Predicting postoperative delirium after cardiovascular surgeries from preoperative portable electroencephalography oscillations

Introduction

Postoperative delirium (POD) is common and life-threatening, however, with intensive interventions, a potentially preventable clinical syndrome. Although electroencephalography (EEG) is a promising biomarker of delirium, standard 20-leads EEG holds difficulties for screening usage in clinical practice.

Objective

We aimed to develop an accurate algorithm to predict POD using EEG data obtained from portable device.

Methods

We recruited 128 patients who underwent scheduled cardiovascular surgery. Cognitive function assessments were conducted, and portable EEG recordings were obtained prior to surgery.

Results

Among the patients, 47 (36.7%) patients with POD were identified and they did not significantly differ from patients without POD in sex ratio, age, cognitive function, or treatment duration of intensive care unit. However, significant differences were observed in the preoperative EEG power spectrum densities at various frequencies, especially gamma activity, between patients with and without POD. POD was successfully predicted using preoperative EEG data with a machine learning algorithm, yielding accuracy of 86% and area under the receiver operating characteristic curve of 0.93.

Discussion

This study provides new insights into the objective and biological vulnerability to delirium. The developed algorithm can be applied in general hospitals without advanced equipment and expertise, thereby enabling the reduction of POD occurrences with intensive interventions for high-risk patients.

Extraction and Reconstruction of Arbitrary 3D Frequency Features from the Potassium Dihydrogen Phosphate Surfaces Machined by Different Cutting Parameters

To comprehensively analyze the effect of cutting parameters on the 3D surface topography of machined potassium dihydrogen phosphate crystals, 2D power spectrum density and continuous wavelet transform are used to extract and reconstruct the arbitrary actual 3D frequency features of machined potassium dihydrogen phosphate crystal surfaces. The 2D power spectrum density method is used to quantitatively describe the 3D surface topography of machined potassium dihydrogen phosphate crystals. The continuous wavelet transform method is applied to extract and reconstruct 3D topographies of arbitrary actual spatial frequency features in machined surfaces. The main spatial frequency features f_x of the machined surfaces are 0.0056 μm^-1, 0.0112 μm^-1, and 0.0277 μm^-1 with the cutting depth from 3 μm to 9 μm. With the feed rate changing from 8μm/r to 18 μm/r, the main spatial frequency features f_x are 0.0056 μm^-1-0.0277 μm^-1. With the spindle speed from 1300 r/min to 1500 r/min, the main spatial frequency features f_x are same as the main spatial frequency features of the cutting depths. The results indicate that the variation of cutting parameters affects the main spatial frequency features on the 3D surface topography. The amplitudes of the spatial middle-frequency features are increased with the increasing of cutting depth and spindle speed. The spatial low-frequency features are mainly affected via the feed rate. The spatial high-frequency features are related to the measurement noise and material properties of potassium dihydrogen phosphate. The distributional directions of the frequency features in the reconstructed 3D surface topography are consistent with the distribution directions of actual frequency features in the original surface topography. The reconstructed topographies of the spatial frequency features with maximum power spectrum density are the most similar to the original 3D surfaces. In this machining, the best 3D surface topography of the machined KDP crystals is obtained with a cutting depth a_p = 3 μm, feed rate f = 8 μm/r and a spindle speed n = 1400 r/min.

Dynamics of nanoscale triangular features on Ge surfaces

Ion beam sputtering, known as potential technique for producing nanoripple on various surfaces having wide range of applications. Along with nanoripple, triangular features are also superimposed, limiting their use for some potential applications. Here we are reporting evolution of triangular features on Ge (100) surfaces under low energy (300-1000 eV) Xe ion irradiation at room temperature for angles of incidence (61°-80°) and ion fluences of (5.34 × 10¹⁷-8.01 × 10¹⁸ions cm^-2). Triangular features appear with the onset of ripple formation and disappear when the ripple periodicity is lost. These features formation depend not only on material but also depend on the ratio of the ion/target mass. In comparison with numerical simulations based on modified anisotropic Kuramoto-Sivanshinsky equation, we find good agreement for the evolution of base angle and lateral length for the triangular features with ion incidence angle. The dynamics of triangular feature with ion incidence angle and ion fluence have been reported. Ion-incidence angle dependency is adequately replicated in numerical simulations. Experimentally the base angle and lateral length increases with increase in ion incidence angle, similar trend is observed in numerical simulation.

