Journal Articles
2015(3 publications)
[publication], J.R. and Mollaei, M.R.K. and Bandarabadi, M. and Teixeira, C. and António Dourado , "Epileptic seizure prediction based on ratio and differential linear univariate features", Journal of Medical Signals and Sensors,  vol. 5, 2015
[citation][year=2016]Gadhoumi, K., Lina, J.M., Mormann, F. and Gotman, J., 2016. Seizure prediction for therapeutic devices: A review. Journal of neuroscience methods, 260, pp.270-282.
[citation][year=2016]Zhang, Z. and Parhi, K.K., 2016. Low-complexity seizure prediction from iEEG/sEEG using spectral power and ratios of spectral power. IEEE transactions on biomedical circuits and systems, 10(3), pp.693-706.
[citation][year=2016]Ulate-Campos, A., Coughlin, F., Gaínza-Lein, M., Fernández, I.S., Pearl, P.L. and Loddenkemper, T., 2016. Automated seizure detection systems and their effectiveness for each type of seizure. Seizure, 40, pp.88-101.
[citation][year=2016]Behnam, M. and Pourghassem, H., 2016. Real-time seizure prediction using RLS filtering and interpolated histogram feature based on hybrid optimization algorithm of Bayesian classifier and Hunting search. Computer methods and programs in biomedicine, 132, pp.115-136.
[citation][year=2016]Zainuddin, Z., Lai, K.H. and Ong, P., 2016. An enhanced harmony search based algorithm for feature selection: Applications in epileptic seizure detection and prediction. Computers & Electrical Engineering, 53, pp.143-162.
[citation][year=2016]Shiao, H.T., Cherkassky, V., Lee, J., Veber, B., Patterson, N., Brinkmann, B. and Worrell, G., 2016. SVM-Based System for Prediction of Epileptic Seizures from iEEG Signal. IEEE Transactions on Biomedical Engineering.
[citation][year=2016]Song, Y., Viventi, J. and Wang, Y., Unsupervised Learning of Spike Pattern for Seizure Detection and Wavefront Estimation of High Resolution Micro Electrocorticographic (?ECoG) Data. (http://vision.poly.edu/papers/technical_report/seizure_tech_report.pdf)
[citation][year=2016]Song, Y., Viventi, J. and Wang, Y., 2016. Diversity encouraged learning of unsupervised LSTM ensemble for neural activity video prediction. arXiv preprint arXiv:1611.04899.
[citation][year=2016]Lin, L.C., Chen, S.C.J., Chiang, C.T., Wu, H.C., Yang, R.C. and Ouyang, C.S., 2016. Classification Preictal and Interictal Stages via Integrating Interchannel and Time-Domain Analysis of EEG Features. Clinical EEG and neuroscience, p.1550059416649076.
[citation][year=2016]Dong, H., Supratak, A., Pan, W., Wu, C., Matthews, P.M. and Guo, Y., 2016. Mixed Neural Network Approach for Temporal Sleep Stage Classification. arXiv preprint arXiv:1610.06421.
