Major depression, also termed as major depressive disorder (MDD),
unipolar depression, clinical depression, or even simply depression, is a
mental illness. According to the World Health Organization (WHO),
depression has been identified as a leading cause of functional disability, worldwide. About 300 million people have been reported suffering
from depression, globally.1 In addition to the functional disability caused
by depression, it may lead to suicide ideations. Moreover, the treatment
management for depression has been challenging due to multiple factors,
such as the suitable selection of medication for a patient being based on
the subjective experience of clinicians and which might not be appropriate for the patient and could result into unsuccessful treatment trials.
Another implication is that the patient may stop the treatment.
In this chapter, the topics covered in this book are introduced by providing a basic explanation of the relevant concepts which will be elaborated on in later chapters. More specifically, this chapter explores the
possibilities of utilizing electroencephalogram (EEG) as an objective
method for the diagnosis and treatment efficacy assessment for depression. Also, depression will be discussed from different perspectives
such as its subtypes, signs and symptoms, the challenges associated
with treating depression, an overview of the literature involving EEG
studies for depression, EEG as a modality, and the basics of an EEGbased machine learning (ML) approach
Classification of physiological diseases using eeg signals and machine learni...eSAT Journals
Abstract
In rural areas providing advanced diagnostics for various health disorders is not possible in countries like India. With latest technological breakthrough, brain signals (EEG signal) capturing devices are available at rate less 50$. If these brain signals can be used to predict any Physiological disorders like heart problem, kidney problems etc., then these EEG devices can be provided to rural health care centre for preliminary investigation and on diagnosis the patient can move to city hospitals for diagnostics and treatment. In this project, we provide a solution of identifying physiological problems using EEG signals and use machine learning techniques for diagnosis.
Keywords: EEG Signals, EEG Frame, Feature Extraction
Artificial neural networks (ANN) consider classification as one of the most dynamic research and
application areas. ANN is the branch of Artificial Intelligence (AI). The neural network was trained by
back propagation algorithm. The different combinations of functions and its effect while using ANN as a
classifier is studied and the correctness of these functions are analyzed for various kinds of datasets. The
back propagation neural network (BPNN) can be used as a highly successful tool for dataset classification
with suitable combination of training, learning and transfer functions. When the maximum likelihood
method was compared with backpropagation neural network method, the BPNN was more accurate than
maximum likelihood method. A high predictive ability with stable and well functioning BPNN is possible.
Multilayer feed-forward neural network algorithm is also used for classification. However BPNN proves to
be more effective than other classification algorithms.
Improved feature exctraction process to detect seizure using CHBMIT-dataset ...IJECEIAES
One of the most dangerous neurological disease, which is occupying worldwide, is epilepsy. Fraction of second nerves in the brain starts impulsion i.e. electrical discharge, which is higher than the normal pulsing. So many researches have done the investigation and proposed the numerous methodology. However, our methodology will give effective result in feature extraction. Moreover, we used numerous number of statistical moments features. Existing approaches are implemented on few statistical moments with respect to time and frequency. Our proposed system will give the way to find out the seizure-effected part of the brain very easily using TDS, FDS, Correlation and Graph presentation. The resultant value will give the huge difference between normal and seizure effected brain. It also explore the hidden features of the brain.
Application of Hybrid Genetic Algorithm Using Artificial Neural Network in Da...IOSRjournaljce
The main purpose of data mining is to extract knowledge from large amount of data. Artificial Neural network (ANN) has already been applied in a variety of domains with remarkable success. This paper presents the application of hybrid model for stroke disease that integrates Genetic algorithm and back propagation algorithm. Selecting a good subset of features, without sacrificing accuracy, is of great importance for neural networks to be successfully applied to the area. In addition the hybrid model that leads to further improvised categorization, accuracy compared to the result produced by genetic algorithm alone. In this study, a new hybrid model of Neural Networks and Genetic Algorithm (GA) to initialize and optimize the connection weights of ANN so as to improve the performance of the ANN and the same has been applied in a medical problem of predicting stroke disease for verification of the results.
Classification of physiological diseases using eeg signals and machine learni...eSAT Journals
Abstract
In rural areas providing advanced diagnostics for various health disorders is not possible in countries like India. With latest technological breakthrough, brain signals (EEG signal) capturing devices are available at rate less 50$. If these brain signals can be used to predict any Physiological disorders like heart problem, kidney problems etc., then these EEG devices can be provided to rural health care centre for preliminary investigation and on diagnosis the patient can move to city hospitals for diagnostics and treatment. In this project, we provide a solution of identifying physiological problems using EEG signals and use machine learning techniques for diagnosis.
Keywords: EEG Signals, EEG Frame, Feature Extraction
Artificial neural networks (ANN) consider classification as one of the most dynamic research and
application areas. ANN is the branch of Artificial Intelligence (AI). The neural network was trained by
back propagation algorithm. The different combinations of functions and its effect while using ANN as a
classifier is studied and the correctness of these functions are analyzed for various kinds of datasets. The
back propagation neural network (BPNN) can be used as a highly successful tool for dataset classification
with suitable combination of training, learning and transfer functions. When the maximum likelihood
method was compared with backpropagation neural network method, the BPNN was more accurate than
maximum likelihood method. A high predictive ability with stable and well functioning BPNN is possible.
Multilayer feed-forward neural network algorithm is also used for classification. However BPNN proves to
be more effective than other classification algorithms.
Improved feature exctraction process to detect seizure using CHBMIT-dataset ...IJECEIAES
One of the most dangerous neurological disease, which is occupying worldwide, is epilepsy. Fraction of second nerves in the brain starts impulsion i.e. electrical discharge, which is higher than the normal pulsing. So many researches have done the investigation and proposed the numerous methodology. However, our methodology will give effective result in feature extraction. Moreover, we used numerous number of statistical moments features. Existing approaches are implemented on few statistical moments with respect to time and frequency. Our proposed system will give the way to find out the seizure-effected part of the brain very easily using TDS, FDS, Correlation and Graph presentation. The resultant value will give the huge difference between normal and seizure effected brain. It also explore the hidden features of the brain.
