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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

Predicting clinical events using non-wearable sensors in elderly

Mihail Popescu

MU Informatics Institute

challenges in medical pattern recognition
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
challenge no 2
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)

challenge no 3
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)

brief event ontology
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)
data source tigerplace
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)
introduction
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)
intended fade architecture

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
available data
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)
methodology
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”
methodology cont
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
results
Results
  • Noisy environment: the nurse (standing, not shown) was instructing the actor
results for the nn cont
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?
more intuitive sound features
More intuitive sound features

Fall

Bag drop

Door knock

use sub band energy ratios ersb features
Use sub-band energy ratios (ERSB) features
  • ERSB1(0-330Hz), ERSB2(331-2205Hz), ERSB3(2206-5513Hz)
fuzzy rule system frs
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”
frs results
FRS results
  • Dataset: 30 falls+50 fa
  • FRS performed as well as cepstral features+ nearest neighbor
a typical apartment sensor network
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
the data logger
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):
question
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
why pulse pressure pp
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
feature description
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)
available data25
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!
classifiers used
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
results classifier comparison
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
one class classification methods
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
one class classification methods cont
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
some experiments on male1 data
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.
comparison of svm and svdd
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”
conclusions
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
acknowledgement
Acknowledgement
  • Elena Florea
  • Yun Li
  • Eldertech team
thank you
Thank you!
  • Questions?