Real world Structural Health Monitoring (SHM) systems consist of sensors in the scale of hundreds, each sensor generating extremely large amounts of data, often arousing the issue of the cost associated with data transfer and storage. Sensor energy is a major component included in this cost factor, especially in Wireless Sensor Networks (WSN). Data compression is one of the techniques that is being explored to mitigate the effects of these issues. In contrast to traditional data compression techniques, Compressive Sensing (CS)-a very recent development-introduces the means of accurately reproducing a signal by acquiring much less number of samples than that defined by the Nyquist's theorem. CS achieves this task by exploiting the sparsity of the signal. By the reduced amount of data samples, CS may help reduce the energy consumption and storage costs associated with SHM systems. This paper investigates CS based data acquisition in SHM, in particular, the implications of CS on damage detection and localization. CS is implemented in a simulation environment to compress structural response data from a Reinforced Concrete (RC) structure. Promising results were obtained from the compressed data reconstruction process as well as the subsequent damage identification process using the reconstructed data. A reconstruction accuracy of 99% could be achieved at a Compression Ratio (CR) of 2.48 using the experimental data. Further analysis using the reconstructed signals provided accurate damage detection and localization results using two damage detection algorithms, showing that CS has not compromised the crucial information on structural damages during the compression process.

IMPORTANCE The pathogenesis of transfusion-associated necrotizing enterocolitis remains elusive. Splanchnic hypoperfusion associated with packed red blood cell transfusion (PRBCT) and feeding has been implicated, but studies of splanchnic tissue oxygenation with respect to feeding plus PRBCT are lacking. OBJECTIVE To investigate the oxygen utilization efficiency of preterm gut and brain challenged with bolus feeding during anemia and after transfusion using near-infrared spectroscopy. DESIGN, SETTING, AND PARTICIPANTS This prospective cohort study conducted from September 1, 2014, to November 30, 2016, at a tertiary neonatal intensive care unit included 25 hemodynamically stable infants with gestational age less than 32 weeks, birth weight less than 1500 g, and postmenstrual age younger than 37 weeks. Data analysis was performed from August 1, 2017, to October 31, 2018. EXPOSURES Infants received PRBCT (15 mL/kg for 4 hours) and at least 120 mL/kg daily of second hourly bolus feedings. MAIN OUTCOMES AND MEASURES Splanchnic fractional tissue oxygen extraction (FTOEs) and cerebral fractional tissue oxygen extraction (FTOEc) measures were made during 75-minute feeding cycles that comprised a 15-minute preprandial feeding phase (FP0) and 4 contiguous 15-minute postprandial feeding phases (FP1, FP2, FP3, and FP4; each 15 minutes long). The intraindividual comparisons of feeding-related changes were evaluated during the pretransfusion epoch (TE0: 4 hours before onset of transfusion) and 3 TEs after transfusion (TE1: first 8 hours after PRBCT completion; TE2: 9-16 hours after PRBCT completion; and TE3: 17-24 hours after PRBCT completion). RESULTS Of 25 enrolled infants (13 [52%] female; median birth weight, 949 g [interquartile range {IQR}, 780-1100 g]; median gestational age, 26.9 weeks [IQR, 25.9-28.6 weeks]; median enrollment weight, 1670 g [IQR, 1357-1937 g]; and median postmenstrual age, 34 weeks [IQR, 32.9-35 weeks]), 1 infant was excluded because of corrupted near-infrared spectroscopy data. No overall association was found between FTOEs and FPs in a multivariable repeated-measures model that accounted for transfusion epochs (primary analysis approach) (FP0: mean estimate, 11.64; 95% CI, 9.55-13.73; FP1:

An automatic algorithm for processing simultaneously acquired electrocardiogram (ECG) and oximetry signals that identifies epochs of pure central apnoea, epochs containing obstructive apnoea and epochs of normal breathing is presented. The algorithm uses time and spectral features from the ECG derived heart-rate and respiration information, as well as features capturing desaturations from the oximeter sensor. Evaluation of performance of the system was achieved by using leave-one-record-out cross validation on the St. Vincent's University Hospital / University College Dublin Sleep Apnea Database from the Physionet collections of recorded physiologic signals. When classifying the three epoch types, our system achieved a specificity of 80%, a sensitivity to central apnoea of 44% and sensitivity to obstructive apnoea of 35%. A sensitivity of 81% was achieved when the central and obstructive epochs were combined into one class.

The computational cost of our algorithm is low as all features are derived from RR intervals and are processed by a single hidden layer neural network. This makes it potentially suitable for low-power devices.

