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Noa Braverman Forecasting epilepsy from the heart rate signal.

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Presentation on theme: "Noa Braverman Forecasting epilepsy from the heart rate signal."— Presentation transcript:

1 Noa Braverman Forecasting epilepsy from the heart rate signal

2 Introduction  potential seizure detector  EEG as brain-state mirror  instantaneous heart rate  ictal (sinus)tachycardia

3 Introduction cont.  the brain-heart axis  Vagus Nerve  The existence of pre- ictal phase

4 Introduction cont.  This study  Forecasting seizures  Partial complex – humans  Generalized - rats  Novel method for HRV analysis  Ph.D. D.H.Kerem  Ph.D. A.B.Geva

5 Known Methods  Spectral analysis of the time series of R-R intervals  non-linear dynamics  shortcoming -  inability to account for non-stationary states and transients

6 Known Methods cont.  time-varying power spectral density estimation  Attractors and correlation dimensions  Karhunen-Love transform-based signal analysis method

7 Fuzzy clustering approach  comet or torpedo-shaped  unsupervised method advantage

8 Chosen method  EEG-contained information of HRV.  (GEVA and KEREM, 1998)  an unsupervised method designed to deal with merging and overlapping states  ability to spot and classify

9 Data resources HumansRats  Humans  21 patients records, archived records  The recording machinery  simultaneous EEG and video recording  ECG channel  visual inspection by an EEG expert  The actual database  Rats  Hyperbaric-oxygen  ECG and EEG filtering and recording  Rats effects  Time period analyzing  Control rats Vs. research rats

10 Method cont.  Choice of analysis parameters  |∆RRI| Vs. RRI  embedding dimension N  For this experiment –  Both features  N = 3  number of clusters

11 Method cont.  Forecasting criteria  Appearance  Disappearance  Dominant  False negative - False positive

12 Results HumansRats Successful forecasting Tachycardia period success rate 86% |∆RRI| Vs. RRI forecasting times 1.5-11 min. Successful forecasting Bradycardia period success rate 82% |∆RRI| Vs. RRI forecasting times 2.5-9 min.

13 Results cont. HumansRats prediction failures false negative One case false positive Two cases Longer records prediction failures false negative none false positive Two cases Ignoring changes shown in control rats

14 Discussion  information in the pre-ictal ECG signal  HRV Time-Frequency analysis by NOVAK  pre-ictal state  time-frequency forecasters  Records length

15 Discussion cont.  the sleeping state  Alerting systems  generalized seizures forecasting

16 Individual opinion  Next step –  Testing State-rely data  Non-arbitrary patient selection  Age specific

17 Any Questions?


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