Automatic front-crawl temporal phase detection using adaptive filtering of inertial signals

This study introduces a novel approach for automatic temporal phase detection and inter-arm coordination estimation in front-crawl swimming using inertial measurement units (IMUs). We examined the validity of our method by comparison against a video-based system. Three waterproofed IMUs (composed of 3D accelerometer, 3D gyroscope) were placed on both forearms and the sacrum of the swimmer. We used two underwater video cameras in side and frontal views as our reference system. Two independent operators performed the video analysis. To test our methodology, seven well-trained swimmers performed three 300 m trials in a 50 m indoor pool. Each trial was in a different coordination mode quantified by the index of coordination. We detected different phases of the arm stroke by employing orientation estimation techniques and a new adaptive change detection algorithm on inertial signals. The difference of 0.2 +/- 3.9% between our estimation and video-based system in assessment of the index of coordination was comparable to experienced operators' difference (1.1 +/- 3.6%). The 95% limits of agreement of the difference between the two systems in estimation of the temporal phases were always less than 7.9% of the cycle duration. The inertial system offers an automatic easy-to-use system with timely feedback for the study of swimming.

Change points detection in crime-related time series: an on-line fuzzy approach based on a shape space representation

The extension of traditional data mining methods to time series has been effectively applied to a wide range of domains such as finance, econometrics, biology, security, and medicine. Many existing mining methods deal with the task of change points detection, but very few provide a flexible approach. Querying specific change points with linguistic variables is particularly useful in crime analysis, where intuitive, understandable, and appropriate detection of changes can significantly improve the allocation of resources for timely and concise operations. In this paper, we propose an on-line method for detecting and querying change points in crime-related time series with the use of a meaningful representation and a fuzzy inference system. Change points detection is based on a shape space representation, and linguistic terms describing geometric properties of the change points are used to express queries, offering the advantage of intuitiveness and flexibility. An empirical evaluation is first conducted on a crime data set to confirm the validity of the proposed method and then on a financial data set to test its general applicability. A comparison to a similar change-point detection algorithm and a sensitivity analysis are also conducted. Results show that the method is able to accurately detect change points at very low computational costs. More broadly, the detection of specific change points within time series of virtually any domain is made more intuitive and more understandable, even for experts not related to data mining.

Screening for wound-induced oxylipins in Arabidopsis thaliana by differential HPLC-APCI/MS profiling of crude leaf extracts and subsequent characterisation by capillary-scale NMR

A simple non-targeted differential HPLC-APCI/MS approach has been developed in order to survey metabolome modifications that occur in the leaves of Arabidopsis thaliana following wound-induced stress. The wound-induced accumulation of metabolites, particularly oxylipins, was evaluated by HPLC-MS analysis of crude leaf extracts. A generic, rapid and reproducible pressure liquid extraction procedure was developed for the analysis of restricted leaf samples without the need for specific sample preparation. The presence of various oxylipins was determined by head-to-head comparison of the HPLC-MS data, filtered with a component detection algorithm, and automatically compared with the aid of software searching for small differences in similar HPLC-MS profiles. Repeatability was verified in several specimens belonging to different series. Wound-inducible jasmonates were efficiently highlighted by this non-targeted approach without the need for complex sample preparation as is the case for the 'oxylipin signature' procedure based on GC-MS. Furthermore this HPLC-MS screening technique allowed the isolation of induced compounds for further characterisation by capillary-scale NMR (CapNMR) after HPLC scale-up. In this paper, the screening method is described and applied to illustrate its potential for monitoring polar and non-polar stress-induced constituents as well as its use in combination with CapNMR for the structural assignment of wound-induced compounds of interest

Detection of millimetric deformation using a terrestrial laser scanner: experiment and application to a rockfall event

Abstract. Terrestrial laser scanning (TLS) is one of the most promising surveying techniques for rockslope characteriza- tion and monitoring. Landslide and rockfall movements can be detected by means of comparison of sequential scans. One of the most pressing challenges of natural hazards is com- bined temporal and spatial prediction of rockfall. An outdoor experiment was performed to ascertain whether the TLS in- strumental error is small enough to enable detection of pre- cursory displacements of millimetric magnitude. This con- sists of a known displacement of three objects relative to a stable surface. Results show that millimetric changes cannot be detected by the analysis of the unprocessed datasets. Dis- placement measurement are improved considerably by ap- plying Nearest Neighbour (NN) averaging, which reduces the error (1σ ) up to a factor of 6. This technique was ap- plied to displacements prior to the April 2007 rockfall event at Castellfollit de la Roca, Spain. The maximum precursory displacement measured was 45 mm, approximately 2.5 times the standard deviation of the model comparison, hampering the distinction between actual displacement and instrumen- tal error using conventional methodologies. Encouragingly, the precursory displacement was clearly detected by apply- ing the NN averaging method. These results show that mil- limetric displacements prior to failure can be detected using TLS.

