The Application of a Self-Made Integrated Three-in-One Microsensor and Commercially Available Wind Speed Sensor to the Cold Air Pipe of the Heating, Ventilation, and Air Conditioning in a Factory for Real-Time Wireless Measurement.

In this study, the integrated three-in-one (temperature, humidity, and wind speed) microsensor was made through the technology of the Micro-electro-mechanical Systems (MEMS) to measure three important physical quantities of the internal environment of the cold air pipe of the Heating, Ventilation and Air Conditioning (HVAC) in the factory, plan the installation positions of the integrated three-in-one microsensor and commercially available wind speed sensor required by the internal environment of the cold air pipe, and conduct the actual 310-h long term test and comparison. In the experiment, it was also observed that the self-made micro wind speed sensor had higher stability compared to the commercially available wind speed sensor (FS7.0.1L.195). The self-made micro wind speed sensor has a variation range of ±200 mm/s, while the commercially available wind speed sensor a variation range of ±1000 mm/s. The commercially available wind speed sensor (FS7.0.1L.195) can only measure the wind speed; however, the self-made integrated three-in-one microsensor can conduct real-time measurements of temperature and humidity according to the environment at that time, and use different calibration curves to know the wind speed. As a result, it is more accurate and less costly than commercially available wind speed sensors. The material cost of self-made integrated three-in-one microsensor includes chemicals, equipment usage fees, and wires. In the future, factories may install a large number of self-made integrated three-in-one microsensors in place of commercially available wind speed sensors. Through real-time wireless measurements, the self-made integrated three-in-one microsensors can achieve the control optimization of the HVAC cold air pipe's internal environment to improve the quality of manufactured materials.

Fractal Properties of Heart Rate Dynamics: A New Biomarker for Anesthesia-Biphasic Changes in General Anesthesia and Decrease in Spinal Anesthesia.

Processed electroencephalogram (EEG) has been considered a useful tool for measuring the depth of anesthesia (DOA). However, because of its inability to detect the activities of the brain stem and spinal cord responsible for most of the vital signs, a new biomarker for measuring the multidimensional activities of the central nervous system under anesthesia is required. Detrended fluctuation analysis (DFA) is a new technique for detecting the scaling properties of nonstationary heart rate (HR) behavior. This study investigated the changes in fractal properties of heart rate variability (HRV), a nonlinear analysis, under intravenous propofol, inhalational desflurane, and spinal anesthesia. We compared the DFA method with traditional spectral analysis to evaluate its potential as an alternative biomarker under different levels of anesthesia. Eighty patients receiving elective procedures were randomly allocated different anesthesia. HRV was measured with spectral analysis and DFA short-term (4-11 beats) scaling exponent (DFAα1). An increase in DFAα1 followed by a decrease at higher concentrations during propofol or desflurane anesthesia is observed. Spinal anesthesia decreased the DFAα1 and low-/high-frequency ratio (LF/HF ratio). DFAα1 of HRV is a sensitive and specific method for distinguishing changes from baseline to anesthesia state. The DFAα1 provides a potential real-time biomarker to measure HRV as one of the multiple dimensions of the DOA.

Comparison of Deep Learning Algorithms in Predicting Expert Assessments of Pain Scores during Surgical Operations Using Analgesia Nociception Index.

