Two Algorithms for Real-Time Seizure Prediction and
Detection, for an Implanted, Closed-Loop, Epilepsy
Prosthesis In Vivo
P. Rajdev1, S. Raghunathan2, P. Irazoqui3
1, 2 Graduate
Student, Purdue University, 3 Asst. Professor, Purdue University
Outline
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Motivation
Experimental Setup
Prediction Algorithm
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–
–
–
•
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Algorithm
Digital Signal Processor
Thresholds
Results
Hardware Constraints in Implantable Applications
Event Based Seizure Detection
– Algorithm
– Hardware Implementation
– Results
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Applications and Future Directions
Motivation
• Real time Implementation
– Algorithm with low computational complexity.
– Implementable on Digital Signal Processor or Application Specific Integrated
Circuit.
• Local Field Potential (LFP) Vs Electroencephalogram (EEG)
– Disadvantages of EEG
– Disadvantages of LFPs
Experimental Setup
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Animal Model of epilepsy: Kainate model
– Commonly used model for temporal lobe
epilepsy.
– Primary site of action of kainic acid is the
CA3 cells of the hippocampus.
– Pathological, clinical, and electrographic
characteristics of the seizures caused by
kainic acid treatment strongly resemble
those seen in human temporal lobe
epilepsy
Digital Signal Processor
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The prediction algorithm was first developed in Matlab.
•
A real-time implementation was then realized on a floating point TMS320C6713T
digital signal processor (DSP).
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DSP chip operating at 225MHz, along with 16Mbytes of SDRAM, 415Kbytes of Flash memory and a JTAG
emulator.
Weiner Prediction Based Algorithm
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Four-step process
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signal enhancement,
adaptive auto-regressive modeling and prediction,
envelope detection,
and a binomial decision rule.
Algorithm (contd…)
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Implemented a real-time wiener-prediction
based algorithm on a digital signal processor.
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Quasi-stationary signal
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Adaptive nature of the algorithm ensures that
the prediction coefficients provide effective
prediction of baseline activity.
–
Lower Computational complexity
In an autoregressive (AR) model, the future
value is modeled as a linear combination of
the p past values of the signal.

p
s[n 1]   a[k]s[n  k]
k 0
Algorithm (contd…)
40 sec
prior to
seizure
onset
1400
1200
Mean
=124.36
1000
Mean =
22.45
800
600
400
200
Mean =
4.93
0
Baseline
Pre-ictal
Ictal
Thresholds
Sensitivity
T
False positive rate (FPR)
Latency
Latency (sec)
2

50
0
1
1
1.2
1.4
1.6
Lambda (λ)
Lambda (λ)
1.8
2
0
False positives (/hr)
Sensitivity
100
1 N 2
 e [n]
N n1
Results
Results
ID
# of Seizures
Sensitivity
FP/Min
(%)
Median
Mean latency Std of latency
Latency (sec)
(sec)
(sec)
1
32
96.87
0.0064
19.96
26.02
20.84
2
27
96.29
0.0095
34.79
33.29
18.12
3
25
88
0.0063
29.18
31.34
19.61
4
25
96
0.0143
13.82
15.51
10.50
Median Latency
Mean latency
Std of latency
(sec)
(sec)
(sec)
ID
# of Seizures
Sensitivity (%)
FP/Min
1
14
92.85
0.115
6.33
5.63
4.29
2
14
92.85
0.077
7.68
8.35
7.63
3
24
91.67
0.074
5.18
6.35
4.87
4
18
88.89
0.080
6.91
7.24
5.34
Hardware constraints in an implantable
application
1.
2.
3.
4.
Power consumed / Battery life
Total area /size
Programmability/Communication link to an external monitor
Integration capabilities
Goals for feedback algorithm:
1.
2.
3.
4.
Simplicity in implementation
Good sensitivity
Adaptability, allowing for patient to patient variations
Integration capabilities
Temporal evolution and spread
1. Radial spread at speeds up to 60cm/sec (Jung , 2003)
2. 2-70 seconds from hippocampal focus to neo-cortex (Spencer
,1987)
3. Animal studies indicate a delay of ~ 20 s before spreading away
from the temporal lobe focus (Litt, 2003)
Event based seizure detection
1. Amplitude of recorded signal (Kamp )
2. Measure of frequency content obtained from inter-event interval (IEIth )
3. Measure of rhythmicity obtained from sustained levels of increasing
amplitude, high frequency content in recorded signal (NStage)
Distributing the event threshold (Kamp
)
Distributing the IEI threshold (IEIth)
Hardware implementation
Hardware implementationcircuit timing results
Where do the hardware trade-offs figure?
Optimizing threshold selection
Results
Power consumption
350 nW
Supply voltage
250 mV
Area
370 x 130 um (per channel)
Sensitivity
0.94
Selectivity
0.90
Detection delay
6.4 s
*references available on request
ASIC Design/ Where are we going?
Microchip
Reid Harrison Lab, Univ. of
Utah
Center for Wireless Integrated Microsystems,
Univ. of Michigan
Applications of device
Integration with multi-channel neural recording devices
Integration with implantable neural stimulators
Seizure focus identification and tracking
Acknowledgments
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Research partners
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BCI Lab members
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Funding
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Robert Worth, M.D., Ph.D.
Thomas Sutula, Ph.D.
Jenna Rickus, Ph.D.
Edward Bartlett, Ph.D.
Kaushik Roy, Ph.D.
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Cyberonics, Inc.
Wallace H. Coulter Early Career
Award
Additional industry collaborators
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Texas Instruments
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https://engineering.purdue.edu/BCILab
Professor: Pedro P. Irazoqui
Research Associate: Casey Ellison
Post Doctorate: Kate Musick
PhD Students
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Travis Hassell, BME
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Eric Chow, ECE
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Pooja Rajdev, BME
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Shriram Raghunathan, BME
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Matt Ward, BME
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Brooke Beier, BME
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Art Chlebowski, BME
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Bhupendra Manola, BME
Masters Students
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Matthew Graves, BME
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Adam Kahn, BME
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Gabriel Albors, BME