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BACKGROUND: Hippocampal atrophy (HA) is one of the biomarkers for Alzheimer's disease (AD).OBJECTIVE: To identify the best biomarkers and develop models for prediction of HA over 24 months using baseline data.METHODS: The study included healthy elderly controls, subjects with mild cognitive impairment, and subjects with AD, obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI 1) and the Australian Imaging Biomarkers and Lifestyle Flagship Study of Ageing (AIBL) databases. Predictor variables included cognitive and neuropsychological tests, amyloid-β, tau, and p-tau from cerebrospinal fluid samples, apolipoprotein E, and features extracted from magnetic resonance images (MRI). Least-mean-squares regression with elastic net regularization and least absolute deviation regression models were tested using cross-validation in ADNI 1. The generalizability of the models including only MRI features was evaluated by training the models with ADNI 1 and testing them with AIBL. The models including the full set of variables were not evaluated with AIBL because not all needed variables were available in it.RESULTS: The models including the full set of variables performed better than the models including only MRI features (root-mean-square error (RMSE) 1.76-1.82 versus 1.93-2.08). The MRI-only models performed well when applied to the independent validation cohort (RMSE 1.66-1.71). In the prediction of dichotomized HA (fast versus slow), the models achieved a reasonable prediction accuracy (0.79-0.87).CONCLUSIONS: These models can potentially help identifying subjects predicted to have a faster HA rate. This can help in selection of suitable patients into clinical trials testing disease-modifying drugs for AD.

Oropharyngeal dysphagia is prevalent in several at-risk populations, including post-stroke patients, patients in intensive care and the elderly. Dysphagia contributes to longer hospital stays and poor outcomes, including pneumonia. Early identification of dysphagia is recommended as part of the evaluation of at-risk patients, but available bedside screening tools perform inconsistently. In this study, we developed algorithms to detect swallowing impairment using a novel accelerometer-based dysphagia detection system (DDS). A sample of 344 individuals was enrolled across seven sites in the United States. Dual-axis accelerometry signals were collected prospectively with simultaneous videofluoroscopy (VFSS) during swallows of liquid barium stimuli in thin, mildly, moderately and extremely thick consistencies. Signal processing classifiers were trained using linear discriminant analysis and 10,000 random training–test data splits. The primary objective was to develop an algorithm to detect impaired swallowing safety with thin liquids with an area under receiver operating characteristic curve (AUC) > 80% compared to the VFSS reference standard. Impaired swallowing safety was identified in 7.2% of the thin liquid boluses collected. At least one unsafe thin liquid bolus was found in 19.7% of participants, but participants did not exhibit impaired safety consistently. The DDS classifier algorithms identified participants with impaired thin liquid swallowing safety with a mean AUC of 81.5%, (sensitivity 90.4%, specificity 60.0%). Thicker consistencies were effective for reducing the frequency of penetration–aspiration. This DDS reached targeted performance goals in detecting impaired swallowing safety with thin liquids. Simultaneous measures by DDS and VFSS, as performed here, will be used for future validation studies. Electronic supplementary material The online version of this article (10.1007/s00455-018-09974-5) contains supplementary material, which is available to authorized users.

The SNIFFPHONE device is a portable multichannel gas sensor, aiming to detect gastric cancer (GC) from breath samples. It employs gold nanoparticle (GNP) sensors reacting to volatile organic compounds (VOCs) in the exhaled breath, a non-invasive technique to support early diagnosis. This study evaluates the repeatability of the SNIFFPHONE classification result for measurements conducted on healthy subjects over a short period of time of less than 10 minutes. Due to the portable nature of the device, repeatability is studied with respect to varying measurement location. We find the classification results repeatable with a statistically significant 81 % Pearson correlation coefficient, even though the raw sensor responses are not concluded repeatable.

