Abdominal Signals Research Articles

A Hierarchical Attention-Based Method for Sleep Staging Using Movement and Cardiopulmonary Signals.

Sleep monitoring typically requires the uncomfortable and expensive polysomnography (PSG) test to determine the sleep stages. Body movement and cardiopulmonary signals provide an alternative way to perform sleep staging. In recent years, long-short term memory (LSTM) networks and convolutional neural networks (CNN) have dominated automatic sleep staging due to their better learning ability than machine learning classifiers. However, LSTM may lose information when dealing with long sequences, while CNN is not good at sequence modeling. As an improvement, we develop a hierarchical attention-based deep learning method for sleep staging using body movement, electrocardiogram (ECG), and abdominal breathing signals. We apply the multi-head self-attention to model the global context of feature sequences and coupled it with CNN to achieve a hierarchical self-attention weight assignment. We evaluate the performance of the method using two public datasets. Our method outperforms other baselines in the three sleep stages, achieving an accuracy of 84.3%, an F1 score of 0.8038, and a Cohen's Kappa coefficient of 0.7036. The result demonstrates the effectiveness of the hierarchical self-attention mechanism when processing feature sequences in the sleep stage classification problem. This paper provides new possibilities for long-term sleep monitoring using movement and cardiopulmonary signals obtained from non-invasive devices. The code can be found at: https://github.com/scutrd/attention-sleep-staging.

Automatic signal quality assessment of raw trans-abdominal biopotential recordings for non-invasive fetal electrocardiography.

Introduction: Wearable monitoring systems for non-invasive multi-channel fetal electrocardiography (fECG) can support fetal surveillance and diagnosis during pregnancy, thus enabling prompt treatment. In these embedded systems, power saving is the key to long-term monitoring. In this regard, the computational burden of signal processing methods implemented for the fECG extraction from the multi-channel trans-abdominal recordings plays a non-negligible role. In this work, a supervised machine-learning approach for the automatic selection of the most informative raw abdominal recordings in terms of fECG content, i.e., those potentially leading to good-quality, non-invasive fECG signals from a low number of channels, is presented and evaluated. Methods: For this purpose, several signal quality indexes from the scientific literature were adopted as features to train an ensemble tree classifier, which was asked to perform a binary classification between informative and non-informative abdominal channels. To reduce the dimensionality of the classification problem, and to improve the performance, a feature selection approach was also implemented for the identification of a subset of optimal features. 10336 5-s long signal segments derived from a real dataset of multi-channel trans-abdominal recordings acquired from 55 voluntary pregnant women between the 21st and the 27th week of gestation, with healthy fetuses, were adopted to train and test the classification approach in a stratified 10-time 10-fold cross-validation scheme. Abdominal recordings were firstly pre-processed and then labeled as informative or non-informative, according to the signal-to-noise ratio exhibited by the extracted fECG, thus producing a balanced dataset of bad and good quality abdominal channels. Results and Discussion: Classification performance revealed an accuracy above 86%, and more than 88% of those channels labeled as informative were correctly identified. Furthermore, by applying the proposed method to 50 annotated 24-channel recordings from the NInFEA dataset, a significant improvement was observed in fetal QRS detection when only the channels selected by the proposed approach were considered, compared with the use of all the available channels. As such, our findings support the hypothesis that performing a channel selection by looking directly at the raw abdominal signals, regardless of the fetal presentation, can produce a reliable measurement of fetal heart rate with a lower computational burden.

