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Flexible and wearable electronics are experiencing rapid growth due to the increasing demand for multifunctional, lightweight, and portable devices. However, the growing demands of interactive applications driven by the rise of AI reveal the inadequate connectivity of current connection technologies. In this work, we successfully leverage memristive technology to develop a flexible radio frequency (RF) switch, optimized for 6G-compatible communication systems and adaptable to flexible applications. The flexible RF switch demonstrates a low insertion loss (2 dB) and a cutoff frequency exceeding 840 GHz, and performance metrics are maintained after 10 6 switching cycles and 2500 mechanical bending cycles, showing excellent reliability and robustness. Furthermore, the RF switch is fully integrable with a photolithography-processable polyimide (PSPI) substrate, enabling efficient 2.5D integration with other RF components, such as RF antennas and interconnects. This technology holds significant promise to advance 6G communications in flexible electronics, offering a scalable solution for high-speed data transmission in next-generation wearable devices.

The phenomenon that the depression risk among doctoral students is higher than that of other groups should not be ignored. Despite this, studies specifically addressing depression risk in doctoral students are relatively scarce, and existing findings are not universally applicable. Using neural network feature extraction technology, this study aims to investigate the factors contributing to the high depression risk of doctoral students and effectively identify doctoral students at depression risk, so as to propose corresponding improvement strategies to prevent and intervene doctoral students with depression risk for universities. Based on the data from the 2019 Nature Global Doctoral Student Survey, we first screened 13 highly relevant features from a total of 37 features potentially related to the risk of depression among doctoral students by Random Forest algorithm. Subsequently, we trained the optimal prediction model to predict the doctoral students with depression risk using a Multilayer Perceptron (MLP), achieving an accuracy of 89.09% on the test set. Additionally, this study constructed a group portrait of doctoral students at risk of depression, and found that overwork, poor work-life balance, and poor supervisor-student relationship, etc., were typical characteristics among these students. Finally, we proposed several improvement strategies for higher education institutions. Our research offers a new perspective on utilising artificial intelligence (AI) methods to tackle educational challenges, particularly in the identification and support of doctoral students at risk of depression, thereby enhancing their mental health., Competing Interests: Declarations. Competing interests: The authors declare no competing interests., (© 2024. The Author(s).)

Energy-dispersive X-ray spectroscopy (EDXS) mapping with a scanning transmission electron microscope (STEM) is commonly used for chemical characterization of materials. However, STEM-EDXS quantification becomes challenging when the phases constituting the sample under investigation share common elements and overlap spatially. In this paper, we present a methodology to identify, segment, and unmix phases with a substantial spectral and spatial overlap in a semi-automated fashion through combining non-negative matrix factorization with a priori knowledge of the sample. We illustrate the methodology using a sample taken from an electron beam-sensitive mineral assemblage representing Earth's deep mantle. With it, we retrieve the true EDX spectra of the constituent phases and their corresponding phase abundance maps. It further enables us to achieve a reliable quantification for trace elements having concentration levels of ∼100 ppm. Our approach can be adapted to aid the analysis of many materials systems that produce STEM-EDXS datasets having phase overlap and/or limited signal-to-noise ratio (SNR) in spatially-integrated spectra., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

