# A Non-Invasive Fetal QRS Complex Detection Method Based on a Multi-Feature Fusion Neural Network
###### Abstract  
Fetal heart monitoring, as a crucial part of fetal monitoring, can accurately reflect the fetus's health status in a timely manner. To address the issues of high computational cost, inability to observe fetal heart morphology, and insufficient accuracy associated with the traditional method of calculating the fetal heart rate using a four-channel maternal electrocardiogram (ECG), a method for extracting fetal QRS complexes from a single-channel non-invasive fetal ECG based on a multi-feature fusion neural network is proposed. Firstly, a signal entropy data quality detection algorithm based on the blind source separation method is designed to select maternal ECG signals that meet the quality requirements from all channel ECG data, followed by data preprocessing operations such as denoising and normalization on the signals. After being segmented by the sliding window method, the maternal ECG signals are calculated as data in four modes: time domain, frequency domain, time-frequency domain, and data eigenvalues. Finally, the deep neural network using three multi-feature fusion strategies--feature-level fusion, decision-level fusion, and model-level fusion--achieves the effect of quickly identifying fetal QRS complexes. Among the proposed networks, the one with the best performance has an accuracy of 95.85% and sensitivity of 97%. deep neural networks; fetal QRS detection; maternal ECG; multi-feature fusion +
Footnote †: journal: MedicalTo address the challenges of fetal heart rate extraction, several mathematical methods have been proposed, including blind source separation algorithms based on Independent Component Analysis (ICA) and Singular Value Decomposition (SVD) [7], algorithms combining power spectral density and matched filtering techniques [8], adaptive filtering algorithms combining SVD and smooth window methods [9], wavelet separation methods combining clustering algorithms with extrema pairs and ICA [10], and a negative entropy-based blind source separation method combined with a template subtraction method [11]. [PERSON] et al. proposed a method combining ICA and LMS, achieving Se, PPV, and F1 scores of 89.41%, 90.42%, and 89.19%, respectively [12]. [PERSON] et al. proposed a method for extracting fetal QRS complexes from single-channel abdominal ECG signals based on an Adaptive Improved Permutation Entropy (AIPE) algorithm, achieving an F1 score and Se of 90.95% and 90.77%, respectively [13]. Other researchers have used methods such as Principal Component Analysis [14; 15], Recursive Least Squares [16], Tensor Decomposition [17], and Linear Combination Algorithms [18] for fetal heart rate detection research, all achieving satisfactory experimental outcomes. With significant advancements in deep learning technologies, numerous studies have used neural networks to extract fetal ECG signals without separating the maternal ECG [19; 20; 21]. [PERSON] et al. proposed a novel method based on Conditional Generative Adversarial Networks (cGANs) for extracting fetal electrocardiogram (ECG) signals in the time-frequency domain using the Short-Time Fourier Transform (STFT) method. This method demonstrates higher robustness and resistance to interference compared to traditional approaches [22]. [PERSON] et al. employed a one-dimensional convolutional neural network (CNN) to detect fetal QRS complexes without removing the maternal ECG signal, aiming for a similar goal as this study, achieving an accuracy of 75.33%, a recall rate of 80.54%, and an F1 score of 77.85% [23]. [PERSON] et al. proposed a more profound CNN architecture that utilizes four-channel original ECG signals to detect fetal QRS, resulting in a positive predictive value (PPV) of 92.77% and an average sensitivity of 89.06% [24]. [PERSON] et al. used the Short-Time Fourier Transform (STFT) method to prepare features for training, employing ResNet and a one-dimensional octave convolution network to design a four-class deep learning model, which achieved an accuracy of 91.1% [25].