Majority of global neonatal deaths is due to sepsis. A vast portion of these deaths occurs in developing countries due to inaccessibility of hospitals or lack of resources. Blood culture is the test to confirm sepsis, but it requires the presence of laboratory and is time-consuming. Therefore, we require simple, easy to use methods to predict sepsis in homes. Majority of the available prediction models need invasive parameters and hence become useless in the rural areas of developing countries where laboratory facilities do not exist. Non-invasive prediction models overcome these challenges to predict neonatal sepsis in places where there is a scarcity of laboratories. The aim and objective of this study are as follows: (i) to develop a practical, non-invasive prediction-model for neonatal sepsis which can be used in the rural areas of developing countries and to validate its performance. (ii) To compare the prognostic performance of the non-invasive prediction model with invasive prediction model and (iii) to create a prototype of the hardware which calculates the probability of the sepsis in neonates and sends the real-time data to the cloud. For this retrospective analysis, we extracted the data of 1446 neonates from Medical Information Mart for Intensive care III (MIMIC) database. Using stepwise logistic regression analysis, we developed and validated two prediction models. These two models were named as model NI and model O. Model O contains invasive as well as non-invasive parameters whereas model NI contains only non-invasive parameters. Model NI performed equally well in comparison to Model O despite using different predictors. The area under ROC curves for model NI and model O were 0.879 (95% CI: 0.857 to 0.899) and 0.861 (95% CI: 0.838 to 0.881) respectively. Both models were statistically significant with [Formula: see text]-value[Formula: see text].
Monitoring of fetal and maternal well-being is extremely critical during pregnancy and during labor to reduce the occurrence of fetal and maternal distress in high risk pregnancies. There are many approaches to identify and quantify the real-time feto-maternal well-being by measuring physiological parameters viz. fetal movement, fetal temperature, fetal respiration rate, fetal kick count, fetal heart rate, maternal ECG acquired from both chest and abdomen region, uterine contraction, blood SpO2 concentration, amniotic fluid pH etc. In this paper, different techniques to acquire and measure these physiological parameters non-invasively are evaluated and compared for the purpose of monitoring fetomaternal well-being.
Abstract:In this paper, a robust method of feto-maternal heart rate extraction from the non-invasive composite abdominal Electrocardiogram (aECG) signal is presented. The proposed method is based on the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) method, in which a composite aECG signal is decomposed into its constituent frequency components called Intrinsic Mode Functions (IMFs) or simply "modes", with better spectral separation. Decomposed IMFs are then selected manually according to probable maternal and fetal heart rate information and are processed further for quantification of maternal and fetal heart rate and variability analysis. The proposed method was applied to aECG recordings collected from three different sources: (i) the PhysioNet (adfecgdb) database; (ii) the PhysioNet (nifecgdb) database; and (iii) synthetic aECG signal generated from mathematical modeling in the LabVIEW software environment. An overall sensitivity of 98.83%, positive diagnostic value of 97.97%, accuracy of 96.93% and performance index of 96.75% were obtained in the case of Maternal Heart Rate (MHR) quantification, and an overall sensitivity of 98.13%, positive diagnostic value of 97.62%, accuracy of 95.91% and performance index of 95.69% were obtained in case of Fetal Heart Rate (FHR) quantification. The obtained results confirm that CEEMDAN is a very robust and accurate method for extraction of feto-maternal heart rate components from aECG signals. We also conclude that non-invasive aECG is an effective and reliable method for long-term FHR and MHR monitoring during pregnancy and labor. The requirement of manual intervention while selecting the probable maternal and fetal components from "n" number of decomposed modes limits the real-time application of the proposed methodology. This is due to the fact that the number of modes "n" produced by the CEEMDAN decomposition is unpredictable. However, the proposed methodology is well suited for applications where a small time-delay or offset in feto-maternal monitoring can be acceptable. In future, application-specific modification of the CEEMDAN algorithm can be implemented to eliminate manual intervention completely and will be suitable for long-term feto-maternal monitoring.