People with obstructive sleep apnea hypopnea syndrome (OSAHS) are affected by disruption in normal breathing patterns during sleep. In the literature, it is common to find acquisition of Thoracic (THO) and abdominal (ABD) movements with piezo-electric bands included in a full polysomnography. These movements convey valuable information related to sleep apnea events, and for this reason, contactless methods, such as the Pressure Bed Sensor (PBS), have been developed to extract this information. The main goal of this study is to analyze apnea and hypopnea fluctuations based on the spectral analysis of nasal airflow measure (as a reference signal), thoraco–abdominal effort and PBS respiration signal. To this end, features from the respiratory spectrum such as entropy, Gaussian modeling and instantaneous frequency were computed. These spectral properties were evaluated in three windows for each sensor: control point (CP) which is a window randomly extracted for the sleep time without apnea event, before event (BE) a window before an apnea episode and during event (DE) a window during an apnea episode. Apnea and hypopnea events were analyzed separately. According to a database of seventeen subjects, DE windows showed significant differences with respect to the CP window in most of the computed indices for both apnea and hypopnea events for all sensors. Significant differences were also found when DE and BE windows were compared in the case of apnea for all the sensors. In conclusion, the analyzed spectral characteristics could be a good tool to detect apnea and hypopnea. Finally, PBS signal which is a unobtrusive sensor, maintains the spectral properties of the standard respiratory effort measurements, and the use of this sensor could be useful for the monitoring outside of a clinical environment, simplifying the acquisition process.

We evaluate the performance of an unobtrusive sleep monitoring system in the detection of the sleep apnea-hypopnea syndrome (SAHS). The proposed system is a pressure bed sensor (PBS) that incorporates multiple pressure sensors into a bed mattress to measure several physiological signals of the sleeping subject: respiration; heart rate; and body movements. An automatic algorithm is developed to calculate a respiratory event index (REI). The recordings of 24 patients with suspected sleep problems are analyzed, and the results are compared with the gold standard methods; first with manual scoring of polysomnography to calculate the apnea-hypopnea index (AHI), and second with automatic detection of REI from the respiratory inductive plethysmography belts. The correlation coefficient between AHI and REI from PBS is up to 0.93. Evaluating the ability of PBS in the diagnosis of pathologic (AHI $\ge 5$ ) and nonpathologic ( ${\rm AHI} ) subjects, we obtained a sensitivity, specificity, and accuracy of 100%, 92%, and 96%, respectively. To diagnose three levels of SAHS, mild, moderate, and severe, the Cohen’s kappa value is 0.76. These findings support that PBS recording could provide a simple and unobtrusive method for detection of SAHS in home monitoring.

Automatic sleep staging based on inter-beat fluctuations and motion signals recorded through a pressure bed sensor during sleep is presented. The analysis of the sleep was based on the three major divisions of the sleep time: Wake, non-rapid eye movement (nREM) and rapid eye movement (REM) sleep stages. Twelve sleep recordings, from six females working alternate shift, with their respective annotations were used in the study. Six recordings were acquired during the night and six during the day after a night shift. A Time-Variant Autoregressive Model was used to extract features from inter-beat fluctuations which later were fed to a Support Vector Machine classifier. Accuracy, Kappa index, and percentage in wake, REM and nREM were used as performance measures. Comparison between the automatic sleep staging detection and the standard clinical annotations, shows mean values of [Formula: see text]% for accuracy [Formula: see text] for kappa index, and mean errors of 5% for sleep stages. The performance measures were similar for night and day sleep recordings. In this sample of recordings, the results suggest that inter-beat fluctuations and motions acquired in non-obtrusive way carried valuable information related to the sleep macrostructure and could be used to support to the experts in extensive evaluation and monitoring of sleep.

SummaryIntroduction: This article is part of the Focus Theme of Methods of Information in Medicine on “Biosignal Interpretation: Advanced Methods for Studying Cardiovascular and Respiratory Systems”.Objectives: The aim of this study is to assess the reliability of the estimated Nocturnal Heart Rate (HR), recorded through a bed sensor, compared with the one obtained from standard electrocardiography (ECG).Methods: Twenty-eight sleep deprived patients were recorded for one night each through matrix of piezoelectric sensors, integrated into the mattress, through polysomnography (PSG) simultaneously. The two recording methods have been compared in terms of signal quality and differences in heart beat detection.Results: On average, coverage of 92.7% of the total sleep time was obtained for the bed sensor, testifying the good quality of the recordings. The average beat-to-beat error of the inter-beat intervals was 1.06%. These results suggest a good overall signal quality, however, considering fast heart rates (HR > 100 bpm), performances were worse: in fact, the sensitivity in the heart beat detection was 28.4% while the false positive rate was 3.8% which means that a large amount of fast beats were not detected.Conclusions: The accuracy of the measurements made using the bed sensor has less than 10% of failure rate especially in periods with HR lower than 70 bpm. For fast heart beats the uncertainty increases. This can be explained by the change in morphology of the bed sensor signal in correspondence of a higher HR.

