Chapter 12Chapter 12Case StudiesCase Studies

Case Study - EEG Spike Detection

Outline:

•Problem definition

•Data acquisition and preprocessing

•Alternative network paradigms and structures

•Results

Acknowledgments

At Johns Hopkins University Applied Physics Laboratory: Russ Eberhart, Roy Dobbins, Chuck Spaur

At Johns Hopkins Hospital: Bob Webber, Ron Lesser, Dale Roberts

What is an EEG?

EEG is an abbreviation for ‘electroencephalogram.’

It is the recording of brain electric potentials varying in time at frequencies up to a few tens of Hz and measuring from a few microvolts to a few millivolts.

For diagnostic purposes, it is usually taken by placing electrodes at standard locations. The most commonly used electrode arrangement is the International 10-20 montage.

EEG Electrode Placement

Diagram of top view of the scalp with the nose (front of scalp) up, illustrating the EEG electrode positions in the 10-20 International System. C is central, F is frontal, P is parietal, O is occipital, and T is temporal. Odd numbers designate leads on the left side of the scalp, even numbers designate leads on the right side, and Z designates zero or midline. (Many more channels may be used.)

Usually, electrodes are attached to the scalp. However, sometimes they are put directly on the

brain:

EEG Spike Detection Project•A team effort

•Always remember that “medicine drives engineering” and that the customer is always right

•Data were taken real-time from multiple channels (32-64 typical)

•Data rate is often 200–250 Hz continuously, resulting in 10–100 Mbytes per hour of data

•Accurate interpretation is critical: surgical procedures often depend on results obtained

•Replace expensive and time-consuming manual interpretation of EEG

Problems to be addressed

Primary: On-line multi-channel analysis of EEG waveforms, including spike and seizure detection

Secondary: Reduction in amount of data to be recorded and archived, especially paper records

Design Considerations

•Design process was iterative

•Raw data versus pre-processed parameters

•Network architectures

•Minimize system cost (< $10K 1990 dollars)

•Ambulatory system desirable

System Performance Specifications

•Multi-channel analysis capability

•“Real-time” analysis

•Minimal training required for each patient

•Spikes are defined by (4 out of 6) neurologists

•Recall and precision (each > 0.8) are performance measures

Note: The first three were relatively easy to formulate; the last two were difficult.

Spike Detection Performance Metrics

Recall: The number of spikes correctly identified by the system divided by the number of spikes identified by the neurologists

Precision: The number of spikes correctly identified by the system divided by the total number of spikes identified by the system (includes false positives)

Contingency Matrix

System Diagnosis

PositiveNegative

Positive TP(true pos.)

FN(false neg.)

Negative FP(false pos.)

TN(true neg.)

GoldStandardDiagnosis

Recall is TP/(TP + FN)

Precision is TP/(TP+FP)

Data Preprocessing and Categorization

Design effort focused in two main areas* Preprocessing of data for NN input* Development of NN analysis tools

Three main processing alternatives were considered1. Raw data using sliding windows (max. 200 ms spike

results in 250 ms window)2. Preprocess raw data; present raw candidate spike

centered in window3. Preprocess to produce parameters & spike center time

Methods Selected: 2 and 3

JHU Hospital software set to minimize false negatives(resulted in 2-3 false positives for each spike)

Data “of interest” had an average of about 1 spike per second, so processing load was about 3-4 calculations per second

Used the JHU Hospital spike viewer, which produced 9 parameters for each candidate spike

Spike Parameters

Input Data Scaling

Had significant effect

OK to normalize uniformly across all channels for raw data

For parametric data, scaling across all channels didn’t work; neither did scaling on each one individually

For parametric data, success was achieved by scaling channels with same units together

amplitudestimessharpnesses

ROC Curve for Output 1

ROC Curve for Output 2

The “Universal Solution” Myth

Minimization of false negatives is more important than minimization of false positives in the U.S. It is the reverse in New Zealand.

System performance specifications are customer and application dependent.

Case Study:Determining Battery State of Charge

Using Computational Intelligence

R. Eberhart, Y. Chen and S. LyashevskiyPurdue School of Engineering and Technology

Indianapolis, Indiana

S. Sullivan and R. BrostDelphi Energy and Engine Management Systems

Indianapolis, Indiana

The Situation

Existing state of charge (SOC) estimation methods don’t perform satisfactorily for many applications. Problems arise due to:•Charge-discharge cycles•Load profiles•Environmental conditions

Accuracy of existing systems was not better than about 10 percent.

Dynamic Load Profile

The Project

Develop a system to determine SOC for a string of two or more lead-acid batteries

Goal: Achieve errors significantly less than 5% over charge-discharge cycles, load and environmental variations

Prior Technology

* Amp-hour integration* High accumulation errors

* Peukert’s relation* Requires constant current discharge, and

invariant temperature and environmental conditions * Discharge to 0% SOC required (hard on batteries!)

* Families of capacity-voltage-current curves* Battery aging invalidates calibration

Electrochemical Principles

* Need current, voltage, temperature, and amp-hours of individualbatteries

•Results in input vector with 4n inputs for n batteries

* Makes online system difficult to implement computationallyand economically

* Additional goal: Reduce dimensionality of inputs

Data Acquisition Approach

• Acquired numerous data sets

• Included constant and dynamic loads

• Varied temperature

Initial Input Parameters

1. Discharge current of battery pack2. Total ampere hours used3. Average temperature of battery pack4. Minimum battery voltage5. Maximum battery voltage6. Average battery voltage7. Voltage difference between average and minimum battery voltages8. Minimum battery voltage at previous sampling time

Initial Design •Various approaches were tried

•Selected feedforward supervised training neural network

•Initial design used all 8 parameters in previous list in an 8-5-1 network configuration

•Sigmoidal activation function used for hidden and output PEs

•Each input was scaled between 0 and 1

•Levenberg-Marquardt algorithm chosen

•Second-order Butterworth filter implemented for test/run only

•Largest errors were at beginning and end of the cycle

Errors Worse Near 0 and 100 Percent

Final Design

• Reduced number of processing elements in neural networkto a 5-3-1 configuration (used inputs 1-4, 8)

• Used linear output processing element

• Trained on 2,500 patterns

• Tested on 24 data sets with various load and temperature profiles

• Average sum-squared error/pattern ~0.0006

• Errors in SOC estimation generally less than 1%, always less than 2%

Performance Results

Learnings/Conclusions

1. The methodology chosen met all project goals.2. Use only the data necessary to train the network; more

is not always better.3. Match output processing element activations to

problem.4. Computational methods may be useful for either training

or testing that are not useful/needed for both.

U. S. Patent 6,064,180 was issued May 16, 2000 for this technology.