A new approach to the spatio-temporal pattern identification in neuronal multi-electrode registrations

A lot of methods were created in last decade for the spatio-temporal analysis of multi-electrode array (MEA) neuronal data sets. All these methods were implemented starting from a channel to channel analysis, with a great computational effort and onerous spatial pattern recognition task. 
Our idea is to approach the MEA data collection from a different point of view, i.e. considering all channels simultaneously. We transform the 2D plus time MEA signal in a mono-dimensional plus time signal and elaborate it as a normal 1D signal, using the Space-Amplitude Transform method. 
This geometrical transformation is completely invertible and allows to employ very fast processing algorithms. 
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A NEW APPROACH TO THE SPATIO-TEMPORAL PATTERN IDENTIFICATION IN NEURONAL MULTI-ELECTRODE REGISTRATIONS

A lot of methods were created in last decade for the spatio-temporal analysis of multi-electrode array (MEA) neuronal data sets. All these methods were implemented starting from a channel to channel analysis, with a great computational effort and onerous spatial pattern recognition task. Our idea is to approach the MEA data collection from a different point of view, i.e. considering all channels simultaneously. We transform the 2D plus time MEA signal in a mono-dimensional plus time signal and elaborate it as a normal 1D signal, using the Space-Amplitude Transform method. This geometrical transformation is completely invertible and allows to employ very fast processing algorithms

A deep learning mixed-data type approach for the classification of FHR signals

The Cardiotocography (CTG) is a widely diffused monitoring practice, used in Ob-Gyn Clinic to assess the fetal well-being through the analysis of the Fetal Heart Rate (FHR) and the Uterine contraction signals. Due to the complex dynamics regulating the Fetal Heart Rate, a reliable visual interpretation of the signal is almost impossible and results in significant subjective inter and intra-observer variability. Also, the introduction of few parameters obtained from computer analysis did not solve the problem of a robust antenatal diagnosis. Hence, during the last decade, computer aided diagnosis systems, based on artificial intelligence (AI) machine learning techniques have been developed to assist medical decisions. The present work proposes a hybrid approach based on a neural architecture that receives heterogeneous data in input (a set of quantitative parameters and images) for classifying healthy and pathological fetuses. The quantitative regressors, which are known to represent different aspects of the correct development of the fetus, and thus are related to the fetal healthy status, are combined with features implicitly extracted from various representations of the FHR signal (images), in order to improve the classification performance. This is achieved by setting a neural model with two connected branches, consisting respectively of a Multi-Layer Perceptron (MLP) and a Convolutional Neural Network (CNN). The neural architecture was trained on a huge and balanced set of clinical data (14.000 CTG tracings, 7000 healthy and 7000 pathological) recorded during ambulatory non stress tests at the University Hospital Federico II, Napoli, Italy. After hyperparameters tuning and training, the neural network proposed has reached an overall accuracy of 80.1%, which is a promising result, as it has been obtained on a huge dataset

Entropy Information of Cardiorespiratory Dynamics in Neonates during Sleep

Abstract: Sleep is a central activity in human adults and characterizes most of the newborn infant life. During sleep, autonomic control acts to modulate heart rate variability (HRV) and respiration. Mechanisms underlying cardiorespiratory interactions in different sleep states have been studied but are not yet fully understood. Signal processing approaches have focused on cardiorespiratory analysis to elucidate this co-regulation. This manuscript proposes to analyze heart rate (HR), respiratory variability and their interrelationship in newborn infants to characterize cardiorespiratory interactions in different sleep states (active vs. quiet). We are searching for indices that could detect regulation alteration or malfunction, potentially leading to infant distress. We have analyzed inter-beat (RR) interval series and respiration in a population of 151 newborns, and followed up with 33 at 1 month of age. RR interval series were obtained by recognizing peaks of the QRS complex in the electrocardiogram (ECG), corresponding to the ventricles depolarization. Univariate time domain, frequency domain and entropy measures were applied. In addition, Transfer Entropy was considered as a bivariate approach able to quantify the bidirectional information flow from one signal (respiration) to another (RR series). Results confirm the validity of the proposed approach. Overall, HRV is higher in active sleep, while high frequency (HF) power characterizes more quiet sleep. Entropy analysis provides higher indices for SampEn and Quadratic Sample entropy (QSE) in quiet sleep. Transfer Entropy values were higher in quiet sleep and point to a major influence of respiration on the RR series. At 1 month of age, time domain parameters show an increase in HR and a decrease in variability. No entropy differences were found across ages. The parameters employed in this study help to quantify the potential for infants to adapt their cardiorespiratory responses as they mature. Thus, they could be useful as early markers of risk for infant cardiorespiratory vulnerabilities

