Classification of Epileptic Seizure Predictors in EEG
|
|
- Albert Campbell
- 5 years ago
- Views:
Transcription
1 Classification of Epileptic Seizure Predictors in EEG Problem: Epileptic seizures are still not fully understood in medicine. This is because there is a wide range of potential causes of epilepsy which may or may not correlate to the severity, regularity, and frequency of a patient s seizures. Furthermore, the physiological pathway causing seizures may vary between patients, even if both patients epilepsy is attributed to the same apparent cause. Despite modern diagnostic techniques, 6/10 seizure events are still diagnosed as idiopathic and attributed to epilepsy [1]. The amount of physiological variability in seizures makes a cure difficult in some cases and impossible in others. However, nearly all seizures can be detected as aberrant behavior in electrical signals produced by the brain, which provides an avenue for treatment of chronic seizures. Chronic seizures are treated with a combination of medication and preparedness/action plans. The utility and practicality of a seizure action plan increases if the patient has some method of monitoring when the next seizure will occur. Recent clinical studies have shown that features of EEG waveforms present during seizure activity can be detected as pre-seizure activity minutes before the onset of symptoms [2]. A practical method to warn patients of future seizure events is required. Solution: Patient Needs are not being met by the current applications of machine learning in EEG processing. To date, seizure detection has been a higher priority than seizure prediction. To detect ongoing seizures, pattern classification of bio-signals and EEG data is used daily in emergency rooms to diagnose unresponsive patients [3]. Seizure prediction is the use of pattern classification to detect neuroelectric markers of an oncoming seizure before symptoms begin. In a survey of 191 epileptic patients, 90% stated that they believe practical seizure prediction would be important for treatment [4]. Those same patients expressed a desire for sensitivity or specificity [4]. These patient needs were translated into technical specifications for a pattern classification algorithm and resulting deterministic mathematical model. The following are criteria the final model is based on patient needs. Feature space dimensions <= 25 Computationally simple feature extraction Time group predictions Maximize true negative probability with highest possible true positive The feature space must remain small and computationally simple to extract due to the intention that the final patient-specific deterministic models get loaded onto a microprocessors and combined with wearable EEG technology. This combination of hardware and software will act as an everyday monitoring device providing a continuous, real-time assessment of oncoming seizure probability.
2 To improve patient utility, this study also attempted to locate feature markers in training data that indicated if seizure event is likely as well as when that seizure is most likely to begin. This will be achieved by adding an additional value to the feature space labeling which time group the processed sample was extracted from as shown in Table 1. Table 1: The label of each time group. Control samples labeled 0 are taken from time windows that are 1+ hours away from the next seizure as well as 1+ hours away from the previous seizure to ensure a clean control. Label Sample Window (time before next seizure) 0 1+ hours minutes minutes minutes minutes minutes The relative classification probability criteria are also based on patient needs. As this is intended to be a patient-monitored device, it is important not to warn falsely warn of seizures at the risk of the patient losing confidence in the device. However, it is also important to consider the survey results calling for sensitivity over specificity. Patients would rather be warned of a higher percentage of oncoming seizures and suffer a few false alarms than not receive a warning at all. These two criteria are conflicting but can both be achieved with time dependent classification. False positives are minimized by requiring an overall high specificity in the deterministic model. This lowers the sensitivity for recognizing pre-seizure samples in each time group at any given instant. Low sensitivity is compensated for by the proposed real-time implementation through the accumulation of pre-seizure markers. The false negative probability between groups 1-5 is accepted as high and variable between patients. This is a physiological consequence of the assumption that even though there are markers that predict seizures, these markers will not exist at all locations in EEG data at all times. Training Data [5]: Collected from 22 patients with 23 electrode continuous EEG systems. Data is recorded at 256 Hz at 0.1 mv resolution. In total, 198 seizure events occurred during EEG collection. Each seizure is annotated with start and end time. Since epilepsy is patient-specific, each model was trained on data from only one patient.
3 Approach: The complexity of EEG signals and a lack of universal pre-seizure markers excludes a template matching method for pre-seizure wave characteristic classification. Instead, the EEG signal was preprocessed into data representing the mathematical features of the waveform. A pattern classification algorithm compares the preprocessed data from the five minutes leading up to a seizure against preprocessed data pulled from the same patient s normal EEG patterns (1+ hours away from the nearest seizure). This yielded lists of waveform features that commonly occur at known time ranges before the onset of seizure symptoms. Using the final results of the pattern classification algorithm run in each time range, a multilayer perceptron (MLP) network will be generated to simultaneously analyze all electrode inputs from a real-time EEG signal. The output of the MLP is a real-time probability assessment of when the patient will have their next seizure or an indication that there is no pre-seizure activity. S = Number of seizures in training data Fs = 256 Hz E = # of electrodes in EEG = 23 for most patients X = # of features = 16 Sampling: Each patient s EEG data was handled separately. First the data was divided into control data and seizure data based on provided annotations. The control data was sourced 1+ hours from any recorded seizure activity. Seizure data was collected in the 5 minutes leading up to each recorded seizure in the training data set. Each patients training data was composed of 30 minutes of control data the five minutes leading up to at least four seizures. Data = [(30 + (S * 5 )) * 60 * Fs, E] matrix Preprocessing: Each data set was run through a preprocessing function to convert the raw waveform data into labeled feature vectors for the pattern classification algorithm. The preprocessing function extracted frequency and amplitude metrics, and relative power spectrographic results. The final 16-dimensional feature space was defined to include: Relative power of the 8, 4-Hz-wide frequency windows from 0-32 Hz Relative high frequency power in the Hz range Max/min peak signal values Max/min peak duration Max/min values of signal after digital high pass filtering with Fc =.5 Hz Average frequency
4 The input to the feature extraction was a time scrolling window of data set, collecting one second long samples every half second from each electrode individually. The time group labels described in Table 1 were also added in this step. Finally the rows of the data set were randomized to get an unordered training set. Data = [(30 + (S * 5 )) * 60 * 2 * E, X] matrix Single-Input Pattern Classification: The data set after preprocessing was analyzed with MATLAB s classification learner. The ideal pattern classification method for this study was arrived at experimentally. Support vector machines, nearest neighbor classifiers, and decision trees were tested. Due to the number of features and size of the data set, ensemble methods were the most effective. The two best approaches were a bootstrapped aggregate of decision trees (sometimes referred to as a decision forest) and a subspace KNN which analyzes the results of many KNN algorithms in different subsets of the feature space. Ensemble methods are not ideal for the future platform of a microcontroller. In this case, the computational simplicity involved in computing the output of a decision forest makes it possible. Additionally, the decision forest has a faster training time than the subspace KNN makes it the best pattern classification algorithm for this study.
