Key result
An XGBoost machine learning model applied to ECG waveforms accurately predicted regular uterine contraction cycle-induced labor pain with an ROC of 0.98.
Why the study?
A reliable, validated pain-level assessment approach to evaluate consciousness status has yet to be established.
Can machine learning applied to ECG waveforms accurately predict labor pain during uterine contractions in pregnant women?
Cross-Sectional (n=16)
No
Can machine learning applied to ECG waveforms accurately predict labor pain during uterine contractions in pregnant women?
Effect estimate: ROC 0.98
Machine learning models, particularly XGBoost, applied to ECG waveforms can accurately predict labor pain during uterine contractions.
ECG-ML models for labor pain monitoring are hypothesis-generating; prospective validation required before any clinical consideration.
BACKGROUND To date, a reliable, validated pain-level assessment approach to evaluate consciousness status has yet to be established. With appropriate algorithms, computers can learn to detect patterns and associations in large datasets. OBJECTIVE This study aimed to apply machine learning to electrocardiography (ECG) waveforms to create an algorithm to predict and monitor regular uterine contraction cycle-induced labor pain. METHODS Pregnant women undergoing natural spontaneous delivery (NSD) with regular uterine contraction pain were recruited from National Taiwan University Hospital for prospective data collection and cross-sectional analysis. Machine learning was used to fine-tune a nociception-related algorithm (NoP) based initially on the complex analysis of features in high-fidelity ECG waveform recordings in 4 pregnant women. The algorithm was then validated in another 12 pregnant women. Data on uterine contractions were collected as raw data from tocometry and ECG waveforms by re-cording simultaneously using the same computer. Every uterine contraction cycle with VAS pain score was also confirmed and recorded by an investigating nurse for further analysis. RESULTS The machine learning model that most accurately predicted pain was the XGBoost model, which exhibited the highest ROC (0.98), followed by the random forest and GBDT models with an ROC of 0.89. NoP threshold values and NoP indexes were the highest and most important features in the XGBoost models. CONCLUSIONS The newly developed algorithm supports obstetricians in clinical practice by providing early detection of alterations in ECG waveforms during uterine contraction cycles. The integration of ECG systems should be considered in artificial intelligence (AI) models for future parturient care.
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Chu et al. (2022) conducted a cross-sectional in Labor pain during natural spontaneous delivery (n=16). Machine learning-based pain prediction using ECG waveforms (XGBoost model) vs. Random forest and GBDT models was evaluated on Accuracy of pain prediction (ROC) (ROC 0.98). An XGBoost machine learning model applied to ECG waveforms accurately predicted regular uterine contraction cycle-induced labor pain with an ROC of 0.98.
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