effective in various types of signal classifications and how to apply convolutional neural networks for a specific task such as identifying hand motions from EEG recordings.
See more details on our website: http://rnd.azoft.com/classification-eeg-signals-brain-computer-interface/
5. detection and separation of eeg artifacts using wavelet transform nov 11, ...IAESIJEECS
Bio-medical signal processing is one of the most important techniques of multichannel sensor network and it has a substantial concentration in medical application. However, the real-time and recorded signals in multisensory instruments contains different and huge amount of noise, and great work has been completed in developing most favorable structures for estimating the signal source from the noisy signal in multichannel observations. Methods have been developed to obtain the optimal linear estimation of the output signal through the Wide-Sense-Stationary (WSS) process with the help of time-invariant filters. In this process, the input signal and the noise signal are assumed to achieve the linear output signal. During the process, the non-stationary signals arise in the bio-medical signal processing in addition to it there is no effective structure to deal with them. Wavelets transform has been proved to be the efficient tool for handling the non-stationary signals, but wavelet provide any possible way to approach multichannel signal processing. Based on the basic structure of linear estimation of non-stationary multichannel data and statistical models of spatial signal coherence acquire through the wavelet transform in multichannel estimation. The above methods can be used for Electroencephalography (EEG) signal denoising through the original signal and then implement the noise reduction technique to evaluate their performance such as SNR, MSE and computation time.
MHEALTH APPLICATIONS DEVELOPED BY THE MINISTRY OF HEALTH FOR PUBLIC USERS INK...hiij
mHealth applications have shown promise in supporting the delivery of health services in peoples’ daily life. Recently, the Ministry of Health in the Kingdom of Saudi Arabia (MOH) has launched several mHealth applications to develop work mechanisms. Our study aimed to identify and understand the design of mHealth apps by classifying their persuasive features using the Persuasive Systems Design (PSD) model and expert evaluation method. This paper presents the distinct persuasive features applied in recent applications launched by MOH for public users called “Sehha & Mawid” Apps. The results revealed the extensive use of persuasive features; particularly features related to credibility support, dialogue support and primary task support respectively. The implementation and design of social support features were found to be poor; this could be due to the nature of the apps or lack of knowledge from the developers’
perspectives. The findings suggest some features that may improve the persuasion for the evaluated apps.
Feature Extraction Techniques and Classification Algorithms for EEG Signals t...Editor IJCATR
EEG (Electroencephalogram) signal is a neuro signal which is generated due the different electrical activities in the brain.
Different types of electrical activities correspond to different states of the brain. Every physical activity of a person is due to some
activity in the brain which in turn generates an electrical signal. These signals can be captured and processed to get the useful information
that can be used in early detection of some mental diseases. This paper focus on the usefulness of EGG signal in detecting the human
stress levels. It also includes the comparison of various preprocessing algorithms ( DCT and DWT.) and various classification algorithms
(LDA, Naive Bayes and ANN.). The paper proposes a system which will process the EEG signal and by applying the combination of
classifiers, will detect the human stress levels.
International Journal of Engineering Research and Applications (IJERA) is a team of researchers not publication services or private publications running the journals for monetary benefits, we are association of scientists and academia who focus only on supporting authors who want to publish their work. The articles published in our journal can be accessed online, all the articles will be archived for real time access.
Our journal system primarily aims to bring out the research talent and the works done by sciaentists, academia, engineers, practitioners, scholars, post graduate students of engineering and science. This journal aims to cover the scientific research in a broader sense and not publishing a niche area of research facilitating researchers from various verticals to publish their papers. It is also aimed to provide a platform for the researchers to publish in a shorter of time, enabling them to continue further All articles published are freely available to scientific researchers in the Government agencies,educators and the general public. We are taking serious efforts to promote our journal across the globe in various ways, we are sure that our journal will act as a scientific platform for all researchers to publish their works online.
A New Approach to Denoising EEG Signals - Merger of Translation Invariant Wav...CSCJournals
In this paper we present a new algorithm using a merger of Independent Component Analysis and Translation Invariant Wavelet Transform. The efficacy of this algorithm is evaluated by applying contaminated EEG signals. Its performance was compared to three fixed-point ICA algorithms (FastICA, EFICA and Pearson-ICA) using Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR), Signal to Distortion Ratio (SDR), and Amari Performance Index. Experiments reveal that our new technique is the most accurate separation method.