5 Results: After the sampling and preprocessing steps defined above, the implemented learning algorithm successfully generated decision forests that met the criteria defined by patient needs as shown in Figure 1 and Figure 2. All test were run with 5-fold verification. Total number of tree was experimentally determined to yield the level of classification shown below is Figure 1: Confusion of patient 03 in data set. Maximum values on the diagonal of the matrix shows successful pattern classification.
6 Figure 2: True positive and false negative of patient 03. High true positive in class 0 and relatively high true positives for groups 1-5 compared to false positives in groups 1-5 shows success.
7 The success of this study with respect to its goals are summarized in Table 2: Table 2: Success of each criteria is described in table two Criteria Small feature space Simple feature extraction Time group predictions Evidence Only 16 features and one label are used for this classification. Calculations do not require large amounts of memory or computations. When a preseizure marker is detected, there is a ~50% chance it is placed in the right time group. There is a 60-80% chance the marker is placed in adjacent time group. Maximum true negative probability True positive classification for group 0 is 99%. High true positive probability The true positive calculation of groups 1-5 is ~20% with relatively smaller false positive probabilities within groups 1-5 * As expected false negative probabilities are high. This is shown in the high rate of misclassification of each group 1-5 into group 0. Real-Time Implementation: The real-time implementation of the neural network based on this pattern classification model was not generated due to the scope of this course. The NN would need to operate recursively, storing an array of past processed samples. The output of the RNN would be based on a weighted combination of all past stored samples. The classification of more recent samples would have higher weights than the samples farther away in the time domain to calculate the probability and time of an approaching seizure. The RNN would also employ a heuristic threshold to set a minimum confidence required to warn a patient a seizure is approaching. The pattern classification modeling technique described in this report can be used in the future to implement such a neural network. Conclusion: This study achieved the goal of applying machine learning to epileptic EEG monitoring while focusing on patient priorities. The process of developing pattern classification models proposed in this study could be used to build a real time RNN operating on a microcontroller with a wearable EEG input. The high specificity for group 0 samples shows the system would almost never instantaneously come to the conclusion that a seizure is about to occur if there is not seizure activity in the next five minutes. Once this is applied to the threshold and aggregate analysis of the proposed RNN, the number of false warnings from this system would be nearly 0. Additionally, the pattern classification models have a high probability of classifying a pre-seizure marker to the correct time group or an adjacent group relative to other time groups 1-5. The proposed device and the software developed in this study have the potential to function as a real-time seizure warning system advising a patient when his or her next seizure symptoms will begin.
8 References [1] Steven, S. (2017). What Causes Epilepsy and Seizures? Epilepsy Foundation. [online] Epilepsy Foundation. Available at: [Accessed 16 Dec. 2017]. [2] G. Minasyan, J. Chatten, M. Chatten and R. Harner, "Patient-Specific Early Seizure Detection From Scalp Electroencephalogram", Journal of Clinical Neurophysiology, vol. 27, no. 3, pp , [3] Ramgopal, S., Thome-Souza, S., Jackson, M., Kadish, N., Sánchez Fernández, I., Klehm, J., Bosl, W., Reinsberger, C., Schachter, S. and Loddenkemper, T. (2017). Seizure detection, seizure prediction, and closed-loop warning systems in epilepsy. [online] Available at: [Accessed 16 Dec. 2017]. [4] Schulze-Bonhage, A., Sales, F., Wagner, K., Teotonio, R., Carius, A., Schelle, A. and Ihle, M. (2017). Views of patients with epilepsy on seizure prediction devices. [online] Available at: [Accessed 16 Dec. 2017]. [5] Goldberger AL, Amaral LAN, Glass L, Hausdorff JM, Ivanov PCh, Mark RG, Mietus JE, Moody GB, Peng C-K, Stanley HE. PhysioBank, PhysioToolkit, and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals. Circulation 101(23):e215-e220 [Circulation Electronic Pages; (June 13).