There are many surgical operations performed daily in operation rooms worldwide. Adequate anesthesia is needed during an operation. Besides hypnosis, adequate analgesia is critical to prevent autonomic reactions. Clinical experience and vital signs are usually used to adjust the dosage of analgesics. Analgesia nociception index (ANI), which ranges from 0 to 100, is derived from heart rate variability (HRV) via electrocardiogram (ECG) signals, for pain evaluation in a non-invasive manner. It represents parasympathetic activity. In this study, we compared the performance of multilayer perceptron (MLP) and long short-term memory (LSTM) algorithms in predicting expert assessment of pain score (EAPS) based on patient's HRV during surgery. The objective of this study was to analyze how deep learning models differed from the medical doctors' predictions of EAPS. As the input and output features of the deep learning models, the opposites of ANI and EAPS were used. This study included 80 patients who underwent operations at National Taiwan University Hospital. Using MLP and LSTM, a holdout method was first applied to 60 training patients, 10 validation patients, and 10 testing patients. As compared to the LSTM model, which had a testing mean absolute error (MAE) of 2.633 ± 0.542, the MLP model had a testing MAE of 2.490 ± 0.522, with a more appropriate shape of its prediction curves. The model based on MLP was selected as the best. Using MLP, a seven-fold cross validation method was then applied. The first fold had the lowest testing MAE of 2.460 ± 0.634, while the overall MAE for the seven-fold cross validation method was 2.848 ± 0.308. In conclusion, HRV analysis using MLP algorithm had a good correlation with EAPS; therefore, it can play role as a continuous monitor to predict intraoperative pain levels, to assist physicians in adjusting analgesic agent dosage. Further studies may consider obtaining more input features, such as photoplethysmography (PPG) and other kinds of continuous variable, to improve the prediction performance.

Anticonvulsive and Neuroprotective Effects of Eupafolin in Rats Are Associated with the Inhibition of Glutamate Overexcitation and Upregulation of the Wnt/ß-Catenin Signaling Pathway.

Several plant compounds have been found to possess neuroactive properties. The aim of this study was to investigate the anticonvulsant effect of eupafolin, a major active component extracted from Salvia plebeia, a herb used in traditional medicine for its anti-inflammatory properties. To this end, we assessed the anticonvulsant effects of eupafolin in rats intraperitoneally (i.p.) injected with kainic acid (KA) to elucidate this mechanism. Treatment with eupafolin (i.p.) for 30 min before KA administration significantly reduced behavioral and electrographic seizures induced by KA, similar to carbamazepine (i.p.), a widely used antiepileptic drug. Eupafolin treatment also significantly decreased KA seizure-induced neuronal cell death and glutamate elevation in the hippocampus. In addition, eupafolin notably reversed KA seizure-induced alterations in α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptor subunit GluR2, glutamate decarboxylase 67 (GAD67, GABAergic enzyme), and Wnt signaling-related proteins, including porcupine, Wnt1, phosphorylated-glycogen synthase kinase-3ß, ß-catenin, and Bcl-2 in the hippocampus. Furthermore, the increased level of Dickkopf-related protein 1 (Dkk-1, a Wnt signaling antagonist) and the decreased level of Disheveled1 (Dvl-1, a Wnt signaling activator) in the hippocampus of KA-treated rats were reversed by eupafolin. This study provides evidence of the anticonvulsant and neuroprotective properties of eupafolin and of the involvement of regulation of glutamate overexcitation and Wnt signaling in the mechanisms of these properties. These findings support the benefits of eupafolin in treating epilepsy.

Special Issue "Advanced Signal Processing in Wearable Sensors for Health Monitoring".

Wearable sensors are becoming very popular recently due to their ease of use and flexibility in recording data from home [...].

ECG Recurrence Plot-Based Arrhythmia Classification Using Two-Dimensional Deep Residual CNN Features.

In this paper, an effective electrocardiogram (ECG) recurrence plot (RP)-based arrhythmia classification algorithm that can be implemented in portable devices is presented. Public databases from PhysioNet were used to conduct this study including the MIT-BIH Atrial Fibrillation Database, the MIT-BIH Arrhythmia Database, the MIT-BIH Malignant Ventricular Ectopy Database, and the Creighton University Ventricular Tachyarrhythmia Database. ECG time series were segmented and converted using an RP, and two-dimensional images were used as inputs to the CNN classifiers. In this study, two-stage classification is proposed to improve the accuracy. The ResNet-18 architecture was applied to detect ventricular fibrillation (VF) and noise during the first stage, whereas normal, atrial fibrillation, premature atrial contraction, and premature ventricular contractions were detected using ResNet-50 in the second stage. The method was evaluated using 5-fold cross-validation which improved the results when compared to previous studies, achieving first and second stage average accuracies of 97.21% and 98.36%, sensitivities of 96.49% and 97.92%, positive predictive values of 95.54% and 98.20%, and F1-scores of 95.96% and 98.05%, respectively. Furthermore, a 5-fold improvement in the memory requirement was achieved when compared with a previous study, making this classifier feasible for use in resource-constricted environments such as portable devices. Even though the method is successful, first stage training requires combining four different arrhythmia types into one label (other), which generates more data for the other category than for VF and noise, thus creating a data imbalance that affects the first stage performance.