[citation][year=2016]Vinette, S.A., Premji, S., Beers, C.A., Gaxiola-Valdez, I., Pittman, D.J., Slone, E.G., Goodyear, B.G. and Federico, P., 2016. Pre-ictal BOLD alterations: Two cases of patients with focal epilepsy. Epilepsy Research, 127, pp.207-220.
[citation][year=2016]Cancelli, A., Cottone, C., Tecchio, F., Truong, D.Q., Dmochowski, J. and Bikson, M., 2016. A simple method for EEG guided transcranial electrical stimulation without models. Journal of neural engineering, 13(3), p.036022.
[citation][year=2016]Khalid, M.I., Aldosari, S.A., Alshebeili, S.A. and Alotaiby, T., 2016, December. Threshold based MEG data classification for healthy and epileptic subjects. In Electronic Devices, Systems and Applications (ICEDSA), 2016 5th International Conference on (pp. 1-3). IEEE.
[citation][year=2016]Zheng, Y., Wang, G. and Wang, J., 2016. Is Using Threshold-Crossing Method and Single Type of Features Sufficient to Achieve Realistic Application of Seizure Prediction?. Clinical EEG and neuroscience, 47(4), pp.305-316.
[citation][year=2016]Supratak, A., Wu, C., Dong, H., Sun, K. and Guo, Y., 2016. Survey on Feature Extraction and Applications of Biosignals. In Machine Learning for Health Informatics (pp. 161-182). Springer International Publishing.
[citation][year=2015]Zhang, Z. and Parhi, K.K., 2015, August. Seizure prediction using polynomial SVM classification. In Engineering in Medicine and Biology Society (EMBC), 2015 37th Annual International Conference of the IEEE (pp. 5748-5751). IEEE.
[citation][year=2015]Zhang, Z. and Parhi, K.K., 2015, August. Seizure detection using regression tree based feature selection and polynomial SVM classification. In Engineering in Medicine and Biology Society (EMBC), 2015 37th Annual International Conference of the IEEE (pp. 6578-6581). IEEE.
[citation][year=2015]Zhang, Z., Henry, T.R. and Parhi, K.K., 2015, November. Seizure prediction using cross-correlation and classification. In Signals, Systems and Computers, 2015 49th Asilomar Conference on (pp. 775-779). IEEE.
[citation][year=2015]Panichev, O., Popov, A. and Kharytonov, V., 2015, June. Patient-specific epileptic seizure prediction using correlation features. In Signal Processing Symposium (SPSympo), 2015 (pp. 1-5). IEEE.
[citation][year=2015]Village, D., 2015. 2015 Signal Processing Symposium (SPSympo).
[citation][year=2015]Tewolde, S., Oommen, K., Lie, D.Y., Zhang, Y. and Chyu, M.C., 2015. Epileptic Seizure Detection and Prediction Based on Continuous Cerebral Blood Flow Monitoring–a Review. Journal of healthcare engineering, 6(2), pp.159-178.
[citation][year=2015]Khalid, M.I., Aldosari, S.A., Alshebeili, S.A. and Alotaiby, T., 2015, December. Enhancing the reliability of epileptic seizure alarms for scalp EEG signals. In Signal and Information Processing (GlobalSIP), 2015 IEEE Global Conference on (pp. 1302-1306). IEEE.