Objective Quantification of Detrusor Overactivity Using Spectral Measures of Cystometry Data

To develop scalable objective methods for differentiating patients with and without detrusor overactivity (DO) using quantitative Fast Fourier Transform (FFT)-based measures and routinely captured cystometry data. Retrospective cystometry data were collected as prevoid vesical and abdominal pressure signals from 18 DO and 10 SUI (non-DO) cystometry recordings. Data were filtered and divided into two equal-duration segments, Early and Late Fill, representing the first and second halves of filling. FFT was applied, followed by subtraction of abdominal spectra from vesical spectra. Spectral Power (SP) and Weighted Average Frequency (WAF) measures were calculated for each segment spectra within 1-6 cycles min-1. Compared to non-DO, the mean SP was significantly higher in DO patients for both Early and Late Fill segments. WAF was significantly lower in DO patients for both segments. Changes in spectral pressures appeared to be linked to the presence of detrusor contractions (DCs) and were especially visible when DCs were present in the Early Fill segments of cystometry. FFT-based spectral measures derived from routinely captured cystometry data are significantly different between DO and non-DO patients. This preliminary method is clinically scalable and can be further developed to facilitate the detection of DO, classify disease phenotype, and capture therapeutic efficacy.

The neural pathway of the hyperthermic response to antagonists of the transient receptor potential vanilloid-1 channel

ABSTRACT We identified the neural pathway of the hyperthermic response to TRPV1 antagonists. We showed that hyperthermia induced by i.v. AMG0347, AMG 517, or AMG8163 did not occur in rats with abdominal sensory nerves desensitized by pretreatment with a low i.p. dose of resiniferatoxin (RTX, TRPV1 agonist). However, neither bilateral vagotomy nor bilateral transection of the greater splanchnic nerve attenuated AMG0347-induced hyperthermia. Yet, this hyperthermia was attenuated by bilateral high cervical transection of the spinal dorsolateral funiculus (DLF). To explain the extra-splanchnic, spinal mediation of TRPV1 antagonist-induced hyperthermia, we proposed that abdominal signals that drive this hyperthermia originate in skeletal muscles – not viscera. If so, in order to prevent TRPV1 antagonist-induced hyperthermia, the desensitization caused by i.p. RTX should spread into the abdominal-wall muscles. Indeed, we found that the local hypoperfusion response to capsaicin (TRPV1 agonist) in the abdominal-wall muscles was absent in i.p. RTX-desensitized rats. We then showed that the most upstream (lateral parabrachial, LPB) and the most downstream (rostral raphe pallidus) nuclei of the intrabrain pathway that controls autonomic cold defenses are also required for the hyperthermic response to i.v. AMG0347. Injection of muscimol (inhibitor of neuronal activity) into the LPB or injection of glycine (inhibitory neurotransmitter) into the raphe blocked the hyperthermic response to i.v. AMG0347, whereas i.v. AMG0347 increased the number of c-Fos cells in the raphe. We conclude that the neural pathway of TRPV1 antagonist-induced hyperthermia involves TRPV1-expressing sensory nerves in trunk muscles, the DLF, and the same LPB-raphe pathway that controls autonomic cold defenses.

A Dual Attention-Based Autoencoder Model for Fetal ECG Extraction From Abdominal Signals

Fetal electrocardiogram (FECG) signals contain important information about the conditions of the fetus during pregnancy. Currently, pure FECG signals can only be obtained through an invasive acquisition process, which is life threatening to both mother and fetus. In this study, single-channel ECG signals from the mother’s abdomen are analyzed with the aim of extracting the clean FECG waveform. This is a challenging task due to the very low amplitude of the FECG, various noises involved in the signal acquisition, and the overlap of R waves. To address this problem, we propose a novel convolutional autoencoder (AE) network architecture to learn and extract the FECG patterns. The proposed model is equipped with a dual attention mechanism, composed of squeeze-and-excitation and channel-wise (CW) modules, in the encoder and decoder blocks, respectively. It also benefits from a bidirectional long short-term memory (LSTM) layer. This unique combination allows the proposed network to accurately attend to and extract the FECG signals from abdominal data. Three well-established datasets are considered in our experiments. The obtained results of FECG extraction are promising and confirm the effectiveness of using attention modules within the deep learning model. The results also suggest that the proposed AE network can accurately extract the FECG signals where no information about maternal ECG (MECG) is available.

ASW-Net: Adaptive Spectral Wavelet Network for Accurate Fetal ECG Extraction.

