1

Seizure Time Series Analysis I:Seizure Detection, Optimization and

Assessment of Seizure Detection Algorithms

Sridhar Sunderam, Ph.D.Center for Neural Engineering

The Pennsylvania State University

4th Intl. Workshop on Seizure PredictionKansas City, MOJune 4, 2009

1. What to detect, and why:Targets

2

Markers of epilepsy Interictal spikes Ripples, Fast ripples High frequency oscillations

Electrographic seizures Clinical seizures

Seizure precursor/Preictal state States of vigilance (?)

Fast ripples or HFOs: 80-500 Hz

Andrzejak et al

Staba et al., 2002

Clinical seizure

1. What to detect, and why:Applications

Treatment GoalsSeizure controlImproved QOL

3

www.clevelandclinicmeded.com

TargetsIon channelsReceptors

Antiepileptic drugs

Side-effectsCognitionAllergiesInteractions

SurgeryLocalizationFunction mapping

Electrical stimulation

Uses of detection Diagnose seizure

types and foci Evaluate for surgery Seizure warning Responsive therapy Treatment evaluation

1. What to detect, and why: Performance goals

4

All of the above:

Early detection

Sensitivity: frequent hits

Specificity: rare misses

Low false alarm rate: patient anxiety, treatment dose

Low cost (computation/power): implanted devices

Quantitative description:

Not just onset: intensity, duration, spread, dynamics

2. Seizure detection:The jungle out there…

5

Any sufficiently advanced technology is indistinguishable from magic

- Arthur C. Clarke

2. Seizure detection:What you really want…

6

Signal source Cohort

Focus/semiology Performance

2. Seizure detection:General Framework

7

Source

Daly and Wolpaw Lancet Neurology 2008

EEG

ECoG

LFP andspikes

SIGNALCONDITIONING

FEATUREEXTRACTION

SEIZUREFILTERING

POST-PROCESSING

DETECTION

THRESHOLDSEIZURE!

TASKS Source selection Feature selection Filter design Detector design Performance evaluation

2. Seizure detection:Signal conditioning

8

SIGNALCONDITIONING

Amplification Antialiasing/bandpass (e.g., 0.5-80 Hz) Sampling (e.g., 200 Hz) Artifact rejection:

Line noise, motion, stim, etc.

Here comes the good data!

Signal

Gotman EEG & Clin Neurophysiol 1982

Elimination of line noise

(Topic of Litt lecture)

2. Seizure detection:Feature extraction

9

FEATUREEXTRACTION

One or more features that “look” different during seizure

Good data

Example features

Wave morphology:

Amplitude

Shape…

Spectral characteristics:

Band power

Edge frequency…

Statistics:

Rhythmicity

Entropy…

Seizure Interictal

Mormann et al. Epilepsia 2005

Analysis window

Half wave geometryGotman EEG & Clin Neurophysiol 1982

Line length featureEsteller et al. IEEE-EMBC 2001

2. Seizure detection:Seizure filtering

11

SEIZUREFILTERING

Features

Correlate ofSeizure content

Example 1: Wavelet-based FIR filtersOsorio et al, 1998-2007

x(t) * b(t)

X(f) x B(f)

2. Seizure detection:Seizure filtering

12

SEIZUREFILTERING

Features

Correlate ofSeizure content

Example 2: Spectrographic signatures of epileptic seizures Schiff et al., Clin Neurophysiol 2000

“Brain chirps”

Stacked in sequential windows

Correlation with seizure chirp

template

2. Seizure detection:Post-processing

13

POST-PROCESSING

Seizure contentWell-behaved

output

Background estimationMedian filtering

Meng et al. Med Eng Phys 2004Osorio et al.

Epilepsia 1998

Gotman EEG & Clin Neurophysiol 1982

2. Seizure detection:Detection/Classification

14

DETECTION

SEIZURE!