We describe a system for the evaluation of the sleep macrostructure on the basis of Emfit sensor foils placed into bed mattress and of advanced signal processing. The signals on which the analysis is based are heart-beat interval (HBI) and movement activity obtained from the bed sensor, the relevant features and parameters obtained through a time-variant autoregressive model (TVAM) used as feature extractor, and the classification obtained through a hidden Markov model (HMM). Parameters coming from the joint probability of the HBI features were used as input to a HMM, while movement features are used for wake period detection. A total of 18 recordings from healthy subjects, including also reference polysomnography, were used for the validation of the system. When compared to wake-nonrapid-eye-movement (NREM)-REM classification provided by experts, the described system achieved a total accuracy of 79+/-9% and a kappa index of 0.43+/-0.17 with only two HBI features and one movement parameter, and a total accuracy of 79+/-10% and a kappa index of 0.44+/-0.19 with three HBI features and one movement parameter. These results suggest that the combination of HBI and movement features could be a suitable alternative for sleep staging with the advantage of low cost and simplicity.

In MRI-guided high-intensity focused ultrasound (MR-HIFU) therapy, temperature-dependent proton resonance frequency (PRF) shift is a key factor to quantify and visualize the spatial heating pattern in treated and surrounding tissue. However, during treatment, multiple scanner-related changes can impact the accuracy of the temperature measurements obtained with the PRF shift method and cause an over/under estimation of temperature, which can be a major safety issue for treatments involving real-time temperature control. Hence, it is necessary to apply corrections to ensure accurate temperature measurements during heating. Prior to image acquisition, the central MR frequency F0 can be measured to adjust the F0 of next image acquisition. After acquisition, corrections can be applied to the acquired images to remove scanner-related influences, most importantly phase drift. Different phase drift correction algorithms such as conventional and polynomial adaptive drift correction estimate the background phase by fitting a linear or polynomial to the image phase outside the treatment area and perform the correction accordingly. The goal of this study was to understand the performance of these algorithms for long heating durations as would be experienced during hyperthermia or transurethral HIFU (>20 minutes).

The Pressure Bed Sensor (PBS), which is presented as a contactless sensor for physiological signals recording, allows the acquisition of respiration movements signal. The aim of the present study is to identify spectral parameters from the PBS respiratory signal that allow the discrimination between normal and abnormal breathing epochs. The nasal airflow and the PBS respiratory signal acquired on 19 subjects were pre-processed in order to obtain their positive envelope signals. Both of them were analyzed by means of an optimized Time-Variant Autoregressive Model (TVAM). Total sleep time was divided into consecutive epochs of 60 s classified as normal and abnormal (at least one apnea or hypopnea). The mean Power Spectral Density (PSD) for each sleep epoch was estimated from the averaged TVAM coefficients. Spectral features were extracted from both the nasal airflow and the PBS respiratory signal. A statistically significant difference (p-value

The authors go through the open source for product life cycle management (PLM) and the efforts done from communities such as the open source initiative. The characteristics of the open source solutions are highlighted as well. Next, the authors go through the requirements for PLM. This is an area where more attention has been given as the manufacturers are competing with the quality and life cycle costs of their products. Especially, the need of companies to try to get a strong position in providing services for their products and thus to make themselves less vulnerable to changes in the market has led to high interest in product life cycle simulation. The potential of applying semantic data management to solve these problems discussed in the light of recent developments. In addition, a basic roadmap is presented as to how the above-described problems could be tackled with open software solutions.

