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

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 2

Prognostics and health management (PHM) 3  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

Aerospace medicine 4  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.

EEG in aerospace medicine 5  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.

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 6

EEG signal’s rhythmic pattern 7

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. 8 Extract dominant amplitude and phase components as signal descriptors, i.e., physiological cues!

Observations 9  Inclusive EEG rhythms  Estimated frequency components

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

Machine learning 11  “… 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 messages to learn to distinguish between spam and non- spam messages. After learning, it can then be used to classify new messages into spam and non-spam folders. …” (from Wikipedia)

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

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

Epilepsy 14  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.

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

EEG with epileptic seizure 16  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.

Seizure diagnosis tasks 17 TaskRequirementsApplication 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.

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). 18 Efficient signal analysis method that can produce physically meaningful and effective features is highly desirable!

Freiburg EEG database 19  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.

StageParameterDescription Data At least 24 hrDuration of interictal record At least 150 minDuration of preictal record Feature extraction 5 secEEG epoch length 6Number of EEG channels Training5 foldCross validation SVM classification log 2 γ ~ [-10, 10]SVM radial basis function kernel parameter log 2 C ~ [-10, 10]Cost parameter 20 Classification

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

22 Detailed results

EMG with proposed framework 23  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.

Conclusion 24  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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