Objective: Electroencephalography (EEG) has an influential role in neuroscience and commercial applications. Most of the tools available for EEG signal analysis use machine learning to extract the required information. So, the study of robust techniques for feature extraction and classification is an important thing to understand the practical use of EEG. The paper aims that if there is any special tool for a particular task. Which feature domain or classifier has a significant role in EEG signal analysis? Approach: It presents a detailed report of the current trend for bio-electrical signals classification focusing on various classifiers’ advantages and disadvantages. This study includes literature from 2000 to 2021 with a brief description of EEG signal origin and advancement in classification techniques. Results: Randomly used classifiers for EEG signal can be categorized into five classes, namely Linear Classifiers, Nearest Neighbor Classifiers, Nonlinear Bayesian Classifiers, Neural Networks, and Combinations of Classifiers. Approximately 40% of studies use Support Vector Machine, Nearest Neighbor, and their combination with others. For specific tasks, particular classifiers are recommended in the survey. Features can be defined into four categories, namely TDFs, FDFs, TFDFs, and statistical features, where 39% of studies used TFDFs. Multi-domains features are preferred when the required information cannot be obtained from one domain. Significance: The paper summarizes the recent approaches for feature extraction and classification of EEG signals. It describes the brain waves with their classification, related behavior, and task with the physiological correlation. The comparative analysis of different classifiers, toolbox, the channel used, accuracy, and the number of subjects from various studies can help the practitioners choose a suitable classifier. Furthermore, future directions can cope up with the relevant problems and can lead to accurate classification.
Sepsis is one of the major causes of neonatal deaths worldwide. Majority of these neonatal deaths occurs in resource-poor countries due to inaccessibility of hospitals and absence of laboratories. Blood culture which is the gold standard to confirm sepsis is time-consuming and requires the presence of a laboratory. To start the antimicrobial therapy at the earliest, prediction models have been developed. A vast number of available prediction models require laboratory tests and cannot be used in the developing countries where such facilities do not exist. Therefore, there is a need for non-invasive prediction models. The objectives of this study are as follows: -(i) to train and test non-invasive prediction models for neonatal sepsis (ii) to compare the performance of the invasive with non-invasive prediction models. For this retrospective study, we extracted the data of 1446 neonates from the Medical Information Mart for Intensive Care (MIMIC) III data set. We trained and tested six prediction models using this data set. Three of these six models were trained using non-invasive parameters (model LR(NI), model ANN(NI) and model MDA (NI)) and three were trained using invasive and non-invasive parameters (model LR(O), model ANN(O) and model MDA(O)). The sensitivity of model LR(NI), model ANN(NI), model MDA(NI), model LR(O), model ANN(O) and model MDA(O ) at their optimum threshold values were 81.68%, 79.39%, 82.44%, 77.10%, 79.39% and 78.63% respectively. Whereas, specificity of the above mentioned models were 82.27%, 81.82%, 80.00%, 84.77%, 82.05% and 78.30% respectively. To decrease the neonatal mortality rate in resource-poor areas one may use non-invasive prediction models where invasive parameters are not available due to lack of resources, as shown by our study that non-invasive prediction models can achieve similar predictive capability as the invasive prediction models.
Presently, non-invasive techniques are in vogue and preferred standard clinical approach because of its limitless advantages in monitoring real time phenomenon occurring within our human body without much interference. Many techniques such as ultrasound, magnetocardiography, CT scan, MRI etc., are used for real time monitoring but are generally not recommended for continuous monitoring. The limitations created by above used techniques are overcome by a proposed technique called non-invasive bio-impedance technique such as Electrical Impedance Technique (EIT). EIT imaging technique is based on internal electrical conductivity distribution of the body. The reconstruction of cross sectional image of resistivity required sufficient data collection by finite element method using MATLAB software. The EIT technique offers some benefits over other imaging modalities. It is economical, non-invasive, user friendly and emits no radiation thus appears to be one of the best fit technology for mass health care to be used by the basic health worker at a community level.
Background: Magnetic-Resonance guided Focused Ultrasound (FUS) thalamotomy is a new and less invasive surgical technique for treating Parkinson’s disease (PD). During therapy the required part of cerebral (as STN, Internal Globus Pallidus, and Ventral Intermediate Nucleus) is ablated with less possibility of infection and brain hemorrhage as normally happen in invasive procedures. Introduction: New advancement in the technique enable it for transcranial transportation of US. Now a days, US coupling with MRI confirms the accurate energy transferring and monitoring. So, MRI guided FUS lesioning is discovered for various psychiatrics and brain disorders. Methods: A technical overview of non-invasive MRI-FUS thalamotomy to treat various tremors is described here. Research, review articles, and book chapters are extracted from online resources using related search strings from year 1994-2020. Results: MRgFUS is concluded a non-invasive, satisfactory and safe technique to reduce the tremor. Table 1 shows the significance of it.
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