Fuzzy logic algorithm to extract specific interaction forces from atomic force microscopy data

The atomic force microscope is not only a very convenient tool for studying the topography of different samples, but it can also be used to measure specific binding forces between molecules. For this purpose, one type of molecule is attached to the tip and the other one to the substrate. Approaching the tip to the substrate allows the molecules to bind together. Retracting the tip breaks the newly formed bond. The rupture of a specific bond appears in the force-distance curves as a spike from which the binding force can be deduced. In this article we present an algorithm to automatically process force-distance curves in order to obtain bond strength histograms. The algorithm is based on a fuzzy logic approach that permits an evaluation of "quality" for every event and makes the detection procedure much faster compared to a manual selection. In this article, the software has been applied to measure the binding strength between tubuline and microtubuline associated proteins.

Quantifying consistency of Event Related Potentials across subjects based on single-trial topographic analysis

Introduction: Non-invasive brain imaging techniques often contrast experimental conditions across a cohort of participants, obfuscating distinctions in individual performance and brain mechanisms that are better characterised by the inter-trial variability. To overcome such limitations, we developed topographic analysis methods for single-trial EEG data [1]. So far this was typically based on time-frequency analysis of single-electrode data or single independent components. The method's efficacy is demonstrated for event-related responses to environmental sounds, hitherto studied at an average event-related potential (ERP) level. Methods: Nine healthy subjects participated to the experiment. Auditory meaningful sounds of common objects were used for a target detection task [2]. On each block, subjects were asked to discriminate target sounds, which were living or man-made auditory objects. Continuous 64-channel EEG was acquired during the task. Two datasets were considered for each subject including single-trial of the two conditions, living and man-made. The analysis comprised two steps. In the first part, a mixture of Gaussians analysis [3] provided representative topographies for each subject. In the second step, conditional probabilities for each Gaussian provided statistical inference on the structure of these topographies across trials, time, and experimental conditions. Similar analysis was conducted at group-level. Results: Results show that the occurrence of each map is structured in time and consistent across trials both at the single-subject and at group level. Conducting separate analyses of ERPs at single-subject and group levels, we could quantify the consistency of identified topographies and their time course of activation within and across participants as well as experimental conditions. A general agreement was found with previous analysis at average ERP level. Conclusions: This novel approach to single-trial analysis promises to have impact on several domains. In clinical research, it gives the possibility to statistically evaluate single-subject data, an essential tool for analysing patients with specific deficits and impairments and their deviation from normative standards. In cognitive neuroscience, it provides a novel tool for understanding behaviour and brain activity interdependencies at both single-subject and at group levels. In basic neurophysiology, it provides a new representation of ERPs and promises to cast light on the mechanisms of its generation and inter-individual variability.

Multi-scale support vector algorithms for hot spot detection and modelling

The algorithmic approach to data modelling has developed rapidly these last years, in particular methods based on data mining and machine learning have been used in a growing number of applications. These methods follow a data-driven methodology, aiming at providing the best possible generalization and predictive abilities instead of concentrating on the properties of the data model. One of the most successful groups of such methods is known as Support Vector algorithms. Following the fruitful developments in applying Support Vector algorithms to spatial data, this paper introduces a new extension of the traditional support vector regression (SVR) algorithm. This extension allows for the simultaneous modelling of environmental data at several spatial scales. The joint influence of environmental processes presenting different patterns at different scales is here learned automatically from data, providing the optimum mixture of short and large-scale models. The method is adaptive to the spatial scale of the data. With this advantage, it can provide efficient means to model local anomalies that may typically arise in situations at an early phase of an environmental emergency. However, the proposed approach still requires some prior knowledge on the possible existence of such short-scale patterns. This is a possible limitation of the method for its implementation in early warning systems. The purpose of this paper is to present the multi-scale SVR model and to illustrate its use with an application to the mapping of Cs137 activity given the measurements taken in the region of Briansk following the Chernobyl accident.

