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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.

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Presentation Transcript
introduction
Introduction

potential seizure detector

EEG as brain-state mirror

instantaneous heart rate

ictal (sinus)tachycardia

introduction cont
Introduction cont.

the brain-heart axis

Vagus Nerve

The existence of

pre- ictal phase

introduction cont1
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

known methods
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

known methods cont
Known Methods cont.

time-varying power spectral density estimation

Attractors and correlation dimensions

Karhunen-Love transform-based signal analysis method

fuzzy clustering approach
Fuzzy clustering approach

comet or torpedo-shaped

unsupervised method advantage

chosen method
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

data resources
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
method cont
Method cont.

Choice of analysis parameters

|∆RRI| Vs. RRI

embedding dimension N

For this experiment –

Both features

N = 3

number of clusters

method cont1
Method cont.

Forecasting criteria

Appearance

Disappearance

Dominant

False negative - False positive

results
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.

results cont
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

discussion
Discussion

information in the pre-ictal ECG signal

HRV Time-Frequency analysis by NOVAK

pre-ictal state

time-frequency forecasters

Records length

discussion cont
Discussion cont.

the sleeping state

Alerting systems

generalized seizures forecasting

individual opinion
Individual opinion

Next step –

Testing State-rely data

Non-arbitrary patient selection

Age specific