[publication]Bandarabadi, M. and , J.R. and Teixeira, C. and Mollaei, M.R.K. and António Dourado , "On the proper selection of preictal period for seizure prediction", Epilepsy & Behavior, 2015
[citation][year=2016]Ulate-Campos, A., Coughlin, F., Gaínza-Lein, M., Fernández, I.S., Pearl, P.L. and Loddenkemper, T., 2016. Automated seizure detection systems and their effectiveness for each type of seizure. Seizure, 40, pp.88-101.
[citation][year=2016]Shiao, H.T., Cherkassky, V., Lee, J., Veber, B., Patterson, N., Brinkmann, B. and Worrell, G., 2016. SVM-Based System for Prediction of Epileptic Seizures from iEEG Signal. IEEE Transactions on Biomedical Engineering.
[citation][year=2016]Mula, M., 2016. New trends and hot topics in epileptology: An analysis of top articles published in Epilepsy & Behavior in 2015. Epilepsy & behavior: E&B, 63, p.125.

[publication]Bandarabadi, M. and , J.R. and Teixeira, C. and Netoff, T. and Parhi, K.K. and António Dourado , "EARLY SEIZURE DETECTION USING NEURONAL POTENTIAL SIMILARITY: A GENERALIZED LOW-COMPLEXITY AND ROBUST MEASURE", International Journal of Neural Systems,  vol. 25,  pp. 1550019-1550037, 2015
[citation][year=2016]Tonoyan, Y., Looney, D., Mandic, D.P. and Van Hulle, M.M., 2016. Discriminating Multiple Emotional States from EEG Using a Data-Adaptive, Multiscale Information-Theoretic Approach. International journal of neural systems, 26(02), p.1650005.
[citation][year=2016]Hsu, W.Y., 2016. A hybrid approach for brain image registration with local constraints. Integrated Computer-Aided Engineering, (Preprint), pp.1-13.
[citation][year=2016]Chen, D., Wan, S. and Bao, F.S., 2016. Epileptic Focus Localization Using Discrete Wavelet Transform Based on Interictal Intracranial EEG. IEEE Transactions on Neural Systems and Rehabilitation Engineering.
[citation][year=2015]Xia, Yudan, et al. "Seizure detection approach using S-transform and singular value decomposition." Epilepsy & Behavior 52 (2015): 187-193.
[citation][year=2015]Acharya, U. Rajendra, et al. "Nonlinear Dynamics Measures for Automated EEG-Based Sleep Stage Detection." European neurology 74.5-6 (2015): 268-287.

2014(2 publications)
[publication]Teixeira, C. and Bruno Direito and Bandarabadi, M. and LeVanQuyen, M. and Valderrama, M. and Schelter, B. and Bonhage, A.S. and Navarro, V. and Sales, F. and António Dourado , "Epileptic seizure predictors based on computational intelligence techniques: A comparative study with 278 patients", Computer Methods and Programs in Biomedicine, 2014
[citation][year=2016]Zhang, Z. and Parhi, K.K., 2016. Low-complexity seizure prediction from iEEG/sEEG using spectral power and ratios of spectral power. IEEE transactions on biomedical circuits and systems, 10(3), pp.693-706.
[citation][year=2016]Brinkmann, B.H., Wagenaar, J., Abbot, D., Adkins, P., Bosshard, S.C., Chen, M., Tieng, Q.M., He, J., Muñoz-Almaraz, F.J., Botella-Rocamora, P. and Pardo, J., 2016. Crowdsourcing reproducible seizure forecasting in human and canine epilepsy. Brain, 139(6), pp.1713-1722.
[citation][year=2016]Lehnertz, K., Dickten, H., Porz, S., Helmstaedter, C. and Elger, C.E., 2016. Predictability of uncontrollable multifocal seizures–towards new treatment options. Scientific reports, 6.
[citation][year=2016]Ulate-Campos, A., Coughlin, F., Gaínza-Lein, M., Fernández, I.S., Pearl, P.L. and Loddenkemper, T., 2016. Automated seizure detection systems and their effectiveness for each type of seizure. Seizure, 40, pp.88-101.
[citation][year=2016]Satapathy, S.K., Dehuri, S. and Jagadev, A.K., 2016. An Empirical Analysis of Different Machine Learning Techniques for Classification of EEG Signal to Detect Epileptic Seizure. International Journal of Applied Engineering Research, 11(1), pp.120-129.
[citation][year=2015]Seizure prediction using polynomial SVM classification
Z Zhang, KK Parhi - … in Medicine and Biology Society (EMBC), …, 2015 - ieeexplore.ieee.org
Abstract—This paper presents a novel patient-specific algorithm for prediction of seizures in
epileptic patients with low hardware complexity and low power consumption. In the
proposed approach, we first compute the spectrogram of the input fragmented EEG ...
[citation][year=2015]Low-Complexity Seizure Prediction From iEEG/sEEG Using Spectral Power and Ratios of Spectral Power
Z Zhang, KK Parhi - 2015 - ieeexplore.ieee.org
EEG patterns are not wide-sense stationary and change from seizure to seizure, electrode to
electrode, and from patient to patient. This paper presents a novel patient-specific algorithm
for prediction of seizures in epileptic patients from either one or two single-channel or ...
[citation][year=2015]Transcranial Magnetic Stimulation Combined with EEG Reveals Covert States of Elevated Excitability in the Human Epileptic Brain
VK Kimiskidis, C Koutlis, A Tsimpiris… - … journal of neural …, 2015 - World Scientific
Background: Transcranial magnetic stimulation combined with electroencephalogram (TMS-
EEG) can be used to explore the dynamical state of neuronal networks. In patients with
epilepsy, TMS can induce epileptiform discharges (EDs) with a stochastic occurrence ...

