A Machine-Learning Approach to the Detection of Fetal Hypoxia during Labor and Delivery

By Warrick, Philip A.; Hamilton, Emily F. et al. | AI Magazine, Summer 2012 | Go to article overview

A Machine-Learning Approach to the Detection of Fetal Hypoxia during Labor and Delivery


Warrick, Philip A., Hamilton, Emily F., Kearney, Robert E., Precup, Doina, AI Magazine


The lifelong disability that can result from oxygen deprivation during childbirth is rare but devastating for families, clinicians, and the health-care system. Between 1 and 7 in 1000 fetuses experience oxygen deprivation during labor that is severe enough to cause fetal death or brain injury (Saphier et al. 1998); the range of this estimate reflects considerable regional variation and some clinical debate on the definition of brain injury. The main source of information used by clinicians to assess the fetal state during labor is cardiotocography (CTG), which measures maternal uterine pressure (UP) and fetal heart rate (FHR). These signal are routinely recorded during labor, using monitors of the type presented in figure 1.

Clinicians look at these signals and use visual pattern recognition and their prior experience to decide whether the fetus is in distress and to pick an appropriate course of action (such as performing a Cesarean section). However, there is great variability among physicians in terms of how they perform this task (Parer et al. 2006). Furthermore, because significant hypoxia is rare, false alarms are common, leading physicians to disregard truly abnormal signals. Indeed, approximately 50 percent of birth-related brain injuries are deemed preventable, with incorrect CTG interpretation leading the list of causes (Draper et al. 2002; Freeman, Garite, and Nageotte 2003). The social costs of such errors are massive: intrapartum care generates the most frequent malpractice claims and the greatest liability costs of all medical specialties (Saphier et al. 1998). Thus, there is great motivation to find better methods to discriminate between healthy and hypoxic conditions.

[FIGURE 1 OMITTED]

In this article, we summarize our recent work on a novel approach to this problem, which relies heavily on machine-learning methods; a more detailed account of the methods is presented in two biomedical journal publications (Warrick et al. 2009; 2010) as well as in a Ph.D. dissertation (Warrick 2010). Although the system we present Was designed specifically for labor monitoring, we believe that the general steps we took can inform other AI medical monitoring systems, as well as, more generally, applications for time-series prediction and analysis.

We built an automated detector of fetal distress by using data from normal and pathological cases. We had access to a unique database, which contains labor monitoring data from an unusually large number of births; a significant number of the cases are pathological examples (well above the natural frequency of occurrence of such problems). All the data has been collected under clinical conditions; as a result, it is very noisy. To handle this problem, we modeled the fetal heart rate signal through several components. The parameters of these models, which have been learned from data, are then used to build a classifier for a given time period. Because the state of the fetus can change during labor, classification is performed repeatedly on data segments of limited duration. A majority vote of recent labels determines if and when a fetus is considered pathological.

The article is organized as follows. First, we give some background on the problem and type of data used. Then, we describe our general approach. We present empirical results and a discussion of the main findings, as well as the next steps towards clinical deployment.

Background

Clinicians' interpretation of intrapartum CTG signals relies on the temporary decreases in FHR (FHR decelerations) in response to uterine contractions. FHR decelerations are due mainly to two contraction-induced events: (1) umbilical-cord compression and (2) a decrease in oxygen delivery through an impaired utero-placental unit. There is general consensus that deceleration depth, frequency, and timing with respect to contractions are indicators of both the insult and the ability of the fetus to withstand it. …

The rest of this article is only available to active members of Questia

Already a member? Log in now.

Notes for this article

Add a new note
If you are trying to select text to create highlights or citations, remember that you must now click or tap on the first word, and then click or tap on the last word.
One moment ...
Default project is now your active project.
Project items
Notes
Cite this article

Cited article

Style
Citations are available only to our active members.
Buy instant access to cite pages or passages in MLA 8, MLA 7, APA and Chicago citation styles.

(Einhorn, 1992, p. 25)

(Einhorn 25)

(Einhorn 25)

1. Lois J. Einhorn, Abraham Lincoln, the Orator: Penetrating the Lincoln Legend (Westport, CT: Greenwood Press, 1992), 25, http://www.questia.com/read/27419298.

Note: primary sources have slightly different requirements for citation. Please see these guidelines for more information.

Cited article

A Machine-Learning Approach to the Detection of Fetal Hypoxia during Labor and Delivery
Settings

Settings

Typeface
Text size Smaller Larger Reset View mode
Search within

Search within this article

Look up

Look up a word

  • Dictionary
  • Thesaurus
Please submit a word or phrase above.
Print this page

Print this page

Why can't I print more than one page at a time?

Help
Full screen
Items saved from this article
  • Highlights & Notes
  • Citations
Some of your highlights are legacy items.

Highlights saved before July 30, 2012 will not be displayed on their respective source pages.

You can easily re-create the highlights by opening the book page or article, selecting the text, and clicking “Highlight.”

matching results for page

    Questia reader help

    How to highlight and cite specific passages

    1. Click or tap the first word you want to select.
    2. Click or tap the last word you want to select, and you’ll see everything in between get selected.
    3. You’ll then get a menu of options like creating a highlight or a citation from that passage of text.

    OK, got it!

    Cited passage

    Style
    Citations are available only to our active members.
    Buy instant access to cite pages or passages in MLA 8, MLA 7, APA and Chicago citation styles.

    "Portraying himself as an honest, ordinary person helped Lincoln identify with his audiences." (Einhorn, 1992, p. 25).

    "Portraying himself as an honest, ordinary person helped Lincoln identify with his audiences." (Einhorn 25)

    "Portraying himself as an honest, ordinary person helped Lincoln identify with his audiences." (Einhorn 25)

    "Portraying himself as an honest, ordinary person helped Lincoln identify with his audiences."1

    1. Lois J. Einhorn, Abraham Lincoln, the Orator: Penetrating the Lincoln Legend (Westport, CT: Greenwood Press, 1992), 25, http://www.questia.com/read/27419298.

    Cited passage

    Thanks for trying Questia!

    Please continue trying out our research tools, but please note, full functionality is available only to our active members.

    Your work will be lost once you leave this Web page.

    Buy instant access to save your work.

    Already a member? Log in now.

    Search by... Author
    Show... All Results Primary Sources Peer-reviewed

    Oops!

    An unknown error has occurred. Please click the button below to reload the page. If the problem persists, please try again in a little while.