Stereometric characterization of Dinizia excelsa Ducke wood from Amazon rainforest using atomic force microscopy

Dinizia excelsa Ducke under three different cut conditions were carefully analyzed. The morphology and stereometry of different wood cutting surfaces (longitudinal radial, longitudinal tangential, and transversal) were studied by SEM and AFM. The results obtained in this study suggest that both the height parameters and the advanced stereometric parameters of the surfaces did not reveal a significant difference, indicating that the spatial patterns do not change according to the type of cut. In this way, the surface microtexture does not vary depending on the cut type. Similarly, the Hurst's coefficients did not show any significant difference in the spectrum of the PSD fractal region. On the other hand, Minkowski functionals presented a morphological difference between the samples. These results showed that the microtexture of the wood surface does not change as a function of the type of cut submitted to the same polishing process.

Identification of Major Psychiatric Disorders From Resting-State Electroencephalography Using a Machine Learning Approach

We aimed to develop a machine learning (ML) classifier to detect and compare major psychiatric disorders using electroencephalography (EEG). We retrospectively collected data from medical records, intelligence quotient (IQ) scores from psychological assessments, and quantitative EEG (QEEG) at resting-state assessments from 945 subjects [850 patients with major psychiatric disorders (six large-categorical and nine specific disorders) and 95 healthy controls (HCs)]. A combination of QEEG parameters including power spectrum density (PSD) and functional connectivity (FC) at frequency bands was used to establish models for the binary classification between patients with each disorder and HCs. The support vector machine, random forest, and elastic net ML methods were applied, and prediction performances were compared. The elastic net model with IQ adjustment showed the highest accuracy. The best feature combinations and classification accuracies for discrimination between patients and HCs with adjusted IQ were as follows: schizophrenia = alpha PSD, 93.83%; trauma and stress-related disorders = beta FC, 91.21%; anxiety disorders = whole band PSD, 91.03%; mood disorders = theta FC, 89.26%; addictive disorders = theta PSD, 85.66%; and obsessive-compulsive disorder = gamma FC, 74.52%. Our findings suggest that ML in EEG may predict major psychiatric disorders and provide an objective index of psychiatric disorders.

Peak Alpha Frequency in Relation to Cognitive Performance

Objective  Peak alpha frequency (PAF) is reported to be a nervous system property which is genetically endowed and reflected in individual’s cognitive functioning. Cognitive performance denotes the mental processes for effective changes in response to situations in general and mental task in particular. None of the study has till now used reading comprehension as a measure of cognitive functioning, which is considered to be a measure of higher cognitive processes. The reading comprehension task used in the study implies certain cognitive processes such as reading ability, comprehension, memory performance involving information maintenance, and retrieval. Therefore, the study was conducted to test the hypothesis of differences in cognitive reading comprehension performance between high- and low-peak alpha subjects.

Materials and Methods  A group of 300 healthy participants were selected on the basis of incidental-cum-probabilistic sampling from seven districts of Indian state of Haryana. In the present study, reading comprehension task (Hindi–English; bilingual format) was used to assess the cognitive performance and multichannel electroencephalography alpha frequencies were recorded to measure PAF through power spectrum density analysis of each subject.

Results  The findings revealed that individuals with high-peak alpha frequency had significantly higher score ( p < 0.05) on reading comprehension task as compared with individuals with low PAF.

Conclusion  The present study concluded that the reading comprehension task is effected by peak alpha frequency of an individual. PAF can be considered to be a correlate of cognitive performance.

Stumbling prediction based on plantar pressure distribution

BACKGROUND

Stumbles are common accidents that can result in falls and serious injuries, particularly in the workplace where back and forth movements are involved and in offices where high heels are imperative. Currently, the characteristics of plantar pressure during a stumble and the differences between stumbling and a normal gait remain unclear.

OBJECTIVE

This paper is aimed at providing insights into the feasibility of the data mining technique for interventions in stumble-related occupational safety issues.

METHODS

The characteristics of plantar pressure distribution during stumbling and normal gait were analyzed by using the power spectrum density (PSD) and the Support Vector Machine (SVM). The PSD, a novel pattern recognition feature, was used to mathematically describe the image signal. The SVM, a powerful data mining technique, was used as the classifier to recognize a stumble. Dynamic plantar pressures were measured from twelve healthy participants as they walked.