These are the slides that I presented at the first Brain Control Club hackathon in Paris, see http://cri-paris.org/scientific-clubs/brain-control-club/
Non invasive modalities of neurocognitive science used for brain mappingeSAT Journals
Abstract The brain plays a pivotal role in the study neurocognitive science. It is the seat of intelligence and is a complex organ which is being widely studied. Although mapping different areas of the brain has been carried a lot still remains for the study of cognition. Various non invasive modalities used for brain mapping are classified according to the measurement techniques used. Electromagnetic Technique uses two modalities for studying electromagnetic and electrical activity of the brain EEG (Electroencephalography) MEG (Magnetoencephalogrphy).Whereas hemodynamic technique uses recording of hemodynamic activity of the brain, these modalities are MRI (Magnetic Resonance Imaging).fMRI (functional Magnetic Resonance Imaging) PET (Positron Emission Tomography), SPECT (Single Photon Emission Computed Tomography) NRIS (Near Infrared Spectroscopy ). Hemodynamic techniques like MRI, fMRI, PET and SPECT provide excellent spatial resolution while electromagnetic modalities provide excellent temporal resolution .This paper explains various noninvasive modalities, their working principle and a comparative study of all the modalities. Index Terms: modalities, spatial and temporal resolution, Radionuclide, Artifacts
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Classification of EEG Signal for Epileptic Seizure DetectionusingEMD and ELMIJTET Journal
Abstract—This paper proposes the classification of EEG signal for epilepsy diagnosis. Epilepsy is a neurological disorder which occurs due to synchronous neuronal activity in brain. Empirical Mode Decomposition (EMD), Extreme Learning Machine (ELM) are the techniquedelivered in the proposed method.Input EEG signal, which is available in online as Bonn Database is decomposed into five Intrinsic Mode Functions (IMFs) using EMD.Higher Order Statistical moments such as Variance, Skewness and Kurtosis are drawn out as features from the decomposed signals. Extreme Learning Machine is used as a classifier to classify the EEG signals with the taken features, under various categories that include healthy and ictal, interictal and ictal, Non seizure and seizure, healthy, interictal and ictal. The proposed method gives 100%accuracy, 100%sensitivity in discriminating interictal and ictal, non seizure and seizure, healthy and ictal, healthy, interictal and ictal, 100% specificity in classifying healthy and ictal, interictal and ictal and 100% and 99%accuracy in case of discriminating interictal and ictal, non seizure and seizure.
HuddleUp promotes internal networking by generating warm contacts. The first step and the hardest is to approach someone you don't know. Breakdown silos and build a social culture.
Connect - Catch Up - Collaborate
Heart Rate Variability (HRV) plays an important role for reporting several cardiological and noncardiological
diseases. Also, the HRV has a prognostic value and is therefore quite important in modelling
the cardiac risk. The nature of the HRV is chaotic, stochastic and it remains highly controversial. Because
the HRV has utmost importance, it needs a sensitive tool to analyze the variability. In previous work,
Rosenstein and Wolf had used the Lyapunov exponent as a quantitative measure for HRV detection
sensitivity. However, the two methods diverge in determining the HRV sensitivity. This paper introduces a
modification to both the Rosenstein and Wolf methods to overcome their drawbacks. The introduced
Mazhar-Eslam algorithm increases the sensitivity to HRV detection with better accuracy.
In vivo characterization of breast tissue by non-invasive bio-impedance measu...ijbesjournal
Biological tissues have complex electrical impedance related to the tissue dimension, the internal structure
and the arrangement of the constituent cells. Since different tissues have different conductivities and
permittivities, the electrical impedance can provide useful information based on heterogeneous tissue
structures, physiological states and functions. In vivo bio-impedance breast measurements proved to be a
dependable method where these measurements can be adopted to characterize breast tissue into normal
and abnormal by a developed normalized coefficient of variation (NCV) as a numerical criterion of the bioimpedance
measurements. In this study 26 breasts in 26 women have been scanned with a homemade
Electrical Bio-impedance System (EBS). Characteristic breast conductivity and permittivity measurements
emerged for Mammographically normal and abnormal cases. CV and NCV are calculated for each case,
and the value of NCVs greater than 1.00 corresponds to abnormalities, particularly tumours while NCVs
less than 1.00 correspond to normal cases. The most promising results of (NCV) for permittivity at 1 MHz,
it detects 73% of abnormal cases including 100% tumor cases while it detects 82% of normal cases. The
numerical criterion NCV of in-vivo bio-impedance measurements of the breast appears to be promising in
breast cancer screening.