A Pressure Bed Sensor (PBS) can offer an unobtrusive method for sleep monitoring. This study focuses on the detection of the sleep related breathing disorders using a PBS in comparison to the methods used in a sleep laboratory. A newly developed PCA modeling approach for the eight sensor signals of the PBS is evaluated using the Reduced Respiratory Amplitude Index (RRAI) as a central measure. The method computes the respiration amplitude with the Hilbert transform, and then detects the events based on a 20% amplitude reduction from the baseline signal. A similar calculation was used for the sleep laboratory RIP measurements, and both PBS and RIP were compared against the reference based on the nasal flow signal. In the reference RRAI method, the respiratory-disordered events were obtained using RemLogic respiration analyzer to detect over 50% amplitude reduction in the nasal respiratory flow, but removing the RemLogic standard hypopnea event associations on the oxygen desaturation events and the sleep arousals. The movement artifacts were automatically detected based on the movement activity signal of the PBS. Twenty-five (25) out of 28 patients were finally analysed. On average 87% of a night measurement has been covered by the system. The correlation coefficient was 0.92 between the PBS and the reference RRAI, and the performance of the PBS was similar with the RIP belts. Classifying the severity of the sleep related breathing by dividing RRAI in groups according to the severity criteria, the sensitivity was 92% and the specificity was 70% for the PBS. The results suggest that PBS recording can provide an easy and un-obstructive alternative method for the detection of the sleep disordered breathing and thus has a great promise for the home monitoring.

Application of computational methods, including multibody system simulation, has become a common approach in the product development of mechanical systems and machines. Computational methods enable the efficient study of the kinematics and dynamics of mechanical systems being developed, and thus reduce development costs of the product. The increasing application of computational methods in product development processes has resulted in a new problem; the management and efficient utilisation of massive amounts of new design data. Because of the amounts, intelligent computational systems are needed for efficient use of the data coming, e.g. from multibody system simulations. This article introduces the use of semantic restrictions and rules in an ontology-based multibody system modelling approach. It discusses the benefit of separating modelling data and domain data semantics from the modelling and simulation tools and develops an improved multibody system modelling ontology with general modelling restrictions and rules, demonstrating its application with a simple example. The application of modelling case-specific rules is also demonstrated with an example.

Magnetic resonance imaging (MRI) thermometry is applied for guiding thermal therapy of tumorous tissues using high-intensity focused ultrasound (HIFU). Fast imaging sequences are needed for the on-line MRI operation, and strong artifacts on the temperature map can be found especially for image regions having poor signal-to-noise (S/N) ratio. Detection and masking of the regions with unreliable temperature measurement would be important to avoid unnecessary notification of the HIFU operator or even cancelation of the sonication procedure. This paper presents a specific image processing procedure to improve detection of typical MRI thermometry artifacts. First a region for well behaving temperature map is selected. Then, the remaining region of distorted temperature surface is processed with a novel method using oriented filter on the surface gradient. The result gives a more stable surface over the artifact regions, which can be used further to improve consistency in detection and masking of the temperature errors. Evaluation data is from N=7 patients having thermal therapy for uterine fibroid, including about 300 different sonications and 30,000 MRI thermometry images. The results show that the suggested method decreases variance of the temperature difference between consequent image frames in comparison with a conventional method using 2D phase unwrapping.

This study proposes an automatic method for the sleep-wake staging in normal and pathologic sleep based only on respiratory effort acquired from a Pressure Bed Sensor (PBS). Motion and respiratory movements were obtained through a PBS and sleep-wake staging was evaluated from those time series. 20 all night polysomnographies, with annotations, used as gold standard and the time series coming from the PBS were used to develop and to evaluate the automatic wake-sleep staging. The database was built up by: 10 healthy subjects and 10 patients with severe sleep apnea. The agreement of the statistical measures between the automatic classification and the human scoring were: 83.59 ± 6.79 of sensitivity, 83.60 ± 15.13 of specificity and 81.91 ± 6.36 of accuracy. These results suggest that some important indexes, such as sleep efficiency, could be computed through a contactless technique

This paper proposes a new real-time method to measure the driver’s useful field of view (UFOV) while driving a car in ordinary traffic situations in an urban environment. This is called the real-time useful field of view (rUFOV) method to discriminate it from conventional UFOV measurement, which is typically performed offline and with laboratory equipment developed by Visual Awareness Inc. The proposed real-time method first tracks traffic objects that appear in the driver’s peripheral vision using a road video camera, checks the degree of the driver’s attention to these objects using a driver monitoring camera, and finally calculates the percentage reduction in the driver’s UFOV using a database acquired over an extended period of time. Preliminary results showed better performance than originally expected. The rUFOV method was then incorporated into a driving simulation environment to enable more precise measurement of the driver’s gaze angle. This enabled the performance of safer tests for identifying conditions under which mental load reduced the driver’s visual capabilities, thus increasing the possibility of hasty driving, as well as the incorporation of more accurate control parameters into simulation software for risky driving scenarios. Consequently, this paper proposes a new methodology for measuring the driver’s UFOV as a potential real-time driver support system with automatic intrusive HMI adaptation and immediate alarm functions. The evaluation was conducted in two phases. First, the system was tested in real traffic using typical vehicle equipment and technically worked with a performance level of 81%.In the second phase, more test runs were performed in the simulator environment, which enabled near accident scenarios to be created without risking traffic safety and it was also measured its reaction time..