RESULTS

The plantar pressures of the stumbling gaits had significantly different patterns compared to the normal ones, from either a qualitative or quantitative perspective. The mean recognition accuracy of the proposed method reached 96.7%.

CONCLUSIONS

This study helps better understand stumbles and provides a theoretical basis for stumble-related occupational injuries. In addition, the stumble is the precursor of a fall and the research on stumble recognition would be of value to predict and provide warnings of falls and to design anti-fall devices for potential victims.

High-frequency stimulation of afferent axons alters firing rhythms of downstream neurons

Deep brain stimulation is an emerging treatment for brain disorders. However, the mechanisms of high-frequency brain stimulation are unclear. Recent studies have suggested that high-frequency stimulation might produce therapeutic effects by eliminating pathological rhythms in neuronal firing. To test the hypothesis, the present study investigated whether stimulation of axonal afferent fibers might alter firing rhythms of downstream neurons in in-vivo experiments with Sprague-Dawley rats. Stimulation trains of 100 Hz with one minute duration were applied to the Schaffer collaterals of hippocampus Area CA1 in anaesthetized rats. Spikes of single interneurons and pyramidal neurons in the downstream region were analyzed. The spike rhythms before, during, and after the stimulations were evaluated by analyzing the power spectrum density of autocorrelograms of the spiking sequences. The rhythms of local field potentials were also evaluated by power spectrum density. During baseline recordings, theta rhythms were obvious in the spiking sequences of both types of neuron and in the local field potentials of the stratum radiatum. However, these theta rhythms were all suppressed significantly during the stimulations. Additionally, the results of Pearson's correlation analysis showed that 20-30% variation in the theta rhythms of neuronal firing could be explained by changes of the theta rhythms in local field potentials. High-frequency axonal stimulation might prevent the original rhythmic excitation in afferent fibers and generate new excitation by stimulation pulses per se, thereby suppressing the theta rhythms of individual neuron firing and of local field potentials in the region downstream from stimulation. The results provide new evidence to support the hypothesis that high-frequency stimulation can alter the firing rhythms of neurons, which may underlie the therapeutic effects of deep brain stimulation.

A Hybrid System for Distinguishing between Brain Death and Coma Using Diverse EEG Features

Electroencephalography (EEG) signals may provide abundant information reflecting the developmental changes in brain status. It usually takes a long time to finally judge whether a brain is dead, so an effective pre-test of brain states method is needed. In this paper, we present a hybrid processing pipeline to differentiate brain death and coma patients based on canonical correlation analysis (CCA) of power spectral density, complexity features, and feature fusion for group analysis. In addition, time-varying power spectrum and complexity were observed based on the analysis of individual patients, which can be used to monitor the change of brain status over time. Results showed three major differences between brain death and coma groups of EEG signal: slowing, increased complexity, and the improvement on classification accuracy with feature fusion. To the best of our knowledge, this is the first scheme for joint general analysis and time-varying state monitoring. Delta-band relative power spectrum density and permutation entropy could effectively be regarded as potential features of discrimination analysis on brain death and coma patients.

A Novel Complex-Coefficient In-Band Interference Suppression Algorithm for Cognitive Ultra-Wide Band Wireless Sensors Networks

With the rapid development of wireless communication systems and electronic techniques, the limited frequency spectrum resources are shared with various wireless devices, leading to a crowded and challenging coexistence circumstance. Cognitive radio (CR) and ultra-wide band (UWB), as sophisticated wireless techniques, have been considered as significant solutions to solve the harmonious coexistence issues. UWB wireless sensors can share the spectrum with primary user (PU) systems without harmful interference. The in-band interference of UWB systems should be considered because such interference can severely affect the transmissions of UWB wireless systems. In order to solve the in-band interference issues for UWB wireless sensor networks (WSN), a novel in-band narrow band interferences (NBIs) elimination scheme is proposed in this paper. The proposed narrow band interferences suppression scheme is based on a novel complex-coefficient adaptive notch filter unit with a single constrained zero-pole pair. Moreover, in order to reduce the computation complexity of the proposed scheme, an adaptive complex-coefficient iterative method based on two-order Taylor series is designed. To cope with multiple narrow band interferences, a linear cascaded high order adaptive filter and a cyclic cascaded high order matrix adaptive filter (CCHOMAF) interference suppression algorithm based on the basic adaptive notch filter unit are also presented. The theoretical analysis and numerical simulation results indicate that the proposed CCHOMAF algorithm can achieve better performance in terms of average bit error rate for UWB WSNs. The proposed in-band NBIs elimination scheme can significantly improve the reception performance of low-cost and low-power UWB wireless systems.

