use of machine learning which is subset of artificial intelligence.

1. A Machine Learning Framework for Space Medicine
Predictive Diagnostics with Physiological Signals
Ning Wang, Michael R. Lyu
Dept. of Computer Science & Engineering, Chinese University of Hong Kong, Hong Kong
Chenguang Yang
School of Computing and Mathematics, Plymouth University, United Kingdom

2. Outline
 Introduction
 Electroencephalogram (EEG) in Aerospace Medicine
 Amplitude and Frequency Properties in EEG
 Predictive Diagnostics Framework
 Case study: Epileptic Seizure Prediction with EEG
 Discussion & Conclusion
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3. Prognostics and health management (PHM)
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 For space missions
 Focuses on fundamental issues of system failures
 To predict when failures may occur
 For healthcare in space
 Preventive, occupational
 To predict and prevent health problems timely
 Subjects are pilots, astronauts, or persons involved in spaceflight
 Critical to aviation safety

4. Aerospace medicine
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 Predictive diagnostics
Autonomously predict, prevent and manage potential health problems
 Identify negative health trends with concerned premonitory symptoms.
 Predict future health condition.
 Raise alarms in case of emergency.
 Disease prediction & health monitoring
Computer-based, self-diagnosis, and self-directed treatment programs
 Forecast acute disease onset.
 Monitor health condition.
 Patient-specific.

5. EEG in aerospace medicine
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 Long been employed in crew selection and training.
 Considered as an essential health metric of people involved in
space missions.
 Diagnosis for neurologic events.
 Help in determining an acute cardiovascular disease, etc.

6. How to acquire EEG data?
 Data recording
 Noninvasive electrodes uniformly arrayed on the scalp.
 Channel signal = difference between potentials measured at two
electrodes.
 Annotated to be clinical events or not by medical experts.
 Scalp EEG
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7. EEG signal’s rhythmic pattern
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8. Amplitude and frequency properties in EEG
 An EEG signal is typically described in terms of rhythmic
activities.
 Contains multiple frequency components.
 Differs in structure among subjects.
 A band-limited signal that describes the kth EEG rhythm
is characterized by two sequences:
 -- amplitude of rhythm;
 -- phase of rhythm.
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Extract dominant amplitude and phase components as signal descriptors,
i.e., physiological cues!

9. Observations
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 Inclusive EEG
rhythms
 Estimated
frequency
components

10. Predictive Diagnostics Framework
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 Physiological signal analysis algorithm
 Identify primary components
 Disease prediction and health monitoring architecture
 Machine learning based, subject-specific

11. Machine learning
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 “… a computer program that can learn from experience with
respect to some class of tasks and performance measure …”
(Mitchell, 1997)
 “Machine learning, a branch of artificial intelligence, is about
the construction and study of systems that can learn from data.
For example, a machine learning system could be trained on
email messages to learn to distinguish between spam and
non-spam messages. After learning, it can then be used to
classify new email messages into spam and non-spam
folders. …”
(from Wikipedia)

12. About support vector machine (SVM)
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 Linear discriminant function
 Maximal margin  best hyperplane.
 Support vectors: data points closest to the hyperplane.

13. Case study: epileptic seizure prediction
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 Epilepsy diagnosis
 EEG with epileptic seizure
 Prediction system specification
 Performance

14. Epilepsy
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 Neurological disorder characterized by
sudden recurring seizures.
 Affecting 1% of world’s population.
 Second only to stroke.
 Frequently encountered in-flight
medical events
 Unpredictable time and occasions.
 Second only to dizziness.

15. What happens today?
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 Diagnosis using electroencephalogram (EEG)
 Recording electrical activity of brain using multiple electrodes
 Machine learning techniques applied to classify EEG data
 Restricted to clinical environment

16. EEG with epileptic seizure
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 Preictal – the period before seizure onset occurs.
 Ictal – the period during which seizure takes place.
 Postictal – the period after the seizure ends.
 Interictal – the time between seizures.

17. Seizure diagnosis tasks
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Task Requirements Application scenarios
Seizure event
detection
 greatest possible accuracy,
 not necessarily shortest delay.
Apps. requiring an accurate account of
seizure activity over a period of time.
Seizure onset
detection
 shortest possible delay,
 not necessarily highest accuracy.
Apps. requiring a rapid response to a seizure.
e.g., initiating functional neuro-imaging studies to
localize cerebral origin of a seizure.
Seizure
prediction
 highest possible sensitivity,
 lowest possible false alarms,
 actionable warning time.
Apps. requiring quick reaction to a seizure by
delivering therapy or notifying a caregiver,
before seizure onset.

18. Current approaches
 Pattern recognition issue
 Two-step processing strategy
 Feature extraction front-end
 Usually computationally expensive.
 Standard machine learning techniques
 Artificial neural networks;
 Decision trees;
 Mixture Gaussian models;
 Support vector machine (SVM).
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Efficient signal analysis method that can produce physically meaningful
and effective features is highly desirable!

19. Freiburg EEG database
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 Epilepsy Center, the University Hospital of Freiburg, Germany.
 Intracranial EEG data:
recorded during invasive presurgical epilepsy monitoring.
 21 patients:
8 males, 13 females.
 For each patient:
at least 100 min preictal data + approximately 24 hr interictal data.

20. Stage Parameter Description
Data
At least 24 hr Duration of interictal record
At least 150 min Duration of preictal record
Feature extraction
5 sec EEG epoch length
6 Number of EEG channels
Training 5 fold Cross validation
SVM classification
log2γ ~ [-10, 10] SVM radial basis function kernel parameter
log2C ~ [-10, 10] Cost parameter
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Classification

21.  Sensitivity
 95.2%: 79 out of 83
seizures predicted
successfully;
 Perfect results for 16
out of 19 patients.
 Specificity
 0.144 FAs per hour;
 Two-in-a-row post-
processing: filtering
out single positive
detection.
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Performance

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Detailed results

23. EMG with proposed framework
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 Neuromuscular abnormality detection and muscular fatigue
prediction.
 Long-duration spaceflight and absence of gravity greatly impacts
astronauts’ neural-muscular system.
 Diagnosis using electromyogram (EMG).
 Indicate human’s physical status.
 Reflect electrical activity produced by skeletal muscles.
 Amplitude is closely related to muscle force.

24. Conclusion
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 Physiological cues as physical indicators in aerospace medicine
predictive diagnostics has been investigated.
 Primary amplitude and frequency components.
 A new framework for improved medical operation autonomy
during space missions has been developed.
 With state-of-the-art machine learning techniques.
 For disease prediction and health monitoring proposes.
 On a subject-by-subject basis.
 Promising epileptic seizure prediction performance in case
study has been achieved.

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