Application of Hybrid Genetic Algorithm Using Artificial Neural Network in Da...IOSRjournaljce
The main purpose of data mining is to extract knowledge from large amount of data. Artificial Neural network (ANN) has already been applied in a variety of domains with remarkable success. This paper presents the application of hybrid model for stroke disease that integrates Genetic algorithm and back propagation algorithm. Selecting a good subset of features, without sacrificing accuracy, is of great importance for neural networks to be successfully applied to the area. In addition the hybrid model that leads to further improvised categorization, accuracy compared to the result produced by genetic algorithm alone. In this study, a new hybrid model of Neural Networks and Genetic Algorithm (GA) to initialize and optimize the connection weights of ANN so as to improve the performance of the ANN and the same has been applied in a medical problem of predicting stroke disease for verification of the results.
Prognosticating Autism Spectrum Disorder Using Artificial Neural Network: Lev...Avishek Choudhury
Autism spectrum condition (ASC) or autism spectrum disorder (ASD) is primarily identified with the help of behavioral indications encompassing social, sensory and motor characteristics. Although categorized, recurring motor actions are measured during diagnosis, quantifiable measures that ascertain kinematic physiognomies in the movement configurations of autistic persons are not adequately studied, hindering the advances in understanding the etiology of motor mutilation. Subject aspects such as behavioral characters that influences ASD need further exploration. Presently, limited autism datasets concomitant with screening ASD are available, and a majority of them are genetic. Hence, in this study, we used a dataset related to autism screening enveloping ten behavioral and ten personal attributes that have been effective in diagnosing ASD cases from controls in behavior science. ASD diagnosis is time exhaustive and uneconomical. The burgeoning ASD cases worldwide mandate a need for the fast and economical screening tool. Our study aimed to implement an artificial neural network with the Levenberg- Marquardt algorithm to detect ASD and examine its predictive accuracy. Consecutively, develop a clinical decision support system for early ASD identification.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Functional magnetic resonance imaging-based brain decoding with visual semant...IJECEIAES
The activity pattern of the brain has been activated to identify a person in mind. Using the function magnetic resonance imaging (fMRI) to decipher brain decoding is the most accepted method. However, the accuracy of fMRI-based brain decoder is still restricted due to limited training samples. The limitations of the brain decoder using fMRI are passed through the design features proposed for many label coding and model training to predict these characteristics for a particular label. Moreover, what kind of semantic features for deciphering the neurological activity patterns are unclear. In current work, a new calculation model for learning decoding labels that is consistent with fMRI activity responses. The approach demonstrates the proposed corresponding label's success in terms of accuracy, which is decoded from brain activity patterns and compared with conventional text-derived feature technique. Besides, experimental studies present a training model based on multi-tasking to reduce the problems of limited training data sets. Therefore, the multi-task learning model is more efficient than modern methods of calculation, and decoding features may be easily obtained.
An Artificial Neural Network Model for Neonatal Disease DiagnosisWaqas Tariq
The significance of disease diagnosis by artificial intelligence is not obscure now days. The increasing demand of Artificial Neural Network application for predicting the disease shows better performance in the field of medical decision making. This paper represents the use of artificial neural networks in predicting neonatal disease diagnosis. The proposed technique involves training a Multi Layer Perceptron with a BP learning algorithm to recognize a pattern for the diagnosing and prediction of neonatal diseases. A comparative study of using different training algorithm of MLP, Quick Propagation, Conjugate Gradient Descent, shows the higher prediction accuracy. The Backpropogation algorithm was used to train the ANN architecture and the same has been tested for the various categories of neonatal disease. About 94 cases of different sign and symptoms parameter have been tested in this model. This study exhibits ANN based prediction of neonatal disease and improves the diagnosis accuracy of 75% with higher stability. Key words: Artificial Intelligence, Multi Layer Perceptron, Neural Network, Neonate
EEG Classification using Semi Supervised Learningijtsrd
The major challenge in the current brain–computer interface research is the accurate classification of time varying electroencephalographic EEG signals. The labeled EEG samples are usually scarce, while the unlabeled samples are available in large quantities and easy to collect in real applications. Semi supervised learning SSL methods can utilize both labeled and unlabeled data to improve performance over supervised approaches. However, it has been reported that the unlabeled data may undermine the performance of SSL in some cases. This study proposes a three stages technique for automatic detection of epileptic seizure in EEG signals. In practical application of pattern recognition, there are often diverse features extracted from raw data which needs to be recognized. Proposed method is based on time series signal, spectral analysis and recurrent neural networks RNNs . Decision making was performed in three stages i feature extraction using Welch method power spectrum density estimation PSD ii dimensionality reduction using statistics over extracted features and time series signal samples iii EEG classification using recurrent neural networks. This study shows that Welch method power spectrum density estimation is an appropriate feature which well represents EEG signals. We achieved higher classification accuracy specificity, sensitivity, classification accuracy in comparison with other researches to classify EEG signals exactly 100 in this study. To improve the safety of SSL, we proposed a new safety control mechanism by analyzing the differences between unlabeled data analysis in supervised and semi supervised learning. We then develop and implement a safe classification method based on the semi supervised extreme learning machine SS ELM . Following this approach, the Wasserstein distance is used to measure the similarities between the predictions obtained from ELM and SS ELM algorithms, and a different risk degree is thereby calculated for each unlabeled data instance. A risk based regularization term is then constructed and embedded into the objective function of the SS ELM. Extensive experiments were conducted using benchmark and EEG datasets to evaluate the effectiveness of the proposed method. Experimental results show that the performance of the new algorithm is comparable to SS ELM and superior to ELM on average. It is thereby shown that the proposed method is safe and efficient for the classification of EEG signals. Shivshankar Kumar Yadav | Veena S. ""EEG Classification using Semi Supervised Learning"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-3 , April 2019, URL: https://www.ijtsrd.com/papers/ijtsrd23355.pdf
Paper URL: https://www.ijtsrd.com/engineering/electrical-engineering/23355/eeg-classification-using-semi-supervised-learning/shivshankar-kumar-yadav
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.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
The Dawn of the Age of Artificially Intelligent NeuroprostheticsSagar Hingal
A summary or an overview of the existing technologies that encapsulate the concepts of NeuroScience and Bio-Technology using the enhanced methods of Artificial-intelligence.