Electroencephalogram variability analysis for monitoring depth of anesthesia.

Objective. In this paper, a new approach of extracting and measuring the variability in electroencephalogram (EEG) was proposed to assess the depth of anesthesia (DOA) under general anesthesia.Approach. The EEG variability (EEGV) was extracted as a fluctuation in time interval that occurs between two local maxima of EEG. Eight parameters related to EEGV were measured in time and frequency domains, and compared with state-of-the-art DOA estimation parameters, including sample entropy, permutation entropy, median frequency and spectral edge frequency of EEG. The area under the receiver-operator characteristics curve (AUC) and Pearson correlation coefficient were used to validate its performance on 56 patients.Main results. Our proposed EEGV-derived parameters yield significant difference for discriminating between awake and anesthesia stages at a significance level of 0.05, as well as improvement in AUC and correlation coefficient on average, which surpasses the conventional features of EEG in detection accuracy of unconscious state and tracking the level of consciousness.Significance. To sum up, EEGV analysis provides a new perspective in quantifying EEG and corresponding parameters are powerful and promising for monitoring DOA under clinical situations.

Depth of anesthesia prediction via EEG signals using convolutional neural network and ensemble empirical mode decomposition.

According to a recently conducted survey on surgical complication mortality rate, 47% of such cases are due to anesthetics overdose. This indicates that there is an urgent need to moderate the level of anesthesia. Recently deep learning (DL) methods have played a major role in estimating the depth of Anesthesia (DOA) of patients and has played an essential role in control anesthesia overdose. In this paper, Electroencephalography (EEG) signals have been used for the prediction of DOA. EEG signals are very complex signals which may require months of training and advanced signal processing techniques. It is a point of debate whether DL methods are an improvement over the already existing traditional EEG signal processing approaches. One of the DL algorithms is Convolutional neural network (CNN) which is very popular algorithm for object recognition and is widely growing its applications in processing hierarchy in the human visual system. In this paper, various decomposition methods have been used for extracting the features EEG signal. After acquiring the necessary signals values in image format, several CNN models have been deployed for classification of DOA depending upon their Bispectral Index (BIS) and the signal quality index (SQI). The EEG signals were converted into the frequency domain using and Empirical Mode Decomposition (EMD), and Ensemble Empirical Mode Decomposition (EEMD). However, because of the inter mode mixing observed in EMD method; EEMD have been utilized for this study. The developed CNN models were used to predict the DOA based on the EEG spectrum images without the use of handcrafted features which provides intuitive mapping with high efficiency and reliability. The best trained model gives an accuracy of 83.2%. Hence, this provides further scope and research which can be carried out in the domain of visual mapping of DOA using EEG signals and DL methods.

Non-Invasive Hemodynamics Monitoring System Based on Electrocardiography via Deep Convolutional Autoencoder.

