Detecting Slow Wave Sleep Using a Single EEG Signal Channel

Background: In addition to the cost and complexity of processing multiple signal channels, manual sleep staging is also tedious, time consuming, and error-prone. The aim of this paper is to propose an automatic slow wave sleep (SWS) detection method that uses only one channel of the electroencephalography (EEG) signal. New Method: The proposed approach distinguishes itself from previous automatic sleep staging methods by using three specially designed feature groups. The first feature group characterizes the waveform pattern of the EEG signal. The remaining two feature groups are developed to resolve the difficulties caused by interpersonal EEG signal differences. Results and comparison with existing methods: The proposed approach was tested with 1,003 subjects, and the SWS detection results show kappa coefficient at 0.66, an accuracy level of 0.973, a sensitivity score of 0.644 and a positive predictive value of 0.709. By excluding sleep apnea patients and persons whose age is older than 55, the SWS detection results improved to kappa coefficient, 0.76; accuracy, 0.963; sensitivity, 0.758; and positive predictive value, 0.812. Conclusions: With newly developed signal features, this study proposed and tested a single-channel EEG-based SWS detection method. The effectiveness of the proposed approach was demonstrated by applying it to detect the SWS of 1003 subjects. Our test results show that a low SWS ratio and sleep apnea can degrade the performance of SWS detection. The results also show that a large and accurately staged sleep dataset is of great importance when developing automatic sleep staging methods

Developing a Low-Cost Force Treadmill via Dynamic Modeling

By incorporating force transducers into treadmills, force platform-instrumented treadmills (commonly called force treadmills) can collect large amounts of gait data and enable the ground reaction force (GRF) to be calculated. However, the high cost of force treadmills has limited their adoption. This paper proposes a low-cost force treadmill system with force sensors installed underneath a standard exercise treadmill. It identifies and compensates for the force transmission dynamics from the actual GRF applied on the treadmill track surface to the force transmitted to the force sensors underneath the treadmill body. This study also proposes a testing procedure to assess the GRF measurement accuracy of force treadmills. Using this procedure in estimating the GRF of “walk-on-the-spot motion,” it was found that the total harmonic distortion of the tested force treadmill system was about 1.69%, demonstrating the effectiveness of the approach

Assessing Postural Stability Via the Correlation Patterns of Vertical Ground Reaction Force Components

Background Many methods have been proposed to assess the stability of human postural balance by using a force plate. While most of these approaches characterize postural stability by extracting features from the trajectory of the center of pressure (COP), this work develops stability measures derived from components of the ground reaction force (GRF). Methods In comparison with previous GRF-based approaches that extract stability features from the GRF resultant force, this study proposes three feature sets derived from the correlation patterns among the vertical GRF (VGRF) components. The first and second feature sets quantitatively assess the strength and changing speed of the correlation patterns, respectively. The third feature set is used to quantify the stabilizing effect of the GRF coordination patterns on the COP. Results In addition to experimentally demonstrating the reliability of the proposed features, the efficacy of the proposed features has also been tested by using them to classify two age groups (18–24 and 65–73 years) in quiet standing. The experimental results show that the proposed features are considerably more sensitive to aging than one of the most effective conventional COP features and two recently proposed COM features. Conclusions By extracting information from the correlation patterns of the VGRF components, this study proposes three sets of features to assess human postural stability during quiet standing. As demonstrated by the experimental results, the proposed features are not only robust to inter-trial variability but also more accurate than the tested COP and COM features in classifying the older and younger age groups. An additional advantage of the proposed approach is that it reduces the force sensing requirement from 3D to 1D, substantially reducing the cost of the force plate measurement system

Impact of Heavy Metals in Ambient Air in Insulin Resistance of Shipyard Welders in Northern Taiwan