[publication]Bandarabadi, M. and Teixeira, C. and , J.R. and António Dourado , "Epileptic Seizure Prediction Using Relative Spectral Power Features", Clinical Neurophysiology, 2014
[citation][year=2015]Seizure prediction for therapeutic devices: a review
K Gadhoumi, JM Lina, F Mormann, J Gotman - Journal of neuroscience …, 2015 - Elsevier
Abstract Research in seizure prediction has come a long way since its debut almost 4
decades ago. Early studies suffered methodological caveats leading to overoptimistic results
and lack of statistical significance. The publication of guidelines addressing mainly the ...
[citation][year=2015][Z Zhang, KK Parhi - … in Medicine and Biology Society (EMBC), …, 2015 - ieeexplore.ieee.org
Abstract—This paper presents a novel patient-specific algorithm for prediction of seizures in
epileptic patients with low hardware complexity and low power consumption. In the
proposed approach, we first compute the spectrogram of the input fragmented EEG ...
[citation][year=2015]Low-Complexity Seizure Prediction From iEEG/sEEG Using Spectral Power and Ratios of Spectral Power
Z Zhang, KK Parhi - 2015 - ieeexplore.ieee.org
EEG patterns are not wide-sense stationary and change from seizure to seizure, electrode to
electrode, and from patient to patient. This paper presents a novel patient-specific algorithm
for prediction of seizures in epileptic patients from either one or two single-channel or ...
[citation][year=2015]Is Using Threshold-Crossing Method and Single Type of Features Sufficient to Achieve Realistic Application of Seizure Prediction?
Y Zheng, G Wang, J Wang - Clinical EEG and neuroscience, 2015 - eeg.sagepub.com
Objective. This study aims to verify whether the simple threshold-crossing method can work
well enough to achieve the realistic application of seizure prediction on the basis of a large
public database, and examines how a more complex classifier can improve prediction ...
[citation][year=2015]Patient-specific epileptic seizure prediction using correlation features
O Panichev, A Popov… - Signal Processing …, 2015 - ieeexplore.ieee.org
Abstract—In this contribution, several classifiers are employed to study patient-specific
epileptic seizure prediction quality using intracranial electroencephalogram signal (iEEG) for
dogs and humans suffering from epilepsy. New approach to extraction of correlation- ...
[citation][year=2015]Seizure detection using regression tree based feature selection and polynomial SVM classification
Z Zhang, KK Parhi - … in Medicine and Biology Society (EMBC), …, 2015 - ieeexplore.ieee.org
Abstract—This paper presents a novel patient-specific algorithm for detection of seizures in
epileptic patients with low hardware complexity and low power consumption. In the
proposed approach, we first compute the spectrogram of the input fragmented EEG ...
[citation][year=2015][PDF] 2015 Signal Processing Symposium (SPSympo)
D Village - 2015 - researchgate.net
Abstract—This paper focuses on the Time of Arrival (TOA) estimation problem related to new
application of pulsar signals for airplane-based navigation. The aim of the paper is to
propose and evaluate a possible algorithm for TOA estimation that consists of epoch ...
[citation][year=2015]Epileptic Seizure Detection and Prediction Based on Continuous Cerebral Blood Flow Monitoring–a Review
S Tewolde, K Oommen, DYC Lie… - Journal of …, 2015 - multi-science.atypon.com
Epilepsy is the third most common neurological illness, affecting 1% of the world's
population. Despite advances in medicine, about 25 to 30% of the patients do not respond to
or cannot tolerate the severe side effects of medical treatment, and surgery is not an ...