Study Objectives: Although wrist-worn consumer wearables are widely used for home sleep monitoring, few have been validated. It is unclear whether consumer wearables could be an alternative to Actiwatch. This study aimed to establish and validate an automatic sleep staging system (ASSS) utilizing photoplethysmography and acceleration data collected from a wrist-worn wearable device., Methods: Seventy-five participants from a community population underwent overnight polysomnography (PSG) while wearing a smartwatch (MT2511) and Actiwatch Spectrum Plus (Philips Respironics, Inc; Murrysville, PA, USA). Photoplethysmography and acceleration data collected from the smartwatches were utilized to build a 4-stage (wake, light sleep, deep sleep, and rapid eye movement [REM] sleep) classifier, which was validated against PSG. The performance of the sleep/wake classifier was compared with Actiwatch. All analyses were conducted separately for participants with PSG sleep efficiency (SE) ≥ 80% and SE < 80%., Results: The 4-stage classifier and PSG showed fair overall epoch-by-epoch agreement (kappa, 0.55; 95% confidence interval, 0.52 to 0.57). The deep sleep and REM times were comparable between ASSS and PSG, while ASSS underestimated the wake time and overestimated the light sleep time among participants with SE < 80%. Moreover, ASSS underestimated sleep-onset latency and wake after sleep onset and overestimated total sleep time and SE among participants with SE < 80%, while all were comparable among participants with SE ≥ 80%. The biases were smaller for ASSS than for Actiwatch., Conclusions: Our photoplethysmography- and acceleration-based ASSS was reliable for participants with SE ≥ 80% and had a smaller bias than Actiwatch among those with SE < 80%. Thus, ASSS may be a promising alternative to Actiwatch., Clinical Trial Registration: Registry: ClinicalTrials.gov; Name: Validation of Sleep Healthcare System; URL: https://clinicaltrials.gov/study/NCT04252482; Identifier: NCT04252482., Citation: Liu P-K, Ting N, Chiu H-C, et al. Validation of photoplethysmography- and acceleration-based sleep staging in a community sample: comparison with polysomnography and Actiwatch. J Clin Sleep Med . 2023;19(10):1797-1810., (© 2023 American Academy of Sleep Medicine.)

With the development of graph neural networks, how to handle large-scale graph data has become an increasingly important topic. Currently, most graph neural network models which can be extended to large-scale graphs are based on random sampling methods. However, the sampling process in these models is detached from the forward propagation of neural networks. Moreover, quite a few works design sampling based on statistical estimation methods for graph convolutional networks and the weights of message passing in GCNs nodes are fixed, making these sampling methods not scalable to message passing networks with variable weights, such as graph attention networks. Noting the end-to-end learning capability of neural networks, we propose a learnable sampling method. It solves the problem that random sampling operations cannot calculate gradients and samples nodes with an unfixed probability. In this way, the sampling process is dynamically combined with the forward propagation process of the features, allowing for better training of the networks. And it can be generalized to all message passing models. In addition, we apply the learnable sampling method to GNNs and propose two models. Our method can be flexibly combined with different graph neural network models and achieves excellent accuracy on benchmark datasets with large graphs. Meanwhile, loss function converges to smaller values at a faster rate during training than past methods., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

This paper proposes a deep learning-based mmWave radar and RGB camera sensor early fusion method for object detection and tracking and its embedded system realization for ADAS applications. The proposed system can be used not only in ADAS systems but also to be applied to smart Road Side Units (RSU) in transportation systems to monitor real-time traffic flow and warn road users of probable dangerous situations. As the signals of mmWave radar are less affected by bad weather and lighting such as cloudy, sunny, snowy, night-light, and rainy days, it can work efficiently in both normal and adverse conditions. Compared to using an RGB camera alone for object detection and tracking, the early fusion of the mmWave radar and RGB camera technology can make up for the poor performance of the RGB camera when it fails due to bad weather and/or lighting conditions. The proposed method combines the features of radar and RGB cameras and directly outputs the results from an end-to-end trained deep neural network. Additionally, the complexity of the overall system is also reduced such that the proposed method can be implemented on PCs as well as on embedded systems like NVIDIA Jetson Xavier at 17.39 fps.

