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Forecasting epilepsy from the heart rate signal

Forecasting epilepsy from the heart rate signal. Noa Braverman. Introduction. potential seizure detector EEG as brain-state mirror instantaneous heart rate ictal (sinus)tachycardia. Introduction cont. the brain-heart axis Vagus Nerve The existence of pre- ictal phase.

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Forecasting epilepsy from the heart rate signal

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  1. Forecasting epilepsy from theheart rate signal Noa Braverman

  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 Humans Rats 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 • OUTPUT

  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 Humans Rats 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. Humans Rats 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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