Automatic detection of complete and measurable cardiac cycles in antenatal pulsed-wave Doppler signals

Background and objective: Pulsed-wave Doppler (PWD) echocardiography is the primary tool for antenatal cardiological diagnosis. Based on it, different measurements and validated reference parameters can be extracted. The automatic detection of complete and measurable cardiac cycles would represent a useful tool for the quality assessment of the PWD trace and the automated analysis of long traces. Methods: This work proposes and compares three different algorithms for this purpose, based on the preliminary extraction of the PWD velocity spectrum envelopes: template matching, supervised classification over a reduced set of relevant waveshape features, and supervised classification over the whole waveshape potentially representing a cardiac cycle. A custom dataset comprising 43 fetal cardiac PWD traces (174,319 signal segments) acquired on an apical five-chamber window was developed and used for the assessment of the different algorithms. Results: The adoption of a supervised classifier trained with the samples representing the upper and lower envelopes of the PWD, with additional features extracted from the image, achieved significantly better results (p < 0.0001) than the other algorithms, with an average accuracy of 98% ± 1% when using an SVM classifier and a leave-one-subject-out cross-validation. Further, the robustness of the results with respect to the classifier model was proved. Conclusions: The results reveal excellent detection performance, suggesting that the proposed approach can be adopted for the automatic analysis of long PWD traces or embedded in ultrasound machines as a first step for the extraction of measurements and reference clinical parameters

Annotated real and synthetic datasets for non-invasive foetal electrocardiography post-processing benchmarking

Non-invasive foetal electrocardiography (fECG) can be obtained at different gestational ages by means of surface electrodes applied on the maternal abdomen. The signal-to-noise ratio (SNR) of the fECG is usually low, due to the small size of the foetal heart, the foetal-maternal compartment, the maternal physiological interferences and the instrumental noise. Even after powerful fECG extraction algorithms, a post-processing step could be required to improve the SNR of the fECG signal. In order to support the researchers in the field, this work presents an annotated dataset of real and synthetic signals, which was used for the study “Wavelet Denoising as a Post-Processing Enhancement Method for Non-Invasive Foetal Electrocardiography” [1]. Specifically, 21 15 s-long fECG, dual-channel signals obtained by multi-reference adaptive filtering from real electrophysiological recordings were included. The annotation of the foetal R peaks by an expert cardiologist was also provided. Recordings were performed on 17 voluntary pregnant women between the 21st and the 27th week of gestation. The raw recordings were also included for the researchers interested in applying a different fECG extraction algorithm. Moreover, 40 10 s-long synthetic non-invasive fECG were provided, simulating the electrode placement of one of the abdominal leads used for the real dataset. The annotation of the foetal R peaks was also provided, as generated by the FECGSYN tool used for the signals’ creation. Clean fECG signals were also included for the computation of indexes of signal morphology preservation. All the signals are sampled at 2048 Hz. The data provided in this work can be used as a benchmark for fECG post-processing techniques but can also be used as raw signals for researchers interested in foetal QRS detection algorithms and fECG extraction methods

A non-invasive multimodal foetal ECG–Doppler dataset for antenatal cardiology research

Non-invasive foetal electrocardiography (fECG) continues to be an open topic for research. The development of standard algorithms for the extraction of the fECG from the maternal electrophysiological interference is limited by the lack of publicly available reference datasets that could be used to benchmark different algorithms while providing a ground truth for foetal heart activity when an invasive scalp lead is unavailable. In this work, we present the Non-Invasive Multimodal Foetal ECG-Doppler Dataset for Antenatal Cardiology Research (NInFEA), the first open-access multimodal early-pregnancy dataset in the field that features simultaneous non-invasive electrophysiological recordings and foetal pulsed-wave Doppler (PWD). The dataset is mainly conceived for researchers working on fECG signal processing algorithms. The dataset includes 60 entries from 39 pregnant women, between the 21st and 27th week of gestation. Each dataset entry comprises 27 electrophysiological channels (2048 Hz, 22 bits), a maternal respiration signal, synchronised foetal trans-abdominal PWD and clinical annotations provided by expert clinicians during signal acquisition. MATLAB snippets for data processing are also provided

Wavelet denoising as a post-processing enhancement method for non-invasive foetal electrocardiography