Phonocardiogram based diagnostic systemijbesjournal
A Phonocardiogram or PCG is a plot of high fidelity recording of the sounds and murmurs made by the
heart with the help of the machine called phonocardiograph. It has developed continuously to perform an
important role in the proper and accurate diagnosis of the defects of the heart. As usually with the
stethoscope, it requires highly and experienced physicians to read the phonocardiogram. A diagnostic
system based on Artificial Neural Networks (ANN) is implemented as a detector and classifier of heart
diseases. The output of the system is the classification of the sound as either normal or abnormal, if it is
abnormal what type of abnormality is present. In this paper, Based on the extracted time domain and
frequency domain features such as energy, mean, variance and Mel Frequency Cepstral Coefficients
(MFCC) various heart sound samples are classified using Support Vector Machine (SVM), K Nearest
Neighbour (KNN), Bayesian and Gaussian Mixture Model (GMM) Classifiers. The data used in this paper
was obtained from Michigan university website.
A novel reliable method assess hrv forijbesjournal
In a simple words, the heart rate variability (HRV) refers to the divergence in heart complex wave (beat- to-beat) intervals. It is a reliable repercussion of many, psychological, physiological, also environmental factors modulating therhythm of the heart. Seriously, the HRV act as a powerful tool for observation the interaction between the sympathetic and parasympathetic nervous systems. However, it has a frequency that is great for supervision, surveillance, and following up the cases. Finally, the generating structure of heart complex wave signal is not simply linear, but also it involves the nonlinear contributions. Those two contributions are totally correlated.
HRV is stochastic and chaotic (stochaotic) signal. It has utmost importance in heart diseases diagnosis, and it needs a sensitive tool to analyze its variability. In early works, Rosenstein and Wolf had used the Lyapunov exponent (LE) as a quantitative measure for HRV detection sensitivity, but the Rosenstein and Wolf methods diverge in determining the main features of HRV sensitivity, while Mazhar-Eslam introduced a modification algorithm to overcome the Rosenstein and Wolf drawbacks.
The present work introduces a novel reliable method to analyze the linear and nonlinear behaviour of heart complex wave variability, and to assess the use of the HRV as a versatile tool for heart disease diagnosis. This paper introduces a declaration for the concept of the LE parameters to be used for HRV diagnosis and proposes a modified algorithm for a more sensitive parameters computation
In this paper designing of a battery operated portable single channel electroencephalography (EEG) signal acquisition system is presented. The advancement in the field of hardware and signal processing tools made possible the utilization of brain waves for the communication between humans and computers. The work presented in this paper can be said as a part of bigger task, whose purpose is to classify EEG signals belonging to a varied set of mental activities in a real time Brain Computer Interface (BCI). Keeping in mind the end goal is to research the possibility of utilizing diverse mental tasks as a wide correspondence channel in the middle of individuals and PCs. This work deals with EEG based BCI, intent on the designing of portable EEG signal acquisition system. The EEG signal acquisition system with a cut off frequency band of 1-100 Hz is designed by the use of integrated circuits such as low power instrumentation amplifier INA128P, high gain operational amplifiers LM358P. Initially the amplified EEG signals are digitized and transmitted to a PC by a data acquisition module NI DAQ (SCXI-1302). These transmitted signals are then viewed and stored in the LAB VIEW environment. From a varied set of experimental observation it can be said that the system can be implemented in the acquisition of EEG signals and can stores the data to a PC efficiently and the system would be of advantage to the use of EEG signal acquisition or even BCI application by adapting signal processing tools.
