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Predicting clinical events using non-wearable sensors in elderly

Predicting clinical events using non-wearable sensors in elderly. Mihail Popescu MU Informatics Institute. Challenges in medical pattern recognition. Challenge no 1 : Hard to get data in sufficient quantity and quality Patient confidentiality (HIPAA) Hard to perform experiments

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Predicting clinical events using non-wearable sensors in elderly

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  1. Predicting clinical events using non-wearable sensors in elderly Mihail Popescu MU Informatics Institute

  2. Challenges in medical pattern recognition • Challenge no 1: Hard to get data in sufficient quantity and quality • Patient confidentiality (HIPAA) • Hard to perform experiments • insufficient and incomplete data • Algorithm validation is difficult • Possible solution: hospital data warehouse

  3. Challenge no. 2 • Hard to obtain data for the “other” class  • severe class imbalance problem • hard to train a 2-class classifier, • Ex: • if we want to detect falls in elderly, we can’t collect fall data • If we want to detect heart attacks, we can’t provoke them  use methods that do not require training (expert systems, fuzzy rules) or one-class classifiers (anomaly detection)

  4. Challenge no 3 • Data = mixed numeric and symbolic (categorical) • Example: P1=(ICD9: 232.2, 421, age:62,chlesterol: 200, smoke:Y) P2=(ICD9: 230, 430, age:69,cholesterol: 120, smoke:N). Question: what is d(P1, P2)? use ontologies, cathegorical distances ( Burnaby, Goodal, etc) and relational algorithms (VAT, relational fuzzy c-means)

  5. Brief event ontology • Event • Clinical event • Chronic event • Abnormal blood pressure (BP) • Arthritis pain • Angina pain • Depression • Acute event • Fall • Medication adverse effect • Unspecified clinical event (“do not feel well”) • Non-clinical event (visitor, interesting book)

  6. Data source: TigerPlace • Location: Columbia, MO, USA • Mission: Aging in place • residents stay as active and functionally independent as possible • What is there: • First apartment instrumented 3 years ago. • Currently, 17 apartments on line • Sensors: • Present: motion, bed. • To come: video (silhouette sensor) and acoustic (fall detector)

  7. Fall Detection - using a two class classifier - using a fuzzy rule system

  8. Introduction • Fall detection approaches • Wearable devices (accelerometers, etc) • Non wearable: • Video sensors (cameras…) • Audio sensors (microphones…) • Others (radar, IR, magic carpet, etc) • The approaches are complementary: • Wearable sensors work outside (in the garden) • Non-wearable sensors are less intrusive and more suitable for seniors with mental disabilities • Audio sensors work during the night (bathroom visit)

  9. z Mic 1 Data Acquisition Card NI 9162 Fall signal (phone call, email) To caregiver 2 ft Mic 2 Motion detector 2 ft Mic 3 Microprocessor board x y FADE- Acoustic Fall Detection System Intended FADE architecture • Privacy concern: • FADE will be encapsulated with only (wireless) “fall” signals going out. • No sound will be stored. • Main technical problem: false alarms • Use an array of sensors for better location and confirmation • Use an integrated motion detector

  10. Available data • Falls performed by a stunt actor instructed to fall as an “elderly person” • Each fall session: • 10-15 min long • Had 3-5 falls • 6 fall sessions= 23 falls, 1.3 hours total • 1 extra session with 14 fall and 25 false alarms (steps, table knocks, object drops) was recorded for algorithm training • The training data was extracted manually in files with 1000 samples (1 s long)

  11. Methodology • 0. Consider windows with N=1000 samples and 0.5 overlap • 1. Signal preprocessing (for each channel) • Wiener filter • windows w with energy Ew< ETHR “no fall”

  12. Methodology (cont.) • 2. Remove false alarms using height • Perform spectrum cross-correlation • Compute the delay between the channels • Label the window “no fall” if 12>0 (signal came from above 2ft) • 3. Extract the cepstral features (mfcc) with C=7 (C0 was not used)  6 features • 4. Identify the sound using the NN. • 5. A “fall” has to be identified in both channels

  13. Results • Noisy environment: the nurse (standing, not shown) was instructing the actor

  14. Results for the NN (cont.) • ROC was obtained by varying ETHR • The false alarms were reported vs. time and not versus total number of false alarms (unknown) • Best performance: 100% detection with 5 alarms/hour  too much! an acceptable rate could be 1 false alarm/day (two order of magnitude lower)  How do we get there?