2013(1 publication)
[publication], J.R. and Mollaei, M.R.K. and Bandarabadi, M. and Teixeira, C. and António Dourado , "Preprocessing effects of 22 univariate features on the performance of seizure prediction methods", Journal of Neuroscience Methods, 2013
[citation][year=2016]Parvez, M.Z. and Paul, M., 2016. Epileptic seizure prediction by exploiting spatiotemporal relationship of EEG signals using phase correlation. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 24(1), pp.158-168.
[citation][year=2016]Fergus, P., Hussain, A., Hignett, D., Al-Jumeily, D., Abdel-Aziz, K. and Hamdan, H., 2016. A machine learning system for automated whole-brain seizure detection. Applied Computing and Informatics, 12(1), pp.70-89.
[citation][year=2016]Bhardwaj, A., Tiwari, A., Krishna, R. and Varma, V., 2016. A novel genetic programming approach for epileptic seizure detection. Computer methods and programs in biomedicine, 124, pp.2-18.
[citation][year=2016]Gómez, C., Poza, J., Gutiérrez, M.T., Prada, E., Mendoza, N. and Hornero, R., 2016. Characterization of EEG patterns in brain-injured subjects and controls after a Snoezelen® intervention. computer methods and programs in biomedicine, 136, pp.1-9.
[citation][year=2016]Ghaderyan, P. and Abbasi, A., 2016. An efficient automatic workload estimation method based on electrodermal activity using pattern classifier combinations. International Journal of Psychophysiology, 110, pp.91-101.
[citation][year=2016]Shiao, H.T., Cherkassky, V., Lee, J., Veber, B., Patterson, N., Brinkmann, B. and Worrell, G., 2016. SVM-Based System for Prediction of Epileptic Seizures from iEEG Signal. IEEE Transactions on Biomedical Engineering.
[citation][year=2016]Babu, U.R. and Sridhar, C.N.V., Design and Classification of EEG and ECG Signals for Detection of Seizures based on Prototype Recognition.
[citation][year=2015]Automatic Epileptic Seizure Detection Using Scalp EEG and Advanced Artificial Intelligence Techniques
P Fergus, D Hignett, A Hussain, D Al-Jumeily… - BioMed research …, 2015 - hindawi.com
The epilepsies are a heterogeneous group of neurological disorders and syndromes
characterised by recurrent, involuntary, paroxysmal seizure activity, which is often
associated with a clinicoelectrical correlate on the electroencephalogram. The diagnosis ...
[citation][year=2015]An Innovative Genetic Programming Framework in modelling a real time Epileptic Seizure detection system
A Bhardwaj, A Tiwari, M RameshKrishna… - 2015 - ase360.org
Epilepsy, sometimes called seizure disorder, is a neurological condition that substantiates
itself as a susceptibility to seizures. A seizure is a sudden burst of rhythmic discharges of
electrical activity in the brain that causes an alteration in behavior, sensation, or ...
[citation][year=2015]Reliable seizure prediction from EEG data
V Cherkassky, B Veber, J Lee, HT Shiao… - … Joint Conference on, 2015 - ieeexplore.ieee.org
Abstract-There is a growing interest in data-analytic modeling for prediction and/or detection
of epileptic seizures from EEG recording of brain activity [1-10]. Even though there is clear
evidence that many patients have changes in EEG signal prior to seizures, development ...
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[citation][year=2015]Band-sensitive seizure onset detection via CSP-enhanced EEG features
M Qaraqe, M Ismail, E Serpedin - Epilepsy & Behavior, 2015 - Elsevier
Abstract This paper presents two novel epileptic seizure onset detectors. The detectors rely
on a common spatial pattern (CSP)-based feature enhancement stage that increases the
variance between seizure and nonseizure scalp electroencephalography (EEG). The ...
[citation][year=2015]Machine learning for seizure prediction: A revamped approach
A Sai Kumar, L Nigam, D Karnam… - Advances in …, 2015 - ieeexplore.ieee.org
Abstract—Occurrence of multiple seizures is a common phenomenon observed in patients
with epilepsy: a neurological malfunction that affects approximately 50 million people in the
world. Seizure prediction is widely acknowledged as an important problem in the ...
[citation][year=2015]A novel genetic programming approach for epileptic seizure detection
A Bhardwaj, A Tiwari, R Krishna, V Varma - Computer methods and …, 2015 - Elsevier
Abstract The human brain is a delicate mix of neurons (brain cells), electrical impulses and
chemicals, known as neurotransmitters. Any damage has the potential to disrupt the
workings of the brain and cause seizures. These epileptic seizures are the manifestations ...