ANALYSIS OF BRAIN SIGNAL FOR THE DETECTION OF EPILEPTIC SEIZURE
ANALYSIS OF BRAIN SIGNAL FOR THE DETECTION OF EPILEPTIC SEIZURE Sumit Kumar Srivastava 1, Sharique Ahmed 2, Mohd Maroof Siddiqui 3 1,2,3 Department of EEE, Integral University ABSTRACT The electroencephalogram
More informationElectroencephalography II Laboratory
Introduction Several neurological disorders exist that can have an impact on brain function. Often these disorders can be examined by reviewing the electroencephalograph, or EEG signal. Quantitative features
More informationarxiv: v1 [cs.lg] 4 Feb 2019
Machine Learning for Seizure Type Classification: Setting the benchmark Subhrajit Roy [000 0002 6072 5500], Umar Asif [0000 0001 5209 7084], Jianbin Tang [0000 0001 5440 0796], and Stefan Harrer [0000
More informationApplying Data Mining for Epileptic Seizure Detection
Applying Data Mining for Epileptic Seizure Detection Ying-Fang Lai 1 and Hsiu-Sen Chiang 2* 1 Department of Industrial Education, National Taiwan Normal University 162, Heping East Road Sec 1, Taipei,
More informationECG Signal Classification with Deep Learning Techniques
ECG Signal Classification with Deep Learning Techniques Chien You Huang, B04901147 Ruey Lin Jahn, B02901043 Sung-wei Huang, B04901093 Department of Electrical Engineering, National Taiwan University, Taipei,
More informationRobust Detection of Atrial Fibrillation for a Long Term Telemonitoring System
Robust Detection of Atrial Fibrillation for a Long Term Telemonitoring System B.T. Logan, J. Healey Cambridge Research Laboratory HP Laboratories Cambridge HPL-2005-183 October 14, 2005* telemonitoring,
More informationAUTOMATIC CLASSIFICATION OF HEARTBEATS
AUTOMATIC CLASSIFICATION OF HEARTBEATS Tony Basil 1, and Choudur Lakshminarayan 2 1 PayPal, India 2 Hewlett Packard Research, USA ABSTRACT We report improvement in the detection of a class of heart arrhythmias
More informationOptimal preictal period in seizure prediction
Optimal preictal period in seizure prediction Mojtaba Bandarabadi, Jalil Rasekhi, Cesar A. Teixeira, António Dourado CISUC/DEI, Center for Informatics and Systems of the University of Coimbra, Department
More informationTHE data used in this project is provided. SEIZURE forecasting systems hold promise. Seizure Prediction from Intracranial EEG Recordings
1 Seizure Prediction from Intracranial EEG Recordings Alex Fu, Spencer Gibbs, and Yuqi Liu 1 INTRODUCTION SEIZURE forecasting systems hold promise for improving the quality of life for patients with epilepsy.
More informationDynamic Time Warp Distances as Feedback for EEG Feature Density
1 Dynamic Time Warp Distances as Feedback for EEG Feature Density Christian R. Ward 1 and Iyad Obeid, PhD Abstract This work presents a feature detection method built around a dynamic time-warping (DTW)
More informationDETECTION AND CORRECTION OF EYE BLINK ARTIFACT IN SINGLE CHANNEL ELECTROENCEPHALOGRAM (EEG) SIGNAL USING A SIMPLE k-means CLUSTERING ALGORITHM
Volume 120 No. 6 2018, 4519-4532 ISSN: 1314-3395 (on-line version) url: http://www.acadpubl.eu/hub/ http://www.acadpubl.eu/hub/ DETECTION AND CORRECTION OF EYE BLINK ARTIFACT IN SINGLE CHANNEL ELECTROENCEPHALOGRAM
More informationThis presentation is the intellectual property of the author. Contact them for permission to reprint and/or distribute.
Modified Combinatorial Nomenclature Montage, Review, and Analysis of High Density EEG Terrence D. Lagerlund, M.D., Ph.D. CP1208045-16 Disclosure Relevant financial relationships None Off-label/investigational
More informationEfficient Feature Extraction and Classification Methods in Neural Interfaces
Using a simple decision tree model, we introduce efficient hardware architectures to predict abnormal neurological states in various disorders. Efficient Feature Extraction and Classification Methods in
More informationEKG Monitoring and Arrhythmia Detection
EKG Monitoring and Arrhythmia Detection Amaris Chen Department of Computer Science & Engineering University of Washington Box 352350 Seattle, WA 98195-2350 amarisch@cs.washington.edu ABSTRACT Cardiovascular
More informationComparison of Feature Extraction Techniques: A Case Study on Myocardial Ischemic Beat Detection
International Journal of Pure and Applied Mathematics Volume 119 No. 15 2018, 1389-1395 ISSN: 1314-3395 (on-line version) url: http://www.acadpubl.eu/hub/ http://www.acadpubl.eu/hub/ Comparison of Feature
More informationClassification of EEG signals in an Object Recognition task
Classification of EEG signals in an Object Recognition task Iacob D. Rus, Paul Marc, Mihaela Dinsoreanu, Rodica Potolea Technical University of Cluj-Napoca Cluj-Napoca, Romania 1 rus_iacob23@yahoo.com,
More informationAutomatic Detection of Epileptic Seizures in EEG Using Machine Learning Methods
Automatic Detection of Epileptic Seizures in EEG Using Machine Learning Methods Ying-Fang Lai 1 and Hsiu-Sen Chiang 2* 1 Department of Industrial Education, National Taiwan Normal University 162, Heping