Noninvasive fetal ECG (FECG) is of great significance for monitoring fetal health. However, it is challenging to extract FECG signals from the abdominal ECG signal (AECG) due to the complexity of the task: 1) FECG signals are routinely mixed with noise; 2) FECG signals are aliased with maternal ECG signals in the time and frequency domain. To solve such problems, an adaptive spectral wavelet network (ASW-Net) is proposed for FECG extraction, where the adaptive spectral wavelet module, which can improve the computational efficiency by replacing convolution operation with element-wise Hadamard product in the frequency domain, is first developed to extract FECG components with different frequencies; then, the residual attention module is devised to distinguish FECG signals from noise by capturing waveform details; finally, the inverse spectral wavelet module is designed to reconstruct FECG signals from multi-resolution FECG components. Experiments conducted on the benchmarks demonstrate that the proposed ASW-Net outperforms the state-of-the-art methods.

Fetal ECG extraction using temporal convolutional encoder-decoder network

To extract weak fetal ECG signals from mixed ECG signals recorded from maternal abdominal wall for accurate analysis of fetal heart rate and fetal ECG patterns. By exploiting the superior nonlinear mapping ability of deep convolutional network, we developed a nonlinear adaptive noise cancelling (nonlinear ANC) extraction framework based on a temporal convolutional encoder-decoder network for extracting fetal ECG signals. We first constructed a deep temporal convolutional network (TCED-Net) model for fetal ECG signal extraction, and with the maternal chest ECG signal as the reference signal, the maternal ECG component in the abdominal mixed signal was estimated using this model. The estimated maternal ECG component was subtracted from the mixed abdominal ECG signals to obtain the fetal ECG component. Experimental analyses were performed using synthetic ECG signals (FECGSYNDB) and clinical ECG signals (NIFECGDB, PCDB) to test the performance of the propose method. The results of experiments on the FECGSYNDB dataset showed that the proposed approach achieved good performance in F1-score (98.89%), mean-square-error (MSE; 0.20) and quality signalto-noise ratio (qSNR; 7.84). The F1- score reached 99.1% on the NIFECGDB dataset and 98.61% on the PCDB dataset. The R peak detection accuracy index of the proposed method was higher than the existing best-performing algorithms such as EKF (F1=93.84%), ES-RNN (F1=97.20%) and AECG-DecompNet (F1=95.43%) by 5.05%, 1.9% and 3.18%, respectively. The fetal ECG signals extracted using the proposed method are clearer than those by the existing algorithms, suggesting the potential value this method for effective fetal health monitoring during pregnancy.

Heuristic RNN-based Kalman filter for fetal electrocardiogram extraction

Fetal Electrocardiogram (FECG) analysis helps in diagnosis of fetal heart. Extracting FECG from composite abdominal signal that contains noises like maternal ECG (MECG), electrical interference etc is a topic of great research interest, and several approaches have been reported. The proposed method is Heuristic RNN-based Kalman Filter for Fetal Electrocardiogram Extraction (HRKFFEE) which is based on redundant noise and signal patterns in the residual signal of FECG and MECG. Two functional blocks are used in the proposed method. The first functional block is based on Heuristic RNN equipped with legacy Long Short-Term Memory (LSTM) for assembling a knowledgebase and the second functional block is RNN-based Kalman filter. Upon testing, the proposed method delivers better average values of accuracy, F Score, Precision and Specificity as 93.118%, 93.106%, 92.9495 % and 92.98% respectively.

Real-Time Visual Feedback in Singing Pedagogy: Current Trends and Future Directions