[citation][year=2015][PDF] A Machine Learning System for Automated Whole-Brain Seizure Detection
B Street, P de Moulon - 2015 - researchgate.net
ABSTRACT Epilepsy is a chronic neurological condition that affects approximately 70 million
people worldwide. Characterised by sudden bursts of excess electricity in the brain,
manifesting as seizures, epilepsy is still not well understood when compared with other ...
[citation][year=2015]Epileptic Seizure Prediction by Exploiting Spatiotemporal Relationship of EEG Signals using Phase Correlation
MZ Parvez, M Paul - 2015 - ieeexplore.ieee.org
Abstract—Automated seizure prediction has a potential in epilepsy monitoring, diagnosis,
and rehabilitation. Electroencephalogram (EEG) is widely used for seizure detection and
prediction. This paper proposes a new seizure prediction approach based on ...
[citation][year=2015][HTML] A machine learning system for automated whole-brain seizure detection
P Fergus, A Hussain, D Hignett, D Al-Jumeily… - Applied Computing and …, 2015 - Elsevier
Abstract Epilepsy is a chronic neurological condition that affects approximately 70 million
people worldwide. Characterised by sudden bursts of excess electricity in the brain,
manifesting as seizures, epilepsy is still not well understood when compared with other ...
[citation][year=2015][PDF] Detection of Pre-stage of Epileptic Seizure by Exploiting Temporal Correlation of EMD Decomposed EEG Signals
MZ Parvez, M Paul, M Antolovich - Journal of Medical and Bioengineering …, 2015 - jomb.org
Abstract—Epilepsy is one of the common neurological disorders characterized by a sudden and
recurrent malfunction of the brain that is termed “seizure”, affecting over 50 million individuals
worldwide. The Electroencephalogram (EEG) is the most influential technique in ...
[citation][year=2015]Seizure prediction by analyzing EEG signal based on phase correlation
MZ Parvez, M Paul - … in Medicine and Biology Society (EMBC), …, 2015 - ieeexplore.ieee.org
Abstract—Epilepsy is a common neurological disorders characterized by sudden recurrent
seizures. Electroencephalogram (EEG) is widely used to diagnose possible epileptic
seizure. Many research works have been devoted to predict epileptic seizure by analyzing ...
[citation][year=2015]MZ Parvez, M Paul - csusap.csu.edu.au
ABSTRACT Epilepsy is one of the common neurological disorders characterized by a
sudden and recurrent malfunction of the brain that is termed “seizure”, affecting around 65
million individuals worldwide. Epileptic seizure may lead to many injuries such as ...
[citation][year=2015][PDF] An Enhanced Wavelet Neural Network Model with Metaheuristic Harmony Search Algorithm for Epileptic Seizure Prediction
Z Zainuddin, KH Lai, P Ong - International Journal of Modeling and …, 2015 - ijmo.org
Abstract—The task of epileptic seizure prediction aims at differentiating between two classes
of electroencephalography (EEG) signals, namely interictal and pre-ictal signals. The
development of an automated classifier that is capable of performing such task with high ...
[citation][year=2015][PDF] Epileptic Seizure Prediction by Exploiting Signal Transitions Phenomena
MZ Parvez, M Paul - waset.org
Abstract—A seizure prediction method is proposed by extracting global features using
phase correlation between adjacent epochs for detecting relative changes and local
features using fluctuation/deviation within an epoch for determining fine changes of ...
[citation][year=2015]Parvez, Mohammad Zavid, Manoranjan Paul, and Michael Antolovich. "Detection of Pre-stage of Epileptic Seizure by Exploiting Temporal Correlation of EMD Decomposed EEG Signals." Journal of Medical and Bioengineering Vol 4.2 (2015).
[citation][year=2014]Parvez, Mohammad Zavid, Manoranjan Paul, and Michael Antolovich. "Detection of Pre-stage of Epileptic Seizure by Exploiting Temporal Correlation of EMD Decomposed EEG Signals." Journal of Medical and Bioengineering Vol 4.2 (2015).
[citation][year=2014]Bhardwaj, Arpit, et al. "Classification of EEG signals using a novel genetic programming approach." Proceedings of the 2014 conference companion on Genetic and evolutionary computation companion. ACM, 2014.
[citation][year=2014]Ghaderyan, Peyvand, Ataollah Abbasi, and Mohammad Hossein Sedaaghi. "An efficient seizure prediction method using KNN-based undersampling and linear frequency measures." Journal of neuroscience methods (2014).
[citation][year=2013]Prediction and Detection of Epileptic Seizure by Analysing EEG Signals
MZ Parvez, M Paul - researchgate.net