In this review paper, there are several case studies and methodologies of implementations of neuroprosthetics as well as how A.I (Artificial Intelligence) is evolved over the period of time and what is next on the future.....
Evaluation of Default Mode Network In Mild Cognitive Impairment and Alzheimer...CSCJournals
Although progressive functional brain network disorders has been one of the indication of Alzheimer's disease, The current research on aging and dementia focus on diagnostics of the cognitive changes of normal aging and Alzheimer Disease (AD), these changes known as Mild Cognitive Impairment (MCI). The default mode network (DMN) is a network of interacting brain regions known to have activity highly correlated with each other and distinct from other networks in the brain, the default mode network is active during passive rest and consists of a set of brain areas that are tightly functionally connected and distinct from other systems within the brain. Anatomically, the DMN includes the posterior cingulated cortex (PCC), dorsal and ventral medial prefrontal cortex, the lateral parietal cortex, and the medial temporal lobes. DMN involves multiple anatomical networks that converge on cortical hubs, such as the PCC, ventral medial prefrontal, and inferior parietal cortices. The aim of this study was to evaluate the default mode network functional connectivity in MCI patients. While no treatments are recommended for MCI currently, Mild Cognitive Impairment is becoming a very important subject for researchers and deserves more recognition and further study, In order to increase the ability to recognize earlier symptoms of Alzheimer's disease.
Survey analysis for optimization algorithms applied to electroencephalogramIJECEIAES
This paper presents a survey for optimization approaches that analyze and classify electroencephalogram (EEG) signals. The automatic analysis of EEG presents a significant challenge due to the high-dimensional data volume. Optimization algorithms seek to achieve better accuracy by selecting practical features and reducing unwanted features. Forty-seven reputable research papers are provided in this work, emphasizing the developed and executed techniques divided into seven groups based on the applied optimization algorithm particle swarm optimization (PSO), ant colony optimization (ACO), artificial bee colony (ABC), grey wolf optimizer (GWO), Bat, Firefly, and other optimizer approaches). The main measures to analyze this paper are accuracy, precision, recall, and F1-score assessment. Several datasets have been utilized in the included papers like EEG Bonn University, CHB-MIT, electrocardiography (ECG) dataset, and other datasets. The results have proven that the PSO and GWO algorithms have achieved the highest accuracy rate of around 99% compared with other techniques.
METHODS FOR IMPROVING THE CLASSIFICATION ACCURACY OF BIOMEDICAL SIGNALS BASED...IAEME Publication
Biomedical signals are long records of electrical activity within the human body, and they faithfully represent the state of health of a person. Of the many biomedical signals, focus of this work is on Electro-encephalogram (EEG), Electro-cardiogram (ECG) and Electro-myogram (EMG). It is tiresome for physicians to visually examine the long records of biomedical signals to arrive at conclusions. Automated classification of these signals can largely assist the physicians in their diagnostic process. Classifying a biomedical signal is the process of attaching the signal to a disease state or healthy state. Classification Accuracy (CA) depends on the features extracted from the signal and the classification process involved. Certain critical information on the health of a person is usually hidden in the spectral content of the signal. In this paper, effort is made for the improvement in CA when spectral features are included in the classification process.
Prognosticating Autism Spectrum Disorder Using Artificial Neural Network: Lev...Avishek Choudhury
Autism spectrum condition (ASC) or autism spectrum disorder (ASD) is primarily identified with the help of behavioral indications encompassing social, sensory and motor characteristics. Although categorized, recurring motor actions are measured during diagnosis, quantifiable measures that ascertain kinematic physiognomies in the movement configurations of autistic persons are not adequately studied, hindering the advances in understanding the etiology of motor mutilation. Subject aspects such as behavioral characters that influences ASD need further exploration. Presently, limited autism datasets concomitant with screening ASD are available, and a majority of them are genetic. Hence, in this study, we used a dataset related to autism screening enveloping ten behavioral and ten personal attributes that have been effective in diagnosing ASD cases from controls in behavior science. ASD diagnosis is time exhaustive and uneconomical. The burgeoning ASD cases worldwide mandate a need for the fast and economical screening tool. Our study aimed to implement an artificial neural network with the Levenberg- Marquardt algorithm to detect ASD and examine its predictive accuracy. Consecutively, develop a clinical decision support system for early ASD identification.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Functional magnetic resonance imaging-based brain decoding with visual semant...IJECEIAES
The activity pattern of the brain has been activated to identify a person in mind. Using the function magnetic resonance imaging (fMRI) to decipher brain decoding is the most accepted method. However, the accuracy of fMRI-based brain decoder is still restricted due to limited training samples. The limitations of the brain decoder using fMRI are passed through the design features proposed for many label coding and model training to predict these characteristics for a particular label. Moreover, what kind of semantic features for deciphering the neurological activity patterns are unclear. In current work, a new calculation model for learning decoding labels that is consistent with fMRI activity responses. The approach demonstrates the proposed corresponding label's success in terms of accuracy, which is decoded from brain activity patterns and compared with conventional text-derived feature technique. Besides, experimental studies present a training model based on multi-tasking to reduce the problems of limited training data sets. Therefore, the multi-task learning model is more efficient than modern methods of calculation, and decoding features may be easily obtained.