A magnetoelectric antenna (ME) can exhibit the dual capabilities of wireless energy harvesting and sensing at different frequencies. In this article, a behavioral circuit model for hybrid ME antennas is described to emulate the radio frequency (RF) energy harvesting and sensing operations during circuit simulations. The ME antenna of this work is interfaced with a CMOS energy harvester chip towards the goal of developing a wireless communication link for fully integrated implantable devices. One role of the integrated system is to receive pulse-modulated power from a nearby transmitter, and another role is to sense and transmit low-magnitude neural signals. The measurements reported in this paper are the first results that demonstrate simultaneous low-frequency wireless magnetic sensing and high-frequency wireless energy harvesting at two different frequencies with one dual-mode ME antenna. The proposed behavioral ME antenna model can be utilized during design optimizations of energy harvesting circuits. Measurements were performed to validate the wireless power transfer link with an ME antenna having a 2.57 GHz resonance frequency connected to an energy harvester chip designed in 65nm CMOS technology. Furthermore, this dual-mode ME antenna enables concurrent sensing using a carrier signal with a frequency that matches the second 63.63 MHz resonance mode. A wireless test platform has been developed for evaluation of ME antennas as a tool for neural implant design, and this prototype system was utilized to provide first experimental results with the transmission of magnetically modulated action potential waveforms.

Sequential activity reflecting previously experienced temporal sequences is considered a hallmark of learning across cortical areas. However, it is unknown how cortical circuits avoid the converse problem: producing spurious sequences that are not reflecting sequences in their inputs. We develop methods to quantify and study sequentiality in neural responses. We show that recurrent circuit responses generally include spurious sequences, which are specifically prevented in circuits that obey two widely known features of cortical microcircuit organization: Dale's law and Hebbian connectivity. In particular, spike-timing-dependent plasticity in excitation-inhibition networks leads to an adaptive erasure of spurious sequences. We tested our theory in multielectrode recordings from the visual cortex of awake ferrets. Although responses to natural stimuli were largely non-sequential, responses to artificial stimuli initially included spurious sequences, which diminished over extended exposure. These results reveal an unexpected role for Hebbian experience-dependent plasticity and Dale's law in sensory cortical circuits., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2022 The Authors. Published by Elsevier Inc. All rights reserved.)

The majority of digital sensors rely on von Neumann architecture microprocessors to process sampled data. When the sampled data require complex computation for 24×7, the processing element will a consume significant amount of energy and computation resources. Several new sensing algorithms use deep neural network algorithms and consume even more computation resources. High resource consumption prevents such systems for 24×7 deployment although they can deliver impressive results. This work adopts a Computing-In-Memory (CIM) device, which integrates a storage and analog processing unit to eliminate data movement, to process sampled data. This work designs and evaluates the CIM-based sensing framework for human pose recognition. The framework consists of uncertainty-aware training, activation function design, and CIM error model collection. The evaluation results show that the framework can improve the detection accuracy of three poses classification on CIM devices using binary weights from 33.3% to 91.5% while that on ideal CIM is 92.1%. Although on digital systems the accuracy is 98.7% with binary weight and 99.5% with floating weight, the energy consumption of executing 1 convolution layer on a CIM device is only 30,000 to 50,000 times less than the digital sensing system. Such a design can significantly reduce power consumption and enables battery-powered always-on sensors.

Objective: Both continuous positive airway pressure (CPAP) pressure and polysomnographic phenotypes have been associated with mandibular advancement device (MAD) treatment response, but the precise relationship has not been fully elucidated. We hypothesized that utilizing CPAP pressure would predict the MAD response in treatment-naïve patients with moderate-severe obstructive sleep apnea (OSA), and the MAD response would be associated with two polysomnographic phenotypes, including sleep stage dependency and positional dependency., Methods: OSA treatment-naïve patients with an apnea-hypopnea index (AHI) ≥15/h who declined CPAP treatment and received MAD treatment for 3-6 months were enrolled. The MAD treatment response was defined as 1) residual AHI under MAD (AHI MAD ) <5/h and 2) AHI MAD <10/h. Logistic regression was applied to identify the association between CPAP pressure and MAD treatment responders. The predictability of the MAD responder status utilizing CPAP pressure was assessed with the area under the receiver operating characteristic (AUROC)., Results: A total of 128 enrolled patients (AHI ≥30/h in 74.2%) were recruited, of whom 119 patients and 80 patients were included for analysis of sleep stage and positional dependency, respectively. REM-predominant OSA had lower AHI than stage-independent OSA, while the supine-predominant phenotype had lower anthropometrics than the nonpositional-dependent phenotype. The response rates for AHI MAD <5/h and AHI MAD <10/h were 25.8% and 48.4%, respectively. Lower anthropometrics, baseline AHI, and supine predominance were associated with the responder status, while CPAP pressure was an independent predictor. The AUROCs for the prediction of AHI MAD <5/h and AHI MAD <10/h responders were 0.635 and 0.664, respectively. Utilizing a CPAP level >14 cmH 2 O as the cutoff to predict criterion 1 and 2 nonresponders, the sensitivity was 93.9% and 95.2%, respectively., Conclusion: In treatment-naïve patients with moderate-severe OSA, the supine-predominant phenotype and lower CPAP pressure were associated with the MAD response, while the sleep stage dependency phenotype was not. Utilization of a CPAP level >14 cmH 2 O could be a sensitive measure to identify nonresponders., Competing Interests: All authors declare that they have no conflicts of interest in this work., (© 2022 Lee et al.)