  15. More intuitive sound features Fall Bag drop Door knock

  16. Use sub-band energy ratios (ERSB) features • ERSB1(0-330Hz), ERSB2(331-2205Hz), ERSB3(2206-5513Hz)

  17. Fuzzy rule system (FRS) • If ERSB1=HIGH1and ERSB2 = LOW2and ERSB3=LOW3then “fall” • If ERSB1=HIGH1and ERSB2 = HIGH2and ERSB3=LOW3then “no fall” • … • If ERSB1=LOW1and ERSB2 = HIGH2and ERSB3=HIGH3then “no fall”

  18. FRS results • Dataset: 30 falls+50 fa • FRS performed as well as cepstral features+ nearest neighbor

  19. Abnormal blood pressure prediction

  20. A typical apartment sensor network • 5-8 motion sensors • 1 bed sensor (motion in bed-restlessness, pulse, breathing) • Other sensors: • Stove (temperature) • Refrigerator • Kitchen cabinets • Drawers • Video and audio sensors are under development

  21. The Data Logger • The sensors transmit events (on, activated) wirelessly to the data logger that adds time stamps and stores the events in a database • The sensor with continuous values (pulse) are quantized in 4 levels (we use only level 1 here) • Ex: level 1: move 5 seconds, level 2, move 10 seconds, etc… • Typical database record (firing):

  22. Question • Is it possible to correlate the sensor reading with abnormal clinical events? • Why?: alert nursing staff to check the resident (elderly do not report their status…) • Intuition: If the patient does not feel well he does not sleep well (during the night) and does not move as much (during the day) • This translates in: high restlessness during the night and low motion during the day

  23. Why pulse pressure (PP)? • PP=systolic BP – diastolic BP (mmHg) • PP is elevated (abnormal) when • Systolic BP is high • Diastolic BP is low (more often in elderly) • PP > 60 is associated with myocardial infarctions, renal and cerebral incidents • Problems • The threshold (60 mmHg)-normal PP, is questioned • The normal PP increases with age (ignored here) • It seems that mean arterial pressure might have been better • MAP~DP+PP/3

  24. Feature description • Divided (arbitrarily) the day in two • Night (9pm, previous day -7am) • Day (7am -9pm) • A better way would be to compute the go_to_bed and wake_up events (sleep duration !) • Used 4 features to describe the day of a resident: • Total night motion firings • Total day motion firings • Total day bed restlessness (level 1) • Total night bed restlessness (level 1)

  25. Available data • The study was retrospective not many BP readings were available Future solution: use a vital sign meter (Honeywell) • Out of the room: the resident was out of the room for more than 3 hours in the previous day the data was not used  Future solution: use firing density instead of the sum • Not that bad: there are hundreds of publications about classification of microarray data with less samples than this!

  26. Classifiers used • Divided data in two classes: abnormal PP (PP>=60) and normal (PP<60) • Used a classifier to predict the PP based on the previous day sensor readings: • Neural network M-M-1 • M=# of features (4 or 8) • Output: the degree of abnormality • Robust logistic regression: PP=f(feat1, …,featM) • Support Vector Machine (SVM) • Validation: • ROC curves and • leave-one-out cross-validation

  27. Results: classifier comparison Male1 Female1 • The robust regression seems to perform best in our conditions (insufficient data) • The NN did not have enough training data • We did not compute the ROC for the SVM

  28. Pulse pressure prediction

  29. Abnormal behavior pattern detection (“bad night”)

  30. One-class Classification Methods • Aka “Abnormality detection”, “novelty detection”, etc • Used where the “other” class is not available such as in intrusion detection, credit card fraud, medical surveillance. • Density methods: • estimate the density of the training data and set a threshold on this density • Trick: use a rejected fraction in training to remove possible outliers • Ex: Parzen density estimator, Gaussian model

  31. One-class Classification Methods - cont • Boundary methods: • Focus only on the boundary of the data • Deal better with small datasets • Ex: NN (nearest neighbor) and the SVDD (Support Vector Data Description)- an type SVM method • R= the distance from the center of the data set, “a”, to any support vectors • z is an outlier if ||z-a||>R

  32. Some experiments on Male1 data • What is an abnormal event: • Bad night complaints in the journal (only 2) • Bad nurse assessment during a day visit (as interpreted by Elena) • Abnormal pulse pressure • Sensor data was recorded hourly (bed restlessness, motion, heart rate, breathing) since we want to act as fast as possible (not next day) about 10,000 data points in total • Unfortunately: the abnormal events were recorded for the whole day • We focused on the nights, only want to detect a bad night.

  33. Comparison of SVM and SVDD • For data between 1am-2am. • The training data was smoothed with a running average over a month • SVDD (reject fraction 5%) does better than SVM : • Only 9 bad training cases • There might be more unlabeled “bad nights”

  34. Conclusions • Sound sensor arrays seem to be a viable technology for fall detection • Fuzzy rule systems work well for fall sound classification • One-class classifiers are a promising approach to medical surveillance

  35. Acknowledgement • Elena Florea • Yun Li • Eldertech team

  36. Thank you! • Questions?

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