improving cardiotocography monitoring: a memory-less stream...
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Improving cardiotocography monitoring:a memory-less stream learning approach
position paper
Pedro Pereira Rodrigues, Raquel Sebastião, Cristina Costa [email protected] [email protected] [email protected]
University of Porto, Portugal
6th July 2011
Research ProjectsKDUDS (PTDC/EIA-EIA/98355/2008)CSI2 (PTDC/EIA-CCO/099951/2008)
2LEMEDS @ AIME 2011Pedro Pereira Rodrigues
Roadmap
Problem setting
● Biomedical signals gathered before and during labor
Data stream management problem
● Improve visualization of usual signal features
Machine learning problem
● Detection of problematic features
● Prediction of birth outcome
Future directions
● Memory-less processing of data streams
● Event detection for outcome prediction
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Cardiotocography (CTG)
Monitors two signals:
● fetal heart rate (FHR)
● uterine contractions (UC)
Used before (antepartum) and during (intrapartum) labor
Helps to detect fetuses in danger of death or permanent damage
Outcome is usually measured by the Apgar score (0-10)
Apgar 1 minute after birth
Apgar 5 minute after birth
Bad outcome considered if first-minute Apgar score less than seven.
4LEMEDS @ AIME 2011Pedro Pereira Rodrigues
Cardiotocography (CTG)
Omniview-SisPorto® usually extracted features:
● FHR baseline
mean FHR during stable segments (no fetal move or UC)● # accelerations
increases in the FHR above the baseline, lasting 15-120 seconds and reaching a peak of at least 15 bpm
● % of tracing with abnormal short-term variability (STV)
difference to adjacent FHR signals is less than 1 bpm● % of tracing with abnormal long-term variability (LTV)
difference between maximum and minimum FHR values of a sliding 60 seconds window does not exceed 5 bpm
● average STV and LTV
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Cardiotocography (CTG)
Omniview-SisPorto® alarms
● 'Normal' – green
● 'Suspicious' – yellow/orange
● 'Problematic' – red
6LEMEDS @ AIME 2011Pedro Pereira Rodrigues
Cardiotocography (CTG)
Omniview-SisPorto® alarms
● 'Normal' – green
● 'Suspicious' – yellow/orange
● 'Problematic' – red
Up to 10 minute delay
In detection!
7LEMEDS @ AIME 2011Pedro Pereira Rodrigues
Biomedical Signals Data Streams
Biomedical signals usually measured at high rates
common cardiotocograms with readings at 1-4 Hz
Visual analysis of tracings is hard for the human eye.
Alarm delays (up to 10 minutes) should be improved.
This high rate production creates a continuous flow of data.
These continuous flows of data are called data streams.
After processing, a data point is either discarded or archived.
8LEMEDS @ AIME 2011Pedro Pereira Rodrigues
Biomedical Signals Data Streams
Particular issues to address include:
● summarization of stream data
● real-time monitoring of changes
● novelty detection
This is an incremental task that requires:
● incremental learning algorithms that
● integrate artificial intelligence in medical domains.
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Summarization of Stream Data
Usual simple smoothers based on moving averages.
● insert-delete or turnstile model
each new observation forces an old one to be deleted
Window models include:
● landmark model
● sliding model
● time-biased model
tilted or weighted (e.g. exponentially weighted factors)
10LEMEDS @ AIME 2011Pedro Pereira Rodrigues
Summarization of Stream Data
All previous models use a catastrophic forgetting mechanism:
an observation is either in or out of the model!
We believe old data is less but still important.
α-fading window model:
compute exponentially weighted fading factor model with all data
works in the accumulative stream model.
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Fading Statistics
α-fading increment
α-fading sum
α-fading average
12LEMEDS @ AIME 2011Pedro Pereira Rodrigues
Fading Statistics
α-fading average approximates α-weighted average
ε, allowed proportion of weight given to points out of the window
w, size of the window
if defined as
Error between two averages is less than 2εR.
with R being the range of previous values.
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Fading Statistics
α-fading variance
α-fading correlation
α-fading histogram (interval frequency counts)
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Aim
Extend the cardiotocography monitoring system with memory-less fading statistics:
● define fading statistics for fetal heart rate and uterine contractions;
● define fading statistics for association between the two tracings;
● assess the relevance of fading statistics evolution for detecting changes of behavior in tracings;
● assess the relevance of fading statistics evolution in the prediction of newborn outcome through the Apgar score at 1 and 5 minutes.
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Visual analysis of FHR and UC
α-fading averages of 4Hz signals with α=0.980 to approximate a window of 1 minute with ε=1%.
FHR STV
α-fading standard deviation of 4Hz signals with α=0.316 to approximate a window of 1 second with ε=1%.
FHR LTV
α-fading standard deviation of 4Hz signals with α=0.980 to approximate a window of 1 minute with ε=1%.
Fading Cardiotocogram
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Fading Cardiotocogram (FHR)
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Fading Cardiotocogram (STV)
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Fading Cardiotocogram (LTV)
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Fading Cardiotocogram (UC)
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Association between FHR and UC
α-fading correlation of 4Hz signals with α=0.9987 to approximate a window of 15 minutes with ε=1%.
Fading Cardiotocogram
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Alarm detection via concept change detection
monitor the fading statistics with known change detectors
e.g. Page-Hinkley Test
Previous work as extended this test with fading factors, making it suitable to detect changes in fading statistics.
Fading Cardiotocogram
22LEMEDS @ AIME 2011Pedro Pereira Rodrigues
Future Directions
● Obtain clinical expert opinion on different statistics:
● FHR
● STV
● LTV
● UC
● cor(FHR,UC)
● Define alerts based on concept drift detectors.
● Estimate relevance of alerts on predicting Apgar score.
23LEMEDS @ AIME 2011Pedro Pereira Rodrigues
Improving CTG Monitoring
Thank you!