More informationAssessment of Reliability of Hamilton-Tompkins Algorithm to ECG Parameter Detection
Proceedings of the 2012 International Conference on Industrial Engineering and Operations Management Istanbul, Turkey, July 3 6, 2012 Assessment of Reliability of Hamilton-Tompkins Algorithm to ECG Parameter
More informationAnalysis of EEG Signal for the Detection of Brain Abnormalities
Analysis of EEG Signal for the Detection of Brain Abnormalities M.Kalaivani PG Scholar Department of Computer Science and Engineering PG National Engineering College Kovilpatti, Tamilnadu V.Kalaivani,
More informationECG Beat Recognition using Principal Components Analysis and Artificial Neural Network
International Journal of Electronics Engineering, 3 (1), 2011, pp. 55 58 ECG Beat Recognition using Principal Components Analysis and Artificial Neural Network Amitabh Sharma 1, and Tanushree Sharma 2
More informatione-glass: A Wearable System for Real-Time Detection of Epileptic Seizures
e-glass: A Wearable System for Real-Time Detection of Epileptic Seizures Dionisije Sopic, Amir Aminifar, David Atienza Embedded Systems Laboratory (ESL), Swiss Federal Institute of Technology Lausanne
More informationSuperchords: the atoms of thought 1
Superchords: the atoms of thought 1 NORMAND, Rogério & FERREIRA, Hugo Alexandre Institute of Biophysics and Biomedical Engineering (IBEB) Faculty of Science, University of Lisbon Abstract Electroencephalography
More informationVALIDATION OF AN AUTOMATED SEIZURE DETECTION SYSTEM ON HEALTHY BABIES Histogram-based Energy Normalization for Montage Mismatch Compensation
VALIDATION OF AN AUTOMATED SEIZURE DETECTION SYSTEM ON HEALTHY BABIES Histogram-based Energy Normalization for Montage Mismatch Compensation A. Temko 1, I. Korotchikova 2, W. Marnane 1, G. Lightbody 1
More informationSUPPLEMENTARY INFORMATION. Table 1 Patient characteristics Preoperative. language testing
Categorical Speech Representation in the Human Superior Temporal Gyrus Edward F. Chang, Jochem W. Rieger, Keith D. Johnson, Mitchel S. Berger, Nicholas M. Barbaro, Robert T. Knight SUPPLEMENTARY INFORMATION
More informationConvulsive seizure detection using a wrist-worn electrodermal activity and accelerometry biosensor
BRIEF COMMUNICATION Convulsive seizure detection using a wrist-worn electrodermal activity and accelerometry biosensor *yming-zher Poh, ztobias Loddenkemper, xclaus Reinsberger, ynicholas C. Swenson, yshubhi
More informationPCA Enhanced Kalman Filter for ECG Denoising
IOSR Journal of Electronics & Communication Engineering (IOSR-JECE) ISSN(e) : 2278-1684 ISSN(p) : 2320-334X, PP 06-13 www.iosrjournals.org PCA Enhanced Kalman Filter for ECG Denoising Febina Ikbal 1, Prof.M.Mathurakani
More informationD8 - Executive Summary
Autonomous Medical Monitoring and Diagnostics AMIGO DOCUMENT N : ISSUE : 1.0 DATE : 01.09.2016 - CSEM PROJECT N : 221-ES.1577 CONTRACT N : 4000113764 /15/F/MOS FUNCTION NAME SIGNATURE DATE Author Expert
More informationSpatiotemporal Cardiac Activation Sites Localization Using ECG Precordial Leads
Spatiotemporal Cardiac Activation Sites Localization Using ECG Precordial Leads Jaime R. De La Cruz BSEE a, Joseph H.Pierluissi PhD a, Ubaldo Robles BSEE a, Zainul Abedin MD b a Electrical and Computer
More informationFeature Parameter Optimization for Seizure Detection/Prediction
Feature Parameter Optimization for Seizure Detection/Prediction R. Esteller* #, J. Echauz #, M. D Alessandro, G. Vachtsevanos and B. Litt,. # IntelliMedix, Atlanta, USA * Universidad Simón Bolívar, Caracas,
More informationVarious Methods To Detect Respiration Rate From ECG Using LabVIEW
Various Methods To Detect Respiration Rate From ECG Using LabVIEW 1 Poorti M. Vyas, 2 Dr. M. S. Panse 1 Student, M.Tech. Electronics 2.Professor Department of Electrical Engineering, Veermata Jijabai Technological
More informationEEG Interictal Spike Detection Using Artificial Neural Networks
Virginia Commonwealth University VCU Scholars Compass Theses and Dissertations Graduate School 2016 EEG Interictal Spike Detection Using Artificial Neural Networks Howard J. Carey III Virginia Commonwealth
More informationError Detection based on neural signals
Error Detection based on neural signals Nir Even- Chen and Igor Berman, Electrical Engineering, Stanford Introduction Brain computer interface (BCI) is a direct communication pathway between the brain
More informationPrecision/Recall Trade-Off Analysis in Abnormal/Normal Heart Sound Classification
Precision/Recall Trade-Off Analysis in Abnormal/Normal Heart Sound Classification Jeevith Bopaiah 2 and Ramakanth Kavuluru 1,2 1 Division of Biomedical Informatics, Department of Internal Medicine 2 Department
More informationClassification of ECG Data for Predictive Analysis to Assist in Medical Decisions.
48 IJCSNS International Journal of Computer Science and Network Security, VOL.15 No.10, October 2015 Classification of ECG Data for Predictive Analysis to Assist in Medical Decisions. A. R. Chitupe S.