In the long history of psycholinguistic research on verifying negative sentences, an often-reported finding is that participants take longer to correctly judge negative sentences true than false, while being faster to judge their positive counterparts true (e.g. Clark & Chase, Cogn Psychol 3(3):472-517, 1972; Carpenter & Just, Psychol Rev 82(1):45-73, 1975). While many linguists and psycholinguists have strongly advocated the idea that the costs and complexity of negation can be explained by appeal to context, context-based approaches have not been able to provide a satisfying account of this polarity*truth-value interaction. By contrast, the alternative theory of negation processing, which says that negation is processed by separately representing the positive, does provide a plausible account. Our proposals provide a means for reconciliation between the two views since we argue that negation is a strong cue to a positive context. Here we present our account of why and when negation is often apparently processed via the positive. We review many of the factors that are seen to be at play in sentence verification involving negation. We present evidence that participants' adoption of the positive-first procedure in sentence-picture verification tasks is conditioned by context., (© 2021. The Author(s).)

Backgrounds: Reduced brain cortical activity over the frontotemporal regions measured by near infrared spectroscopy (NIRS) has been reported in patients with first-episode schizophrenia (FES). This study aimed to differentiate between patients with FES and healthy controls (HCs) on basis of the frontotemporal activity measured by NIRS with a support vector machine (SVM) and deep neural network (DNN) classifier. In addition, we compared the accuracy of performance of SVM and DNN. Methods: In total, 33 FES patients and 34 HCs were recruited. Their brain cortical activities were measured using NIRS while performing letter and category versions of verbal fluency tests (VFTs). The integral and centroid values of brain cortical activity in the bilateral frontotemporal regions during the VFTs were selected as features in SVM and DNN classifier. Results: Compared to HCs, FES patients displayed reduced brain cortical activity over the bilateral frontotemporal regions during both types of VFTs. Regarding the classifier performance, SVM reached an accuracy of 68.6%, sensitivity of 70.1%, and specificity of 64.6%, while DNN reached an accuracy of 79.7%, sensitivity of 88.8%, and specificity of 74.9% in the classification of FES patients and HCs. Conclusions: Compared to findings of previous structural neuroimaging studies, we found that using DNN to measure the NIRS signals during the VFTs to differentiate between FES patients and HCs could achieve a higher accuracy, indicating that NIRS can be used as a potential marker to classify FES patients from HCs. Future additional independent datasets are needed to confirm the validity of our model., Competing Interests: Y-HY, R-XZ, and Y-LL are currently employed by company MediaTek Inc. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Chou, Yao, Zheng, Liou, Liu, Lane, Yang and Wang.)

