Chapter 12 Case Studies. Case Study - EEG Spike Detection Outline: Problem definition Data...
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Chapter 12Case Studies
Case Study - EEG Spike DetectionOutline:
Data acquisition and preprocessing
Alternative network paradigms and structures
AcknowledgmentsAt 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 PlacementDiagram 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 ProjectA 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 200250 Hz continuously, resulting in 10100 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
Minimize system cost (< $10K 1990 dollars)
Ambulatory system desirable
System Performance Specifications Multi-channel analysis capability
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 DiagnosisGoldStandardDiagnosis
Recall is TP/(TP + FN)
Precision is TP/(TP+FP)
PositiveNegativePositiveTP(true pos.)FN(false neg.)NegativeFP(false pos.)TN(true neg.)
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
Input Data Scaling Had significant effect
OK to normalize uniformly across all channels for raw data
For parametric data, scaling across all channels didnt work; neither did scaling on each one individually
For parametric data, success was achieved by scaling channels with same units togetheramplitudestimessharpnesses
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 ChargeUsing Computational Intelligence
R. Eberhart, Y. Chen and S. LyashevskiyPurdue School of Engineering and TechnologyIndianapolis, Indiana
S. Sullivan and R. BrostDelphi Energy and Engine Management SystemsIndianapolis, Indiana
The Situation Existing state of charge (SOC) estimation methods dont perform satisfactorily for many applications. Problems arise due to:Charge-discharge cyclesLoad profilesEnvironmental conditions
Accuracy of existing systems was not better than about 10 percent.
Dynamic Load Profile
The ProjectDevelop 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
* Peukerts 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
Initial Input Parameters Discharge current of battery packTotal ampere hours usedAverage temperature of battery packMinimum battery voltageMaximum battery voltageAverage battery voltageVoltage difference between average and minimum battery voltagesMinimum 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%
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.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.