Bonnerweeks5236

Considering the difference between task types, no difference in power of frontal Theta, central-parietal Alpha and sample entropies at scales more than 10 of parietal regions were found between verbal and object tasks, as well as between two spatial tasks. No difference of frontal Theta/Alpha ratio was found in all the four tasks. The results can provide evidence for the mental workload evaluation in tasks with different information types.Stimulation of target neuronal populations using optogenetic techniques during specific sleep stages has begun to elucidate the mechanisms and effects of sleep. To conduct closed-loop optogenetic sleep studies in untethered animals, we designed a fully integrated, low-power system-on-chip (SoC) for real-time sleep stage classification and stage-specific optical stimulation. The SoC consists of a 4-channel analog front-end for recording polysomnography signals, a mixed-signal machine-learning (ML) core, and a 16-channel optical stimulation back-end. A novel ML algorithm and innovative circuit design techniques improved the online classification performance while minimizing power consumption. The SoC was designed and simulated in 180 nm CMOS technology. In an evaluation using an expert labeled sleep database with 20 subjects, the SoC achieves a high sensitivity of 0.806 and a specificity of 0.947 in discriminating 5 sleep stages. Overall power consumption in continuous operation is 97 µW.Arterial blood pressure (ABP) waveform is a common physiological signal that contains a wealth of cardiovascular information. click here According to the cardiac cycle, the ABP waveform is divided into rapid ejection, systolic and diastolic phases. Therefore, the characteristic points of the arterial blood pressure waveform, i.e. their onsets, systolic peaks, represent the timing of the minimum and maximum pressures. It is important to detect these characteristic points accurately. Recently, many researchers have introduced some feature points detection methods, but the accuracy is not particularly high. In this paper, a deep learning method is proposed to achieve periodic segmentation and feature points detection of ABP signals using a one-dimensional U-Net network. The network can split the ABP signal into two parts and accurately detect the feature points. The method is validated on an ABP dataset of 126 people, 500 people each. Performances are good at different tolerance thresholds, with an average time difference of less than 1.5 ms. Finally, the method performs with 99.79% and 99.79% sensitivity, 99.99% and 99.94% positive predictivity, and 0.23% and 0.27% error rates for both onsets and systolic peaks at a tolerance threshold of 30 ms. To our knowledge, this is the first paper to use deep learning methods for the onsets and systolic peaks detections of ABP signals.Obtaining high quality heart and lung sounds enables clinicians to accurately assess a newborns cardio-respiratory health and provide timely care. However, noisy chest sound recordings are common, hindering timely and accurate assessment. A new Non-negative Matrix Co-Factorisation based approach is proposed to separate noisy chest sound recordings into heart, lung and noise components to address this problem. This method is achieved through training with 20 high quality heart and lung sounds, in parallel with separating the sounds of the noisy recording. The method was tested on 68 10-second noisy recordings containing both heart and lung sounds and compared to the current state of the art Non-negative Matrix Factorisation methods. Results show significant improvements in heart and lung sound quality scores respectively, and improved accuracy of 3.6bpm and 1.2bpm in heart and breathing rate estimation respectively, when compared to existing methods.Wave intensity analysis (WIA) as a framework to assess cardiovascular hemodynamics has been successfully used in many clinical applications. Typically, wave intensity calculations require the simultaneous acquisition of blood velocity and blood pressure at the same vascular site. Unfortunately, many hemodynamic parameters that are used to monitor pre-operative patient hemodynamic state use both invasively acquired blood pressure measurements in catheterization laboratory and non-invasively acquired blood velocity measurements. To utilize wave intensity analysis to assess patients undergoing cardiac interventional procedures, we have developed a graphical user interface (GUI) that uses standard clinical measurements which include invasive blood pressure waveforms and Doppler echocardiography images to calculate wave intensity parameters. The GUI consists of three main subroutines that allow clinicians to import raw data and extract and analyze the blood pressure and blood velocity signals separately. Using the electrocardiogram signals as an alignment marker, the re-formatted signals are aligned, and wave intensity is calculated. Wave intensity features such as forward compression wave (FCW), forward expansion wave (FEW) and wave speed are calculated and output in a table for statistical analysis. The GUI represents the first attempt to create a program that encourages clinicians to use WIA for hemodynamic assessment in patients undergoing cardiac catheterization procedures with the data they have already procured.Blood Pressure (BP) is one of the four primary vital signs indicating the status of the body's vital (life-sustaining) functions. BP is difficult to continuously monitor using a sphygmomanometer (i.e. a blood pressure cuff), especially in everyday-setting. However, other health signals which can be easily and continuously acquired, such as photoplethysmography (PPG), show some similarities with the Aortic Pressure waveform. Based on these similarities, in recent years several methods were proposed to predict BP from the PPG signal. Building on these results, we propose an advanced personalized data-driven approach that uses a three-layer deep neural network to estimate BP based on PPG signals. Different from previous work, the proposed model analyzes the PPG signal in time-domain and automatically extracts the most critical features for this specific application, then uses a variation of recurrent neural networks (RNN) called Long-Short-Term-Memory (LSTM) to map the extracted features to the BP value associated with that time window. Experimental results on two separate standard hospital datasets, yielded absolute errors mean and absolute error standard deviation for systolic and diastolic BP values outperforming prior works.In this paper, we introduce PulseLab, a comprehensive MATLAB toolbox that enables estimating the blood pressure (BP) from electrocardiogram (ECG) and photoplethysmogram (PPG) signals using pulse wave velocity (PWV)-based models. This universal framework consists of 6 sequential modules, covering end-to-end procedures that are needed for estimating BP from raw PPG/ECG data. These modules are "dataset formation", "signal pre-processing", "segmentation", "characteristic-points detection", "pulse transit time (PTT)/ pulse arrival time (PAT) calculation", and "model validation". The toolbox is expandable and its application programming interface (API) is built such that newly-derived PWV-BP models can be easily included. The toolbox also includes a user-friendly graphical user interface (GUI) offering visualization for step-by-step processing of physiological signals, position of characteristic points, PAT/PTT values, and the BP regression results. To the best of our knowledge, PulseLab is the first comprehensive toolbox that enables users to optimize their model by considering several factors along the process for obtaining the most accurate model for cuff-less BP estimation.