Structural Health Monitoring (SHM) and damage detection techniques have captured much interest and attention of researchers and structural engineers owing to their promising ability to provide spatial and quantitative information regarding structural damage and the performance of a structure during its life-cycle. With the development of smart sensors and communication technologies, Wireless Sensor Networks (WSN) has empowered the advancement in SHM. Recently, time series models have been widely used for structural damage detection due to the sensitivity of the model coefficients and residual errors to the damages in the structure. This paper presents a simple index that is computed using the Auto-Regressive (AR) model coefficients as an effective damage sensitive feature (DSF) for the detection of structural damage. Based on this feature, a damage identification method is developed. The Fisher information criterion of the computed DSF is used to statistically decide on the location of damage. This method has been implemented in a simulation environment and the verification of its accuracy in structural damage detection has been carried out experimentally. Experimental data is obtained using wireless sensors from a series of tests performed on a steel beam. The novel damage feature combined with the Fisher criterion for statistical evaluation has shown potential in effective structural damage detection.

This paper presents a study on identifying sleep apnoea using the photoplethysmography (PPG) measurements, which is obtained from the SpO2 sensor. Using a database of polysomnogram (PSG) records of 52 patients, the heart rate and breathing effort information was derived from the PPG measurements and then features are extracted and processed by a classifier to detect one-minute epochs of sleep apnoea. The ground truth labels for the epochs were determined by trained technicians using the full PSG signal. Pulse oximetry (SpO2) measurements from the same sensor are also used in the classification process for comparison and in combination with the PPG results. The results show that both the heart rate and respiratory effort information derived from the PPG signal were able to detect apnoeic epochs with some success. The best classification performance of 87% for correctly labelling the epochs was obtained when the SpO2 features and the PPG features were combined.

High energy consumption, excessive data storage and transfer requirements are prevailing issues associated with structural health monitoring (SHM) systems, especially with those employing wireless sensors. Data compression is one of the techniques being explored to mitigate the effects of these issues. Compressive sensing (CS) introduces a means of reproducing a signal with a much less number of samples than the Nyquist's rate, reducing the energy consumption, data storage and transfer cost. This paper explores the applicability of CS for SHM, in particular for damage detection and localization. CS is implemented in a simulated environment to compress SHM data. The reconstructed signal is verified for accuracy using structural response data obtained from a series of tests carried out on a reinforced concrete (RC) slab. Results show that the reconstruction was close, but not exact as a consequence of the noise associated with the responses. However, further analysis using the reconstructed signal provided successful damage detection and localization results, showing that although the reconstruction using CS is not exact, it is sufficient to provide the crucial information of the existence and location of damage.

Background It is well established that counter-regulation to hypoxia follows a hierarchical pattern, with brain-sparing in preference to peripheral tissues. In contrast, it is unknown if the same hierarchical sequence applies to recovery from hypoxia after correction of anemia with packed red blood cell transfusion (PRBCT). Objective To understand the chronology of cerebral and splanchnic tissue oxygenation resulting after correction of anemia by PRBCT in preterm infants using near-infrared spectroscopy (NIRS). Design Prospective cohort study. Setting Neonatal intensive care. Patients included Haemodynamically stable infants: <32 weeks gestation, <37weeks postmenstrual age, <1500 grams birth weight; and ≥120 mL/kg/day feeds tolerated. Intervention PRBCT at 15 mL/Kg over 4 hours. Main outcome measures Transfusion-associated changes were determined by comparing the 4-hour mean pre-transfusion cerebral and splanchnic fractional tissue oxygen extraction (FTOEc0; FTOEs0) with hourly means during (FTOEc1-4; FTOEs1-4) and for 24 hours after PRBCT completion (FTOEc5-28; FTOEs5-28). Results Of 30 enrolled infants, 14[46.7%] male; median[IQR] birth weight, 923[655–1064]g; gestation, 26.4[25.5–28.1]weeks; enrolment weight, 1549[1113–1882]g; and postmenstrual age, 33.6[32.4–35]weeks, 1 infant was excluded because of corrupted NIRS data. FTOEc significantly decreased during and for 24 hours after PRBCT (p < 0.001), indicating prompt improvement in cerebral oxygenation. In contrast, FTOEs showed no significant changes during and after PRBCT (p>0.05), indicating failure of improvement in splanchnic oxygenation. Conclusion Improvement in regional oxygenation after PRBCT follows the same hierarchical pattern with a prompt improvement of cerebral but not splanchnic tissue oxygenation. We hypothesise that this hierarchical recovery may indicate continued splanchnic hypoxia in the immediate post-transfusion period and vulnerability to transfusion-associated necrotizing enterocolitis (TANEC). Our study provides a possible mechanistic underpinning for TANEC and warrants future randomised controlled studies to stratify its prevention.