EEG SIGNAL CLASSIFICATION USING LDA AND MLP CLASSIFIERhiij
Electroencephalography (EEG) is the recording of electrical activities along the scalp. EEG measures
voltage fluctuations resulting from ionic current flows within the neurons of the brain. Diagnostic
applications generally focus on the spectral content of EEG, which is the type of neural oscillations that
can be observed in EEG signal. EEG is most often used to diagnose epilepsy, which causes obvious
abnormalities in EEG readings. This powerful property confirms the rich potential for EEG analysis and
motivates the need for advanced signal processing techniques to aid clinicians in their interpretations.
This paper describes the application of Wavelet Transform (WT) for the processing of
Electroencephalogram (EEG) signals. Furthermore, the linear discriminant analysis (LDA) is applied for
feature selection and dimensionality reduction where the informative and discriminative two-dimension
features are used as a benchmark for classification purposes through a Multi-Layers Perceptron (MLP)
neural network. For five classification problems, the proposed model achieves a high sensitivity,
specificity and accuracy of 100%.Finally, the comparison of the results obtained with the proposed
methods and those obtained with previous literature methods shows the superiority of our approach for
EEG signals classification and automated diagnosis
An Entropy-based Feature in Epileptic Seizure Prediction Algorithmiosrjce
Epilepsy prediction is a vital demand for people suffering from epileptic onset. Prediction of seizure
onsets could be very useful for drug-resistant epileptic patients. We propose an epileptic seizure prediction
algorithm to predict an onset of epilepsy and discriminate between pre-seizure periods and seizure free periods.
The proposed algorithm is based on entropy features of 60 (1 hour segmented into 60 periods) with free seizure
periods and repeated for 24 hour, and 60 (pre-seizure periods) of the CHB-MIT Scalp EEG Database (Female
less or equal 12 age). Critical values of the sample entropy and approximate entropy are estimated to locate
starting of the seizure onset. These values are taken as warning to a probably seizure starts within a specific
time. The prediction time in order of 1min- 49min is achieved in 60 seizure periods under study in this task.
SVM is used to classify pre-seizure periods from seizure free periods for the mentioned data. The performance
is evaluated and analysed
Computer Aided Detection of Obstructive Sleep Apnea from EEG Signalssipij
Sleep Apnea is an anomaly in sleeping characterized by short pause in breathing. Failure to treat sleep
apnea leads to fatal complications in both psychological and physiological being of human.
Electroencephalogram (EEG) performs an important task in probing for sleep apnea through identifying
and recording the brain’s activities while sleeping. In this study, computer aided detection of sleep apnea
from EEG signals is developed to optimize and increase the prompt recognition and diagnosis of sleep
apnea in patients. The time domain, wavelets, and frequency domain of the EEG signals were computed,
and features were extracted from these domains. These features are inputted into two machine learning
algorithms: Support Vector Machine and K-Nearest Neighbors of different kernel functions and orders.
Evaluation metrics such as specificity, accuracy, and sensitivity are computed and analyzed for the
classifiers. The KNN classifier outperforms the SVM in classifying apnea from non-apnea events in
patients. The KNN order 3 shows the highest performance sensitivity of 85.92%, specificity of 80% and
accuracy of 82.69%.
Computer Aided Detection of Obstructive Sleep Apnea from EEG Signalssipij
Sleep Apnea is an anomaly in sleeping characterized by short pause in breathing. Failure to treat sleep
apnea leads to fatal complications in both psychological and physiological being of human.
Electroencephalogram (EEG) performs an important task in probing for sleep apnea through identifying
and recording the brain’s activities while sleeping. In this study, computer aided detection of sleep apnea
from EEG signals is developed to optimize and increase the prompt recognition and diagnosis of sleep
apnea in patients. The time domain, wavelets, and frequency domain of the EEG signals were computed,
and features were extracted from these domains. These features are inputted into two machine learning
algorithms: Support Vector Machine and K-Nearest Neighbors of different kernel functions and orders.