Conference Articles
2015(1 publication)
[publication]Bandarabadi, M. and , J.R. and Teixeira, C. and António Dourado , "Epileptic Seizure Detection Using Bipolar Singular Value Decomposition", in Biosignals, 2015

2014(3 publications)
[publication]Bandarabadi, M. and , J.R. and Teixeira, C. and António Dourado , "Optimal preictal period in seizure prediction", in 2nd International Work-Conference on Bioinformatics and Biomedical Engineering-IWBBIO 2014, 2014

[publication]Bruno Direito and Teixeira, C. and Bandarabadi, M. and Sales, F. and António Dourado , "Automatic warning of epileptic seizures by SVM: the long road ahead to success", in 19th World Congress of the International Federation of Automatic Control, 2014

[publication]Bandarabadi, M. and Teixeira, C. and Netoff, T. and Parhi, K.K. and António Dourado , "Robust and Low Complexity Algorithms for Seizure Detection", in 36th Annual International IEEE EMBS Conference, 2014
[citation][year=2015]Zhang, Zisheng, and Keshab K. Parhi. "Low-Complexity Seizure Prediction From iEEG/sEEG Using Spectral Power and Ratios of Spectral Power." (2015).

2013(2 publications)
[publication]Bandarabadi, M. and António Dourado and Teixeira, C. and Netoff, T. and Parhi, K.K. , "Seizure Prediction with Bipolar Spectral Power Features using Adaboost and SVM Classifiers", in EMBC'13, 2013
[citation][year=2016]Goldfarb-Rumyantzev, A., Gautam, S. and Brown, R.S., 2016. Practical prediction model for the risk of 2-year mortality of individuals in the general population. Journal of Investigative Medicine, pp.jim-2015.
[citation][year=2016]Alawieh, H., Hammoud, H., Haidar, M., Nassralla, M.H., El-Hajj, A.M. and Dawy, Z., 2016, September. Patient-aware adaptive ngram-based algorithm for epileptic seizure prediction using EEG signals. In e-Health Networking, Applications and Services (Healthcom), 2016 IEEE 18th International Conference on (pp. 1-6). IEEE.
[citation][year=2014]YADOLLAHPOUR, ALI, and MOSTAFA JALILIFAR. "Seizure Prediction Methods: A Review of the Current Predicting Techniques." Studies 153: 7.

[publication]Bandarabadi, M. and , J.R. and Teixeira, C. and António Dourado , "Sub-band mean phase coherence for epileptic seizure detection", in IFMBE International Conference on Health Informatics, 2013

2012(2 publications)
[publication]Bandarabadi, M. and Teixeira, C. and Bruno Direito and António Dourado , "Epileptic Seizure Prediction based on a bivariate spectral power methodology", in 34th Annual International Conference of the IEEE EMBS, 2012
[citation][year=2016]Assi, E.B., Nguyen, D.K., Rihana, S. and Sawan, M., 2017. Towards accurate prediction of epileptic seizures: A review. Biomedical Signal Processing and Control, 34, pp.144-157.
[citation][year=2016]Behbahani, S., Dabanloo, N.J., Nasrabadi, A.M. and Dourado, A., 2016. Prediction of epileptic seizures based on heart rate variability. Technology and Health Care, (Preprint), pp.1-16.
[citation][year=2014]YADOLLAHPOUR, ALI, and MOSTAFA JALILIFAR. "Seizure Prediction Methods: A Review of the Current Predicting Techniques." Studies 153: 7.

