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  • It is not directly clear how one should define a boundary around a target set, how to define the resemblance of an object to a target set and where to put the threshold. In most cases a distance to the target set is defined which is a function of (Euclidean) distances between objects, between the test object and the target objects, and between the target objects themselves. Requires well defined distances in the feature space and well-scaled features.
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    1. 1. Predicting clinical events using non-wearable sensors in elderly Mihail Popescu MU Informatics Institute
    2. 2. Challenges in medical pattern recognition <ul><li>Challenge no 1 : Hard to get data in sufficient quantity and quality </li></ul><ul><ul><li>Patient confidentiality (HIPAA) </li></ul></ul><ul><ul><li>Hard to perform experiments </li></ul></ul><ul><ul><li>insufficient and incomplete data </li></ul></ul><ul><ul><li>Algorithm validation is difficult </li></ul></ul><ul><ul><li>Possible solution: hospital data warehouse </li></ul></ul>
    3. 3. Challenge no. 2 <ul><li>Hard to obtain data for the “other” class  </li></ul><ul><ul><li>severe class imbalance problem </li></ul></ul><ul><ul><li>hard to train a 2-class classifier, </li></ul></ul><ul><ul><li>Ex: </li></ul></ul><ul><ul><ul><li>if we want to detect falls in elderly, we can’t collect fall data </li></ul></ul></ul><ul><ul><ul><li>If we want to detect heart attacks, we can’t provoke them </li></ul></ul></ul><ul><ul><li> use methods that do not require training (expert systems, fuzzy rules) or one-class classifiers (anomaly detection) </li></ul></ul>
    4. 4. Challenge no 3 <ul><li>Data = mixed numeric and symbolic (categorical) </li></ul><ul><ul><li>Example: </li></ul></ul><ul><ul><li>P 1 =(ICD9: 232.2, 421, age:62,chlesterol: 200, smoke:Y) </li></ul></ul><ul><ul><li>P 2 =(ICD9: 230, 430, age:69,cholesterol: 120, smoke:N). </li></ul></ul><ul><ul><li>Question: what is d(P 1 , P 2 )? </li></ul></ul><ul><ul><li> use ontologies, cathegorical distances ( Burnaby, Goodal, etc) and relational algorithms (VAT, relational fuzzy c-means) </li></ul></ul>
    5. 5. Brief event ontology <ul><li>Event </li></ul><ul><ul><li>Clinical event </li></ul></ul><ul><ul><ul><li>Chronic event </li></ul></ul></ul><ul><ul><ul><ul><li>Abnormal blood pressure (BP) </li></ul></ul></ul></ul><ul><ul><ul><ul><li>Arthritis pain </li></ul></ul></ul></ul><ul><ul><ul><ul><li>Angina pain </li></ul></ul></ul></ul><ul><ul><ul><ul><li>Depression </li></ul></ul></ul></ul><ul><ul><ul><li>Acute event </li></ul></ul></ul><ul><ul><ul><ul><li>Fall </li></ul></ul></ul></ul><ul><ul><ul><ul><li>Medication adverse effect </li></ul></ul></ul></ul><ul><ul><ul><ul><li>Unspecified clinical event (“do not feel well”) </li></ul></ul></ul></ul><ul><ul><li>Non-clinical event (visitor, interesting book) </li></ul></ul>
    6. 6. Data source: TigerPlace <ul><li>Location: Columbia, MO, USA </li></ul><ul><li>Mission: Aging in place </li></ul><ul><ul><li>residents stay as active and functionally independent as possible </li></ul></ul><ul><li>What is there: </li></ul><ul><ul><li>First apartment instrumented 3 years ago. </li></ul></ul><ul><ul><li>Currently, 17 apartments on line </li></ul></ul><ul><ul><li>Sensors: </li></ul></ul><ul><ul><ul><li>Present: motion, bed. </li></ul></ul></ul><ul><ul><ul><li>To come: video (silhouette sensor) and acoustic (fall detector) </li></ul></ul></ul>