An efficient method to identify whether mild cognitive impairment (MCI) has progressed to Alzheimer's disease (AD) will be beneficial to patient care. Previous studies have shown that magnetic resonance imaging (MRI) has enabled the assessment of AD progression based on imaging findings. The present work aimed to establish an algorithm based on three features, namely, volume, surface area, and surface curvature within the hippocampal subfields, to model variations, including atrophy and structural changes to the cortical surface. In this study, a new biomarker, the ratio of principal curvatures (RPC), was proposed to characterize the folding patterns of the cortical gyrus and sulcus. Along with volumes and surface areas, these morphological features associated with the hippocampal subfields were assessed in terms of their sensitivity to the changes in cognitive capacity by two different feature selection methods. Either the extracted features were statistically significantly different, or the features were selected through a random forest model. The identified subfields and their structural indices that are sensitive to the changes characteristic of the progression from MCI to AD were further assessed with a multilayer perceptron classifier to help facilitate the diagnosis. The accuracy of the classification based on the proposed method to distinguish whether a MCI patient enters the AD stage amounted to 79.95%, solely using the information from the features selected by a logical feature selection method., Competing Interests: T-HK, Y-RX, and GGCL were employed by company MediaTek Inc., Hsinchu, Taiwan. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Kung, Chao, Xie, Pai, Kuo and Lee.)

In this paper we consider the order of emergence of comprehension of wh-questions and polar-questions. We argue that considerations of complexity and input favour the earlier emergence of polar questions; on the other hand, if one assumes that question understanding emerges as a consequence of interactive learning this favours (certain) wh-questions, as well as a small subclass of polar questions. We offer corpus evidence from the Providence corpus that (a certain class of) wh-questions are in fact understood earlier than the polar-questions. We test this observation using elicitation studies on German and Chinese speaking children. Our results confirm the finding from the corpus study and are in line with an interactive learning perspective for the emergence of understanding of questions.

Study Objectives: Polysomnography is the gold standard for diagnosis of obstructive sleep apnea (OSA) but it is costly and access is often limited. The aim of this study is to develop a clinically useful support vector machine (SVM)-based prediction model to identify patients with high probability of OSA for nonsleep specialist physician in clinical practice., Methods: The SVM model was developed using the features routinely collected at the clinical evaluation from 6,875 Chinese patients referred to sleep clinics for suspected OSA. Three apnea-hypopnea index (AHI) cutoffs, ≥5/h, ≥15/h, and ≥30/h were used to define the severity of OSA. The continuous and categorized features were selected separately and were further selected through stepwise forward feature selection. The modeling was achieved through fivefold cross-validation. The model discriminative ability was evaluated for the whole data set and four subgroups categorized with gender and age (<65 versus ≥65 years old [y/o])., Results: Two features were selected to predict AHI cutoff ≥5/h with six features selected for ≥15/h, and six features selected for ≥30/h, respectively, to reach Area under the Receiver Operating Characteristic (AUROC) 0.82, 0.80, and 0.78, respectively. The sensitivity was 74.14%, 75.18%, and 70.26%, while the specificity was 74.71%, 68.73%, and 70.30%, respectively. Compared to logistic regression, Berlin questionnaire, NoSAS Score, and Supersparse Linear Integer Model (SLIM) scoring system, the SVM model performs better with a more balanced sensitivity and specificity. The discriminative ability was best for male <65 y/o and modest for female ≥65 y/o., Conclusion: Our model provides a simple and accurate modality for early identification of patients with OSA and may potentially help prioritize them for sleep study., (© Sleep Research Society 2020. Published by Oxford University Press on behalf of the Sleep Research Society.)

Study Objectives: Reliable sleep staging is difficult to obtain from home sleep testing for diagnosis of obstructive sleep apnea (OSA), especially when it is self-applied. Hence, the current study aimed to develop a single frontal electroencephalography-based automatic sleep staging system (ASSS)., Methods: The ASSS system was developed on a clinical dataset, with a high percentage of participants with OSA. The F4-M1 signal extracted from 62 participants (62.9% having OSA) was used to build a four-stage classifier. Performance of the ASSS was tested in a holdout set of 58 patients (60.3% having OSA) with epoch-by-epoch and whole-night agreement for sleep staging compared with expert scoring of polysomnography., Results: Mean all-stage percentage agreement was 75.52% (95% confidence interval, 72.90 to 78.13) (kappa 0.62; 95% confidence interval, 0.58 to 0.65), with mean percentage agreement for wake, light sleep, deep sleep (DS), and rapid eye movement of 78.04%, 70.97%, 83.65%, and 75.00%, respectively. The whole-night agreement was good-excellent (intraclass correlation coefficient, 0.74 to 0.88) for sleep onset latency, wake after sleep onset, total sleep time, and sleep efficiency. Compared to the non-OSA subset, the OSA subset had lower agreement for DS., Conclusions: Our results indicate that a single-channel F4-M1 based ASSS was sufficient for sleep staging in a population with a high percentage of participants with OSA., (© 2019 American Academy of Sleep Medicine.)