“Smooth” output

DecisionThreshold

SEIZURE

SLEEP

AWAKE

UNIVARIATE

MULTIVARIATE(and multiclass)

DecisionBoundary

3. Seizure Quantification:Why Quantify?

16

Detection gives binary output: Is there a seizure (onset)? (Y/N)

There are seizures, and there are seizures… Finer distinctions may be useful

Treatment evaluation: Measures of intensity, duration, spread

Seizure dynamics: Mechanism of initiation, progression or generalization

3. Seizure Quantification:Using SDA output

17

Already tracking “seizure content” – just use it

Combine measures to quantify relative severity

Caveat: Must capture the seizure, whole seizure, and nothing but!

Intensity

Duration

Spread

Osorio et al. Epilepsia 1998

3. Seizure Quantification:Time-frequency-energy analysis

18Jouny et al, Clin Neurophysiol 2003

(topic of Franaszczuk lecture)

4. SDA Assessment:Ground Truth

19

Human expert scoring is the gold standard Reproducibility, inter-rater reliability less than perfect

Four experts score one event

Wilson et al, Clin Neurophysiol 2003

4. SDA Assessment:Performance Measures

20

Spread Single event: Onset delay Offset time Area of spread

DelaySDAoutput TP TP TPFP TNFN

Multiple events:

Error rates: Sensitivity = TP/(TP+FN) Specificity = TN/(FP+TN) Positive prediction value = TP/(TP+FP)

Clustering events affects error rates

4. SDA Assessment:ROC Analysis

21

http://www.anaesthetist.com/mnm/stats/roc/Findex.htm

Assess performance Optimize params for:

Early detection Quantification Specific event type Individual/cohort

TPF = Sensitivity FPF = 1-Specificity AUC = area under curve = performance

Non-seizure Seizure

SDA output

Detection threshold

4. SDA Assessment:Optimization

22

1. Parameter sensitivity analysis 2. Seizure filter adaptation

Osorio et al. Epilepsia 1998

Haas et al. Med Eng Phys 2007

5. Practical issues:Artifacts

23

Line noise, motion, saturation, drop-out, cross-talk…

Mask activity, corrupt background Contribute to false detections Cause information loss Conditioning must flag/remove artifact Detector must disregard artifact Artifacts must not corrupt assessment

Eye-blink

Chewing

Saab & Gotman Clin Neurophysiol 2005

Sun et al. Neurotherapeutics 2008

Stimulation

5. Practical issues:What is a True Negative?

24

Specificity = Fraction of non-seizures avoided (some use PPV, or FP/hr) But what is a True Negative?

Whole interval between seizures? But duration varies Interictal epochs = seizure duration? But how selected? Only epochs with interictal activity? Stringent but fair?

Get a superset of detections by relaxing constraints

SDAoutput

Seizure IED

5. Practical issues:Problem with FPR

25

FPR = FP per hour A common index of SDA performance

FPR is a practical measure BUT: FPR ≠ 1-Specificity FPR does not reflect seizure rate

Best used in addition to: Sensitivity = TP/(TP+FN) Specificity = TN/(FP+TN) PPV = TP/(TP+FP)

5. Practical issues:Dealing with nonstationarity

26

Purves et al. 2001

EEG changes with state of vigilanceTherefore, SDA baseline is a moving target

5. Practical issues:Dealing with nonstationarity

27

Malow et al. Epilepsia 1998

Interictal spiking increases with sleep depth in temporal lobe epilepsyState of vigilance monitoring would be useful

5. Practical issues:The price of failure

28

Unwarranted anxiety and treatment: False alarms, triggered stimulations

Flawed treatment evaluation: Altered study design: e.g., closed-loop to open-loop stim

Altered statistical power: e.g., retrospective inclusion of FNs

TP FP TP FP TP TP FP TP FP TP

Before: TP FN TP FN TP TP FN TP FN TP

After: TP TP TP TP TP TP TP TP TP TP

Conclusion:Which SDA is the best?

29

The bare necessities: Precedes clinical onset Enough lead time High specificity

Why? To avoid cognitive impairment, and… We don’t really know when seizures start

But treating subclinical events may be beneficial Ultimately:

What to detect, How, and WhyIS REALLY UP TO YOU! THANK YOU

Or at least good enough…