An Artificial Neural Network Model for Neonatal Disease DiagnosisWaqas Tariq
The significance of disease diagnosis by artificial intelligence is not obscure now days. The increasing demand of Artificial Neural Network application for predicting the disease shows better performance in the field of medical decision making. This paper represents the use of artificial neural networks in predicting neonatal disease diagnosis. The proposed technique involves training a Multi Layer Perceptron with a BP learning algorithm to recognize a pattern for the diagnosing and prediction of neonatal diseases. A comparative study of using different training algorithm of MLP, Quick Propagation, Conjugate Gradient Descent, shows the higher prediction accuracy. The Backpropogation algorithm was used to train the ANN architecture and the same has been tested for the various categories of neonatal disease. About 94 cases of different sign and symptoms parameter have been tested in this model. This study exhibits ANN based prediction of neonatal disease and improves the diagnosis accuracy of 75% with higher stability. Key words: Artificial Intelligence, Multi Layer Perceptron, Neural Network, Neonate
EEG Classification using Semi Supervised Learningijtsrd
The major challenge in the current brain–computer interface research is the accurate classification of time varying electroencephalographic EEG signals. The labeled EEG samples are usually scarce, while the unlabeled samples are available in large quantities and easy to collect in real applications. Semi supervised learning SSL methods can utilize both labeled and unlabeled data to improve performance over supervised approaches. However, it has been reported that the unlabeled data may undermine the performance of SSL in some cases. This study proposes a three stages technique for automatic detection of epileptic seizure in EEG signals. In practical application of pattern recognition, there are often diverse features extracted from raw data which needs to be recognized. Proposed method is based on time series signal, spectral analysis and recurrent neural networks RNNs . Decision making was performed in three stages i feature extraction using Welch method power spectrum density estimation PSD ii dimensionality reduction using statistics over extracted features and time series signal samples iii EEG classification using recurrent neural networks. This study shows that Welch method power spectrum density estimation is an appropriate feature which well represents EEG signals. We achieved higher classification accuracy specificity, sensitivity, classification accuracy in comparison with other researches to classify EEG signals exactly 100 in this study. To improve the safety of SSL, we proposed a new safety control mechanism by analyzing the differences between unlabeled data analysis in supervised and semi supervised learning. We then develop and implement a safe classification method based on the semi supervised extreme learning machine SS ELM . Following this approach, the Wasserstein distance is used to measure the similarities between the predictions obtained from ELM and SS ELM algorithms, and a different risk degree is thereby calculated for each unlabeled data instance. A risk based regularization term is then constructed and embedded into the objective function of the SS ELM. Extensive experiments were conducted using benchmark and EEG datasets to evaluate the effectiveness of the proposed method. Experimental results show that the performance of the new algorithm is comparable to SS ELM and superior to ELM on average. It is thereby shown that the proposed method is safe and efficient for the classification of EEG signals. Shivshankar Kumar Yadav | Veena S. ""EEG Classification using Semi Supervised Learning"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-3 , April 2019, URL: https://www.ijtsrd.com/papers/ijtsrd23355.pdf
Paper URL: https://www.ijtsrd.com/engineering/electrical-engineering/23355/eeg-classification-using-semi-supervised-learning/shivshankar-kumar-yadav
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.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
The Dawn of the Age of Artificially Intelligent NeuroprostheticsSagar Hingal
A summary or an overview of the existing technologies that encapsulate the concepts of NeuroScience and Bio-Technology using the enhanced methods of Artificial-intelligence.
In this review paper, there are several case studies and methodologies of implementations of neuroprosthetics as well as how A.I (Artificial Intelligence) is evolved over the period of time and what is next on the future.....
Evaluation of Default Mode Network In Mild Cognitive Impairment and Alzheimer...CSCJournals
Although progressive functional brain network disorders has been one of the indication of Alzheimer's disease, The current research on aging and dementia focus on diagnostics of the cognitive changes of normal aging and Alzheimer Disease (AD), these changes known as Mild Cognitive Impairment (MCI). The default mode network (DMN) is a network of interacting brain regions known to have activity highly correlated with each other and distinct from other networks in the brain, the default mode network is active during passive rest and consists of a set of brain areas that are tightly functionally connected and distinct from other systems within the brain. Anatomically, the DMN includes the posterior cingulated cortex (PCC), dorsal and ventral medial prefrontal cortex, the lateral parietal cortex, and the medial temporal lobes. DMN involves multiple anatomical networks that converge on cortical hubs, such as the PCC, ventral medial prefrontal, and inferior parietal cortices. The aim of this study was to evaluate the default mode network functional connectivity in MCI patients. While no treatments are recommended for MCI currently, Mild Cognitive Impairment is becoming a very important subject for researchers and deserves more recognition and further study, In order to increase the ability to recognize earlier symptoms of Alzheimer's disease.
Survey analysis for optimization algorithms applied to electroencephalogramIJECEIAES
This paper presents a survey for optimization approaches that analyze and classify electroencephalogram (EEG) signals. The automatic analysis of EEG presents a significant challenge due to the high-dimensional data volume. Optimization algorithms seek to achieve better accuracy by selecting practical features and reducing unwanted features. Forty-seven reputable research papers are provided in this work, emphasizing the developed and executed techniques divided into seven groups based on the applied optimization algorithm particle swarm optimization (PSO), ant colony optimization (ACO), artificial bee colony (ABC), grey wolf optimizer (GWO), Bat, Firefly, and other optimizer approaches). The main measures to analyze this paper are accuracy, precision, recall, and F1-score assessment. Several datasets have been utilized in the included papers like EEG Bonn University, CHB-MIT, electrocardiography (ECG) dataset, and other datasets. The results have proven that the PSO and GWO algorithms have achieved the highest accuracy rate of around 99% compared with other techniques.
METHODS FOR IMPROVING THE CLASSIFICATION ACCURACY OF BIOMEDICAL SIGNALS BASED...IAEME Publication
Biomedical signals are long records of electrical activity within the human body, and they faithfully represent the state of health of a person. Of the many biomedical signals, focus of this work is on Electro-encephalogram (EEG), Electro-cardiogram (ECG) and Electro-myogram (EMG). It is tiresome for physicians to visually examine the long records of biomedical signals to arrive at conclusions. Automated classification of these signals can largely assist the physicians in their diagnostic process. Classifying a biomedical signal is the process of attaching the signal to a disease state or healthy state. Classification Accuracy (CA) depends on the features extracted from the signal and the classification process involved. Certain critical information on the health of a person is usually hidden in the spectral content of the signal. In this paper, effort is made for the improvement in CA when spectral features are included in the classification process.