This study evaluates cardiovascular and cerebral hemodynamics systems by only using non-invasive electrocardiography (ECG) signals. The Massachusetts General Hospital/Marquette Foundation (MGH/MF) and Cerebral Hemodynamic Autoregulatory Information System Database (CHARIS DB) from the PhysioNet database are used for cardiovascular and cerebral hemodynamics, respectively. For cardiovascular hemodynamics, the ECG is used for generating the arterial blood pressure (ABP), central venous pressure (CVP), and pulmonary arterial pressure (PAP). Meanwhile, for cerebral hemodynamics, the ECG is utilized for the intracranial pressure (ICP) generator. A deep convolutional autoencoder system is applied for this study. The cross-validation method with Pearson's linear correlation (R), root mean squared error (RMSE), and mean absolute error (MAE) are measured for the evaluations. Initially, the ECG is used to generate the cardiovascular waveform. For the ABP system-the systolic blood pressure (SBP) and diastolic blood pressures (DBP)-the R evaluations are 0.894 ± 0.004 and 0.881 ± 0.005, respectively. The MAE evaluations for SBP and DBP are, respectively, 6.645 ± 0.353 mmHg and 3.210 ± 0.104 mmHg. Furthermore, for the PAP system-the systolic and diastolic pressures-the R evaluations are 0.864 ± 0.003 mmHg and 0.817 ± 0.006 mmHg, respectively. The MAE evaluations for systolic and diastolic pressures are, respectively, 3.847 ± 0.136 mmHg and 2.964 ± 0.181 mmHg. Meanwhile, the mean CVP evaluations are 0.916 ± 0.001, 2.220 ± 0.039 mmHg, and 1.329 ± 0.036 mmHg, respectively, for R, RMSE, and MAE. For the mean ICP evaluation in cerebral hemodynamics, the R and MAE evaluations are 0.914 ± 0.003 and 2.404 ± 0.043 mmHg, respectively. This study, as a proof of concept, concludes that the non-invasive cardiovascular and cerebral hemodynamics systems can be potentially investigated by only using the ECG signal.

Applying deep learning to defect detection in printed circuit boards via a newest model of you-only-look-once.

In this paper, a new model known as YOLO-v5 is initiated to detect defects in PCB. In the past many models and different approaches have been implemented in the quality inspection for detection of defect in PCBs. This algorithm is specifically selected due to its efficiency, accuracy and speed. It is well known that the traditional YOLO models (YOLO, YOLO-v2, YOLO-v3, YOLO-v4 and Tiny-YOLO-v2) are the state-of-the-art in artificial intelligence industry. In electronics industry, the PCB is the core and the most basic component of any electronic product. PCB is almost used in each and every electronic product that we use in our daily life not only for commercial purposes, but also used in sensitive applications such defense and space exploration. These PCB should be inspected and quality checked to detect any kind of defects during the manufacturing process. Most of the electronic industries are focused on the quality of their product, a small error during manufacture or quality inspection of the electronic products such as PCB leads to a catastrophic end. Therefore, there is a huge revolution going on in the manufacturing industry where the object detection method like YOLO-v5 is a game changer for many industries such as electronic industries.

Phorbol myristate acetate induces differentiation of THP-1 cells in a nitric oxide-dependent manner.

INTRODUCTION: THP-1 cells, a human leukemia monocytic cell line, differentiated by phorbol myristate acetate (PMA) are widely used as surrogate of human macrophages. Differentiated THP-1 cells acquire macrophage-like characteristics including more adherence and altered cell function. Nitric oxide (NO), an intracellular messenger, is critical in regulating cell differentiation. Here we elucidated whether NO relates to PMA-induced monocyte-to-macrophage differentiation of THP-1 cells. The mutual regulation of calcium and NO was also investigated. MATERIAL & METHODS: THP-1 cells were incubated with PMA for 24 h, followed by assay of adherence, morphological change, migration or IL-1ß release. L-NG-Nitroarginine methyl ester (l-NAME, a nitric oxide synthase inhibitor) or BAPTA-AM (a calcium chelator) was added before PMA stimulation, and levels of calcium and NO were measured. Furthermore, a selective inhibitor of inducible nitric oxide synthase (iNOS) activity was employed to study the role of iNOS. RESULTS AND DISCUSSION: Effects of PMA on upregulation of adherence, lipopolysaccharide-triggered IL-1ß, and migration ability of THP-1 cells were consistent with NO concentrations. Both l-NAME and BAPTA-AM mitigated effects of PMA on THP-1 cells differentiation. BAPTA-AM decreased levels of NO, while l-NAME had no effect on calcium levels. Of note, inhibition of iNOS activity decreased PMA-triggered upregulation of NO. CONCLUSION: PMA induced differentiation of THP-1 cells partially in a NO-dependent manner. The calcium signaling may mediate PMA-triggered upregulation of NO.