Evaluation metrics such as specificity, accuracy, and sensitivity are computed and analyzed for the
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Eeg time series data analysis in focal cerebral ischemic rat model
1. International journal of Biomedical Engineering and Science (IJBES), Vol. 2, No. 1, January 2015
1
EEG TIME SERIES DATA ANALYSIS IN FOCAL
CEREBRAL ISCHEMIC RAT MODEL
Sudip Paul1, 3*
, Tapas Kumar Sinha2*
, Ranjana Patnaik3
1
Department of Biomedical Engineering, North-Eastern Hill University, India
2
Computer Centre, North-Eastern Hill University, India
3
School of Biomedical Engineering, Indian Institute of Technology, Banaras Hindu
University, India
*Corresponding Author:
Mr. Sudip Paul
Department of Biomedical EngineeringNorth-Eastern Hill University, India
ABSTRACT
The mammalian brain exists in a number of attractors. In order to characterize these attractors we have
collected the time series data from the EEG recording of rat models. The time series was obtained by
recording of the frontoparietal, occipital and temporal regions of the rat brain. Significant changes have
been observed in the dimensionalities of these brain attractors between the normal state, focal ischemic
state and the drug induced state. Thus, these three states were characterized by unique lyapunov exponents,
correlation dimensions and embedding dimensions. The inverse of the lyapunov exponent gave us the long
term coherence of the rat brain and was found to differ for the three states. The autocorrelation function
measured the mean similarity of the EEG signal with itself after a time t. The degree of decay was high
indicating that there was maximum correlation in the time series. Thus, the autocorrelation functions
clearly indicate the effect of focal cerebral ischemia and drugs induced on the rat brain.
KEYWORDS
EEG; Focal Cerebral Ischemia; Lyapunov Exponent; Embedding Dimension; Correlation Coefficient
1. INTRODUCTION
Brain stroke is a very common and serious neurological disorder. Every year, 15 million people
worldwide suffer a stroke. Nearly six million die and another five million are left permanently
disabled. A stroke is a condition in which the brain cells suddenly die because of a lack of
oxygen. This can be caused by an obstruction in the blood flow, or the rupture of an artery that
feeds the brain. Characterization of the brain stroke EEG plays a vital role.
Electroencephalography (EEG) provides a direct and comprehensive measure of cortical activity
with millisecond resolution. The EEG recordings, which are aperiodic time series, are a result of
the contributions of large number neuronal potentials. We have investigated induced focal
cerebral ischemia and subsequent recovery by application of the Piroxicam drug. Piroxicam drug
2. International journal of Biomedical Engineering and Science (IJBES), Vol. 2, No. 1, January 2015
2
has been used earlier studies [1]. Ischemic model of rat brain has been extensively studied [2].
However, the EEG data of focal cerebral ischemia has not yet been analyzed in terms of inverse
lyapunov exponent, correlation dimension and autocorrelation functions. In this paper, we have
analyzed the focal cerebral ischemia data in terms of the above parameters and were able to
classify EEG spectra in terms of inverse Lyapunov exponent and correlation dimensions. Via
analysis of the time series [3-7] using MATLAB R2010a based programs useful parameters series
such as lyapunov coefficients, Correlation dimension and autocorrelation function were extracted
from the time series. We have used these parameters to study the effect of induced stroke and
subsequent drug (Piroxicam) based recovery on the rat brain.
2. MATERIALS AND METHODS
Male Charles Foster rats (6 weeks, 270 ± 10 g) were in-bred at the Central Animal House,
Banaras Hindu University were used for the experiments. Animals were kept under standard
laboratory conditions maintained with animal care and housing. In our study, a total of 24 animals
were divided in three groups consisting of control (n=8), induced stroke (n=8) and Piroxicam
drug treated (n=8) for each group. Piroxicam and other chemicals were purchased from Sigma,
USA. They were allowed free access to food and water and maintained at 12 h day/night cycle.
Focal cerebral ischemia was induced by occlusion of the middle cerebral artery (MCA) using a
modification of the intraluminal technique. Animals were anesthetized with ketamine and
xylazine (75 mg/kg and 10 mg/kg i.p, respectively). The neck muscles were separated further to
expose external carotid artery (ECA) and internal carotid artery (ICA). A 4.0 cm length 3-0
monofilament nylon suture (Ethicon) was introduced into the ECA lumen through a small nick
and gently advanced from to the ICA lumen to block the origin of MCA. The approximate length
of filament inserted near the bifurcation point to the MCA blockade site was about 18-22mm. The
ECA stump was tightened by thread around the intraluminal nylon suture to prevent bleeding.
Reperfusion was allowed by gently removing the monofilament after 1 h of ischemia. In sham-
operated animals, all the procedures except for the insertion of the nylon filament were carried
out. Animals were allowed to recover from anesthesia and on regaining the righting reflex, were
transferred to cages in the animal room, with temperature maintained at 26 ± 2.5o
C.