Generalized Noise Study of Solid-State Nanopores at Low Frequencies

Nanopore technology has been extensively investigated for analysis of biomolecules, and a success story in this field concerns DNA sequencing using a nanopore chip featuring an array of hundreds of biological nanopores (BioNs). Solid-state nanopores (SSNs) have been explored to attain longer lifetime and higher integration density than what BioNs can offer, but SSNs are generally considered to generate higher noise whose origin remains to be confirmed. Here, we systematically study low-frequency (including thermal and flicker) noise characteristics of SSNs measuring 7 to 200 nm in diameter drilled through a 20-nm-thick SiN_x membrane by focused ion milling. Both bulk and surface ionic currents in the nanopore are found to contribute to the flicker noise, with their respective contributions determined by salt concentration and pH in electrolytes as well as bias conditions. Increasing salt concentration at constant pH and voltage bias leads to increase in the bulk ionic current and noise therefrom. Changing pH at constant salt concentration and current bias results in variation of surface charge density, and hence alteration of surface ionic current and noise. In addition, the noise from Ag/AgCl electrodes can become predominant when the pore size is large and/or the salt concentration is high. Analysis of our comprehensive experimental results leads to the establishment of a generalized nanopore noise model. The model not only gives an excellent account of the experimental observations, but can also be used for evaluation of various noise components in much smaller nanopores currently not experimentally available.

Electrohysterography of labor contractions: propagation velocity and direction

OBJECTIVE

Electrohysterographic assessment of the propagation velocity of uterine depolarization has been introduced as a promising predictor of preterm labor. Therefore, the objectives of this study were to characterize the uterine electrohysterographic signals during labor and to determine the propagation velocity and propagation direction of electrohysterographic signals.

DESIGN

Descriptive study.

SETTING

Department of Obstetrics and Gynecology, Aarhus University Hospital, Denmark.

POPULATION

Eight women in active labor at term.

METHODS

Electrohysterograms (three channels) were recorded using surface electrodes placed abdominally along the vertical median axis with an inter-electrode distance of 6.5-11.2 cm. In total, 89 contractions were analyzed.

RESULTS

Electrohysterographic characteristics: The duration of the contractions was 61.0 ± 18.0 s (mean ± SD). The median frequency of the power spectrum density was 0.51 (0.44; 0.51) Hz (median; 10th; 90th percentile). The greatest signal magnitude was obtained by the electrode in the centermost position. The propagation velocity: 2.15 (0.66; 13.8) cm/s in the upper part and 1.53 (0.58; 6.7) cm/s in the lower part of the uterus. Propagation direction: Both downward (58%) and upward (42%) propagation of the electrohysterographic signals occurred. Moreover, downward and upward propagations were recorded simultaneously in the upper and lower part of the uterus, suggesting a multidirectional propagation pattern.

CONCLUSIONS

Labor contractions, expressed by electrohysterographic signals, propagate both in the downward and upward direction, a phenomenon that must be taken into account when determining the propagation velocity for preterm labor diagnostics.

An efficient method of addressing ectopic beats: new insight into data preprocessing of heart rate variability analysis

Heart rate variability (HRV) analysis is affected by ectopic beats. An efficient method was proposed to deal with the ectopic beats. The method was based on trend correlation of the heart timing signal. Predictor of R-R interval (RRI) value at ectopic beat time was constructed by the weight calculation and the slope estimation of preceding normal RRI. The type of ectopic beat was detected and replaced by the predictor of RRI. The performance of the simulated signal after ectopic correction was tested by the standard value using power spectrum density (PSD) estimation, whereas the results of clinical data with ectopic beats were compared with the adjacent ectopic-free data. The result showed the frequency indexes after ectopy corrected had less error than other methods with the test of simulated signal and clinical data. It indicated our method could improve the PSD estimation in HRV analysis. The method had advantages of high accuracy and real time properties to recover the sinus node modulation.