Pain and Stress Detection Using Wearable Sensors and Devices-A Review.

Pain is a subjective feeling; it is a sensation that every human being must have experienced all their life. Yet, its mechanism and the way to immune to it is still a question to be answered. This review presents the mechanism and correlation of pain and stress, their assessment and detection approach with medical devices and wearable sensors. Various physiological signals (i.e., heart activity, brain activity, muscle activity, electrodermal activity, respiratory, blood volume pulse, skin temperature) and behavioral signals are organized for wearables sensors detection. By reviewing the wearable sensors used in the healthcare domain, we hope to find a way for wearable healthcare-monitoring system to be applied on pain and stress detection. Since pain leads to multiple consequences or symptoms such as muscle tension and depression that are stress related, there is a chance to find a new approach for chronic pain detection using daily life sensors or devices. Then by integrating modern computing techniques, there is a chance to handle pain and stress management issue.

Electroencephalography complexity in infantile spasms and its association with treatment response.

OBJECTIVE: To investigate the potential of EEG multiscale entropy and complexity as biomarkers in infantile spasms. METHODS: We collected EEG data retrospectively from 16 newly diagnosed patients, 16 age- and gender-matched healthy controls, and 15 drug-resistant patients. The multiscale entropy (MSE) and total EEG complexity before anti-epileptic drug (AED) treatment, before adrenocorticotropic hormone (ACTH) treatment, 14 days after ACTH therapy, and after 6 months of follow-up were calculated. RESULTS: The total EEG complexity of 16 newly diagnosed infantile spasms patients was lower than the 16 healthy controls (median [IQR]: 351.5 [323.1-388.1] vs 461.6 [407.7-583.4]). The total EEG complexity before treatment was higher in the six patients with good response to AED than the 10 patients without response (median [IQR]: 410.0 [388.1-475.0] vs 344.5 [319.6-352.0]). The total EEG complexity before and after 14-days of ACTH therapy was not different between 13 ACTH therapy responders and nine non-responders. After 6-months follow-up, the total EEG complexity of ACTH therapy responders were higher than non-responders (median [IQR]: 598.5 [517.4-623.3] vs 448.6 [347.1-536.3]). CONCLUSIONS: The total EEG complexity before AED and 6 months after ACTH are associated with spasm-freedom. SIGNIFICANCE: The total EEG complexity is a potential biomarker to predict and monitor the treatment effect in infantile spasms.

Genetic Deep Convolutional Autoencoder Applied for Generative Continuous Arterial Blood Pressure via Photoplethysmography.

Hypertension affects a huge number of people around the world. It also has a great contribution to cardiovascular- and renal-related diseases. This study investigates the ability of a deep convolutional autoencoder (DCAE) to generate continuous arterial blood pressure (ABP) by only utilizing photoplethysmography (PPG). A total of 18 patients are utilized. LeNet-5- and U-Net-based DCAEs, respectively abbreviated LDCAE and UDCAE, are compared to the MP60 IntelliVue Patient Monitor, as the gold standard. Moreover, in order to investigate the data generalization, the cross-validation (CV) method is conducted. The results show that the UDCAE provides superior results in producing the systolic blood pressure (SBP) estimation. Meanwhile, the LDCAE gives a slightly better result for the diastolic blood pressure (DBP) prediction. Finally, the genetic algorithm-based optimization deep convolutional autoencoder (GDCAE) is further administered to optimize the ensemble of the CV models. The results reveal that the GDCAE is superior to either the LDCAE or UDCAE. In conclusion, this study exhibits that systolic blood pressure (SBP) and diastolic blood pressure (DBP) can also be accurately achieved by only utilizing a single PPG signal.

Nicardipine Inhibits Priming of the NLRP3 Inflammasome via Suppressing LPS-Induced TLR4 Expression.

