# A Non-Invasive Fetal QRS Complex Detection Method Based on a Multi-Feature Fusion Neural Network
## 5 Conclusions  
High-quality fetal ECG signals are of significant clinical importance for diagnosing the health status of fetuses. Our method leverages a sliding window approach to identify fetal QRS complexes from maternal ECG signals sequentially while employing a lightweight deep neural network for the rapid and accurate output of fetal QRS complex waveforms, paving the way for the intelligent morphological analysis of fetal ECG signals. This approach overcomes the limitations of traditional fetal heart rate monitoring, which requires the separation of maternal and fetal ECG signals. Compared to conventional methods that necessitate four-channel detection, our method achieves the reliable detection performance of fetal QRS complexes with data from only a single channel under the premise of ensuring data quality. This means that pregnant women, using a single electrode patch attached to the abdomen, can independently monitor the fetal heartbeat status at home using a smartphone, presenting a broad application prospect. However, the current dataset does not include cases of high-risk pregnancies or multiple pregnancies, which are important scenarios for clinical practice. Future research should focus on adapting fetal heart rate detection methods to effectively function in such challenging conditions. Looking forward, the further training of the network with more extensive and diverse datasets is necessary to enhance analysis accuracy and robustness. Future improvements could also involve integrating advanced noise reduction techniques and adaptive algorithms that account for different maternal conditions and environments, thereby increasing the method's applicability and reliability in clinical practice. Conceptualization, Z.H. and J.Y. ; methodology, Z.H. ; software, Y.S. ; validation, Z.H., J.Y. and X.W. ; formal analysis, Z.H. ; investigation, Y.S. ; resources, J.Y. ; data curation, Z.H. ; writing--original draft preparation, Z.H. ; writing--review and editing, J.Y. and X.W. ; visualization, Y.S. ; supervision, J.Y. ; project administration, J.Y. All authors have read and agreed to the published version of the manuscript. This research received no external funding. The publicly available datasets used in this study have received ethical approval from the relevant institutions, and no new ethical review was required. Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. The datasets analyzed during the current study are available in the PhysioNet repository, [[https://physionet.org/content/challenge-2013/1.0.0/](https://physionet.org/content/challenge-2013/1.0.0/)]([https://physionet.org/content/challenge-2013/1.0.0/](https://physionet.org/content/challenge-2013/1.0.0/)) (accessed on 18 December 2023) and [[https://physionet.org/content/adefcgdb/1.0.0/](https://physionet.org/content/adefcgdb/1.0.0/)]([https://physionet.org/content/adefcgdb/1.0.0/](https://physionet.org/content/adefcgdb/1.0.0/)) (accessed on 20 July 2024), respectively. Author [PERSON] was employed by the company Beijing Health State Monitoring & Consulting Co., Limited, Beijing, China. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interes. References  
[PERSON]; [PERSON]; [PERSON]; [PERSON] Continuous Cardiotocography (CTG) as a Form of Electronic Fetal Monitoring (EFM) for Fetal Assessment during Labour. _Cochrane Database Syst. Rev._**2017**, _2019_, CD006066. [CrossRef] [PubMed]  
[PERSON]; [PERSON]; [PERSON]; [PERSON] Detection of Fetal Cardiac Anomaly from Composite Abdominal Electrocardiogram. _Biomed. Signal Process. Control_**2021**, _65_, 102308. [CrossRef]  
[PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Doppler Ultrasound Technology for Fetal Heart Rate Monitoring: A Review. _IEEE Trans. Ultrason. Ferroelect. Freq. Contr._**2020**, _67_, 226-238.