Singing pedagogy has increasingly adopted guide awareness through the use of meaningful real-time visual feedback. Technology typically used to study the voice can also be applied in a singing lesson, aiming at facilitating students’ awareness of the three subsystems involved in voice production—breathing, oscillatory and resonatory—and their underlying physiological, aerodynamical and acoustical mechanisms. Given the variety of real-time visual feedback tools, this article provides a comprehensive overview of such tools and their current and future pedagogical applications in the voice studio. The rationale for using real-time visual feedback is discussed, including both the theoretical and practical applications of visualizing physiological, aerodynamical and acoustical aspects of voice production. The monitorization of breathing patterns is presented, displaying lung volume as the sum of abdominal and ribcage movements signals. In addition, estimates of subglottal pressure are visually displayed using a subglottal pressure meter to assist with the shaping of musical phrases in singing. As to what concerns vibratory patterns of the vocal folds and phonatory airflow, the use of electroglottography and inverse filters is applied to monitor the phonation types, voice breaks, pitch and intensity range of singers of different music genres. These vocal features, together with intentional voice distortions and intonation adjustments, are also displayed using spectrographs. As the voice is invisible to the eye, the use of real-time visual feedback is proposed as a key pedagogical approach in current and future singing lessons. The use of such an approach corroborates the current trend of developing evidence-based practices in voice education.

Extraction of Fetal ECG From Abdominal and Thorax ECG Using a Non-Causal Adaptive Filter Architecture.

Extracting the Electrocardiogram (ECG) of a fetus from the ECG signal of the maternal abdomen is a challenging task due to different artifacts. The paper proposes a N-tap non-causal adaptive filter (NC-AF) that update the weight by considering the N number of past weights and N-1 number of the reference signal and error signal samples after the processing sample number n. Using the maternal abdominal signal as the primary signal and thorax signal as the reference input, the output e(n) is obtained from the mean of N number of errors. The filtering performance of NC-AF was evaluated using the Synthetic dataset and Daisy dataset with the metrics such as correlation coefficient ( γ), peak root mean square difference (PRD), the output signal to noise ratio (SNR), root mean square error (RMSE), and fetal R-peak detection accuracy (FRPDA). The NC-AF provides a maximum correlation coefficient, PRD, SNR, RMSE and FRPDA of 0.9851, 83.04%, 8.52 dB, 0.208 and 97.09% respectively with filter length N=38. The paper also proposes the architecture of NC-AF that can be implemented in hardware like FPGA. Further, the NC-AF was implemented on Virtex-7 FPGA and its performance is evaluated in terms of resource utilization, throughput, and power consumption. For filter length N=38 and wordlength L=24, the maximum performance of the filter can be attained with a power consumption of [Formula: see text] and a maximum clock frequency of 139.47 MHz.

Enhancing instantaneous oxygen uptake estimation by non-linear model using cardio-pulmonary physiological and motion signals.

Oxygen uptake (VO2) is an important parameter in sports medicine, health assessment and clinical treatment. At present, more and more wearable devices are used in daily life, clinical treatment and health care. The parameters obtained by wearables have great research potential and application prospect. In this paper, an instantaneous VO2 estimation model based on XGBoost was proposed and verified by using data obtained from a medical-grade wearable device (Beijing SensEcho) at different posture and activity levels. Furthermore, physiological characteristics extracted from single-lead electrocardiogram, thoracic and abdominal respiration signal and tri-axial acceleration signal were studied to optimize the model. There were 29 healthy volunteers recruited for the study to collect data while stationary (lying, sitting, standing), walking, Bruce treadmill test and recuperating with SensEcho and the gas analyzer (Metalyzer 3B). The results show that the VO2 values estimated by the proposed model are in good agreement with the true values measured by the gas analyzer (R2 = 0.94 ± 0.03, n = 72,235), and the mean absolute error (MAE) is 1.83 ± 0.59 ml/kg/min. Compared with the estimation method using a separate heart rate as input, our method reduced MAE by 54.70%. At the same time, other factors affecting the performance of the model were studied, including the influence of different input signals, gender and movement intensity, which provided more enlightenment for the estimation of VO2. The results show that the proposed model based on cardio-pulmonary physiological signals as inputs can effectively improve the accuracy of instantaneous VO2 estimation in various scenarios of activities and was robust between different motion modes and state. The VO2 estimation method proposed in this paper has the potential to be used in daily life covering the scenario of stationary, walking and maximal exercise.

Sleep Apnea Detection Using Multi-Error-Reduction Classification System with Multiple Bio-Signals.

