Arrhythmia Foetus Heartbeat Detection Using Optimized Neural Network Based on Phonocardiograph Ensemble Feature and Principal Component Analysis

High-risk maternal health condition is alarming, especially in developing countries. Intensive monitoring is mandatory to prevent such issue. However, the long-term invasive method to pregnant women harms both the baby and the mother. In this research, we proposed a cost-efficient non-invasive foetal heartbeat classification based on a phonocardiograph with feature assembly. Since the high number of features and computationally expensive, we cut the size to half by utilizing Principal Component Analysis. Furthermore, data balancing using SMOTE is incorporated to improve classification performance. We proposed a method based on a neural network and optimized it using Random Search optimization. Eventually, the proposed method gained the top position in all data balancing compared to other machine learning algorithms, with 91.7% for both accuracy and Area Under Curve with a score at 91.6%.