More informationFREQUENCY DOMAIN BASED AUTOMATIC EKG ARTIFACT
FREQUENCY DOMAIN BASED AUTOMATIC EKG ARTIFACT REMOVAL FROM EEG DATA features FOR BRAIN such as entropy COMPUTER and kurtosis for INTERFACING artifact rejection. V. Viknesh B.E.,(M.E) - Lord Jeganath College
More informationHardware efficient seizure prediction algorithm Sergi Consul a, Bashir I. Morshed a, Robert Kozma b a
Hardware efficient seizure prediction algorithm Sergi Consul a, Bashir I. Morshed a, Robert Kozma b a Electrical and Computer Engineering Department, he University of Memphis, Memphis, N, USA b Center
More informationMultichannel Classification of Single EEG Trials with Independent Component Analysis
In J. Wang et a]. (Eds.), Advances in Neural Networks-ISNN 2006, Part 111: 54 1-547. Berlin: Springer. Multichannel Classification of Single EEG Trials with Independent Component Analysis Dik Kin Wong,
More informationEfficient Feature Extraction and Classification Methods in Neural Interfaces
Efficient Feature Extraction and Classification Methods in Neural Interfaces Azita Emami Professor of Electrical Engineering and Medical Engineering Next Generation Therapeutic Devices 2 Where We Started:
More informationEEG signal classification using Bayes and Naïve Bayes Classifiers and extracted features of Continuous Wavelet Transform
EEG signal classification using Bayes and Naïve Bayes Classifiers and extracted features of Continuous Wavelet Transform Reza Yaghoobi Karimoi*, Mohammad Ali Khalilzadeh, Ali Akbar Hossinezadeh, Azra Yaghoobi
More informationLezione 3 Voltmetri Diagnostica strumentale in cardiologia ECG Applicazioni scientifiche della misura in medicina
Corsi di Laurea in Tecniche Di Fisiopatologia Cardiocircolatoria E Perfusione Cardiovascolare Dr. Andrea Malizia 1 Lezione 3 Voltmetri Diagnostica strumentale in cardiologia ECG Applicazioni scientifiche
More informationEpileptic Seizure Classification using Statistical Features of EEG Signal
International Conference on Electrical, Computer and Communication Engineering (ECCE), February 6-8,, Cox s Bazar, Bangladesh Epileptic Seizure Classification using Statistical Features of EEG Signal Md.
More informationSeizure Prediction and Detection
Seizure Prediction and Detection Tay Netoff Yun-Sang Park Michael Brown Lan Luo University of Minnesota Minneapolis, MN Feb. 9, 2011 Outline Seizure prediction using spectral power features and SVMclassification
More informationAccuScreen ABR Screener
AccuScreen ABR Screener Test Methods Doc no. 7-50-1015-EN/02 0459 Copyright notice No part of this Manual or program may be reproduced, stored in a retrieval system, or transmitted, in any form or by any
More informationEE 4BD4 Lecture 11. The Brain and EEG
EE 4BD4 Lecture 11 The Brain and EEG 1 Brain Wave Recordings Recorded extra-cellularly from scalp (EEG) Recorded from extra-cellularly from surface of cortex (ECOG) Recorded extra-cellularly from deep
More informationFuzzy Techniques for Classification of Epilepsy risk level in Diabetic Patients Using Cerebral Blood Flow and Aggregation Operators
Fuzzy Techniques for Classification of Epilepsy risk level in Diabetic Patients Using Cerebral Blood Flow and Aggregation Operators R.Harikumar, Dr. (Mrs).R.Sukanesh Research Scholar Assistant Professor
More informationDevelopment of 2-Channel Eeg Device And Analysis Of Brain Wave For Depressed Persons
Development of 2-Channel Eeg Device And Analysis Of Brain Wave For Depressed Persons P.Amsaleka*, Dr.S.Mythili ** * PG Scholar, Applied Electronics, Department of Electronics and Communication, PSNA College
More informationA micropower support vector machine based seizure detection architecture for embedded medical devices
A micropower support vector machine based seizure detection architecture for embedded medical devices The MIT Faculty has made this article openly available. Please share how this access benefits you.