    7. 7. Fall Detection - using a two class classifier - using a fuzzy rule system
    8. 8. Introduction <ul><li>Fall detection approaches </li></ul><ul><ul><li>Wearable devices (accelerometers, etc) </li></ul></ul><ul><ul><li>Non wearable: </li></ul></ul><ul><ul><ul><li>Video sensors (cameras…) </li></ul></ul></ul><ul><ul><ul><li>Audio sensors (microphones…) </li></ul></ul></ul><ul><ul><ul><li>Others (radar, IR, magic carpet, etc) </li></ul></ul></ul><ul><li>The approaches are complementary: </li></ul><ul><ul><li>Wearable sensors work outside (in the garden) </li></ul></ul><ul><ul><li>Non-wearable sensors are less intrusive and more suitable for seniors with mental disabilities </li></ul></ul><ul><ul><li>Audio sensors work during the night (bathroom visit) </li></ul></ul>
    9. 9. Intended FADE architecture <ul><li>Privacy concern : </li></ul><ul><ul><li>FADE will be encapsulated with only (wireless) “fall” signals going out. </li></ul></ul><ul><ul><li>No sound will be stored. </li></ul></ul><ul><li>Main technical problem: false alarms </li></ul><ul><ul><li>Use an array of sensors for better location and confirmation </li></ul></ul><ul><ul><li>Use an integrated motion detector </li></ul></ul>2 ft 2 ft Mic 1 Mic 2 Mic 3 Data Acquisition Card NI 9162 y x z Motion detector Microprocessor board Fall signal (phone call, email) To caregiver FADE- Acoustic Fall Detection System
    10. 10. Available data <ul><li>Falls performed by a stunt actor instructed to fall as an “elderly person” </li></ul><ul><li>Each fall session: </li></ul><ul><ul><li>10-15 min long </li></ul></ul><ul><ul><li>Had 3-5 falls </li></ul></ul><ul><li>6 fall sessions= 23 falls, 1.3 hours total </li></ul><ul><li>1 extra session with 14 fall and 25 false alarms (steps, table knocks, object drops) was recorded for algorithm training </li></ul><ul><li>The training data was extracted manually in files with 1000 samples (1 s long) </li></ul>
    11. 11. Methodology <ul><li>0. Consider windows with N=1000 samples and 0.5 overlap </li></ul><ul><li>1. Signal preprocessing (for each channel) </li></ul><ul><ul><li>Wiener filter </li></ul></ul><ul><ul><li>windows w with energy E w < E THR  “no fall” </li></ul></ul>
    12. 12. Methodology (cont.) <ul><li>2. Remove false alarms using height </li></ul><ul><ul><li>Perform spectrum cross-correlation </li></ul></ul><ul><ul><li>Compute the delay between the channels </li></ul></ul><ul><ul><li>Label the window “no fall” if  12 >0 (signal came from above 2ft) </li></ul></ul><ul><li>3. Extract the cepstral features ( mfcc ) with C=7 (C 0 was not used)  6 features </li></ul><ul><li>4. Identify the sound using the NN. </li></ul><ul><li>5. A “fall” has to be identified in both channels </li></ul>
    13. 13. Results <ul><li>Noisy environment: the nurse (standing, not shown) was instructing the actor </li></ul>
    14. 14. Results for the NN (cont.) <ul><li>ROC was obtained by varying E THR </li></ul><ul><li>The false alarms were reported vs. time and not versus total number of false alarms (unknown) </li></ul><ul><li>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? </li></ul>
    15. 15. More intuitive sound features Fall Bag drop Door knock
    16. 16. Use sub-band energy ratios (ERSB) features <ul><li>ERSB1(0-330Hz), ERSB2(331-2205Hz), ERSB3(2206-5513Hz) </li></ul>
    17. 17. Fuzzy rule system (FRS) <ul><li>If ERSB1=HIGH 1 and ERSB2 = LOW 2 and ERSB3=LOW 3 then “fall” </li></ul><ul><li>If ERSB1=HIGH 1 and ERSB2 = HIGH 2 and ERSB3=LOW 3 then “no fall” </li></ul><ul><li>… </li></ul><ul><li>If ERSB1=LOW 1 and ERSB2 = HIGH 2 and ERSB3=HIGH 3 then “no fall” </li></ul>