Exposure to metals poses potential health risks, including insulin resistance (IR), to those exposed to them in excess. Limited studies have examined such risks in occupational workers, including welders, and these have yielded inconsistent results. Thus, we examined the associations between exposure to welding metals and IR in welders. We recruited 78 welders and 75 administrative staff from a shipyard located in northern Taiwan. Personal exposure to heavy metals, including chromium (Cr), manganese (Mn), iron (Fe), nickel (Ni), copper (Cu), zinc (Zn), and cadmium (Cd), was monitored through particulate matter with an aerodynamic diameter of less than 2.5 μm (PM2.5) and urine analysis by inductively coupled plasma mass spectrometry (ICP–MS). After each participant fasted overnight, blood samples were collected and analyzed for IR assessment through updated homeostasis model assessment (HOMA2) modeling. Air sampling in the personal breathing zone was performed during a Monday shift prior to the blood and urine sample collection the following morning. The welders’ median personal Cr, Mn, Fe, Ni, Cu, and Zn airborne PM2.5 levels and urinary Cd levels were significantly higher than those of the administrative staff. After adjustment for covariates, logarithmic PM2.5-Mn, PM2.5-Fe, PM2.5-Cu, and PM2.5-Zn levels were positively correlated with logarithmic fasting plasma glucose (P-FGAC) levels (PM2.5-Mn: β = 0.0105, 95% C.I.: 0.0027–0.0183; PM2.5-Fe: β = 0.0127, 95% C.I.: 0.0027–0.0227; PM2.5-Cu: β = 0.0193, 95% C.I.: 0.0032–0.0355; PM2.5-Zn: β = 0.0132, 95% C.I.: 0.0005–0.0260). Logarithmic urinary Zn was positively correlated with logarithmic serum insulin and HOMA2-IR levels and negatively correlated with logarithmic HOMA2-insulin sensitivity (%S; βinsulin = 0.2171, 95% C.I.: 0.0025–0.4318; βIR = 0.2179, 95% C.I.: 0.0027–0.4330; β%S = −0.2180, 95% C.I.: −0.4334 to −0.0026). We observed that glucose homeostasis was disrupted by Mn, Fe, Cu, and Zn exposure through increasing P-FGAC and IR levels in shipyard welders

Resilience-enhancing solution to mitigate risk for sustainable supply chain-an empirical study of elevator manufacturing

As the complexity of supply chains increases, the enhancement of resilience for mitigating sustainable disruption risks in supply chains is an important issue. Quality function deployment (QFD) has been successfully applied in many domains to solve multicriteria decision-making (MCDM) problems. However, research on developing two houses of quality to connect sustainable supply chain disruption risks, resilience capacities, and resilience-enhancing features in elevator manufacturing supply chains by using the MCDM approach is lacking. This study aims to develop a framework for exploring useful decision-making by integrating the MCDM approach and QFD. By applying the framework, supply chain resilience can be improved by identifying the major sustainable risks and the key resilience to mitigate these risks. Important managerial insights and practical implications are obtained from the framework implementation in a case study of the elevator manufacturing industry. To strengthen resilience and thus mitigate key risks, the most urgent tasks are to connect the working site and the backstage to enhance product development and design and to share real-time job information. When these features are strengthened, agility, capacity, and visibility can be improved. Finally, unexpected events lead to changes in supplier delivery dates, and factors such as typhoon and lack of critical capacities/skilled employees with the greatest impact can be alleviated. This framework will provide an effective and pragmatic approach for constructing sustainable supply chain risk resilience in the elevator manufacturing industry.</p

ECG Signal Super-resolution by Considering Reconstruction and Cardiac Arrhythmias Classification Loss

