Slides of my presentation at EMBC 2018, more info on this research is available here: https://www.researchgate.net/project/Bloomlife-improving-prenatal-health-through-longitudinal-physiological-monitoring-at-large-scale?_sg=pbraocCDNc2lJd9v5GESvRhkmffW99OTeeNMkalglCirK5r-ZECp2XRy_5Otk-_B-_dlCalxvKUVtex9MkAHUPFKhHT56GfrO6h3
Towards Non-invasive Labour Detection: A Free- Living Evaluation
1. Towards Non-invasive Labour Detection: A Free-
Living Evaluation
bloomlife.com40th International Conference of the IEEE Engineering in Medicine and Biology Society
Marco Altini, Elisa Rossetti, Michiel J. Rooijakkers and Julien Penders
2. 2|
FREE-LIVING LABOUR DETECTION
bloomlife.com
Being able to detect / estimate labour onset in free-living
conditions, outside of hospital settings, could be beneficial for
several reasons:
- Provide better care in case of early deliveries / preterm
birth
- Avoid unnecessary trips to the hospital
EMBC 2018
3. 3|
REAL DATA, REAL MESS
bloomlife.com
Measuring and collecting data in unsupervised free-living
conditions is finally becoming more common as wearable
sensors are entering the lives of millions of individuals
worldwide.
Even for devices and sensors that have been validated
rigorously under supervised laboratory conditions, it can be
challenging to trust data acquired in free-living, as use
(and misuse) of the system is outside of the researchers
supervision.
EMBC 2018
13. 13|
LABOUR VS PREGNANCY DATA
bloomlife.com
BL_KICK_2016−003−S012_03
BL_CXN_2015−002−S002_06
2400 2700 3000 3300 3600
6000 6250 6500 6750 7000 7250
0
100
200
300
0
50
100
150
Time (s)
RMS
Reference labour class
FALSE
TRUE
Recordings used for labour detection model training
BL_KICK_2016−003−S012_03
BL_CXN_2015−002−S002_06
2400 2700 3000 3300 3600
6000 6250 6500 6750 7000 7250
60
80
100
120
140
80
100
120
140
Time (s)
Heartrate(bpm)
Pregnancy
recording
EMBC 2018
14. 14|
LABOUR VS PREGNANCY DATA
bloomlife.com
BL_KICK_2016−003−S012_03
BL_CXN_2015−002−S002_06
2400 2700 3000 3300 3600
6000 6250 6500 6750 7000 7250
0
100
200
300
0
50
100
150
Time (s)
RMS
Reference labour class
FALSE
TRUE
Recordings used for labour detection model training
BL_KICK_2016−003−S012_03
BL_CXN_2015−002−S002_06
2400 2700 3000 3300 3600
6000 6250 6500 6750 7000 7250
60
80
100
120
140
80
100
120
140
Time (s)
Heartrate(bpm)
Labour
recording
EMBC 2018
15. 15|
LABOUR VS PREGNANCY DATA
bloomlife.com
BL_KICK_2016−003−S012_03
BL_CXN_2015−002−S002_06
2400 2700 3000 3300 3600
6000 6250 6500 6750 7000 7250
0
100
200
300
0
50
100
150
Time (s)
RMS
Reference labour class
FALSE
TRUE
Recordings used for labour detection model training
BL_KICK_2016−003−S012_03
BL_CXN_2015−002−S002_06
2400 2700 3000 3300 3600
6000 6250 6500 6750 7000 7250
60
80
100
120
140
80
100
120
140
Time (s)
Heartrate(bpm)
Heart rate
changes
during
contractions
EMBC 2018
16. 16|
LABOUR PROBABILITY ESTIMATION IN THE LAB
bloomlife.com
●
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0.00
0.25
0.50
0.75
1.00
FALSE TRUE
Reference labour class
Probability
Labour detection − probability
EMBC 2018
18. 18|
LABOUR PROBABILITY ESTIMATION IN FREE-LIVING
bloomlife.com
142 women were included, with an average of 7 hours of clean
data per person. The average labour probability for reference
labour recordings was 54% while it was 21% for reference
pregnancy recordings.
EMBC 2018
19. 19|
LABOUR PROBABILITY ESTIMATION IN FREE-LIVING
bloomlife.com
142 women were included, with an average of 7 hours of clean
data per person. The average labour probability for reference
labour recordings was 54% while it was 21% for reference
pregnancy recordings
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0.00
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Time to delivery (weeks)
Probability
Labour
FALSE
TRUE
Probability of being in labour (HR + EHG + GA)
EMBC 2018
20. 20|
LABOUR PROBABILITY ESTIMATION IN FREE-LIVING
bloomlife.com
142 women were included, with an average of 7 hours of clean
data per person. The average labour probability for reference
labour recordings was 54% while it was 21% for reference
pregnancy recordings
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0.00
0.25
0.50
0.75
1.00
0 1 2 3 4 5 6 7 8 9 10 11 12
Time to delivery (weeks)
Probability
Labour
FALSE
TRUE
Probability of being in labour (HR + EHG + GA)
EMBC 2018
21. 21|
LABOUR PROBABILITY ESTIMATION IN FREE-LIVING
bloomlife.com
During labour, greater physiological changes occur with respect to
pregnancy. As the uterus prepares to expel the fetus, changes in
both EHG and cardiac activity were highlighted in previous
literature in correspondence with uterine contractions.
Additional challenges arise when taking laboratory-based models
to unsupervised free-living settings, mainly due to lack of context,
increased noise, artifacts and possible misuse of the system.
We proposed an approach consisting in the development of
laboratory based models aiming at detecting not only the output of
interest, i.e. labour, but also specifically addressing signal quality
and artifacts detection. Results showed that non-invasive labour
detection in free-living seems promising.
EMBC 2018
22. 22|
LABOUR PROBABILITY ESTIMATION IN FREE-LIVING
bloomlife.com
During labour, greater physiological changes occur with respect to
pregnancy. As the uterus prepares to expel the fetus, changes in
both EHG and cardiac activity were highlighted in previous
literature in correspondence with uterine contractions.
Additional challenges arise when taking laboratory-based
models to unsupervised free-living settings, mainly due to lack
of context, increased noise, artifacts and possible misuse of
the system.
We proposed an approach consisting in the development of
laboratory based models aiming at detecting not only the output of
interest, i.e. labour, but also specifically addressing signal quality
and artifacts detection. Results showed that non-invasive labour
detection in free-living seems promising.
EMBC 2018
23. 23|
LABOUR PROBABILITY ESTIMATION IN FREE-LIVING
bloomlife.com
During labour, greater physiological changes occur with respect to
pregnancy. As the uterus prepares to expel the fetus, changes in
both EHG and cardiac activity were highlighted in previous
literature in correspondence with uterine contractions.
Additional challenges arise when taking laboratory-based models
to unsupervised free-living settings, mainly due to lack of context,
increased noise, artifacts and possible misuse of the system.
We proposed an approach consisting in the development of
laboratory based models aiming at detecting not only the output
of interest, i.e. labour, but also specifically addressing signal
quality and artifacts detection. Results showed that non-invasive
labour detection in free-living seems promising.
EMBC 2018