Revolutionizing Fetal QRS Complex Detection: A Cutting‐Edge Algorithm Employing New Adaptive Filters and Peak Thresholds

Purpose This study proposes an innovative algorithm based on the morphological parameters of noninvasive fetal electrocardiography (NI‐FECG) for the comprehensive analysis of all electrocardiography (ECG) signal components, including the P wave, PR interval, QRS complex, ST segment, T wave, U wave, and QT interval. Accurate identification of these components is critical for a holistic evaluation of fetal heart health. While the QRS complex is crucial for detecting arrhythmias and guiding clinical interventions, a complete diagnostic evaluation requires analyzing all waveform components. The computational efficiency and robustness of the proposed algorithm make it highly suitable for real‐time clinical applications, distinguishing it from conventional methods. Materials and Methods The proposed method utilizes a dataset of 55 multichannel abdominal NI‐FECG recordings collected between gestational weeks 21 and 40. This method focuses on enhancing detection accuracy across various ECG signal components by incorporating adaptive filtering techniques and dynamic peak thresholding to minimize noise interference and improve signal clarity. The study provides a detailed evaluation of the variability in QRS morphology and the influence of arrhythmias on the ECG waveform. Results Experimental results demonstrate the proposed algorithm’s robustness, achieving an average detection error of 1.78% and a standard deviation of 0.46% across all participants for all ECG components, with specific emphasis on the QRS complex. Additionally, the computational complexity is significantly reduced compared to existing approaches, ensuring feasibility for real‐time deployment in clinical settings. Conclusion This study presents a groundbreaking algorithm for fetal ECG analysis using NI‐FECG signals, yielding a high precision for the detection of all morphological features, such as the PR, RR, ST, and QT intervals. The holistic approach ensures a more reliable assessment of fetal heart health. With a swift 0.22‐s execution time, the algorithm is practical for real‐time applications. Our findings highlight the significance of tailored biological signal processing over generic artificial intelligence (AI)‐based models in enhancing accuracy and noise resilience in fetal ECG analysis. Further research is needed to optimize performance in diverse clinical scenarios. Integration of this approach into routine clinical practice could significantly improve fetal cardiovascular health monitoring and overall pregnancy outcomes.