Introduction: Obstructive sleep apnea (OSA) can cause serious health problems such as hypertension or cardiovascular disease. The manual detection of apnea is a time-consuming task, and automatic diagnosis is much more desirable. The contribution of this work is to detect OSA using a multi-error-reduction (MER) classification system with multi-domain features from bio-signals. Methods: Time-domain, frequency-domain, and non-linear analysis features are extracted from oxygen saturation (SaO), ECG, airflow, thoracic, and abdominal signals. To analyse the significance of each feature, we design a two-stage feature selection. Stage 1 is the statistical analysis stage, and Stage 2 is the final feature subset selection stage using machine learning methods. In Stage 1, two statistical analyses (the one-way analysis of variance (ANOVA) and the rank-sum test) provide a list of the significance level of each kind of feature. Then, in Stage 2, the support vector machine (SVM) algorithm is used to select a final feature subset based on the significance list. Next, an MER classification system is constructed, which applies a stacking with a structure that consists of base learners and an artificial neural network (ANN) meta-learner. Results: The Sleep Heart Health Study (SHHS) database is used to provide bio-signals. A total of 66 features are extracted. In the experiment that involves a duration parameter, 19 features are selected as the final feature subset because they provide a better and more stable performance. The SVM model shows good performance (accuracy = 81.68%, sensitivity = 97.05%, and specificity = 66.54%). It is also found that classifiers have poor performance when they predict normal events in less than 60 s. In the next experiment stage, the time-window segmentation method with a length of 60 s is used. After the above two-stage feature selection procedure, 48 features are selected as the final feature subset that give good performance (accuracy = 90.80%, sensitivity = 93.95%, and specificity = 83.82%). To conduct the classification, Gradient Boosting, CatBoost, Light GBM, and XGBoost are used as base learners, and the ANN is used as the meta-learner. The performance of this MER classification system has the accuracy of 94.66%, the sensitivity of 96.37%, and the specificity of 90.83%.

Measuring fractal dynamics of FECG signals to determine the complexity of fetal heart rate

In this research, we study the fetal heart rate from abdominal signals using multi-fractal spectra and fractal analysis. We use the Abdominal and Direct Fetal Electrocardiogram Database contains multichannel fetal electrocardiogram (FECG) recordings obtained from 5 different women in labor, between 38 and 41 weeks of gestation. We apply autocorrelation or power spectral densities (PSD) analysis on these five FECG recordings to estimate the exponent from realizations of these processes and to find out if the signal of interest exhibits a power-law PSD. We perform multi-fractal analysis to discover whether some type of power-law scaling exists for various statistical moments at different scales of these FECG signals. We plot the multi-fractal spectra of this database to compare the width of the scaling exponent for each spectrum. A quantitative analysis commonly known as the Fractal Dimension (FD) using the Higuchi algorithm has been carried out to illustrate the fractal complexity of input signals. Our finding shows that the fractal geometry can be used as a mathematical model and computational framework to further analysis and classification of clinical database. Moreover, it can be considered as a framework to compare the complexity of FECG signals and a useful tool to differentiate between their patterns.

Abdominal cardiovascular sound recording and analysis using cardio-microphones.

In view of using abdominal microphones for fetal heart rate (FHR) monitoring, the analysis of the obtained abdominal phonocardiogram (PCG) signals is complex due to many interferential noises including blood flow sounds. In order to improve the understanding of abdominal phonocardiography, a preliminary study was conducted in one healthy volunteer and designed to characterize the PCG signals all over the abdomen. Acquisitions of PCG signals in different abdominal areas were realized, synchronously with one thoracic PCG signal and one electrocardiogram signal. The analysis was carried out based on the temporal behavior, amplitude and mean pattern of each signal. The synchronized rhythmic signature of each signal confirms that the PCG signals obtained on the abdominal area are resulting from heart function. However, the abdominal PCG patterns are totally different from the thoracic PCG one, suggesting the recording of vascular blood flow sounds on the abdomen instead of cardiac valve sounds. Moreover, the abdominal signal magnitude depends on the sensor position and therefore to the size of the underlying vessel. The sounds characterization of abdominal PCG signals could help improving the processing of such signals in the purpose of FHR monitoring.