The Nod-like receptor protein 3 (NLRP3) inflammasome is a multi-protein complex composed of NLRP3, pro-caspase-1, and apoptosis-associated speck-like protein that contains a caspase recruitment domain (ASC). After NLRP3 priming by lipopolysaccharide (LPS), the ligand of toll-like receptor 4 (TLR4), activation of the NLRP3 inflammasome triggers caspase-1 maturation, leading to pyroptosis and release of interleukin-1beta (IL-1beta). Expression of TLR4 modulates LPS-triggered inflammatory cascades as well as the NLRP3 signaling. L-type calcium channel antagonists are widely used as anti-hypertensive drugs and also exert anti-inflammatory effects through inhibiting release of cytokines including IL-1beta. However, few studies reveal effects of L-type calcium channel antagonists on the NLRP3 inflammasome. In this study, we investigated the effects of nicardipine and verapamil, both L-type calcium channel antagonists, on the NLRP3 inflammasome using differentiated THP-1 cells. Pyroptosis or levels of IL-1beta and caspase-1 were assayed by flow cytometry or enzyme-linked immunosorbent assay, respectively. ASC oligomerization was assayed by immunofluorescence microscopy. Expression of NLRP3 or TLR4 was assayed by polymerase chain reaction and immunoblotting. Nuclear factor-kappaB (NF-kappaB) pathway was also studied. Our results showed that pyroptosis and IL-1beta release were attenuated by nicardipine, but not verapamil. Nicardipine also mitigated caspase-1 activation, inhibited ASC oligomerization, and reduced NLRP3 expression. Furthermore, nicardipine downregulated phosphorylation or nuclear translocation of NF-kappaB p65, consistent with the inhibitory effect of nicardipine on LPS-induced TLR4 expression. In conclusion, nicardipine exerted anti-inflammatory effects through inhibiting NLRP3 inflammasome pathway. Nicardipine may mitigate NLRP3 priming via inhibiting NF-kappaB activation, mediated by suppressing LPS-induced TLR4 expression.

A novel mechanical chest compressor with rapid deployment in all population cardiopulmonary resuscitation.

Cardiopulmonary resuscitation (CPR) resuscitates patients suffering from cardiac arrest. Mechanical chest compression CPR highlights the need for high CPR quality to facilitate survival and neurological recovery. However, current CPR devices cannot be used on pregnant women or infants. These devices' long re-setup times interrupt CPR and can cause cerebral ischemia. This study designed a novel device with a crank-sliding mechanism. The polar coordinate system (r, θ, z) shortened the setup time and enabled adjustment without moving the patient. We compared our device with commercial products (e.g., LUCAS-2) by quantifying the compression pressure. Control groups for manual CPR of trained physicians and untrained citizens were recruited. We used Resusci Anne products as models. Our results indicated that our design exhibited performance similar to that of LUCAS-2 in adults (557.8 vs. 623.6 mmHg, p = 0.217) and met the current CPR standard guidelines. Notably, our device is applicable to pregnant women [565 vs. 564.5 (adults) mmHg, p = 0.987] and infants [570.8 vs. 564.5 (adults) mmHg, p = 0.801] without lowering the compression quality. The overall compression quality and stability of mechanical chest compression CPR were favorable to those of manual CPR. Our device provides an innovative prototype for the next generation of CPR facilities.

Special Issue "Advanced Signal Processing in Intelligent Systems for Health Monitoring".

Recently, significant developments have been achieved in the field of artificial intelligence, in particular the introduction of deep learning technology that has improved the learning and prediction accuracy to unpresented levels, especially when dealing with big data and high-resolution images. Significant developments have occurred in the area of medical signal processing, measurement techniques, and health monitoring, such as vital biological signs for biomedical systems and noise and vibration of mechanical systems, which are carried out by instruments that generate large data sets. These big data sets, ultimately driven by high population growth, would require Artificial Intelligence techniques to analyse and model. In this Special Issue, papers are presented on the latest signal processing and deep learning techniques used for health monitoring of biomedical and mechanical systems.

Integrations between Autonomous Systems and Modern Computing Techniques: A Mini Review.