[publication]Teixeira, C. and Bruno Direito and Bandarabadi, M. and António Dourado , "Output regularization of SVM seizure predictors: Kalman Filter versus the “Firing Power” method", in 34th Annual International Conference of the IEEE EMBS, 2012
[citation][year=2015][HTML] Forecasting seizures using intracranial EEG measures and SVM in naturally occurring canine epilepsy
BH Brinkmann, EE Patterson, C Vite, VM Vasoli… - PloS one, 2015 - journals.plos.org
Abstract Management of drug resistant focal epilepsy would be greatly assisted by a reliable
warning system capable of alerting patients prior to seizures to allow the patient to adjust
activities or medication. Such a system requires successful identification of a preictal, or ...
[citation][year=2015]Early detection of epilepsy seizures based on a weightless neural network
K de Aguiar, FMG Franca, VC Barbosa… - … in Medicine and …, 2015 - ieeexplore.ieee.org
Abstract—This work introduces a new methodology for the early detection of epileptic
seizure based on the WiS-ARD weightless neural network model and a new approach in
terms of preprocessing the electroencephalogram (EEG) data. WiSARD has, among other ...
[citation][year=2014]Couceiro, R., et al. "Neurally mediated syncope prediction based on changes of cardiovascular performance surrogates: Algorithms comparison." Biomedical Engineering and Informatics (BMEI), 2014 7th International Conference on. IEEE, 2014.

2011(1 publication)
[publication]Bandarabadi, M. and Teixeira, C. and António Dourado , "Wepilet, Optimal Orthogonal Wavelets for Epileptic Seizure Prediction with one Single Surface Channel", in EMBC-2011, 33nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2011
[citation][year=2015]Genetic programming and frequent itemset mining to identify feature selection patterns of iEEG and fMRI epilepsy data
O Smart, L Burrell - Engineering applications of artificial intelligence, 2015 - Elsevier
Abstract Pattern classification for intracranial electroencephalogram (iEEG) and functional
magnetic resonance imaging (fMRI) signals has furthered epilepsy research toward
understanding the origin of epileptic seizures and localizing dysfunctional brain tissue for ...
[citation][year=2014]YADOLLAHPOUR, ALI, and MOSTAFA JALILIFAR. "Seizure Prediction Methods: A Review of the Current Predicting Techniques." Studies 153: 7.
[citation][year=2014]Smart, Otis, and Lauren Burrell. "Genetic programming and frequent itemset mining to identify feature selection patterns of iEEG and fMRI epilepsy data." Engineering Applications of Artificial Intelligence 39 (2015): 198-214.

Book Chapters
2014(1 publication)
[publication]Teixeira, C. and Favaro, G. and Bruno Direito and Bandarabadi, M. and Feldwirsch-Drentrup, H. and Ihle, M. and Alvarado-Rojas, C. and LeVanQuyen, M. and Schelter, B. and Bonhage, A.S. and Sales, F. and Navarro, V. and António Dourado , "Brainatic: A System for Real-Time Epileptic Seizure Prediction", in Brain-Computer Interface Research: A State-of-the-Art Summary -2,  vol. 6,  pp. 7-18, 2014
[citation][year=2015]TML] Dynamical disease: Challenges for nonlinear dynamics and medicine
L Glass - Chaos: An Interdisciplinary Journal of Nonlinear …, 2015 - scitation.aip.org
Dynamical disease refers to illnesses that are associated with striking changes in the
dynamics of some bodily function. There is a large literature in mathematics and physics
which proposes mathematical models for the physiological systems and carries out ...