Epileptic Seizure Recognition
01 INTRODUCTION TO EPILEPSY
02 DATA UNDERSTANDING AND PREPARATION
A GENDA GROUP 6 PRESENTATION
03 MODELLING PROCESS 04 STRENGTHS, LIMITATIONS &
Data Transformation
Frequency Domain Dataset
Advantages • Remove the initial time offsets.
• Extract more recognizable features.
Limitation
• Might filter useful information of the original dataset.
Potential
Pure medical/research use
Pervasive domestic use (App/Website)
Continuous EEG record
Data Set for research purpose Individual patient’s customization
ACCURACY
96.73% 95.12%
97.12%
SENSITIVITY
79.23%
95.72% 97.00%
SPECIFICITY
96.99% 99.18%
97.16%
75.00% 80.00% 85.00% 90.00% 95.00% 100.00%
Model A - Neural Network Model C - Logistic Regression
Model B - Discriminant
True Prediction of Either Type True Prediction of Seizure Activity
True Prediction of Non-Seizure Activity
RESULT ANALYSIS & MODEL VALIDATION
Accuracy - True Prediction
98.00% 96.00%
NO.1
95.29%
94.00%
92.00%
90.00%
88.00%
86.00%
84.00%
82.00%
80.00% Actual Seizure
95.39% Potential Seizure
93.45% Healthy
accurate modelling
Auto Classifier
Computer’s job!! Models are constructed and
tested for each group
Validate Model
Analyze testing results of selected models
• Deliver more recognizable dataset.
MODELLING PROCESS
Reclassify Data Types
split or combine into customized groups based on
research purpose
Balance Sample Size of Each Group
02
Symptom
• Recurrent seizures • Loss of consciousness
• Damage to the brain, or even death. ( >30min )
03
Premature Death
Rate of People died prematurely
Epilesy Patients 8.8% Others 0.7%
Ensure even prediction ability for each group
Select Top 3 Accurate Models
Select based on model accuracy in model recalibration
Transformed Data
Reclassify
(University of Oxford and the Karolinska Institute, Stockholm)
Epilepsy EEG
• One of the most common neurological diseases
• Excessive electrical activity within networks of neurons in the brain
Data Partition
Balance
Model Construction
Model Selection
Model Validation
Partition Data Randomly for Different Use in Modelling
Assign proportion of data used in model building, recalibrate and test for
BEST!
Gain Chart How good each model predicting results approach the ideal condition?
Strengths of the Model
• Quick and Accurate. • Models are dynamic, and constantly learn from new data. • Cost effective. Targeted follow-up treatment • Saves times which benefits patients.
1.37
1.34
Deaths in millions Stroke lung cancer Lower respiratory infections
Chronic obstructive pulmonary disease Diabetes Diarrhoeal diseases
Seizure disorders
94.60% Overall
Model A - Neural Network
Model B - Logistic Regression Model C - C5
RESULT ANALYSIS & MODEL VALIDATION
3 Aspects of Validation in T/F Diagnose
• Detection & Prediction • Allow for interventional treatment • Improve current epilepsy diagnosis • Medical signal-pattern recognition
framework
Brain Activity
Properties • 178 Hz sampling rate • 1 second long
Types of EEG Waveforms
Healthy (Type A)
Healthy (Type B)
Potential Seizure (Type C)
Potential Seizure (Type D)
Potential
10
8.76
6.24
5
0
Ischaemic heart disease Trachea and bronchus cancers Dementias Tuberculosis
Top 10 causes of deaths globally
3.19
3.17
1.69
1.59
1.54
1.39
Actual Seizure (Type E)
Time Series
Frequency Series
Data Preparation
Discrete Fourier Transformation
Time Domain Dataset
������=π
4
������=π ������=π
2
f(t)= ������������������(������������ + ������)
Limitations of the Model
• Potential costs of misclassifying a patient. • Model has to be used as a supportive tool. • Model is only as good as data used to build it. • Abnormal EEG may be caused by other neurological disorders.
RESULT ANALYSIS & MODEL VALIDATION
Actual Seizure
Potential Seizure
Healthy
Actual
433
4
20
Seizure
Potential Seizure
10
873
29
Healthy
20
33
863
Coincidence Matrix of Logistic Regression
POTENTIALS
Epilepsy
01
Demography
• 65 million people worldwide suffer from epilepsy (the Epilepsy Foundation)