More informationTowards natural human computer interaction in BCI
Towards natural human computer interaction in BCI Ian Daly 1 (Student) and Slawomir J Nasuto 1 and Kevin Warwick 1 Abstract. BCI systems require correct classification of signals interpreted from the brain
More informationClassification of Epileptic EEG Using Wavelet Transform & Artificial Neural Network
Volume 4, No. 9, July-August 213 International Journal of Advanced Research in Computer Science RESEARCH PAPER Available Online at www.ijarcs.info ISSN No. 976-5697 Classification of Epileptic EEG Using
More informationISSN: ISO 9001:2008 Certified International Journal of Engineering and Innovative Technology (IJEIT) Volume 2, Issue 10, April 2013
ECG Processing &Arrhythmia Detection: An Attempt M.R. Mhetre 1, Advait Vaishampayan 2, Madhav Raskar 3 Instrumentation Engineering Department 1, 2, 3, Vishwakarma Institute of Technology, Pune, India Abstract
More informationEEG Signal Classification Using Wavelet Feature Extraction and Neural Networks
EEG Signal Classification Using Wavelet Feature Extraction and Neural Networks Pari Jahankhani, Vassilis Kodogiannis and Kenneth Revett AbstractDecision Support Systems have been utilised since 196, providing
More informationMental State Recognition by using Brain Waves
Indian Journal of Science and Technology, Vol 9(33), DOI: 10.17485/ijst/2016/v9i33/99622, September 2016 ISSN (Print) : 0974-6846 ISSN (Online) : 0974-5645 Mental State Recognition by using Brain Waves
More informationECG based Atrial Fibrillation Detection using Cuckoo Search Algorithm
ECG based Atrial Fibrillation Detection using Cuckoo Search Algorithm Padmavathi Kora, PhD Gokaraju Rangaraju Institute of Engineering and Technology, Hyderabad V. Ayyem Pillai, PhD Gokaraju Rangaraju
More informationAustralian Journal of Basic and Applied Sciences
Australian Journal of Basic and Applied Sciences, 7(12) October 2013, Pages: 174-179 AENSI Journals Australian Journal of Basic and Applied Sciences Journal home page: www.ajbasweb.com A Probabilistic
More informationMajority Vote of Ensemble Machine Learning Methods for Real-Time Epilepsy Prediction Applied on EEG Pediatric Data
Majority Vote of Ensemble Machine Learning Methods for Real-Time Epilepsy Prediction Applied on EEG Pediatric Data Samed Jukić, Dino Кеčo, Jasmin Kevrić International Burch University, Sarajevo, Bosnia
More informationDETECTION OF EPILEPTIC SEIZURE SIGNALS USING FUZZY RULES BASED ON SELECTED FEATURES
e-issn 2455 1392 Volume 3 Issue 1, January 2017 pp. 22 28 Scientific Journal Impact Factor : 3.468 http://www.ijcter.com DETECTION OF EPILEPTIC SEIZURE SIGNALS USING FUZZY RULES BASED ON SELECTED FEATURES
More informationAn Automated Method for Neuronal Spike Source Identification
An Automated Method for Neuronal Spike Source Identification Roberto A. Santiago 1, James McNames 2, Kim Burchiel 3, George G. Lendaris 1 1 NW Computational Intelligence Laboratory, System Science, Portland
More informationQuick detection of QRS complexes and R-waves using a wavelet transform and K-means clustering
Bio-Medical Materials and Engineering 26 (2015) S1059 S1065 DOI 10.3233/BME-151402 IOS Press S1059 Quick detection of QRS complexes and R-waves using a wavelet transform and K-means clustering Yong Xia
More informationEmotion Detection Using Physiological Signals. M.A.Sc. Thesis Proposal Haiyan Xu Supervisor: Prof. K.N. Plataniotis
Emotion Detection Using Physiological Signals M.A.Sc. Thesis Proposal Haiyan Xu Supervisor: Prof. K.N. Plataniotis May 10 th, 2011 Outline Emotion Detection Overview EEG for Emotion Detection Previous
More informationEEG SPIKE CLASSIFICATION WITH TEMPLATE MATCHING ALGORITHM. Çamlık Caddesi No:44 Sarnıç Beldesi İZMİR 2 Elektrik ve Elektronik Müh.
EEG SPIKE CLASSIFICATION WITH TEMPLATE MATCHING ALGORITHM Selim BÖLGEN 1 Gülden KÖKTÜRK 2 1 Pagetel Sistem Müh. San. Tic. Ltd. Şti. Çamlık Caddesi No:44 Sarnıç Beldesi İZMİR 2 Elektrik ve Elektronik Müh.
More informationComparison of ANN and Fuzzy logic based Bradycardia and Tachycardia Arrhythmia detection using ECG signal
Comparison of ANN and Fuzzy logic based Bradycardia and Tachycardia Arrhythmia detection using ECG signal 1 Simranjeet Kaur, 2 Navneet Kaur Panag 1 Student, 2 Assistant Professor 1 Electrical Engineering
More informationCHAPTER 6 INTERFERENCE CANCELLATION IN EEG SIGNAL
116 CHAPTER 6 INTERFERENCE CANCELLATION IN EEG SIGNAL 6.1 INTRODUCTION Electrical impulses generated by nerve firings in the brain pass through the head and represent the electroencephalogram (EEG). Electrical
More informationTITLE: A Data-Driven Approach to Patient Risk Stratification for Acute Respiratory Distress Syndrome (ARDS)
TITLE: A Data-Driven Approach to Patient Risk Stratification for Acute Respiratory Distress Syndrome (ARDS) AUTHORS: Tejas Prahlad INTRODUCTION Acute Respiratory Distress Syndrome (ARDS) is a condition
More informationKeywords Seizure detection, jerking movement detection, epilepsy seizure, Android app, personal health care
Volume 6, Issue 9, September 2016 ISSN: 2277 128X International Journal of Advanced Research in Computer Science and Software Engineering Research Paper Available online at: www.ijarcsse.com An Android
More informationDesign of Software for an Electrocardiogram Analyzer