Bayesian Sequential Partitioning (BSP) is a statistically effective density estimation method to comprehend the characteristics of a high dimensional data space. The intensive computation of the statistical model and the counting of enormous data have caused serious design challenges for BSP to handle the growing volume of the data. This paper proposes a high performance design of BSP by leveraging a heterogeneous CPU/GPGPU system that consists of a host CPU and a K80 GPGPU. A series of techniques, on both data structures and execution management policies, is implemented to extensively exploit the computation capability of the heterogeneous many-core system and alleviate system bottlenecks. When compared with a parallel design on a high-end CPU, the proposed techniques achieve 48x average runtime enhancement while the maximum speedup can reach 78.76x.

Dynamic voltage and frequency scaling (DVFS) is a well-known method for saving energy consumption. Several DVFS studies have applied learning-based methods to implement the DVFS prediction model instead of complicated mathematical models. This paper proposes a lightweight learning-directed DVFS method that involves using counter propagation networks to sense and classify the task behavior and predict the best voltage/frequency setting for the system. An intelligent adjustment mechanism for performance is also provided to users under various performance requirements. The comparative experimental results of the proposed algorithms and other competitive techniques are evaluated on the NVIDIA JETSON Tegra K1 multicore platform and Intel PXA270 embedded platforms. The results demonstrate that the learning-directed DVFS method can accurately predict the suitable central processing unit (CPU) frequency, given the runtime statistical information of a running program, and achieve an energy savings rate up to 42%. Through this method, users can easily achieve effective energy consumption and performance by specifying the factors of performance loss.

Acute stroke commonly affects cardiac autonomic responses resulting in reduced heart rate variability (HRV). Multiscale entropy (MSE) is a novel non-linear method to quantify the complexity of HRV. This study investigated the influence of intracerebral hemorrhage (ICH) locations and intraventricular hemorrhage (IVH) on the complexity of HRV. We recruited 93 supratentorial ICH patients (male 59%, mean age 61 years), and the locations of ICH included basal ganglia (n = 40), thalamus (n = 35), and lobar (n = 18) group. Continuous one-hour electrocardiography signals were obtained from patients after admission, and the complexity index was defined as the area under the MSE curve. The complexity index was lower in lobar ICH (21.6 ± 7.9) than basal ganglia (27.9 ± 6.4) and thalamus (28.5 ± 7.2) groups. The complexity index was inversely correlated with initial stroke severity (r = -0.26), size of hematoma (r = -0.35) and ICH score (r = -0.26), especially among patients with intraventricular hemorrhage (r = -0.60, -0.60, and -0.41 respectively). A higher complexity can predict a good functional outcome (adjusted odds ratio 1.09, 95% confidence intervals 1.00-1.19) at post-stroke 3 months. In summary, more severe stroke and larger hematoma volume resulted in lower complexity of HRV. Lobar hemorrhage and IVH had great impacts on the cardiac autonomic function.