Health electroencephalogram epileptic classification based on Hilbert probabi...IJECEIAES
This paper has proposed a new classification method based on Hilbert probability similarity to detect epileptic seizures from electroencephalogram (EEG) signals. Hilbert similarity probability-based measure is exploited to measure the similarity between signals. The proposed system consisted of models based on Hilbert probability similarity (HPS) to predict the state for the specific EEG signal. Particle swarm optimization (PSO) has been employed for feature selection and extraction. Furthermore, the used dataset in this study is Bonn University's publicly available EEG dataset. Several metrics are calculated to assess the performance of the suggested systems such as accuracy, precision, recall, and F1-score. The experimental results show that the suggested model is an effective tool for classifying EEG signals, with an accuracy of up to 100% for two-class status.
Efficient electro encephelogram classification system using support vector ma...nooriasukmaningtyas
Complex modern signal processing is used to automate the analysis of electro encephelogram (EEG) signals. For the diagnosis of seizures, approaches that are simple and precise may be preferable rather than difficult and time-consuming. In this paper, efficient EEG classification system using support vector machine (SVM) and Adaptive learning technique is proposed. The database EEG signals are subjected to temporal and spatial filtering to remove unwanted noise and to increase the detection accuracy of the classifier by selecting the specific bands in which most of the EEG data are present. The neural network based SVM is used to classify the test EEG data with respect to training data. The cost-sensitive SVM with proposed Adaptive learning classifies the EEG signals where the adaptive learning with probability based function helps in prediction of the future samples and this leads in improving the accuracy with detection time. The detection accuracy of the proposed algorithm is compared with existing which shows that the proposed algorithm can classify the EEG signal more effectively.
SRGE Workshop on Intelligent system and Application, 27 Dec. 2017 in the framework of the int. conf of computer science, information systems, and operation research, ISSR, Cairo University
Previous research work has highlighted that neuro-signals of Alzheimer’s disease patients are least complex and have low synchronization as compared to that of healthy and normal subjects. The changes in EEG signals of Alzheimer’s subjects start at early stage but are not clinically observed and detected. To detect these abnormalities, three synchrony measures and wavelet-based features have been computed and studied on experimental database. After computing these synchrony measures and wavelet features, it is observed that Phase Synchrony and Coherence based features are able to distinguish between Alzheimer’s disease patients and healthy subjects. Support Vector Machine classifier is used for classification giving 94% accuracy on experimental database used. Combining, these synchrony features and other such relevant features can yield a reliable system for diagnosing the Alzheimer’s disease.
EEG S IGNAL Q UANTIFICATION B ASED ON M ODUL L EVELS sipij
This article proposes a contribution to quantify EE
G signals outline. This technique uses two tools fo
r EEG
signal characteristics extraction. Our tests were r
ealized on the basis of 32 canals EEG canals using
Neuroscan software. EEG example demonstration is re
ferenced CZ and is sampled at 1000HZ. The
principal aim of this technique is to reduce the im
portant volume of EEG signal data Without losing an
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information. EEG signals are quantified on the basi
s of a whole predefined levels The obtained results
show that an EEG alignment can be posted in a quant
ified form.
Effective electroencephalogram based epileptic seizure detection using suppo...IJECEIAES
Epilepsy is one of the widespread disorders. It is a noncommunicable disease that affects the human nerve system. Seizures are abnormal patterns of behavior in the electricity of the brain which produce symptoms like losing consciousness, attention or convulsions in the whole body. This paper demonstrates an effective electroencephalogram (EEG) based seizure detection method using discrete wavelet transformation (DWT) for signal decomposition to extract features. An automatic channel selection method was proposed by the researcher to select the best channel from 23 channels based on maximum variance value. The records were segmented into a nonoverlapping segment with long 1-S. The support vector machine (SVM) model was used to automatically detect segments that contain seizures, using both frequency and time domain statistical moment features. The experimental result was obtained from 24 patients in CHB-MIT database. The average accuracy is 94.1, sensitivity is 93.5, specificity is 94.6 and the false positive rate average is 0.054.
A Comparative Study of Machine Learning Algorithms for EEG Signal Classificationsipij
In this paper, different machine learning algorithms such as Linear Discriminant Analysis, Support vector
machine (SVM), Multi-layer perceptron, Random forest, K-nearest neighbour, and Autoencoder with SVM
have been compared. This comparison was conducted to seek a robust method that would produce good
classification accuracy. To this end, a robust method of classifying raw Electroencephalography (EEG)
signals associated with imagined movement of the right hand and relaxation state, namely Autoencoder
with SVM has been proposed. The EEG dataset used in this research was created by the University of
Tubingen, Germany. The best classification accuracy achieved was 70.4% with SVM through feature
engineering. However, our prosed method of autoencoder in combination with SVM produced a similar
accuracy of 65% without using any feature engineering technique. This research shows that this system of
classification of motor movements can be used in a Brain-Computer Interface system (BCI) to mentally
control a robotic device or an exoskeleton.
