Prediction of epileptic seizures based on heart rate variability.

BACKGROUND Until now, different approaches have been published to resolve the problem of predicting epileptic seizures. The results are reminiscent of a substantial need for improvements in these methods to reach the stage of the clinical application. Our aim is to develop a reliable epileptic seizure prediction algorithm based on the Heart Rate Variability (HRV) analysis. METHODS We analyzed the HRV of sixteen epileptic patients with a total of 170 seizures, to predict the occurrence of seizures based on the dynamic changes of Electrocardiogram (ECG) during the pre-ictal period. Time and frequency-domain features were computed forthe consecutive time windows with a length of five minutes. An adaptive decision threshold method was used for raising alarms. Predictions were made when selected features exceeded the decision thresholds. RESULTS For the seizure occurrence period (SOP) of 4:30 minutes, and intervention time (IT) of 110 Sec, the presented method showed an average sensitivity of 78.59%, and average false prediction rate of 0.21/Hr, which indicates that the system has superiority to the random predictor. CONCLUSION The proposed approach shows a potential in the monitoring of epileptic patients and improving their life quality. The overall performance of the algorithm is a step forward for clinical implementation.

[1]  Y. Stern,et al.  Increased sympathetic and decreased parasympathetic cardiovascular modulation in normal humans with acute sleep deprivation. , 2005, Journal of applied physiology.

[2]  J. W. A. S. Sander,et al.  Mortality from epilepsy: results from a prospective population-based study , 1994, The Lancet.

[3]  Manabu Kano,et al.  Epileptic Seizure Prediction Based on Multivariate Statistical Process Control of Heart Rate Variability Features , 2016, IEEE Transactions on Biomedical Engineering.

[4]  H. Huikuri,et al.  Circadian heart rate variability in Parkinson's disease , 2002, Journal of Neurology.

[5]  César Alexandre Teixeira,et al.  A new algorithm for detection of epileptic seizures based on HRV signal , 2014, J. Exp. Theor. Artif. Intell..

[6]  F. Mormann,et al.  Nonlinear EEG analysis in epilepsy: its possible use for interictal focus localization, seizure anticipation, and prevention. , 2001, Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society.

[7]  J. Martinerie,et al.  Characterizing Neurodynamic Changes Before Seizures , 2001, Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society.

[8]  C. Teixeira,et al.  Epileptic Seizure Prediction based on Ratio and Differential Linear Univariate Features , 2015, Journal of medical signals and sensors.

[9]  H V Huikuri,et al.  Suppressed circadian heart rate dynamics in temporal lobe epilepsy , 2005, Journal of Neurology, Neurosurgery & Psychiatry.

[10]  A. Garde,et al.  Effects of mental and physical demands on heart rate variability during computer work , 2002, European Journal of Applied Physiology.

[11]  M. Dam,et al.  Sudden Unexpected Death in Epilepsy: Is Death by Seizures a Cardiac Disease? , 2005, The American journal of forensic medicine and pathology.

[12]  F. Mormann,et al.  Seizure prediction: the long and winding road. , 2007, Brain : a journal of neurology.

[13]  Ali Motie Nasrabadi,et al.  Ictal Heart Rate Variability Assessment with Focus on Secondary Generalized and Complex Partial Epileptic Seizures , 2013 .

[14]  HEART RATE DURING DAYTIME NAPS: DEPENDENCE ON SLEEP STAGE , 2007 .

[15]  Esen Yildirim,et al.  Patient Specific Seizure Prediction System Using Hilbert Spectrum and Bayesian Networks Classifiers , 2014, Comput. Math. Methods Medicine.

[16]  A. Schulze-Bonhage,et al.  Do False Predictions of Seizures Depend on the State of Vigilance? A Report from Two Seizure‐Prediction Methods and Proposed Remedies , 2006, Epilepsia.

[17]  J. Troncoso,et al.  Evaluation of Sudden Death in Epilepsy , 1986, The American journal of forensic medicine and pathology.

[18]  Theoden Netoff,et al.  Seizure prediction using cost-sensitive support vector machine , 2009, 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[19]  J. Martinerie,et al.  Epileptic seizures can be anticipated by non-linear analysis , 1998, Nature Medicine.

[20]  Leonidas D. Iasemidis,et al.  Quadratic Binary Programming and Dynamical System Approach to Determine the Predictability of Epileptic Seizures , 2001, J. Comb. Optim..

[21]  Zarita Zainuddin,et al.  An Enhanced Wavelet Neural Network Model with Metaheuristic Harmony Search Algorithm for Epileptic Seizure Prediction , 2015 .

[22]  I. Osorio,et al.  Real‐Time Automated Detection and Quantitative Analysis of Seizures and Short‐Term Prediction of Clinical Onset , 1998, Epilepsia.