The emulation of human behavior for autonomous problem solving has been an interdisciplinary field of research. Generally, classical control systems are used for static environments, where external disturbances and changes in internal parameters can be fully modulated before or neglected during operation. However, classical control systems are inadequate at addressing environmental uncertainty. By contrast, autonomous systems, which were first studied in the field of control systems, can be applied in an unknown environment. This paper summarizes the state of the art autonomous systems by first discussing the definition, modeling, and system structure of autonomous systems and then providing a perspective on how autonomous systems can be integrated with advanced resources (e.g., the Internet of Things, big data, Over-the-Air, and federated learning). Finally, what comes after reaching full autonomy is briefly discussed.

Machine Learning Methods Applied to Predict Ventilator-Associated Pneumonia with Pseudomonas aeruginosa Infection via Sensor Array of Electronic Nose in Intensive Care Unit.

One concern to the patients is the off-line detection of pneumonia infection status after using the ventilator in the intensive care unit. Hence, machine learning methods for ventilator-associated pneumonia (VAP) rapid diagnose are proposed. A popular device, Cyranose 320 e-nose, is usually used in research on lung disease, which is a highly integrated system and sensor comprising 32 array using polymer and carbon black materials. In this study, a total of 24 subjects were involved, including 12 subjects who are infected with pneumonia, and the rest are non-infected. Three layers of back propagation artificial neural network and support vector machine (SVM) methods were applied to patients' data to predict whether they are infected with VAP with Pseudomonas aeruginosa infection. Furthermore, in order to improve the accuracy and the generalization of the prediction models, the ensemble neural networks (ENN) method was applied. In this study, ENN and SVM prediction models were trained and tested. In order to evaluate the models' performance, a fivefold cross-validation method was applied. The results showed that both ENN and SVM models have high recognition rates of VAP with Pseudomonas aeruginosa infection, with 0.9479 ± 0.0135 and 0.8686 ± 0.0422 accuracies, 0.9714 ± 0.0131, 0.9250 ± 0.0423 sensitivities, and 0.9288 ± 0.0306, 0.8639 ± 0.0276 positive predictive values, respectively. The ENN model showed better performance compared to SVM in the recognition of VAP with Pseudomonas aeruginosa infection. The areas under the receiver operating characteristic curve of the two models were 0.9842 ± 0.0058 and 0.9410 ± 0.0301, respectively, showing that both models are very stable and accurate classifiers. This study aims to assist the physician in providing a scientific and effective reference for performing early detection in Pseudomonas aeruginosa infection or other diseases.

Frontal EEG Temporal and Spectral Dynamics Similarity Analysis between Propofol and Desflurane Induced Anesthesia Using Hilbert-Huang Transform.

Electroencephalogram (EEG) signal analysis is commonly employed to extract information on the brain dynamics. It mainly targets brain status and communication, thus providing potential to trace differences in the brain's activity under different anesthetics. In this article, two kinds of gamma-amino butyric acid (type A -GABAA) dependent anesthetic agents, propofol and desflurane (28 and 23 patients), were studied and compared with respect to EEG spectrogram dynamics. Hilbert-Huang Transform (HHT) was employed to compute the time varying spectrum for different anesthetic levels in comparison with Fourier based method. Results show that the HHT method generates consistent band power (slow and alpha) dominance pattern as Fourier method does, but exhibits higher concentrated power distribution within each frequency band than the Fourier method during both drugs induced unconsciousness. HHT also finds slow and theta bands peak frequency with better convergence by standard deviation (propofol-slow: 0.46 to 0.24; theta: 1.42 to 0.79; desflurane-slow: 0.30 to 0.25; theta: 1.42 to 0.98) and a shift to relatively lower values for alpha band (propofol: 9.94 Hz to 10.33 Hz, desflurane 8.44 Hz to 8.84 Hz) than Fourier one. For different stage comparisons, although HHT shows significant alpha power increases during unconsciousness stage as the Fourier did previously, it finds no significant high frequency (low gamma) band power difference in propofol whereas it does in desflurane. In addition, when comparing the HHT results within two groups during unconsciousness, high beta band power in propofol is significantly larger than that of desflurane while delta band power behaves oppositely. In conclusion, this study convincingly shows that EEG analyzed here considerably differs between the HHT and Fourier method.