A Comparative Study of Machine Learning Algorithms for EEG Signal Classificationsipij
In this paper, different machine learning algorithms such as Linear Discriminant Analysis, Support vector
machine (SVM), Multi-layer perceptron, Random forest, K-nearest neighbour, and Autoencoder with SVM
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signals associated with imagined movement of the right hand and relaxation state, namely Autoencoder
with SVM has been proposed. The EEG dataset used in this research was created by the University of
Tubingen, Germany. The best classification accuracy achieved was 70.4% with SVM through feature
engineering. However, our prosed method of autoencoder in combination with SVM produced a similar
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Ontheclassificationof ee gsignalbyusingansvmbasedalgorythm
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On the Classification of EEG Signal by Using an SVM Based Algorithm
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2. On the classification of EEG signal by using an
SVM based algorythm
Valeria Sacca´, Maurizio Campolo, Domenico Mirarchi, Antonio Gambardella,
Pierangelo Veltri, and Carlo Francesco Morabito
University Magna Graecia Catanzaro, Italy
University Mediterannea Reggio Calabria, Italy
Abstract. In clinical practice, study of brain functions is fundamental
to notice several diseases potentially dangerous for the health of the sub-
ject. For this goal, Electroencephalography (EEG) can be used to detect
cerebral disorders but EEG study is often difficult to implement, espe-
cially for the signal dimensions and noise presence. For this reason, there
is the necessity to have efficient and accurate methods to overcome these
obstacles. In the field of Signal Processing, there exist many algorithms
and methods to analyze and classify signals that can be used to support
physicians while reducing and extracting useful information from EEG.
Support Vector Machine (SVM) based algorythms can be used as classifi-
cation tool in several field and can be applied for classifying EEG records.
SVM based tools allow to obtain an efficient discrimination between dif-
ferent pathology and to support physicians while studying patients.
In this paper, we report an experience on designing and using an SVM
based algorythm to study and classify EEG signals. We focus on Creutzfeldt-
Jakob disease (CJD) EEG signals by means of studying signal properties.
To reduce the dimension of the dataset, principal component analysis
(PCA) are used. These vectors are used as inputs for the SVM classifier
with two classification classes: pathologic or healthy. The classification
accuracy reaches 96,67% and a validation test has been performed, using
unclassified EEG data.
Key words: Classification; SVM; Early Detection.
1 Introduction
The EEG is a non-invasive analysis used to study brain activity by recording
cerebral waves placing electrodes along the scalp. Each scalp area produces waves
that allow to reflect the state of cerebral health. In case of diseases, EEG anal-
ysis shows several abnormalities in recorded signal waves. The identification of
these abnormalities enables the physician to estimate the disease and its stage,
in order to simplify the clinical diagnosis. This process can be also used in the
biomedical researches to investigate cerebral disease characteristics. One of the
main difficulties in studying EEG signals is represented by signal dimensions
and noise presence, thus that it is often difficult to implement an automatic
3. 2 Sacca’, Campolo, Mirarchi, Gambardella, Veltri, Morabito
abnormality detection [1]. This represents a huge disadvantage because the lack
signal understanding could bring an incorrect diagnosis, causing inconvenience
for patients (both in case of false or positive diagnosis). For this reason, there is
the necessity to have efficient and accurate methods to support signals reading,
preprocessing and storing, allowing appropriate diagnosis and therapy designing.
Nevertheless, due to the high number of features automatic data reduction and
classification of EEG signals is often mandatory to support physicians in diag-
nosis definition. Accordingly signal processing and pattern recognition methods
are available and can be used to support EEG signal analysis.
In the last few years, considerable results have been produced according to
high performing hardware and innovative algorithms allowing the analysis of
EEG signals and the extraction of useful information for brain studies [2]. In
literature there are many classification algorithms used to analyze EEG signals.
The method consists to group signals in classes and to identify differences be-
tween them. By defyning classes, a model can be designed to assign data samples
(i.e., signals or portion of them) into classes. Models can be used then to imple-
ment algorithms (i.e., classifier) to classify input signals into known classes (i.e.,
healthy or non healthy classes) [4] [5] [6] [7] [8] [9].
Artificial Neural Network (ANN) and Support Vector Machine (SVM) based
algorithms are examples of classifiers that can be used to study EEG signals.
E.g., [3] [4] [5] [6] [7] [8] [9] [10] [11] and [12] [13] [14] are examples of brain sig-
nals analysis performed by using classifiers. An advantage of using classification
algorithms is an improvement into error reduction in diagnosis definition, thus
supporting physicians in large scale data analysis. Many examples EEG classi-
fiers are available, each specialized in particular diseases (e.g., epilepsy [3] [4] [5]
[6] [7], brain tumors [8], schizophrenia [9]).
We experienced in analysing and classifying EEG signals in particular to
investigate Alzheimer’s disease and epilepsy [15] [16]. Also onset signals have
been detected in [17] for ECG signals.
In this paper, we report on using SVM based classifier to analyze EEG sig-
nals. The aim is to define an algorithm able to: (i) define a classification tool
trained by means of pathological and healthy patients and (ii) perform test by
using blindly healthy and non healthy signals. We used EEG signals related to
CreutzfeldtJakob disease (CJD) healthy patients. Data signals derived by real
patients conditions and have been extracted from available clinical dataset. We
focus on CJD disease as a rapidly progressive one, characterized by the accu-
mulation of an abnormal protein in the brain [18]. Early and reliable diagnosis
of CJD is crucial to avoid progressive encephalopathies, which could be fatal for
the patients. Since early diagnosis of CJD is complicated by the marked hetero-
geneity of clinical presentation of the disease, the contribution of this work is
to define a classifier that can be used as decision support system for clinicians.
EEG signals exhibit several characteristic in considered pathological condition,
depending on the stage of the disease. The here classification algorithm is thus
based on the identification of the periodic sharp wave complexes (PSWC) that
represent the hallmark EEG finding in patients with CJD. We designed and im-
4. On the classification of EEG signal by using an SVM based algorythm 3
plemented a classifier model of EEG signals, considering the CreutzfeldtJakob
disease (CJD) based on SVM as classification algorithm. The here proposed
method has been testing on clinical data provided by University Magna Graecia
Clinicians group and the obtained results will be used to optimize the process
and for the model tuning.
1.1 Related Works
SVM is a machine learning based method and it has been largely used recent
as kernel for classification tools. E.g., in [19] has been performed a comparison
among SVM and ANN (artificial neural network), to classify eyes blinking show-
ing better accuracy of the SVM based tool. Recently SVM algorithm has mainly
used in EEG classification to study several brain diseases, as for instance in [3]
[4] [5] [6] [7] [8] [9]. In [3] an SVM classifier for EEG signals is presented and used
to detect the onset of epileptic seizures. Features are generated for both seizure
and not seizure activity and a RBF kernel has been chosen, with optimal results.