Fetal Movement Cancellation in Abdominal Electrocardiogram Recordings Using Signal-to-Signal Translation.

This study addresses the cancellation of fetal movement in abdominal electrocardiogram (AECG) recordings through deep neural networks. For this purpose, a generative signal-to-signal translation model consisting of two coupled generators is employed to discover the relations between fetal movement-contaminated and clean AECG recordings. The model is trained on the fetal ECG synthetic database (FECGSYNDB) which provides AECG recordings from 10 pregnancies along with their ground-truth maternal and fetal ECG signals. The signals are initially segmented into 4-second segments and then fed into the network for denoising. It is demonstrated that the signal-to-signal translation method can reconstruct clean AECG signals with average mean-absolute-error (MAE), root-mean-square deviation (RMSD), and Pearson correlation coefficient (PCC) of 0.099, 0.124, and 99.12% respectively, between clean and denoised AECG signals. Furthermore, the robustness of the method to low signal-to-noise ratio (SNR) input values is shown by an RMSD range of (0.047, 0.352) for SNR values within the range of (-3, 3) dB. Clinical Relevance- The proposed framework allows for the denoising of abdominal ECG signals for non-invasive fetal heart rate monitoring. The approach is accurate due to the use of advanced neural network techniques.

Fetal ECG denoising using dynamic time warping template subtraction.

The analysis of the fetal electrocardiogram (ECG) requires to remove the mother ECG (mECG) from the abdominal ECG signals. Template subtraction is a method that consists in modeling and removing the mECG's mean period i.e. the signal waveform defined as the Euclidean mean of all periods. This mean period is then subtracted to all periods to extract the fetal ECG (fECG). Such a method is not accurate because each mECG's period is not correctly aligned with the mean period. We propose to take account of the diffeomorphism of each period to improve the precision of the model and remove the mECG more efficiently. The soft-dynamic time warping (DTW) algorithm is used to compute the mean mECG period and the alignment between the mean period and all periods. Our approach is compared to a classic template subtraction on synthetic and real databases. Results show that considering the dynamic time warping allows a better removal of the mECG. Clinical relevance - The template subtraction is modified in this paper to consider the time warping for each mother ECG's period in order to improve the fetal ECG extraction from the abdominal ECG.

An IoMT enabled deep learning framework for automatic detection of fetal QRS: A solution to remote prenatal care

The amalgamation of the Internet of medical things with artificial intelligence shows tremendous benefits in health care. Accurate detection of the fetal QRS complex is highly demanded in fetal heart rate monitoring. Detecting fetal heart rate using electrophysiological signals obtained from abdominal electrodes seems a promising alternative approach. The challenges in determining fetal heart rate from abdominal ECG (AECG) require eliminating maternal components and other noises from the signal at higher accuracy. We propose a novel approach using an IoT-based deep learning architecture to detect fetal QRS complex without eliminating the maternal components in the abdominal ECG. The novelty of the proposed algorithm is twofold: (1) The method uses the time–frequency image (TFI) of abdominal signals as input to the deep neural network and hence promotes the availability of rich features and improves the accurate detection of the fetal QRS complex. (2) The algorithm adapts pre-trained models based on transfer learning for the classification task and thus improves the fetal QRS detection. Two time–frequency approaches, namely Hilbert Huang Transform (HHT) and Stockwell transform (ST), are used to represent input AECG signals as two-dimensional images. The 2013 challenge database is used to evaluate the performance of the proposed approach. The TFI representations of training data using HHT and ST are independently used to train the pre-trained models MobileNet and ResNet18. A comparative analysis is provided in the results between the TFI and deep network architecture. The proposed solution can be suitable for an IoT environment enabling remote fetal monitoring.

