Why the study?
Interpreting massive sensor-generated ECG signals is challenging, and traditional machine learning techniques for detecting fetal cardiac abnormalities still require improvements.
Do machine learning approaches improve the detection of fetal cardiac anomalies from ECG signals compared to traditional techniques?
Do machine learning approaches improve the detection of fetal cardiac anomalies from ECG signals compared to traditional techniques?
This systematic review highlights the potential of machine learning in analyzing fetal ECG signals to detect cardiac anomalies and identifies key research gaps for future development.
Machine learning merits evaluation for fetal ECG anomaly detection; leaves open standardized comparisons to traditional methods and optimal performance benchmarks.
Globally, the fetal cardiac abnormality is the primary reason for morality. However, understanding the huge amount of "Electrocardiogram (ECG)" signals generated from the sensors is exhausting. The machine learning approaches can be employed to assist the evaluation of these images and support identifying any dangerous cardiac anomalies in the fetus. But, traditional machine learning techniques still need improvements. Thus, this survey analyzes the existing strategies utilized for fetal cardiac abnormality detection tasks. The multimodal data and performance measures supported for the fetal cardiac abnormality detection tasks are categorized. The research gaps and challenges are provided to improve the new fetal cardiac abnormality detection mechanisms in the future.
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A et al. (2024) studied this question.
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