Papers by Peishan Zhou
Algorithms, 2025
Accurate fetal R-peak detection from low-SNR fetal electrocardiogram (FECG) signals remains a cri... more Accurate fetal R-peak detection from low-SNR fetal electrocardiogram (FECG) signals remains a critical challenge as current NI-FECG methods struggle to extract high SNR FECG signals and conventional algorithms fail when signal quality deteriorates. We proposed a U-Net-based method that enables robust R-peak detection directly from low-SNR FECG signals (0-12 dB), bypassing the need for high-SNR inputs that are clinically difficult to acquire. The method was evaluated on both real (A&D FECG) and synthetic (FECGSYN) databases, comparing against ten state-of-the-art detectors. The proposed method significantly reduces false predictions compared to commonly used detection algorithms, achieving a PPV of 99.81%, an SEN of 100.00%, and an F1-score of 99.91% on the A&D FECG database and a PPV of 99.96%, an SEN of 99.93%, and an F1-score of 99.94% on the FECGSYN database. Further investigation of robustness in low-SNR conditions (0 dB, 5 dB, and 10 dB) achieved 87.38% F1-score at 0 dB SNR on real signals, surpassing the best-performing algorithm implemented in Neurokit by 13.58%. In addition, the algorithm showed ≤2.65% performance variation across tolerance windows (50 reduced to 20 ms), further underscoring its detection accuracy. Overall, this work reduces the reliance on high-SNR FECG signals by reliably extracting R-peaks from suboptimal signals, providing implications for the reliability of fetal heart rate variability analysis in real-world noisy environments.
2020 14th International Conference on Signal Processing and Communication Systems (ICSPCS), 2020
In this work, we propose a novel algorithm to achieve real-time R-peak prediction during ECG sign... more In this work, we propose a novel algorithm to achieve real-time R-peak prediction during ECG signal recording. More specifically, from the current frame of ECG signal, we aim to predict how far into the future the next R-peak will occur, taking into account information about the variability in the intervals between beats seen in previous frames of the ECG signal. Currently there has been little work in this area. However, the real-time prediction of the next beat has important research significance, including timing of artificial heart pumps and integration into the cardiopulmonary support heart (CPS-heart), as well as narrowing the search range of R-peaks to assist Rpeak detection. This paper proposes use of an integrated network using one-dimensional convolution network (1D CNN) with long short-term memory (LSTM) network. The deep learning model we have proposed is shown to effectively predict R-peaks with results of preliminary studies achieving a prediction accuracy of 90.61 %.
In this work, we propose a novel algorithm to achieve real-time R-peak prediction during ECG sign... more In this work, we propose a novel algorithm to achieve real-time R-peak prediction during ECG signal recording. More specifically, from the current frame of ECG signal, we aim to predict how far into the future the next R-peak will occur, taking into account information about the variability in the intervals between beats seen in previous frames of the ECG signal. Currently there has been little work in this area. However, the real-time prediction of the next beat has important research significance, including timing of artificial heart pumps and integration into the cardiopulmonary support heart (CPS-heart), as well as narrowing the search range of R-peaks to assist Rpeak detection. This paper proposes use of an integrated network using one-dimensional convolution network (1D CNN) with long short-term memory (LSTM) network. The deep learning model we have proposed is shown to effectively predict R-peaks with results of preliminary studies achieving a prediction accuracy of 90.61%.
Conference Presentations by Peishan Zhou
2024 9th International Conference on Signal and Image Processing (ICSIP), 2024
Electrocardiography (ECG) is a promising approach for continuous fetal heart rate monitoring. Its... more Electrocardiography (ECG) is a promising approach for continuous fetal heart rate monitoring. Its morphology can provide information on fetal health to guide patient care by clinicians. However, fetal ECGs extracted from abdominal ECGs are often too weak to reliably detect fetal heart rate. This study evaluates the application of a U-Net architecture for accurate R-peak detection in low-SNR fetal ECG signals. The proposed method achieves high accuracy with a positive predictive value of 99.81%, sensitivity of 100.00%, and an F1-score of 99.91% on direct fetal ECG from the Abdominal and Direct ECG Database, with significantly reduced false predictions, and outperforming two other baseline methods compared with. Notably, our approach demonstrates robustness, accurately predicting peaks in regions of high distortion, a capability unmatched by other methods evaluated. This finding indicates the suitability and benefits of the U-Net architecture for peak detection in fetal ECG signals.
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Papers by Peishan Zhou
Conference Presentations by Peishan Zhou