    18. 18. FRS results <ul><li>Dataset: 30 falls+50 fa </li></ul><ul><li>FRS performed as well as cepstral features+ nearest neighbor </li></ul>
    19. 19. Abnormal blood pressure prediction
    20. 20. A typical apartment sensor network <ul><li>5-8 motion sensors </li></ul><ul><li>1 bed sensor (motion in bed-restlessness, pulse, breathing) </li></ul><ul><li>Other sensors: </li></ul><ul><ul><li>Stove (temperature) </li></ul></ul><ul><ul><li>Refrigerator </li></ul></ul><ul><ul><li>Kitchen cabinets </li></ul></ul><ul><ul><li>Drawers </li></ul></ul><ul><li>Video and audio sensors are under development </li></ul>
    21. 21. The Data Logger <ul><li>The sensors transmit events (on, activated) wirelessly to the data logger that adds time stamps and stores the events in a database </li></ul><ul><li>The sensor with continuous values (pulse) are quantized in 4 levels (we use only level 1 here) </li></ul><ul><ul><li>Ex : level 1: move 5 seconds, level 2, move 10 seconds, etc… </li></ul></ul><ul><li>Typical database record (firing): </li></ul>12:12:00 Jul 14 2008 35 1002 Time Date Sensor ID Resident ID
    22. 22. Question <ul><li>Is it possible to correlate the sensor reading with abnormal clinical events ? </li></ul><ul><li>Why?: alert nursing staff to check the resident (elderly do not report their status…) </li></ul><ul><li>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) </li></ul><ul><li>This translates in: high restlessness during the night and low motion during the day </li></ul>
    23. 23. Why pulse pressure (PP)? <ul><li>PP=systolic BP – diastolic BP (mmHg) </li></ul><ul><li>PP is elevated (abnormal) when </li></ul><ul><ul><li>Systolic BP is high </li></ul></ul><ul><ul><li>Diastolic BP is low (more often in elderly) </li></ul></ul><ul><li>PP > 60 is associated with myocardial infarctions, renal and cerebral incidents </li></ul><ul><li>Problems </li></ul><ul><ul><li>The threshold (60 mmHg)-normal PP, is questioned </li></ul></ul><ul><ul><li>The normal PP increases with age (ignored here) </li></ul></ul><ul><ul><li>It seems that mean arterial pressure might have been better </li></ul></ul><ul><ul><ul><li>MAP~DP+PP/3 </li></ul></ul></ul>
    24. 24. Feature description <ul><li>Divided (arbitrarily) the day in two </li></ul><ul><ul><li>Night (9pm, previous day -7am) </li></ul></ul><ul><ul><li>Day (7am -9pm) </li></ul></ul><ul><li>A better way would be to compute the go_to_bed and wake_up events (  sleep duration !) </li></ul><ul><li>Used 4 features to describe the day of a resident: </li></ul><ul><ul><li>Total night motion firings </li></ul></ul><ul><ul><li>Total day motion firings </li></ul></ul><ul><ul><li>Total day bed restlessness (level 1) </li></ul></ul><ul><ul><li>Total night bed restlessness (level 1) </li></ul></ul>
    25. 25. Available data <ul><li>The study was retrospective  not many BP readings were available  Future solution: use a vital sign meter (Honeywell) </li></ul><ul><li>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 </li></ul><ul><li>Not that bad: there are hundreds of publications about classification of microarray data with less samples than this! </li></ul>90 (35 PP≥60) 49 139 Female1 41 (30 PP≥60) 42 93 Male1 Total data set Out of the room Total BP records