Background and Objective: The detection of a clean and undistorted foetal electrocardiogram (fECG) from non-invasive abdominal recordings is an open research issue. Several physiological and instrumental noise sources hamper this process, even after that powerful fECG extraction algorithms have been used. Wavelet denoising is widely used for the improvement of the SNR in biomedical signal processing. This work aims to systematically assess conventional and unconventional wavelet denoising approaches for the post-processing of fECG signals by providing evidence of their effectiveness in improving fECG SNR while preserving the morphology of the signal of interest. Methods: The stationary wavelet transform (SWT) and the stationary wavelet packet transform (SWPT) were considered, due to their different granularity in the sub-band decomposition of the signal. Three thresholds from the literature, either conventional (Minimax and Universal) and unconventional, were selected. To this aim, the unconventional one was adapted for the first time to SWPT by trying different approaches. The decomposition depth was studied in relation to the characteristics of the fECG signal. Synthetic and real datasets, publicly available for benchmarking and research, were used for quantitative analysis in terms of noise reduction, foetal QRS detection performance and preservation of fECG morphology. Results: The adoption of wavelet denoising approaches generally improved the SNR. Interestingly, the SWT methods outperformed the SWPT ones in morphology preservation (p<0.04) and SNR (p<0.0003), despite their coarser granularity in the sub-band analysis. Remarkably, the Han et al. threshold, adopted for the first time for fECG processing, provided the best quality improvement (p<0.003). Conclusions: The findings of our systematic analysis suggest that particular care must be taken when selecting and using wavelet denoising for non-invasive fECG signal post-processing. In particular, despite the general noise reduction capability, signal morphology can be significantly altered on the basis of the parameterization of the wavelet methods. Remarkably, the adoption of a finer sub-band decomposition provided by the wavelet packet was not able to improve the quality of the processing

Impact of pulsed-wave-Doppler velocity-envelope tracing techniques on classification of complete fetal cardiac cycles

Fetal echocardiography is an operator-dependent examination technique requiring a high level of expertise. Pulsed-wave Doppler (PWD) is often used as a reference for the mechanical activity of the heart, from which several quantitative parameters can be extracted. These aspects suggest the development of software tools that can reliably identify complete and clinically meaningful fetal cardiac cycles that can enable their automatic measurement. Several scientific works have addressed the tracing of the PWD velocity envelope. In this work, we assess the different steps involved in the signal processing chains that enable PWD envelope tracing. We apply a supervised classifier trained on envelopes traced by different signal processing chains for distinguishing complete and measurable PWD heartbeats from incomplete or malformed ones, which makes it possible to determine the impact of each of the different processing steps on the detection accuracy. In this study, we collected 43 images and labeled 174,319 PWD segments from 25 pregnant women volunteers. By considering seven envelope tracing techniques and the 23 different processing steps involved in their implementation, the results of our study reveal that, compared to the steps investigated in most other works, those that achieve binarisation and envelope extraction are significantly more important (p < 0.05). The best approaches among those studied enabled greater than 98% accuracy on our large manually annotated dataset

High-quality permanent draft genome sequence of Rhizobium sullae strain WSM1592; a Hedysarum coronarium microsymbiont from Sassari, Italy

Rhizobium sullae strain WSM1592 is an aerobic, Gram-negative, non-spore-forming rod that was isolated from an effective nitrogen (N2) fixing root nodule formed on the short-lived perennial legume Hedysarum coronarium (also known as Sulla coronaria or Sulla). WSM1592 was isolated from a nodule recovered from H. coronarium roots located in Ottava, bordering Sassari, Sardinia in 1995. WSM1592 is highly effective at fixing nitrogen with H. coronarium, and is currently the commercial Sulla inoculant strain in Australia. Here we describe the features of R. sullae strain WSM1592, together with genome sequence information and its annotation. The 7,530,820 bp high-quality permanent draft genome is arranged into 118 scaffolds of 118 contigs containing 7.453 protein-coding genes and 73 RNA-only encoding genes. This rhizobial genome is sequenced as part of the DOE Joint Genome Institute 2010 Genomic Encyclopedia for Bacteria and Archaea-Root Nodule Bacteria (GEBA-RNB) project

Bioactive compounds

Bioactive compounds sulla leaf fraction

Phytomass production from Silybum marianum for bioenergy Phytomass production from Silybum marianum for bioenergy

Article available on lin e / Article dispon ible en lign e à l'adresse : To cite th is article / Pou r citer cet article SUMMARY -Silybum marianum Gaertner is a herbaceous thorny member of the Compositae family, common in the native flora of the Mediterranean basin. Silybum marianum seeds contain a complex substance called silymarin, well appreciated for treating human liver diseases and animal wellbeing. Because of its extremely aggressive vegetative growth, causing stress on adjacent plants, it is known as a dangerous weed in cropping areas. On the other hand, the rusticity and the high growth rates of S. marianum make it interesting for bioenergy purposes in rainfed Mediterranean environments. A research work was started in Sardinia in order to evaluate the performance of this species in terms of biomass production under low input conditions. Data on undisturbed dry matter accumulation, yield, and phytomass partitioning into its components (leaves, stems and heads) were collected. The heating value and the energy balance of the crop were also assessed. The first year results are reported in this paper. Key words: Milk thistle, biomass, calorific value, energetic balance, bioenergy. RESUME -"Production de phytomasse d