Moreover the EEG classification can be used to investigate the brain tumors,
as in [8], and to detect drowsiness onset while driving [10]. In the cited papers,
a spectral analysis method has been applied for extracting generic features by
the signals. Nevertheless, the spectral analysis, i.e. the FFT method is based on
simple functions (i.e. sinusoids) and is not suitable for complex signals as EEG
ones. We derive features by using temporal frequency analysis, using a continue
wavelet transform, that is closest with non-stationary signals, as the EEG [20],
by using similar approach than the ones reported in [21] [22]. SVM model has
also be used for schizophrenia diseases in [9], where features extraction is per-
formed by using an autoregressive model (AR) to preprocess data and SVM is
then used to classify signals. To reduce features (data preprocessing) [9] used
linear discriminant analysis (LDA). We used PCA for this purpose that has a
more effective reduction skill than LDA [23].
2 Preprocessing and Data Features extraction
The aim of this paper is to define a classifier to support decision in diagnosis an-
alyzing EEG signals. Data set needs to be preprocessed and normalized to train
SVM based classifier methods. We report about used data set and preprocessing
methods. Sixty EEG signals have been crawled from clinical database referring
to several patients. In particular, thirty signals relate to healthy subjects and
thirty ones refer to CJD patients. These signals have been processed by using the
following workflow: (i) preprocessing phase, aiming to reduce artifacts and noise;
(ii) features extraction phase, by using a wavelet transformation; (iii) principal
component analysis (PCA) and normalization phase, aiming to remove the re-
dundant data; (iv) SVM phase, to generate the classifier model. The input data
set generated for the SVM is represented by an NxM matrix where the rows
represent the EEG signals and the columns represent the extracted features. We
now report on how to extract features and thus how matrix has been created.
5. 4 Sacca’, Campolo, Mirarchi, Gambardella, Veltri, Morabito
Fig. 1. Example of an EEG Signal
EEG signals are gathered by using nineteen electrodes placed along the patient
scalp. Each electrode generates a signal containing information and artifacts gen-
erated from sources external (i.e., electrical interference) or internal (e.g., eyes
movement) to patient (see Figure 2). To eliminate artifacts EEG signals must be
preprocessed. In our data set each signal has been cleaned in a semiautomatic
way, by selecting and cutting the artifact of interest. Each signal has been re-
Fig. 2. Example of artifact
6. On the classification of EEG signal by using an SVM based algorythm 5
Fig. 3. SVM Classifier Block Framework
duced from ten minutes recording to couple of minutes. Physicians support in this
phase is required. We have been supported by physicians of University Magna
Graecia medical school. The preprocessed signals are then manipulated through
a feature extraction process. Extracting the features aims to reduce the data
complexity and to simplify the sequel information process. An analysis in time
domain has been carried out, using the Continuous Wavelet Transform(CWT).
The CWT is an effective tool in signal processing due its attractive properties
such as time/frequency localization (extracting features at various locations in
space at different scales). Using these properties, the desired features can be
extracted from an input signal. In CWT, the signal to be analyzed is matched
and convolved with the wavelet basis function at continuous time and frequency
increments and as result the original signal is expressed as a weighted integral of
the continuous basis wavelet function [20]. In this paper, the considered wavelet
is mexh, which is very close to the type of format wave request. Mean, variance
and skewness features have been evaluated for each signal.
Each signal captured by each electrode has been divided in twenty-four
epochs of five seconds and the CWT has been applied to evaluate the afore-
mentioned features for each signal. Each signal has been divided in three bands,
thus calculating three values for mean, variance and skewness. Finally, the mean
of these three values has been evaluated for each signal, i.e., obtaining for each
signal respectively 4 values for the mean, 4 values for variance and 4 values
for skewness, obtaining twelve features for each EEG signal. Since each EEG
is generated by using 19 electrodes, the final number of features extracted for
each subject is equal to 228 features. In the following the pseudo code used for
features extraction is reported.
For the experienced dataset, we used 60 subjects, thus that the matrix gen-
erated by the preprocessing and features extraction phases is an NxM matrix
where N is 60 and M is equal to 228. We consider an M+1 column value, where
we distinguish healthy from non healthy patients, assigning 1 for the patholog-
7. 6 Sacca’, Campolo, Mirarchi, Gambardella, Veltri, Morabito
ical patients and the value 0 for the healthy patients. We thus obtain a matrix
M(60x229). Latter column in the right part of Table 1 is used to distinguish
healthy from non healthy patients.
Patient Healthy Patient Healthy Patient Healthy Patient Healthy Patient Healthy Patient Healthy
Id Status Id Status Id Status Id Status Id Status Id Status
1 1 11 1 21 1 31 0 41 0 51 0
2 1 12 1 22 1 32 0 42 0 52 0
3 1 13 1 23 1 33 0 43 0 53 0
4 1 14 1 24 1 34 0 44 0 54 0
5 1 15 1 25 1 35 0 45 0 55 0
6 1 16 1 26 1 36 0 46 0 56 0
7 1 17 1 27 1 37 0 47 0 57 0
8 1 18 1 28 1 38 0 48 0 58 0
9 1 19 1 29 1 39 0 49 0 59 0
10 1 20 1 30 1 40 0 50 0 60 0
Table 1. The training vector and classes: Patient Id is the subject identifier while
health status may be 0 or 1 (healthy or non healty). The vector has been divided in
groups of ten
To apply an SVM module and thus to obtain a classifier, data needs to be
reduced in size and also needs to be treated in order to obtain more homo-
geneous values. Indeed, the more large is dataset, the more high is the prob-
ability of obtaining errors or misclassification. Similarly, the more values are
non-homogeneous, it is more likely that SVM classifier is not able to discrimi-
nate between the considered classes. To reduce the size of the dataset, a method
of Blind Source Separation (which is the separation of a set of source signals
from a set of mixed signals) has been used, to improve SVM performance (as in
[24] [25]). We used Principal Component Analysis (PCA) to reduce the number
variables (representing features), because it is the most appropriate technique
for our purpose, allowing to transform the original data into a new set of vari-
ables that preserve the information contained in the original data set without
redundancy. To make values more homogeneous a normalization algorithm has
been applied and values have been mapped into a (-1, +1) range. Finally, the
algorithms have been implemented and run by using the Matlab programming
environment and LibSVM [26].