EEG electrodes were implanted and placed to the skull at positions that were optimized following
stereotactic coordinates (Paxinos) in pilot experiments. A bipolar electrode montage system was
used for recording the rat brain EEG signal. Electrodes were placed bilaterally in the skull over
the frontoparietal, occipital and temporal regions of the rat brain. A reference electrode was
placed posterior to lambda over the transverse sinus.
2.1. Lyapunov exponents
The dynamics of complex systems (such as the mammalian brain) is constrained to certain paths
or flows. Flows may be analyzed in terms of Gauss’s theorem: time rate of flow into a volume
must equal the flow out of the volume. Consider a volume element of radius ε . Grassberger and
Procaccia [5-6] have shown that as result of the flow the number of points in the volume element
is given by the power law as
3. International journal of Biomedical Engineering and Science (IJBES), Vol. 2, No. 1, January 2015
3
( ) d
C r ε≈ (1)
Where, d is the correlation dimension given by the slope of the log ( )C r versus log( )ε
(Fig. 1-9). Thus distances between trajectories grow exponentially:
0( ) t
t eλ
δ δ≈
(2)
The plot of
ln( ( )tδ
versus t gives a straight line with slope λ (Fig. 1-3) where λ is the
Lyapunov exponent. The existence of the linear plots confirms that equations (1) and (3)
are associated with attractors of the rat brain (Fig. 1-3). In general the mammalian brain
exists in a number of attractors [8].
2.2. Correlation dimension:
Consider the time series:{ }( ), ( ),... ( ( 1) )i i iV t V t V t nτ τ+ + − . The integral correlation
coefficient is obtained via the method outlined in [1]. Here one starts with a reference
point iV and its distance to the rest of the points is computed. Then one computes how
many points lie within a given distance r fromV . Repeating this process for all I one
arrives at ( )C r , the integral correlation coefficient of the attractor. For smallr ,
( ) ~C r rν
where ν is the correlation dimension and is given by the slope of the plot of
log( ( ))C r versus log( )r . The slope was computed directly in the Matlab program for
various embedding dimension. The plot of the correlation dimension ν versus embedding
dimension m was obtained (Fig. 4).
2.3. Autocorrelation functions:
The autocorrelation function of a time series is a measure of how the values of the time
series separated by a time τ behave. The autocorrelation coefficient for the time series
defined above is given by [3]
1
2
1
1
( ) ( )
( )
1
( )
N
i i
i
N
i
i
V t V V t V
N
V t V
N
τ
ψ τ =
=
− + −
=
−
∑
∑
(3)
where,
4. International journal of Biomedical Engineering and Science (IJBES), Vol. 2, No. 1, January 2015
4
1
1
( )
N
i
i
V V t
N =
= ∑ (4)
A plot of the autocorrelation function for the control, stroke induced and drug based
recovery is shown in (Fig.5-7).
3. RESULTS
3.1. Lyapunov exponents:
Figure 1. Lyapunov Exponent for Control rat brain EEG Signal
5. International journal of Biomedical Engineering and Science (IJBES), Vol. 2, No. 1, January 2015
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Figure 2. Lyapunov Exponent for Stroke rat brain EEG Signal
6. International journal of Biomedical Engineering and Science (IJBES), Vol. 2, No. 1, January 2015
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Figure 3. Lyapunov Exponent for Drug induced rat brain EEG Signal
Lypaunov exponents for the control data were negative, implying that the neurons in the
rat brain were in the stable state (Fig. 1-3). The Lyapunov exponents for the entire stroke
data were positive indicating that there was some instability had been induced in the
system. After application of drug, in some cases the Lyapunov exponents were in some
cases positive and in some cases were negative. The negative Lyapunov exponents
indicated the neurons of the brain have reached the stable state. On the other hand, when
the Lyapunov exponents were positive it meant that the neurons were in the unstable
state.