Atrial fibrillation (AF) detection is crucial for stroke prevention. We investigated the potential of quantitative analyses of photoplethysmogram (PPG) waveforms to identify AF. Continuous electrocardiogram (EKG) and fingertip PPG were recorded simultaneously in acute stroke patients (n = 666) admitted to an intensive care unit. Each EKG was visually labeled as AF (n = 150, 22.5%) or non-AF. Linear and nonlinear features from the pulse interval (PIN) and peak amplitude (AMP) of PPG waveforms were extracted from the first 1, 2, and 10 min of data. Logistic regression analysis revealed six independent PPG features feasibly identifying AF rhythm, including three PIN-related (mean, mean of standard deviation, and sample entropy), and three AMP-related features (mean of the root mean square of the successive differences, sample entropy, and turning point ratio) (all p < 0.01). The performance of the PPG analytic program comprising all 6 features that were extracted from the 2-min data was better than that from the 1-min data (area under the receiver operating characteristic curve was 0.972 (95% confidence interval 0.951-0.989) vs. 0.949 (0.929-0.970), p < 0.001 and was comparable to that from the 10-min data [0.973 (0.953-0.993)] for AF identification. In summary, our study established the optimal PPG analytic program in reliably identifying AF rhythm.

About one-third of acute stroke patients may experience stroke-in-evolution, which is often associated with a worse outcome. Recently, we showed that multiscale entropy (MSE), a non-linear method for analysis of heart rate variability (HRV), is an early outcome predictor in non-atrial fibrillation (non-AF) stroke patients. We aimed to further investigate MSE as a predictor of SIE. We included 90 non-AF ischemic stroke patients admitted to the intensive care unit (ICU). Nineteen (21.1%) patients met the criteria of SIE, which was defined as an increase in the National Institutes of Health Stroke Scale score of ≥2 points within 3 days of admission. The MSE of HRV was analyzed from 1-hour continuous ECG signals during the first 24 hours of admission. The complexity index was defined as the area under the MSE curve. Compared with patients without SIE, those with SIE had a significantly lower complexity index value (21.3 ± 8.5 vs 26.5 ± 7.7, P = 0.012). After adjustment for clinical variables, patients with higher complexity index values were significantly less likely to have SIE (odds ratio = 0.897, 95% confidence interval 0.818-0.983, P = 0.020). In summary, early assessment of HRV by MSE can be a potential predictor of SIE in ICU-admitted non-AF ischemic stroke patients.

Background/purpose: Mannitol is commonly used in patients with increased intracranial pressure (ICP), but its effect on cerebrovascular pressure reactivity (CVPR) is uncertain. We analyzed the changes of pressure reactivity index (PRx) during the course of mannitol treatment., Methods: Twenty-one patients who received mannitol treatment for increased ICP were recruited prospectively. Continuous waveforms of arterial blood pressure (ABP) and ICP were collected simultaneously for 60 minutes (10 minutes at baseline and 50 minutes since mannitol administration) during 37 events of mannitol treatment. The correlation coefficients between the mean ABP and ICP were averaged every 10 minutes and labeled as the PRx. The linear correlation of six time points of PRx in each event was calculated to represent the trend of CVPR changes. The negative slope of correlation was defined as improvement in CVPR under mannitol treatment and vice versa., Results: At baseline, the average of ICP was 26.0 ± 9.1 mmHg and the values of PRx were significantly correlated with ICP (p = 0.0044, r = 0.46). After mannitol administration, the average of ICP decreased significantly to 21.2 ± 11.1 mmHg (p = 0.036), and CVPR improved in 59.4 % of all events. Further analysis showed that low baseline cerebral perfusion pressure was the only hemodynamic parameter significant association with the improvement of CVPR after mannitol treatment (p = 0.039)., Conclusion: Despite lowering ICP, mannitol may have diverse effects on CVPR in patients with intracranial hypertension. Our study suggests that mannitol infusion may have a beneficial effect on CVPR, particularly in those with a low cerebral perfusion pressure at baseline., (Copyright © 2013. Published by Elsevier B.V.)