3 SVM based classifier
Data set are used to train and develop the SVM based classifier. To perform the
training set we need to use the class labels, that in our cases are the patients
healthy status, and the features. The training data is used to produce a model,
which is able to predict the target values of the test data [27]. We used the
leave-one-out as training method [28]. It consists in calculating the model with
8. On the classification of EEG signal by using an SVM based algorythm 7
the exclusion of one object at a time and predicting its value. Starting from
a data set, it works as follows: (i) remove one element from the data set; (ii)
define a prediction model using the data (less the one removed); (iii) predict and
assign the removed element to a class by using the defined model; (iv) repeat
the procedure for all elements. The workflow of the Leave-one-out algorithm is
reported in Figure 4.
The classification module is then implemented by using the code reported
in the following. To complete the algorithm, the implementation requires the
definition of a kernel function. The 4 most used functions are: (i) Linear; (ii)
Polynomial; (iii) Radial Basis Function (RBF); (iv) Sigmoid. We used an RBF
kernel function because it is most appropriate in the biomedical signal processing
and requires the setting of two parameters: boxconstraint or C and γ. γ defines
how far the influence of a single training example reaches, while the boxconstrain
C controls the classification and the misclassification, due to data overfitting. To
choose these values have been made several iteration tests by evaluating the
classification result and finally the values that gave better accuracy was chosen.
In the SVM training phase, a RBF kernel with C and γ amounting to 1 has been
used and the training vector was the last column of dataset. The leave-one-out
pseudo code is reported in the following.
Leave one out algorithm (pseudo code)
1 %leave one out
d = training vector
3 xs = PCA and normalization output dataset
for i = 1 to 60
5 xtrain =[ xs (1:i-1, 1:59); xs(i+1:60 ,1:59)];
dtrain =[ d(1:i -1); d(i+1:60)];
7 Training svm (’kernel_function ’,
’rbf ’,’RBF_sigma ’, 1,
9 ’boxconstrain ’, 1);
end
Results are used to define a confusion matrix, used to compare the predicted
elements with class belonging (real and predicted). By using the confusion ma-
trix, it can be possible to calculate four parameters: true positives (TP), true
negatives (TN), false positives (FP) and false negatives (FN). These values allow
to calculate the accuracy, the specificity and the sensibility.
Accuracy (acc), sensitivity (sens) and specificity (spec)
Evaluate TP , TN , FP and FN by the confusion matrix
2
acc = (TP+TN)/(TP+TN+FP+FN);
4 sens = TP/(TP+FN);
spec = TN/(TN+FP);
The accuracy indicates the closeness between elements assigned to predicted
classes and their belonging. The metric used consists in evaluating the accuracy
as the sum of TP and TN divided by the sum of all found values (TP, TN, FN,
FP). The specificity measures the ability of correctly predict healthy patient (i.e.,
9. 8 Sacca’, Campolo, Mirarchi, Gambardella, Veltri, Morabito
negative) and can be calculated as TN minus (all the) negative results. Finally,
the sensibility is evaluated as TP minus (all) positive values found, and represents
the ability to predict the non healthy subjects on a reference population.
4 Experimental Results
We used the algorithm reported above to define an SVM based classifier. The
used data set consists of 60 available EEG signals. We used part of the data
as training while part of them as tests. The performance of the SVM classifier
can be used to test and produce diagnosis of CJD, considering EEG track with
PSWC as hallmark of disease. Using the 60 signals in the dataset, the accuracy
of our classifier is 96,67%, the specificity is 93,33% and the sensibility is 100%.
In the Table 2, we report the confusion matrix obtained.
To improve the accuracy, sensibility and specificity, we used 9 additional EEG
signals of subjects suffering from CJD. These signals have been preprocessed
and added to the dataset. The SVM classifier has been trained by using different
values of C and γ. By setting C and γ both to 0,4(also in this case we were carried
out iterative test for the choice of the best parameters), and using the whole
set of data, the value of accuracy is 97,10%, the sensitivity 96,67% while the
Fig. 4. Leave one out algorithm workflow
10. On the classification of EEG signal by using an SVM based algorythm 9
30 0
2 28
Table 2. Confusion matrix results by using the SVM classifier
specificity 97,44%. The addition of these new signals has improved the accuracy
and sensitivity of the classifier, while the specificity is slightly lower. In Table 3,
we report the confusion matrix obtained by using the increased data set.
29 1
1 38
Table 3. Confusion matrix obtained by using 69 EEG signals
5 Conclusion
CJD diagnosis is a difficult task, performed by analyzing EEG signals as well
as clinical background. Nevertheless, EEG signal analysis is a difficult task due
to high dimensional features. CJD early detection is mandatory to reduce risks
or complications for patients. We defined and applied a methodology to define
an SVM based classification tool. First of all EEG signals have been elaborated
by using wavelet based mapping function and by evaluating statistical features.
Principal Component Analysis (PCA) and normalization are used to reduce and
to reduce data heterogeneity. An SVM classifier is then defined. For the training
phase, a leave-one-out based algorithm has been implemented and though the
confusion matrix, we have calculated the accuracy, which had 96,67%. The per-
formance of our SVM classifier confirms the classification ability and a candidate
as decision support system for EEG analysis in case of CJD suspect cases.
Acknowledgments.
The authors would like to thank Rocco Cutellé for his support and experiments in
denoising and preprocessing signals. Also authors thanks here Umberto Aguglia
and Neurological group for supporting us in furnishing supports for EEG signals.
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