Non-contact measurement of physiological vital signs, such as blood pressure (BP), by video-based photoplethysmography (vPPG) is a potential means for remote health monitoring. However, the signal-to-noise ratio of cardiovascular signals within the vPPG is very low.

This study investigates the potential of BP estimation from vPPG.

In 10 healthy volunteers (4 females, 28 ± 7 years), continuous electrocardiogram, finger BP and video of the face and palm of the hand were recorded. BP was varied by isometric hand grip exercise and leg ischemia. Four vPPG methods were compared (i) averages of the green (GREEN) color intensity; (ii) the best linear combination of color channels using independent component analysis (ICA); (iii) a linear combination of chrominance-based (CHROM) signal by standardizing the skin color profile; (iv) plane orthogonal to the skin tone (POS) as vPPG signal. These were applied to 14 regions of interest (ROIs) on the face and 5 ROIs on the palm. Pulse transit time (PTT) between ROIs, for all permutations, were calculated and the correlation with BP quantified.

A significant, negative PTT-BP correlation was defined as success. A maximum success rate of 80% was achieved, occurring for the GREEN, POS and ICA methods only for specific ROIs within the face, but not for any permutation using the hand.

These results indicate that the use of vPPG for estimation of BP will be challenging. A combination of different vPPG methods and within-face ROIs may yield useful information.

These results indicate that the use of vPPG for estimation of BP will be challenging. A combination of different vPPG methods and within-face ROIs may yield useful information.In clinical practice, when a patient is undergoing mechanical ventilation, it is important to identify the optimal moment for extubation, minimizing the risk of failure. However, this prediction remains a challenge in the clinical process. In this work, we propose a new protocol to study the extubation process, including the electromyographic diaphragm signal (diaEMG) recorded through 5-channels with surface electrodes around the diaphragm muscle. First channel corresponds to the electrode on the right. A total of 40 patients in process of withdrawal of mechanical ventilation, undergoing spontaneous breathing tests (SBT), were studied. According to the outcome of the SBT, the patients were classified into two groups successful (SG 19 patients) and failure (FG 21 patients) groups. Parameters extracted from the envelope of each channel of diaEMG in time and frequency domain were studied. After analyzing all channels, the second presented maximum differences when comparing the two groups of patients, with parameters related to root mean square (p = 0.005), moving average (p = 0.001), and upward slope (p = 0.017). The third channel also presented maximum differences in parameters as the time between maximum peak (p = 0.004), and the skewness (p = 0.027). These results suggest that diaphragm EMG signal could contribute to increase the knowledge of the behaviour of respiratory system in these patients and improve the extubation process.Clinical Relevance-This establishes the characterization of success and failure patients in the extubation process.Left ventricular assist devices (LVADs) are mechanical pumps that help patients with chronic heart failure waiting for a heart transplant. Mathematical models of these devices can be used along cardiovascular system (CVS) models to evaluate the assistance performance under different operating modes. The estimation of the CVS model parameters for a particular patient and numerical simulations allow the implementation of adequate LVAD operation mode. This work presents a method to estimate the parameters of a CVS model using only one hemodynamic variable the systemic arterial pressure (Ps). Synthetic signals of Ps are used to solve this ill-posed inverse problem partially, and the results show the high accuracy of the proposed method, which achieves 0.5%.Clinical relevance- The measurements of hemodynamic variables using noninvasive techniques avoid many clinical problems arising from invasive measures such as infections.