Associations of respiratory mechanic instability with respiratory parameters in pediatric patients with obstructive sleep apnea syndrome

ObjectivesThis study aimed to identify the effectiveness of respiratory mechanic instability (RMI) in the diagnosis of pediatric obstructive sleep apnea syndrome (OSAS). We sought to evaluate the correlations of RMI with sleep-related parameters and determine the effectiveness of using RMI for diagnosing OSAS in children. MethodsChildren who underwent polysomnography (PSG) for various reasons were enrolled in this study. Patients' clinical and PSG data at two university hospitals were reviewed retrospectively. During PSG, RMI parameters were automatically calculated according to the phase relationship between thoracic and abdominal movement signals. ResultsAmong 263 children who underwent PSG, 183 (70.4%) were diagnosed with OSAS (apnea-hypopnea index [AHI] ≥ 1). RMI parameters were higher in the OSAS group than in the control group. They also tended to increase with disease severity. RMI scores were well correlated with respiratory parameters, showing a stronger correlation in those with moderate or severe OSAS without central apnea. Areas under the receiver operating characteristics curves (AUROCs) of RMI indicators were over 0.65. The percentage of RMI in stage duration showed the highest value of the AUROCs. ConclusionParadoxical thoraco-abdominal movement assessed by RMI provides additional information. It may be useful in diagnosing OSAS in children.

Non-invasive fetal monitoring: Fetal Heart Rate multimodal estimation from abdominal electrocardiography and phonocardiography

BackgroundFetal cardiac well-being is essential during labor as the delivery is at risk for fetal distress. Continuous monitoring by cardiotocography (CTG) is daily used to record the fetal heart rate (FHR) but this technique has important drawbacks in clinical use. ObjectivesWe propose to monitor FHR with a non-invasive technique, using multimodal recordings of the fetus cardiac activity, associating electrocardiographic (ECG) and phonocardiographic (PCG) sensors. The aim of this study is to evaluate the quality of these multimodal FHR estimations by comparison with CTG, based on clinical criteria. MethodsA clinical protocol was established and a prospective open label study was carried out in the University Hospital of Grenoble. The objective was to record thoracic and abdominal PCG and ECG signals on pregnant women over 37 WG (weeks of gestation), simultaneously with CTG recordings. Adapted signal processing algorithms were then applied on abdominal PCG and ECG signals to extract FHR. Quantitative evaluation was carried out on FHR estimations compared with FHR extracted from CTG. ResultsA total of 40 recordings were performed. Due to technical mistakes the analysis was made possible for 38. 35 recordings allowed a FHR follow-up by ECG or PCG, 30 recordings allowed a FHR follow-up by PCG only, 25 recordings allowed a FHR follow-up by ECG only and 20 recordings allowed a FHR follow-up by both ECG and PCG. ConclusionReliable multimodal recording of FHR associating ECG and PCG sensors is possible during the last month of pregnancy. These positive results encourage the study of multimodal FHR recording during labor and delivery.

A novel fetal ecg signal extraction from maternal ecg signal using conditional generative adversarial networks (CGAN)

Fetal Electrocardiogram (ECG) signal extraction from non-invasive abdominal ECG signal is one of the important clinical practices followed to observe the fetal health state. Information about heart growth and health conditions of a fetus can be observed from fetal ECG signals. However, acquiring fetal ECG from abdominal ECG signals is still considered as a challenging task in biomedical analysis. This is mainly due to corrupted high amplitude maternal ECG signals, low signal to noise ratio of fetal ECG signal, difficulties in reduction of QRS (Q wave, R wave, S wave) complexities, fetal ECG signal superimposed characteristics, other motion, and electromyography artifacts. To reduce these conventional challenges, in fetal ECG analysis of a novel Conditional Generative adversarial network (CGAN) is introduced in this research work to extract the fetal ECG signal. The proposed classification model was classified efficiently in fetal ECG signals from non-invasive abdominal ECG signals. The experimental analysis demonstrates that the proposed network model provides better results in terms of sensitivity, specificity, and accuracy compared to the conventional fetal ECG extraction models like singular value decomposition, periodic component analysis, and Adaptive neuro-fuzzy inference system.