Influence of Gestational Diabetes on Fetal Heart Rate in Antepartum Cardiotocographic Recordings

In pregnancy, diabetes is known to increase the risk of adverse maternal and neonatal outcomes. It would be beneficial to find techniques that allow early investigation of the physio-pathological mechanisms involved to provide clinicians with tools for prevention and therapies. For that, cardiotocography (CTG) is a promising tool. However, the evidence is still scarce and the impact on clinical practice little. In this study, we aim at characterizing the changes induced by gestational diabetes (GDM) on the fetal heart rate series. To do so, we performed a retrospective cohort study on a CTG dataset containing more than 20000 recordings of which 852 belong to 301 GDM-diagnosed patients. We divided the recordings by gestational age (G.A.) into 4 groups (weeks: 31-35, 36, 37, 38 to delivery) and for each we identified a control population of equal size matched by comorbidities. We analyzed a comprehensive set of parameters from the time domain, frequency domain and non-linear analysis and assessed variations in median values on each feature. For all G.A. below the 38th week, we found a significant increase in the power in the movement frequency band (p<0.01) and an increase in the absolute value of Deceleration Reserve (p<0.01) in GDM vs control. Other significant values were also identified and are discussed in more detail in the paper

The Forgotten Role of Central Volume in Low Frequency Oscillations of Heart Rate Variability

The hypothesis that central volume plays a key role in the source of low frequency (LF) oscillations of heart rate variability (HRV) was tested in a population of end stage renal disease patients undergoing conventional hemodialysis (HD) treatment, and thus subject to large fluid shifts and sympathetic activation. Fluid overload (FO) in 58 chronic HD patients was assessed by whole body bioimpedance measurements before the midweek HD session. Heart Rate Variability (HRV) was measured using 24-hour Holter electrocardiogram recordings starting before the same HD treatment. Time domain and frequency domain analyses were performed on HRV signals. Patients were retrospectively classified in three groups according to tertiles of FO normalized to the extracellular water (FO/ECW%). These groups were also compared after stratification by diabetes mellitus. Patients with the low to medium hydration status before the treatment (i.e. 1st and 2nd FO/ECW% tertiles) showed a significant increase in LF power during last 30 min of HD compared to dialysis begin, while no significant change in LF power was seen in the third group (i.e. those with high pre-treatment hydration values). In conclusion, several mechanisms can generate LF oscillations in the cardiovascular system, including baroreflex feedback loops and central oscillators. However, the current results emphasize the role played by the central volume in determining the power of LF oscillations

Evaluation of the Acceleration and Deceleration Phase-Rectified Slope to Detect and Improve IUGR Clinical Management