    26. 26. Classifiers used <ul><li>Divided data in two classes: abnormal PP (PP>=60) and normal (PP<60) </li></ul><ul><li>Used a classifier to predict the PP based on the previous day sensor readings: </li></ul><ul><ul><li>Neural network M-M-1 </li></ul></ul><ul><ul><ul><li>M=# of features (4 or 8) </li></ul></ul></ul><ul><ul><ul><li>Output: the degree of abnormality </li></ul></ul></ul><ul><ul><li>Robust logistic regression: PP=f(feat 1 , …,feat M ) </li></ul></ul><ul><ul><li>Support Vector Machine (SVM) </li></ul></ul><ul><li>Validation: </li></ul><ul><ul><li>ROC curves and </li></ul></ul><ul><ul><li>leave-one-out cross-validation </li></ul></ul>
    27. 27. Results: classifier comparison <ul><li>The robust regression seems to perform best in our conditions (insufficient data) </li></ul><ul><li>The NN did not have enough training data </li></ul><ul><li>We did not compute the ROC for the SVM </li></ul><ul><ul><ul><ul><li>Male1 Female1 </li></ul></ul></ul></ul>
    28. 28. Pulse pressure prediction
    29. 29. Abnormal behavior pattern detection (“bad night”)
    30. 30. One-class Classification Methods <ul><li>Aka “Abnormality detection”, “novelty detection”, etc </li></ul><ul><li>Used where the “other” class is not available such as in intrusion detection, credit card fraud, medical surveillance. </li></ul><ul><li>Density methods: </li></ul><ul><ul><li>estimate the density of the training data and set a threshold on this density </li></ul></ul><ul><ul><li>Trick: use a rejected fraction in training to remove possible outliers </li></ul></ul><ul><ul><li>Ex: Parzen density estimator, Gaussian model </li></ul></ul>
    31. 31. One-class Classification Methods - cont <ul><li>Boundary methods: </li></ul><ul><ul><li>Focus only on the boundary of the data </li></ul></ul><ul><ul><li>Deal better with small datasets </li></ul></ul><ul><ul><li>Ex: NN (nearest neighbor) and the SVDD (Support Vector Data Description)- an type SVM method </li></ul></ul><ul><ul><li>R= the distance from the center of the data set, “ a ”, to any support vectors </li></ul></ul><ul><ul><li>z is an outlier if || z-a ||>R </li></ul></ul>
    32. 32. Some experiments on Male1 data <ul><li>What is an abnormal event: </li></ul><ul><ul><li>Bad night complaints in the journal (only 2) </li></ul></ul><ul><ul><li>Bad nurse assessment during a day visit (as interpreted by Elena) </li></ul></ul><ul><ul><li>Abnormal pulse pressure </li></ul></ul><ul><li>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 </li></ul><ul><li>Unfortunately: the abnormal events were recorded for the whole day </li></ul><ul><li>We focused on the nights, only  want to detect a bad night. </li></ul>6 62 Test (Oct-Nov-Dec) 9 143 Train (April-Sept) Abnormal (based on journal and nursing reports) Normal Available nights
    33. 33. Comparison of SVM and SVDD <ul><li>For data between 1am-2am. </li></ul><ul><li>The training data was smoothed with a running average over a month </li></ul><ul><li>SVDD (reject fraction 5%) does better than SVM : </li></ul><ul><ul><li>Only 9 bad training cases </li></ul></ul><ul><ul><li>There might be more unlabeled “bad nights” </li></ul></ul>
    34. 34. Conclusions <ul><li>Sound sensor arrays seem to be a viable technology for fall detection </li></ul><ul><li>Fuzzy rule systems work well for fall sound classification </li></ul><ul><li>One-class classifiers are a promising approach to medical surveillance </li></ul>
    35. 35. Acknowledgement <ul><li>Elena Florea </li></ul><ul><li>Yun Li </li></ul><ul><li>Eldertech team </li></ul>
    36. 36. Thank you! <ul><li>Questions? </li></ul>