[23]  C. Elger,et al.  CAN EPILEPTIC SEIZURES BE PREDICTED? EVIDENCE FROM NONLINEAR TIME SERIES ANALYSIS OF BRAIN ELECTRICAL ACTIVITY , 1998 .

[24]  Ataollah Abbasi,et al.  Linear features, principal component analysis, and support vector machine for epileptic seizure prediction progress , 2013, 2013 21st Iranian Conference on Electrical Engineering (ICEE).

[25]  Keshab K. Parhi,et al.  Seizure prediction with spectral power of time/space-differential EEG signals using cost-sensitive support vector machine , 2010, 2010 IEEE International Conference on Acoustics, Speech and Signal Processing.

[26]  F W Sharbrough,et al.  Time-frequency mapping of R-R interval during complex partial seizures of temporal lobe origin. , 1999, Journal of the autonomic nervous system.

[27]  A. B. Geva,et al.  Forecasting epilepsy from the heart rate signal , 2005, Medical and Biological Engineering and Computing.

[28]  C. A. Teixeira,et al.  EPILAB: A software package for studies on the prediction of epileptic seizures , 2011, Journal of Neuroscience Methods.

[29]  César Alexandre Teixeira,et al.  Epileptic seizure prediction based on a bivariate spectral power methodology , 2012, 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[30]  Stavros D. Nikolopoulos,et al.  Patient-specific seizure prediction using a multi-feature and multi-modal EEG-ECG classification , 2010 .

[31]  U. Rajendra Acharya,et al.  Heart rate variability: a review , 2006, Medical and Biological Engineering and Computing.

[32]  L. Nilsson,et al.  Risk factors for sudden unexpected death in epilepsy: a case control study , 1999, The Lancet.

[33]  H. Huikuri,et al.  Circadian Rhythms of Frequency Domain Measures of Heart Rate Variabilit in Healthy Subjects and Patients With Coronary Artery Disease: Effects of Arousal and Upright Posture , 1994, Circulation.

[34]  Yann LeCun,et al.  Classification of patterns of EEG synchronization for seizure prediction , 2009, Clinical Neurophysiology.

[35]  K M Kessler,et al.  Alterations in heart rate variability and its circadian rhythm in hypertensive patients with left ventricular hypertrophy free of coronary artery disease. , 1993, American heart journal.

[36]  R. Cohen,et al.  Power spectrum analysis of heart rate fluctuation: a quantitative probe of beat-to-beat cardiovascular control. , 1981, Science.

[37]  J. Martinerie,et al.  Seizure anticipation in human neocortical partial epilepsy. , 2002, Brain : a journal of neurology.

[38]  Kaspar Anton Schindler,et al.  EEG analysis with simulated neuronal cell models helps to detect pre-seizure changes , 2002, Clinical Neurophysiology.

[39]  James R. Williamson,et al.  Epileptic seizure prediction using the spatiotemporal correlation structure of intracranial EEG , 2011, 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[40]  F. Varela,et al.  Spatio‐temporal characterizations of non‐linear changes in intracranial activities prior to human temporal lobe seizures , 2000, The European journal of neuroscience.

[41]  S. Berkovic,et al.  Sudden unexpected death in epilepsy in Victoria , 2000, Journal of Clinical Neuroscience.

[42]  Toshitaka Yamakawa.  A Low-Cost Long-Life R-R Interval Telemeter with Automatic Gain Control for Various ECG Amplitudes , 2012 .

[43]  G. Sammer.  Heart period variability and respiratory changes associated with physical and mental load: non-linear analysis. , 1998, Ergonomics.

[44]  F. Mormann,et al.  Epileptic seizures are preceded by a decrease in synchronization , 2003, Epilepsy Research.

[45]  A. Welch,et al.  Variations of heart rate during sleep as a function of the sleep cycle. , 1973, Electroencephalography and clinical neurophysiology.

[46]  J. Aschoff,et al.  Circadian control of body-temperature , 1983 .

[47]  J. Martinerie,et al.  Anticipating epileptic seizures in real time by a non-linear analysis of similarity between EEG recordings. , 1999, Neuroreport.

[48]  G. Breithardt,et al.  Heart rate variability: standards of measurement, physiological interpretation and clinical use. Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology. , 1996 .

[49]  Theoden Netoff,et al.  Seizure prediction with spectral power of EEG using cost‐sensitive support vector machines , 2011, Epilepsia.

[50]  Adriana Bermeo-Ovalle,et al.  Cardiac and Autonomic Mechanisms Contributing to SUDEP , 2015, Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society.

[51]  Orrin Devinsky,et al.  Interictal Autonomic Nervous System Function in Patients with Epilepsy , 1994, Epilepsia.

[52]  A. Schulze-Bonhage,et al.  Comparison of three nonlinear seizure prediction methods by means of the seizure prediction characteristic , 2004 .