Improving cardiotocography monitoring: a memory-less...

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


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.



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



Omniview-SisPorto® alarms

● 'Normal' – green

● 'Suspicious' – yellow/orange

● 'Problematic' – red



Omniview-SisPorto® alarms

● 'Normal' – green

● 'Suspicious' – yellow/orange

● 'Problematic' – red

Up to 10 minute delay

In detection!


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.


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.


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)


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.


Fading Statistics

α-fading increment

α-fading sum

α-fading average


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.


Fading Statistics

α-fading variance

α-fading correlation

α-fading histogram (interval frequency counts)


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.


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


Fading Cardiotocogram (FHR)


Fading Cardiotocogram (STV)


Fading Cardiotocogram (LTV)


Fading Cardiotocogram (UC)


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%.



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.



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.


Improving CTG Monitoring

Thank you!

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