Background: Heart rate variability (HRV) has been proposed as a predictor of acute stroke outcome. This study aimed to evaluate the predictive value of a novel non-linear method for analysis of HRV, multiscale entropy (MSE) and outcome of patients with acute stroke who had been admitted to the intensive care unit (ICU)., Methods: The MSE of HRV was analysed from 1 h continuous ECG signals in ICU-admitted patients with acute stroke and controls. The complexity index was defined as the area under the MSE curve (scale 1-20). A favourable outcome was defined as modified Rankin scale 0-2 at 3 months after stroke., Results: The trends of MSE curves in patients with atrial fibrillation (AF) (n=77) were apparently different from those in patients with non-AF stroke (n=150) and controls (n=60). In addition, the values of complexity index were significantly lower in the patients with non-AF stroke than in the controls (25.8±.3 vs. 32.3±4.3, p<0.001). After adjustment for clinical variables, patients without AF who had a favourable outcome were significantly related to higher complexity index values (OR=1.15, 95% CI 1.07 to 1.25, p<0.001). Importantly, the area under the receiver operating characteristic curve for predicting a favourable outcome of patients with non-AF stroke from clinical parameters was 0.858 (95% CI 0.797 to 0.919) and significantly improved to 0.903 (95% CI 0.853 to 0.954) after adding on the parameter of complexity index values (p=0.020)., Conclusions: In ICU-admitted patients with acute stroke, early assessment of the complexity of HRV by MSE can help in predicting outcomes in patients without AF., (Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.)

Most face recognition scenarios assume that frontal faces or mug shots are available for enrollment to the database, faces of other poses are collected in the probe set. Given a face from the probe set, one needs to determine whether a match in the database exists. This is under the assumption that in forensic applications, most suspects have their mug shots available in the database, and face recognition aims at recognizing the suspects when their faces of various poses are captured by a surveillance camera. This paper considers a different scenario: given a face with multiple poses available, which may or may not include a mug shot, develop a method to recognize the face with poses different from those captured. That is, given two disjoint sets of poses of a face, one for enrollment and the other for recognition, this paper reports a method best for handling such cases. The proposed method includes feature extraction and classification. For feature extraction, we first cluster the poses of each subject's face in the enrollment set into a few pose classes and then decompose the appearance of the face in each pose class using Embedded Hidden Markov Model, which allows us to define a set of subject-specific and pose-priented (SSPO) facial components for each subject. For classification, an Adaboost weighting scheme is used to fuse the component classifiers with SSPO component features. The proposed method is proven to outperform other approaches, including a component-based classifier with local facial features cropped manually, in an extensive performance evaluation study.

Illumination compensation and normalization play a crucial role in face recognition. The existing algorithms either compensated low-frequency illumination, or captured high-frequency edges. However, the orientations of edges were not well exploited. In this paper, we propose the orientated local histogram equalization (OLHE) in brief, which compensates illumination while encoding rich information on the edge orientations. We claim that edge orientation is useful for face recognition. Three OLHE feature combination schemes were proposed for face recognition: 1) encoded most edge orientations; 2) more compact with good edge-preserving capability; and 3) performed exceptionally well when extreme lighting conditions occurred. The proposed algorithm yielded state-of-the-art performance on AR, CMU PIE, and extended Yale B using standard protocols. We further evaluated the average performance of the proposed algorithm when the images lighted differently were observed, and the proposed algorithm yielded the promising results.

In this paper, we propose a fast algorithm that efficiently selects the temporal prediction type for the dyadic hierarchical-B prediction structure in the H.264/MPEG-4 temporal scalable video coding (SVC). We make use of the strong correlations in prediction type inheritance to eliminate the superfluous computations for the bi-directional (BI) prediction in the finer partitions, 16×8/8×16/8×8 , by referring to the best temporal prediction type of 16 × 16. In addition, we carefully examine the relationship in motion bit-rate costs and distortions between the BI and the uni-directional temporal prediction types. As a result, we construct a set of adaptive thresholds to remove the unnecessary BI calculations. Moreover, for the block partitions smaller than 8 × 8, either the forward prediction (FW) or the backward prediction (BW) is skipped based upon the information of their 8 × 8 partitions. Hence, the proposed schemes can efficiently reduce the extensive computational burden in calculating the BI prediction. As compared to the JSVM 9.11 software, our method saves the encoding time from 48% to 67% for a large variety of test videos over a wide range of coding bit-rates and has only a minor coding performance loss., (© 2011 IEEE)