It is expected that the capability of managing the complete product life cycle in its phases will give the necessary boost for European Manufacturing Industry. Many efforts have been put into the creation of product lifecycle management systems, but it would seem that there is a gap between the existing reality and the specification of expected features. The article addresses this subject from critical point of view and tries to pinpoint the weaknesses of the existing solutions such as standards and database solutions. This work also tries to show the possible ways to follow that could help in solving the problems.

This paper investigates whether a driver's useful field of view (UFOV) can be measured during normal driving tasks without the driver being aware of the system. We were able to develop an online method to measure the driver's UFOV (oUFOV) automatically using a real car driving test, in the city of Tampere. The system was merged with two different technical solutions that estimated if the driver paid attention to the traffic objects that appeared in his or her peripheral vision. Here, the original laboratory method using a PC display was expanded to monitor the driver for much longer periods. By incorporating the oUFOV method into a driving simulation environment in Japan, we were also able to conduct safer tests for the drivers with reduced visual conditions, such as the driver's mental status change (hasty driving), and replace more accurate control parameters with simulation software in cases of risky driving scenarios. Thus, we confirmed that the oUFOV can measure the visual conditions for the driver and be deployed as a real-time driver support system with an automatic intrusive HMI adaptation function or immediate alarming, in the future, without driver awareness, according to the driver's visual attention capability.

The goal of our research work has been to build up an easy-to-use, computationally feasible virtual simulation model of the human body which could be applied to various studies of the vibration transmission and the human exposure to vibration during mobile machine operation. The proposed human model is a 46 degrees-of-freedom multibody model, in which PD-controllers are used to describe the muscular action in driving situations. In the presented case study, random, real driving situation based excitations are applied on the model. The results of the virtual simulation are compared with measurement data from physical test drive simulator. It is shown, that the conventional PD-controller can predict the human behavior in a reasonable accuracy with smaller amplitude excitation. When the human undergoes larger shock loads, there could be added a delay term in the control law.Copyright © 2006 by ASME

This study presents different methods for automatic sleep classification based on heart rate variability (HRV), respiration and movement signals recorded through bed sensors. Two methods for feature extraction have been implemented: time variant-autoregressive model (TVAM) and wavelet discrete transform (WDT); the obtained features are fed into two classifiers: Quadratic (QD) and Linear (LD) discriminant for staging sleep in REM, nonREM and WAKE periods. The performances of all the possible combinations of feature extractors and classifiers are compared in terms of accuracy and kappa index, using clinical polysomographyc evaluation as golden standard. 17 recordings from healthy subjects, including also polisomnography, were used to train and test the algorithms. When automatic classification is compared. QD-TVAM algorithm achieved a total accuracy of 76.81 ± 7.51 % and kappa index of 0.55 ± 0.10, while LD-WDT achieved a total accuracy of 79 ± 10% and kappa index of 0.51 ± 0.17. The results suggest that a good sleep evaluation can be achieved through non-conventional recording systems that could be used outside sleep centers.

This study analyses the spectral components of the heart rate fluctuations of a new contact-less technology for sleep evaluation. Both heart beat interval (HBI) and movement activity were extracted from the multichannel ballistocardiographic (BCG) measurements, based on Emfit sensor foils placed into bed mattress. Powers spectral densities (PSD) of HBI have been compared with the ones obtained from the standard ECG during sleep stage 2. In addition, spectral features obtained from the contact-less technology and standard ECG has been used to automatically classify the sleep macrostructure through a time-varying autoregressive model and a Hidden Markov Model. Whole night recordings from six subjects were analyzed in this study. Spectral components did not show significant differences between the two measurements. Further, contactless technology achieved a total accuracy of 83 % and kappa index of 0.42, while standard ECG achieved an accuracy of 84 % and kappa index of 0.43 when compared to clinical sleep staging from polysomnography.