forecasting epilepsy from the heart rate signal

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Noa Braverman Forecasting epilepsy from the heart rate signal

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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. - PowerPoint PPT Presentation

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Page 1: Forecasting epilepsy from the heart rate signal

Noa Braverman

Forecasting epilepsy from the

heart rate signal

Page 2: Forecasting epilepsy from the heart rate signal

Introduction

potential seizure detectorEEG as brain-state mirrorinstantaneous heart rateictal (sinus)tachycardia

Page 3: Forecasting epilepsy from the heart rate signal

Introduction cont.the brain-heart axis

Vagus Nerve

The existence of pre- ictal phase

Page 4: Forecasting epilepsy from the heart rate signal

Introduction cont.This study

Forecasting seizuresPartial complex – humansGeneralized - rats

Novel method for HRV analysisPh.D. D.H.KeremPh.D. A.B.Geva

Page 5: Forecasting epilepsy from the heart rate signal

Known Methods Spectral analysis of the time series of R-R intervals non-linear dynamicsshortcoming -

inability to account for non-stationary states and transients

Page 6: Forecasting epilepsy from the heart rate signal

Known Methods cont.time-varying power spectral density

estimation

Attractors and correlation dimensions

Karhunen-Love transform-based signal analysis method

Page 7: Forecasting epilepsy from the heart rate signal

Fuzzy clustering approachcomet or torpedo-shaped

unsupervised method advantage

Page 8: Forecasting epilepsy from the heart rate signal

Chosen methodEEG-contained information of HRV.

(GEVA and KEREM, 1998)

an unsupervised method designed to deal with merging and overlapping states

ability to spot and classify

Page 9: Forecasting epilepsy from the heart rate signal

Data resourcesHumans RatsHumans

21 patients records, archived records

The recording machinerysimultaneous EEG

and video recordingECG channelvisual inspection by

an EEG expertThe actual database

RatsHyperbaric-

oxygenECG and EEG

filtering and recording

Rats effectsTime period

analyzingControl rats Vs.

research rats

Page 10: Forecasting epilepsy from the heart rate signal

Method cont.Choice of analysis parameters

|∆RRI| Vs. RRIembedding dimension NFor this experiment –

Both featuresN = 3

number of clusters

Page 11: Forecasting epilepsy from the heart rate signal

Method cont.Forecasting criteria

AppearanceDisappearanceDominant

False negative - False positive

Page 12: Forecasting epilepsy from the heart rate signal

ResultsHumans RatsSuccessful

forecastingTachycardia period

success rate 86%

|∆RRI| Vs. RRIforecasting times

1.5-11 min.

Successful forecastingBradycardia period

success rate 82%

|∆RRI| Vs. RRIforecasting times

2.5-9 min.

Page 13: Forecasting epilepsy from the heart rate signal

Results cont.Humans Ratsprediction

failuresfalse negative

One casefalse positive

Two casesLonger records

prediction failuresfalse negative

nonefalse positive

Two casesIgnoring changes

shown in control rats

Page 14: Forecasting epilepsy from the heart rate signal

Discussioninformation in the pre-ictal ECG signal

HRV Time-Frequency analysis by NOVAK

pre-ictal statetime-frequency forecasters

Records length

Page 15: Forecasting epilepsy from the heart rate signal

Discussion cont.the sleeping state

Alerting systems

generalized seizures forecasting

Page 16: Forecasting epilepsy from the heart rate signal

Individual opinion

Next step – Testing State-rely data

Non-arbitrary patient selectionAge specific

Page 17: Forecasting epilepsy from the heart rate signal

Any Questions?