With recent advances in deep learning algorithms, computer-assisted healthcare services have rapidly grown, especially for those that combine with mobile devices. Such a combination enables wearable and portable services for continuous measurements and facilitates real-time disease alarm based on physiological signals, e.g., cardiac arrhythmias (CAs) from electrocardiography (ECG). However, long-term and continuous monitoring confronts challenges arising from limitations of batteries, and the transmission bandwidth of devices. Therefore, identifying an effective way to improve ECG data transmission and storage efficiency has become an emerging topic. In this study, we proposed a deep-learning-based ECG signal super-resolution framework (termed ESRNet) to recover compressed ECG signals by considering the joint effect of signal reconstruction and CA classification accuracies. In our experiments, we downsampled the ECG signals from the CPSC 2018 dataset and subsequently evaluated the super-resolution performance by both reconstruction errors and classification accuracies. Experimental results showed that the proposed ESRNet framework can well reconstruct ECG signals from the 10-times compressed ones. Moreover, approximately half of the CA recognition accuracies were maintained within the ECG signals recovered by the ESRNet. The promising results confirm that the proposed ESRNet framework can be suitably used as a front-end process to reconstruct compressed ECG signals in real-world CA recognition scenarios

Chinese Herbal Medicine as an Adjunctive Therapy Ameliorated the Incidence of Chronic Hepatitis in Patients with Breast Cancer: A Nationwide Population-Based Cohort Study

We conducted a National Health Insurance Research Database-based Taiwanese nationwide population-based cohort study to evaluate whether Chinese herbal medicine (CHM) treatment decreased the incidence of chronic hepatitis in breast cancer patients receiving chemotherapy and/or radiotherapy. A total of 81171 patients were diagnosed with breast cancer within the defined study period. After randomly equal matching, data from 13856 patients were analyzed. Hazard ratios of incidence rate of chronic hepatitis were used to determine the influence and therapeutic potential of CHM in patients with breast cancer. The patients with breast cancer receiving CHM treatment exhibited a significantly decreased incidence rate of chronic hepatitis even across the stratification of age, CCI score, and treatments. The cumulative incidence of chronic hepatitis for a period of seven years after initial breast cancer diagnosis was also reduced in the patients receiving CHM treatment. The ten most commonly used single herbs and formulas were effective in protecting liver function in patients with breast cancer, where Hedyotis diffusa and Jia-Wei-Xiao-Yao-San were the most commonly used herbal agents. In conclusion, our study provided information that western medicine therapy combined with CHM as an adjuvant modality may have a significant impact on liver protection in patients with breast cancer

Albuminâ bilirubin gradeâ based nomogram of the BCLC system for personalized prognostic prediction in hepatocellular carcinoma

Background & AimsThe prognostic accuracy of individual hepatocellular carcinoma (HCC) patient in each Barcelona Clinic Liver Cancer (BCLC) stage is unclear. We aimed to develop and validate an albuminâ bilirubin (ALBI) gradeâ based nomogram of BCLC to estimate survival for individual HCC patient.MethodsBetween 2002 and 2016, 3690 patients with newly diagnosed HCC were prospectively enrolled and retrospectively analysed. Patients were randomly split into derivation and validation cohort by 1:1 ratio. Multivariate Cox proportional hazards model was used to generate the nomogram from tumour burden, ALBI grade and performance status (PS). The concordance index and calibration plot were determined to evaluate the performance of this nomogram.ResultsBeta coefficients from the Cox model were used to assign nomogram points to different degrees of tumour burden, ALBI grade and PS. The scores of the nomogram ranged from 0 to 24, and were used to predict 3â and 5â year patient survival. The concordance index of this nomogram was 0.77 (95% confidence interval [CI]: 0.71â 0.81) in the derivation cohort and 0.76 (95% CI: 0.71â 0.81) in the validation cohort. The calibration plots to predict both 3â and 5â year survival rate well matched with the 45â degree ideal line for both cohorts, except for ALBIâ based BCLC stage 0 in the validation cohort.ConclusionsThe proposed ALBIâ based nomogram of BCLC system is a simple and feasible strategy in the precision medicine era. Our data indicate it is a straightforward and userâ friendly prognostic tool to estimate the survival of individual HCC patient except for very early stage patients.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/153250/1/liv14249_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/153250/2/liv14249.pd