Objective. This study used a new method called Acceleration (or Deceleration) Phase-Rectified Slope, APRS (or DPRS) to analyze computerized Cardiotocographic (cCTG) traces in intrauterine growth restriction (IUGR), in order to calculate acceleration- and deceleration-related fluctuations of the fetal heart rate, and to enhance the prediction of neonatal outcome. Method. Cardiotocograms from a population of 59 healthy and 61 IUGR fetuses from the 30th gestation week matched for gestational age were included. APRS and DPRS analysis was compared to the standard linear and nonlinear cCTG parameters. Statistical analysis was performed through the -test, ANOVA test, Pearson correlation test and receiver operator characteristic (ROC) curves (). Results. APRS and DPRS showed high performance to discriminate between Healthy and IUGR fetuses, according to gestational week. A linear correlation with the fetal pH at birth was found in IUGR. The area under the ROC curve was 0.865 for APRS and 0.900 for DPRS before the 34th gestation week. Conclusions. APRS and DPRS could be useful in the identification and management of IUGR fetuses and in the prediction of the neonatal outcome, especially before the 34th week of gestation

A system for automatic on-line time detection and classification of neural spikes based on a digital signal processor and a FPGA controller

Definition of single spikes from multiunit spike trains plays a critical role in neurophysiology and in neuroengineering as it does the question of how much information is encoded by single neurons in a neuronal network. Moreover, the possibility to develop a bidirectional communication between electronic devices and neuronal networks provides great perspectives in neuroengineering. Traditionally, the functional properties of neurons and neuronal networks have been investigated using conventional electrodes, such as glass micropipettes, thus allowing neurophysiologists to disclose a detailed picture about the single cell properties. Thirty years ago Micro-Electrode Array devices (MEAs) have been developed as tools providing distributed information about learning, memory and information processing in a cultured neuronal network. Recent applications of this technologies have the problem of the recording and storage of the huge amount of data processed. Here we describe a system based on a FPGA controller coupled to a Digital Signal Processor for the automatic single spike detection, sorting and classification. The first step involves FPGA: its inputs are 60 neuronal signals caming from the 60 channels of MEA and its outputs are time stamps for single electrode and templates of the spikes for single channel. At this level, an adaptative threshold method is used for spike detection. The second step involves the DSP: the principal components of previously recorded templates are computed. Spikes are classified using information about their shape, characterized by different features; the principal component analysis is one method for choosing these features automatically. The challenge is to accurately and reliably separate the spikes from a single neuron from spikes from other neurons and classify them. The on-line application of this method provides an efficient system to reduce the computation time and the space on the storage unit. Our aim is to estimate the number of neurons that are naturally interconnected in complex networks and to discriminate single template’s shape of individual neuronal cell

Ethnic analogies and differences in fetal heart rate variability signal: A retrospective study

Aim: We aimed to analyze computerized cardiotocographic (cCTG) parameters (including fetal heart rate baseline, short-term variability, Delta, long-term irregularity [LTI], interval index [II], low frequency [LF], movement frequency [MF], high frequency [HF], and approximate entropy [ApEn]) in physiological term pregnancies in order to correlate them with ethnic differences. The clinical meaning of numerical parameters may explain physiological or paraphysiological phenomena that occur in fetuses of different ethnic origins. Methods: A total of 696 pregnant women, including 384 from Europe, 246 from sub-Saharan Africa, 45 from South-East Asia, and 21 from South America, were monitored from the 37th to the 41st week of gestation. Statistical analysis was performed with the analysis of variance test, Pearson correlation test and receiver–operator curves (P < 0.05). Results: Our results showed statistically significant differences (P < 0.05) between white and black women for Delta, LTI, LF, MF, HF, and ApEn; between white and Asian women for Delta, LTI, MF, and the LF/(HF + MF) ratio; and between white and Latina women for Delta, LTI, and ApEn. In particular, Delta and LTI performed better in the white group than in the black, Asian, and Latina groups. Instead, LF, MF, HF, and ApEn performed better in the black than in the white group. Conclusion: Our results confirmed the integrity and normal functionality of both central and autonomic nervous system components for all fetuses investigated. Therefore, CTG monitoring should include both linear and nonlinear components of fetal heart rate variability in order to avoid misinterpretations of the CTG trace among ethnic groups