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1. Recovering fetal signals transabdominally through interferometric near-infrared spectroscopy (iNIRS)

   https://doi.org/10.1364/BOE.500898  

   Liu, Shing-Jiuan; Lee, Su_Yeon; Pivetti, Christopher; Kulubya, Edwin; Wang, Aijun; Farmer, Diana_L; Ghiasi, Soheil; Yang, Weijian (October 2023, Biomedical Optics Express)

   Noninvasive transabdominal fetal pulse oximetry can provide clinicians critical assessment of fetal health and potentially contribute to improved management of childbirth. Conventional pulse oximetry through continuous wave (CW) light has challenges measuring the signals from deep tissue and separating the weak fetal signal from the strong maternal signal. Here, we propose a new approach for transabdominal fetal pulse oximetry through interferometric near-infrared spectroscopy (iNIRS). This approach provides pathlengths of photons traversing the tissue, which facilitates the extraction of fetal signals by rejecting the very strong maternal signal from superficial layers. We use a multimode fiber combined with a mode-field converter at the detection arm to boost the signal of iNIRS. Together, we can detect signals from deep tissue (>∼1.6 cm in sheep abdomen and in human forearm) at merely 1.1 cm distance from the source. Using a pregnant sheep model, we experimentally measured and extracted the fetal heartbeat signals originating from deep tissue. This validated a key step towards transabdominal fetal pulse oximetry through iNIRS and set a foundation for further development of this method to measure the fetal oxygen saturation. 

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2. A Deep-Learning-Based Multi-modal ECG and PCG Processing Framework for Label Efficient Heart Sound Segmentation

   https://doi.org/10.1109/CHASE60773.2024.00020  

   Huang, Qijia; Yang, Huanrui; Zeng, Eric; Chen, Yiran (June 2024, IEEE)

   The COVID-19 pandemic has intensified the need for home-based cardiac health monitoring systems. Despite advancements in electrocardiograph (ECG) and phonocardiogram (PCG) wearable sensors, accurate heart sound segmentation algorithms remain understudied. Existing deep learning models, such as convolutional neural networks (CNN) and recurrent neural networks (RNN), struggle to segment noisy signals using only PCG data. We propose a two-step heart sound segmentation algorithm that analyzes synchronized ECG and PCG signals. The first step involves heartbeat detection using a CNN-LSTM-based model on ECG data, and the second step focuses on beat-wise heart sound segmentation with a 1D U-Net that incorporates multi-modal inputs. Our method leverages temporal correlation between ECG and PCG signals to enhance segmentation performance. To tackle the label-hungry issue in AI-supported biomedical studies, we introduce a segment-wise contrastive learning technique for signal segmentation, overcoming the limitations of traditional contrastive learning methods designed for classification tasks. We evaluated our two-step algorithm using the PhysioNet 2016 dataset and a private dataset from Bayland Scientific, obtaining a 96.43 F1 score on the former. Notably, our segment-wise contrastive learning technique demonstrated effective performance with limited labeled data. When trained on just 1% of labeled PhysioNet data, the model pre-trained on the full unlabeled dataset only dropped 2.88 in the F1 score, outperforming the SimCLR method. Overall, our proposed algorithm and learning technique present promise for improving heart sound segmentation and reducing the need for labeled data. 

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3. Heart rate detection using single-channel Doppler radar system

   https://doi.org/10.1109/EMBC48229.2022.9871199  

   Sameera, Jannatun Noor; Droitcour, Amy D.; Boric-Lubecke, Olga (July 2022, Heart rate detection using single-channel Doppler radar system)