CASE 1: According to our experimental result, we can say 5 out of 8 sets in fronto-
parietal region; 6 out of 8 sets in occipital region and, 4 out of 8 sets in temporal region
the inverse Lyapunov exponent of the stroke value was less than the corresponding
control value. That signifies the induced stroke in the neurons of the rat brain. The
different regions of the brain are affected differently by the induced stroke; Occipital
region of the rat brain suffers the most damage while the temporal region suffers the
least. The reason for the differential effect of the stroke on the different region of the rat
brain is not known.
7. International journal of Biomedical Engineering and Science (IJBES), Vol. 2, No. 1, January 2015
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CASE 2: After the drug was administered in our experiment, we got 4 out of 8 sets in
fronto-parietal region; 3 out of 8 sets in occipital region and, 3 out of 8 sets in temporal
region the inverse Lyapunov exponent of the drug induced value is more than the
corresponding induced stroke value and its effect the different region differently. In these
cases the inverse lyapunov exponents indicative the higher coherence length implying
that the recovery from stroke due to the drug have been more. The degree of recovery
was also different.
3.2. Correlation Dimensions:
Figure 4. Correlation dimensions of rat brain: Control, Stroke and Drug
Correlation Dimension decreases due to the induced stroke with respect to the control value and
increases when drug was administered (Fig. 4).
Case 1: According to our experimental result, we can say that 3 out of 8 sets in fronto-parietal
region; 5 out of 8 cases in occipital region and, 6 out of 8 cases in temporal region, the correlation
dimension of the stroke value was less than the corresponding control value. According to the
correlation dimension, temporal region of the rat brain suffers the most damage while the
Frontoparietal region suffers the least. The reason for the differential effect of the stroke on the
different region of the rat brain is not known.
CASE 2: After the drug was administered in our experiment, we got 4 out of 8 sets in fronto-
parietal region; 3 out of 8 sets in occipital region and, 4 out of 8 sets in temporal region the
correlation dimension of the drug induced value is more than the corresponding induced stroke
value and its effect the different region differently. Further in those cases where correlation
dimension in the drug induced data were less compared with the stroke value, the recovery was
not optimum.
8. International journal of Biomedical Engineering and Science (IJBES), Vol. 2, No. 1, January 2015
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3.3. Autocorrelation functions:
Figure 5. Autocorrelation Function- Control rat brain EEG Signal
Figure 6. Autocorrelation Function- Stroke rat brain EEG Signal
9. International journal of Biomedical Engineering and Science (IJBES), Vol. 2, No. 1, January 2015
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Figure 7. Autocorrelation Function- Drug induced rat brain EEG Signal
In the frontoparietal, occipital and temporal lobe of the rat brain the auto correlation function of
the control signal was decaying. The degree of decay was high indicating that there was
maximum correlation in the time series. However in the case of focal cerebral ischemia, the decay
was inhibited considerably. On the application of the drug the decay in the auto correlation
function was somewhat restored, indicating that the drug had restored the initial neuronal
connectivity. (Fig. 5-7)
4. DISCUSSION
Figure 8. Graphical representation of percentage recovery
10. International journal of Biomedical Engineering and Science (IJBES), Vol. 2, No. 1, January 2015
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In this study, we have found that occipital region of the rat brain suffers the most damage and
after inducing the drug we can say that occipital region recovered most compared with the other
regions (Fig. 8). Further in those cases where inverse lyapunov exponents and correlation
dimensions in the drug induced data were less compared with the stroke value, the recovery was
not optimum.
5. CONCLUSION
Significant changes have been observed between the dimensionalities of these brain attractors
between the normal state, focal ischemic state and the drug induced state. We have found that
there are significant changes in both inverse lyapunov exponent and correlation diminutions.
They indicate both the coherence time and the number of interacting neuron are affected by the
induced stroke and restored partially by the application of drug. This result is also visually seeing
in the graphs of the auto correlation functions.
CONFLICT OF INTEREST
The authors declare no conflict in interests. And there are no ethical issues associated
with this work.
REFERENCES
[1] Dirangle, U., Iadecola. C,, Moskowitz, M.A. (1999). Pathobiology of ischemic stroke: an integrated
view. Trends Neurosci, Vol. 22, No.9, pp391–7.
[2] Vartiainen, N., Huang, C.Y., Salminen, A., et al. (2001). Piroxicam and NS-398 rescue neurones from
hypoxia/reoxygenation damage by a mechanism independent of cyclo-oxygenase inhibition. J Neuro-
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