A Machine Learning Framework for Fetal Arrhythmia Detection via Single ECG Electrode

Fetal Arrhythmia is an abnormal heart rhythm caused by a problem in the fetus's heart's electrical system. Monitoring fetal ECG is vital to delivering useful information regarding the fetus's condition. Acute fetal arrhythmia may result in cardiac failure or death. Thus the early detection of fetal arrhythmia is important. Current approaches use several electrodes to acquire abdomen ECG from the mother, which causes discomfort. Moreover, ECG signals acquired are extremely noisy and have artifacts from breathing and muscle contraction, which hardens ECG extraction. In this study, a machine learning framework for fetal arrhythmia detection. The proposed framework uses only a single abdomen ECG. It employs multiple filtering techniques to remove noise and artifacts. It also extracts 16 significant features from multiple domains, including (time, frequency, and time-frequency features. Finally, it utilizes four machine learning classifiers to detect arrhythmia. The highest accuracy of 93.12% is achieved using Boosted decision tree classifier. The performance of the proposed method shows its competing ability compared to other methods.

Automated summary

Fetal arrhythmia, caused by a problem in the fetus's heart's electrical system, is an abnormal heart rhythm that requires monitoring for providing valuable information about the fetus's condition. Current methods involving multiple electrodes for acquiring abdominal ECG from the mother cause discomfort and difficulty in extracting ECG due to noise and artifacts. This study presents a machine learning framework for detecting fetal arrhythmia using a single abdominal ECG, achieving a high accuracy rate.