Design of Software for an Electrocardiogram Analyzer by Robert Tisma Electrical Biomedical Engineering Project Report Submitted in partial fulfillment of the Degree of Bachelor of Engineering McMaster
More informationNeonatal ECG Monitoring: Neonatal QT Interval Measurement System
Neonatal ECG Monitoring: Neonatal QT Interval Measurement System Sanket Mugali 1 Uday Nair 2 1 1 Automatic Control and Systems Engineering, University of Sheffield, Sir Henry Stephenson Building, Mappin
More informationDynamic Time Warping As a Novel Tool in Pattern Recognition of ECG Changes in Heart Rhythm Disturbances
2005 IEEE International Conference on Systems, Man and Cybernetics Waikoloa, Hawaii October 10-12, 2005 Dynamic Time Warping As a Novel Tool in Pattern Recognition of ECG Changes in Heart Rhythm Disturbances
More informationPredicting Sleep Using Consumer Wearable Sensing Devices
Predicting Sleep Using Consumer Wearable Sensing Devices Miguel A. Garcia Department of Computer Science Stanford University Palo Alto, California miguel16@stanford.edu 1 Introduction In contrast to the
More informationOverview Detection epileptic seizures
Overview Detection epileptic seizures Overview Problem Goal Method Detection with accelerometers Detection with video data Result Problem Detection of epileptic seizures with EEG (Golden Standard) Not
More informationGene Selection for Tumor Classification Using Microarray Gene Expression Data
Gene Selection for Tumor Classification Using Microarray Gene Expression Data K. Yendrapalli, R. Basnet, S. Mukkamala, A. H. Sung Department of Computer Science New Mexico Institute of Mining and Technology
More informationAn Enhanced Approach on ECG Data Analysis using Improvised Genetic Algorithm
An Enhanced Approach on ECG Data Analysis using Improvised Genetic Algorithm V.Priyadharshini 1, S.Saravana kumar 2 -------------------------------------------------------------------------------------------------
More informationAutomatic Detection of Heart Disease Using Discreet Wavelet Transform and Artificial Neural Network
e-issn: 2349-9745 p-issn: 2393-8161 Scientific Journal Impact Factor (SJIF): 1.711 International Journal of Modern Trends in Engineering and Research www.ijmter.com Automatic Detection of Heart Disease
More informationGeorge Benke*, Maribeth Bozek-Kuzmicki, David Colella, Garry M. Jacyna, John J. Benedetto**
Wavelet-based analysis of EEG signals for detection and localization of epileptic seizures George Benke*, Maribeth Bozek-Kuzmicki, David Colella, Garry M. Jacyna, John J. Benedetto** The MITRE Corporation,
More informationEmpirical Mode Decomposition based Feature Extraction Method for the Classification of EEG Signal
Empirical Mode Decomposition based Feature Extraction Method for the Classification of EEG Signal Anant kulkarni MTech Communication Engineering Vellore Institute of Technology Chennai, India anant8778@gmail.com
More informationSeizure Prediction using Hilbert Huang Transform on Field Programmable Gate Array
215 IEEE Global Conference on Signal and Information Processing (GlobalSIP) Seizure Prediction using Hilbert Huang Transform on Field Programmable Gate Array Dilranjan S. Wickramasuriya hsenid Mobile Solutions
More informationEpileptic seizure detection using linear prediction filter
11 th International conference on Sciences and Techniques of Automatic control & computer engineering December 19-1, 010, Monastir, Tunisia Epileptic seizure detection using linear prediction filter Introduction:
More informationTesting the Accuracy of ECG Captured by Cronovo through Comparison of ECG Recording to a Standard 12-Lead ECG Recording Device
Testing the Accuracy of ECG Captured by through Comparison of ECG Recording to a Standard 12-Lead ECG Recording Device Data Analysis a) R-wave Comparison: The mean and standard deviation of R-wave amplitudes
More informationSurgical Decision Making in Temporal Lobe Epilepsy by Heterogeneous Classifier Ensembles
Shobeir Fakhraei, Hamid Soltanian-Zadeh, Farshad Fotouhi, Kost Elisevich Surgical Decision Making in Temporal obe Epilepsy by Heterogeneous Classifier Ensembles Epilepsy Epilepsy is a brain disorder involving
More informationNEURAL NETWORK CLASSIFICATION OF EEG SIGNAL FOR THE DETECTION OF SEIZURE
NEURAL NETWORK CLASSIFICATION OF EEG SIGNAL FOR THE DETECTION OF SEIZURE Shaguftha Yasmeen, M.Tech (DEC), Dept. of E&C, RIT, Bangalore, shagufthay@gmail.com Dr. Maya V Karki, Professor, Dept. of E&C, RIT,
More informationCHAPTER IV PREPROCESSING & FEATURE EXTRACTION IN ECG SIGNALS
CHAPTER IV PREPROCESSING & FEATURE EXTRACTION IN ECG SIGNALS are The proposed ECG classification approach consists of three phases. They Preprocessing Feature Extraction and Selection Classification The
More informationPanorama. Arrhythmia Analysis Frequently Asked Questions
Panorama Arrhythmia Analysis Frequently Asked Questions What ECG vectors are used for Beat Detection? 3-wire lead set 5-wire lead set and 12 lead What ECG vectors are used for Beat Typing? 3-wire lead
More informationChapter 1. Introduction
Chapter 1 Introduction 1.1 Motivation and Goals The increasing availability and decreasing cost of high-throughput (HT) technologies coupled with the availability of computational tools and data form a
More informationAn Edge-Device for Accurate Seizure Detection in the IoT
An Edge-Device for Accurate Seizure Detection in the IoT M. A. Sayeed 1, S. P. Mohanty 2, E. Kougianos 3, and H. Zaveri 4 University of North Texas, Denton, TX, USA. 1,2,3 Yale University, New Haven, CT,
More informationMACHINE LEARNING BASED APPROACHES FOR PREDICTION OF PARKINSON S DISEASE
Abstract MACHINE LEARNING BASED APPROACHES FOR PREDICTION OF PARKINSON S DISEASE Arvind Kumar Tiwari GGS College of Modern Technology, SAS Nagar, Punjab, India The prediction of Parkinson s disease is
More informationRobust system for patient specific classification of ECG signal using PCA and Neural Network
International Research Journal of Engineering and Technology (IRJET) e-issn: 395-56 Volume: 4 Issue: 9 Sep -7 www.irjet.net p-issn: 395-7 Robust system for patient specific classification of using PCA
More informationLABVIEW based expert system for Detection of heart abnormalities
LABVIEW based expert system for Detection of heart abnormalities Saket Jain Piyush Kumar Monica Subashini.M School of Electrical Engineering VIT University, Vellore - 632014, Tamil Nadu, India Email address:
More informationEnhanced Detection of Lung Cancer using Hybrid Method of Image Segmentation
Enhanced Detection of Lung Cancer using Hybrid Method of Image Segmentation L Uma Maheshwari Department of ECE, Stanley College of Engineering and Technology for Women, Hyderabad - 500001, India. Udayini
More informationWhite Paper Estimating Complex Phenotype Prevalence Using Predictive Models
White Paper 23-12 Estimating Complex Phenotype Prevalence Using Predictive Models Authors: Nicholas A. Furlotte Aaron Kleinman Robin Smith David Hinds Created: September 25 th, 2015 September 25th, 2015
More informationAutomatic Sleep Arousal Detection based on C-ELM
Automatic Sleep Arousal Detection based on C-ELM Yuemeng Liang, Cyril Leung, Chunyan Miao, Qiong Wu and Martin J. McKeown Department of Electrical and Computer Engineering The University of British Columbia
More informationEpilepsy is the fourth most common neurological disorder
High Performance EEG Feature Extraction for Fast Epileptic Seizure Detection Ramy Hussein, Mohamed Elgendi, Rabab Ward and Amr Mohamed Electrical and Computer Engineering Department, University of British
More informationThe impact of numeration on visual attention during a psychophysical task; An ERP study
The impact of numeration on visual attention during a psychophysical task; An ERP study Armita Faghani Jadidi, Raheleh Davoodi, Mohammad Hassan Moradi Department of Biomedical Engineering Amirkabir University
More informationFinal Report. Implementation of algorithms for QRS detection from ECG signals using TMS320C6713 processor platform
ELG 6163 - DSP Microprocessors, Software, and Applications Final Report Implementation of algorithms for QRS detection from ECG signals using TMS320C6713 processor platform Carleton Student # 100350275
More informationA Sleeping Monitor for Snoring Detection
EECS 395/495 - mhealth McCormick School of Engineering A Sleeping Monitor for Snoring Detection By Hongwei Cheng, Qian Wang, Tae Hun Kim Abstract Several studies have shown that snoring is the first symptom
More informationDIFFERENCE-BASED PARAMETER SET FOR LOCAL HEARTBEAT CLASSIFICATION: RANKING OF THE PARAMETERS
DIFFERENCE-BASED PARAMETER SET FOR LOCAL HEARTBEAT CLASSIFICATION: RANKING OF THE PARAMETERS Irena Ilieva Jekova, Ivaylo Ivanov Christov, Lyudmila Pavlova Todorova Centre of Biomedical Engineering Prof.
More informationElectroencephalography
The electroencephalogram (EEG) is a measure of brain waves. It is a readily available test that provides evidence of how the brain functions over time. The EEG is used in the evaluation of brain disorders.
More informationOPTIMIZING CHANNEL SELECTION FOR SEIZURE DETECTION
OPTIMIZING CHANNEL SELECTION FOR SEIZURE DETECTION V. Shah, M. Golmohammadi, S. Ziyabari, E. Von Weltin, I. Obeid and J. Picone Neural Engineering Data Consortium, Temple University, Philadelphia, Pennsylvania,
More informationECG signal analysis for detection of Heart Rate and Ischemic Episodes
ECG signal analysis for detection of Heart Rate and chemic Episodes Goutam Kumar Sahoo 1, Samit Ari 2, Sarat Kumar Patra 3 Department of Electronics and Communication Engineering, NIT Rourkela, Odisha,
More informationPOWER EFFICIENT PROCESSOR FOR PREDICTING VENTRICULAR ARRHYTHMIA BASED ON ECG
POWER EFFICIENT PROCESSOR FOR PREDICTING VENTRICULAR ARRHYTHMIA BASED ON ECG Anusha P 1, Madhuvanthi K 2, Aravind A.R 3 1 Department of Electronics and Communication Engineering, Prince Shri Venkateshwara
More informationEpileptic Dogs: Advanced Seizure Prediction
CS 74: PROJECT FINAL WRITEUP GITHUB Epileptic Dogs: Advanced Seizure Prediction Taylor Neely & Jack Terwilliger November 22, 2014 INTRODUCTION Epilepsy is a neurological disorder defined by random, spontaneous
More informationREAL-TIME R-SPIKE DETECTION IN THE CARDIAC WAVEFORM THROUGH INDEPENDENT COMPONENT ANALYSIS
REAL-TIME R-SPIKE DETECTION IN THE CARDIAC WAVEFORM THROUGH INDEPENDENT COMPONENT ANALYSIS Harold Martin, Walter Izquierdo, Mercedes Cabrerizo, and Malek Adjouadi Center for Advanced Technology and Education,
More information