Enhanced labor pain monitoring using machine learning and ECG waveform analysis for uterine contraction-induced pain.

ABSTRACT:

Objectives

This study aims to develop an innovative approach for monitoring and assessing labor pain through ECG waveform analysis, utilizing machine learning techniques to monitor pain resulting from uterine contractions.

Methods

The study was conducted at National Taiwan University Hospital between January and July 2020. We collected a dataset of 6010 ECG samples from women preparing for natural spontaneous delivery (NSD). The ECG data was used to develop an ECG waveform-based Nociception Monitoring Index (NoM). The dataset was divided into training (80%) and validation (20%) sets. Multiple machine learning models, including LightGBM, XGBoost, SnapLogisticRegression, and SnapDecisionTree, were developed and evaluated. Hyperparameter optimization was performed using grid se

SUBMITTER: Chu YC 

PROVIDER: S-EPMC11380346 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature