Automated detection of perinatal hypoxia using time-frequency-based heart rate variability features

Novel characterization method of impedance cardiography signals using time-frequency distributions

The purpose of this document is to describe a methodology to select the most adequate time-frequency distribution (TFD) kernel for the characterization of impedance cardiography signals (ICG). The predominant ICG beat was extracted from a patient and was synthetized using time-frequency variant Fourier approximations. These synthetized signals were used to optimize several TFD kernels according to a performance maximization. The optimized kernels were tested for noise resistance on a clinical database. The resulting optimized TFD kernels are presented with their performance calculated using newly proposed methods. The procedure explained in this work showcases a new method to select an appropriate kernel for ICG signals and compares the performance of different time-frequency kernels found in the literature for the case of ICG signals. We conclude that, for ICG signals, the performance (P) of the spectrogram with either Hanning or Hamming windows (P = 0.780) and the extended modified beta distribution (P = 0.765) provided similar results, higher than the rest of analyzed kernels. Graphical abstract Flowchart for the optimization of time-frequency distribution kernels for impedance cardiography signals.

Cardiotocography and beyond: a review of one-dimensional Doppler ultrasound application in fetal monitoring

One-dimensional Doppler ultrasound (1D-DUS) provides a low-cost and simple method for acquiring a rich signal for use in cardiovascular screening. However, despite the use of 1D-DUS in cardiotocography (CTG) for decades, there are still challenges that limit the effectiveness of its users in reducing fetal and neonatal morbidities and mortalities. This is partly due to the noisy, transient, complex and nonstationary nature of the 1D-DUS signals. Current challenges also include lack of efficient signal quality metrics, insufficient signal processing techniques for extraction of fetal heart rate and other vital parameters with adequate temporal resolution, and lack of appropriate clinical decision support for CTG and Doppler interpretation. Moreover, the almost complete lack of open research in both hardware and software in this field, as well as commercial pressures to market the much more expensive and difficult to use Doppler imaging devices, has hampered innovation. This paper reviews the basics of fetal cardiac function, 1D-DUS signal generation and processing, its application in fetal monitoring and assessment of fetal development and wellbeing. It also provides recommendations for future development of signal processing and modeling approaches, to improve the application of 1D-DUS in fetal monitoring, as well as the need for annotated open databases.

An automatic method for arterial pulse waveform recognition using KNN and SVM classifiers

The measurement and analysis of the arterial pulse waveform (APW) are the means for cardiovascular risk assessment. Optical sensors represent an attractive instrumental solution to APW assessment due to their truly non-contact nature that makes the measurement of the skin surface displacement possible, especially at the carotid artery site. In this work, an automatic method to extract and classify the acquired data of APW signals and noise segments was proposed. Two classifiers were implemented: k-nearest neighbours and support vector machine (SVM), and a comparative study was made, considering widely used performance metrics. This work represents a wide study in feature creation for APW. A pool of 37 features was extracted and split in different subsets: amplitude features, time domain statistics, wavelet features, cross-correlation features and frequency domain statistics. The support vector machine recursive feature elimination was implemented for feature selection in order to identify the most relevant feature. The best result (0.952 accuracy) in discrimination between signals and noise was obtained for the SVM classifier with an optimal feature subset .

Collagen nanofibers facilitated presynaptic maturation in differentiated neurons from spinal-cord-derived neural stem cells through MAPK/ERK1/2-Synapsin I signaling pathway

Neural stem cells (NSCs) are deemed to be a potential cell therapy for brain and spinal cord reconstruction and regeneration following injury. In this study, we investigated the role of nanofibrous scaffolds on NSCs-derived neurons in the formation of neural networks. Miniature excitatory postsynaptic currents (mEPSCs) were recorded using the whole-cell patch clamp recording method after the spinal cord-derived NSCs were differentiated into neurons and cultured in vitro for 10-14 days. It was observed that the frequency of mEPSCs in the differentiated neurons cultured on both randomly oriented and aligned collagen nanofibrous scaffolds was higher than that on the collagen-coated control and can be inhibited by an ERK inhibitor (PD98059), indicating that the collagen nanofibers affected the maturation of the synapses from presynaptic sites via the MAPK/ERK1/2 pathway. In addition, both of the collagen nanofibers increased the phosphorylation of Synapsin I and facilitated the interaction of p-ERK1/2 and p-Synapsin I. All these results suggested that the collagen nanofibrous scaffolds contributed to the presynaptic maturation via the ERK1/2-Synapsin I signaling pathway.