   A number of algorithms have been developed to extract heart rate from physiological motion data using Doppler radar system. Yet, it is very challenging to eliminate noise associated with surroundings, especially with a single-channel Doppler radar system. However, single-channel Doppler radars provide the advantage of operating at lower power. Additionally, heart rate extraction using single-channel Doppler radar has remained somewhat unexplored. This has motivated the development of effective signal processing algorithms for signals received from single-channel Doppler radars. Three algorithms have been studied for estimating heart rate. The first algorithm is based on applying FFT on an FIR filtered signal. In the second algorithm, autocorrelation was performed on the filtered data. Thirdly, a peak finding algorithm was used in conjunction with a moving average preceded by a clipper to determine the heart rate. The results obtained were compared with heart rate readings from a pulse oximeter. With a mean difference of 2.6 bpm, the heart rate from Doppler radar matched that from the pulse oximeter most frequently when the peak finding algorithm was used. The results obtained using autocorrelation and peak finding algorithm (with standard deviations of 2.6 bpm and 4.0 bpm) suggest that a single channel Doppler radar system can be a viable alternative to contact heart rate monitors in patients for whom contact measurements are not feasible. 

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4. Investigation of Methods to Extract Fetal Electrocardiogram from the Mother’s Abdominal Signal in Practical Scenarios

   https://doi.org/10.3390/technologies8020033  

   Sarafan, Sadaf; Le, Tai; Naderi, Amir Mohammad; Nguyen, Quoc-Dinh; Kuo, Brandon Tiang-Yu; Ghirmai, Tadesse; Han, Huy-Dung; Lau, Michael P.; Cao, Hung (June 2020, Technologies)

   null (Ed.)

   Monitoring of fetal electrocardiogram (fECG) would provide useful information about fetal wellbeing as well as any abnormal development during pregnancy. Recent advances in flexible electronics and wearable technologies have enabled compact devices to acquire personal physiological signals in the home setting, including those of expectant mothers. However, the high noise level in the daily life renders long-entrenched challenges to extract fECG from the combined fetal/maternal ECG signal recorded in the abdominal area of the mother. Thus, an efficient fECG extraction scheme is a dire need. In this work, we intensively explored various extraction algorithms, including template subtraction (TS), independent component analysis (ICA), and extended Kalman filter (EKF) using the data from the PhysioNet 2013 Challenge. Furthermore, the modified data with Gaussian and motion noise added, mimicking a practical scenario, were utilized to examine the performance of algorithms. Finally, we combined different algorithms together, yielding promising results, with the best performance in the F1 score of 92.61% achieved by an algorithm combining ICA and TS. With the data modified by adding different types of noise, the combination of ICA–TS–ICA showed the highest F1 score of 85.4%. It should be noted that these combined approaches required higher computational complexity, including execution time and allocated memory compared with other methods. Owing to comprehensive examination through various evaluation metrics in different extraction algorithms, this study provides insights into the implementation and operation of state-of-the-art fetal and maternal monitoring systems in the era of mobile health. 

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5. Fetal Electrocardiogram Extraction from the Mother’s Abdominal Signal Using the Ensemble Kalman Filter

   https://doi.org/10.3390/s22072788  

   Sarafan, Sadaf; Le, Tai; Lau, Michael P.; Hameed, Afshan; Ghirmai, Tadesse; Cao, Hung (April 2022, Sensors)

   Fetal electrocardiogram (fECG) assessment is essential throughout pregnancy to monitor the wellbeing and development of the fetus, and to possibly diagnose potential congenital heart defects. Due to the high noise incorporated in the abdominal ECG (aECG) signals, the extraction of fECG has been challenging. And it is even a lot more difficult for fECG extraction if only one channel of aECG is provided, i.e., in a compact patch device. In this paper, we propose a novel algorithm based on the Ensemble Kalman filter (EnKF) for non-invasive fECG extraction from a single-channel aECG signal. To assess the performance of the proposed algorithm, we used our own clinical data, obtained from a pilot study with 10 subjects each of 20 min recording, and data from the PhysioNet 2013 Challenge bank with labeled QRS complex annotations. The proposed methodology shows the average positive predictive value (PPV) of 97.59%, sensitivity (SE) of 96.91%, and F1-score of 97.25% from the PhysioNet 2013 Challenge bank. Our results also indicate that the proposed algorithm is reliable and effective, and it outperforms the recently proposed extended Kalman filter (EKF) based algorithm. 

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