Forest fires cluster detection with space-time scan statistics

Forest fires are defined as uncontrolled fires often occurring in wildland areas, but that can also affect houses or agricultural resources. Causes are both natural (e.g.,lightning phenomena) and anthropogenic (human negligence or arsons).Major environmental factors influencing the fire ignition and propagation are climate and vegetation. Wildfires are most common and severe during drought period and on windy days. Moreover, under water-stress conditions, which occur after a long hot and dry period, the vegetation is more vulnerable to fire. These conditions are common in the United State and Canada, where forest fires represent a big problem. We focused our analysis on the state of Florida, for which a big dataset on forest fires detection is readily available. USDA Forest Service Remote Sensing Application Center, in collaboration with NASA-Goddard Space Flight Center and the University of Maryland, has compiled daily MODIS Thermal Anomalies (fires and biomass burning images) produced by NASA using a contextual algorithm that exploits the strong emission of mid-infrared radiation from fires. Fire classes were converted in GIS format: daily MODIS fire detections are provided as the centroids of the 1 kilometer pixels and compiled into daily Arc/INFO point coverage.

Risk assessment of recurrence in sporadic retinoblastoma using a molecular-based algorithm

Le rétinoblastome (Rb) est une tumeur provenant des cellules rétiniennes progénitrices des photorécepteurs. C'est la tumeur pédiatrique maligne la plus fréquente avec une incidence par naissance évaluée entre 1/15Ό00 et 1/20Ό00. Les enfants atteints de Rb sont diagnostiqué dans leur grande majorité avant l'âge de 4 ans, soit le temps nécessaire à la différentiation et à la maturation des photorécepteurs et donc à la disparition de la cellule d'origine du Rb. La survie du patient, la sauvegarde oculaire et le pronostic visuel restent excellents pour autant que le traitement ne soit pas différé. Dans sa variante non héréditaire (60%) le Rb est toujours unilatéral et sporadique. Le Rb héréditaire de transmission dominante autosomique (40%), se décline sous toutes les formes, familiale (10%) ou sporadique (30%), que l'atteinte soit unilatérale ou bilatérale. La majorité des mutations causales sont uniques et distribuées de façon aléatoire sur la totalité du gène RB1 sans région prédisposante. La détection de ces mutations est couteuse et chronophage, tout en présentant un taux de détection relativement bas; surtout dans les cas de Rb sporadiques unilatéraux. Dans le but d'identifier les patients présentant un risque réel de développer un Rb, et de réduire le nombre d'examens sous narcose requis pour le dépistage de la maladie chez les sujets à risque, nous avons développé une stratégie sensible, rapide, efficace et peu couteuse basée sur une analyse de l'haplotype intragénique. Cet algorithme prend en compte a) la perte d'hétérozygotie intratumorale du gène RB1, b) l'origine paternelle préférentielle des nouvelles mutations germinales et c) un risque a priori dérivé des données empiriques de Vogel. Pendant la période allant de janvier 1994 à décembre 2006, nous avons comparé l'apparition de nouveau Rb parmi la fratrie et la descendance de patient atteints au nombre de nouveaux cas attendus calculé par notre algorithme. 134 familles ont été étudiées. L'analyse moléculaire a été effectuée chez 570 personnes dont 99 patients âgés de moins de 4 ans et donc à risque de développer un Rb. Parmi cette cohorte, nous avons observé l'apparition d'un cas de Rb, alors que les risques cumulés a posteriori calculé par notre algorithme prédisait l'apparition de 1.77 nouveau cas. Dans cette étude, nous avons pu valider notre algorithme prédisant la récurrence de Rb chez les parents de 1er degré de patients atteints. Cet outil devrait grandement faciliter le conseil génétique ainsi que le suivi des patients à risque de développer un Rb, surtout dans les cas ou le séquençage direct du gène RB1 n'est pas disponible ou est resté non informatif. - Purpose: Most RBI mutations are unique and distributed throughout the RBI gene. Their detection can be time-consuming and the yield especially low in cases of conservatively-treated sporadic unilateral retinoblas-toma (Rb) patients. In order to identify patients with true risk of developing Rb, and to reduce the number of unnecessary examinations under anesthesia in all other cases, we developed a universal sensitive, efficient and cost-effective strategy based on intragenic haplotype analysis. Methods: This algorithm allows the calculation of the a posteriori risk of developing Rb and takes into account (a) RBI loss of heterozygosity in tumors, (b) preferential paternal origin of new germline mutations, (c) a priori risk derived from empirical data by Vogel, and (d) disease penetrance of 90% in most cases. We report the occurrence of Rb in first degree relatives of patients with sporadic Rb who visited the Jules Gonin Eye Hospital, Lausanne, Switzerland, from January 1994 to December 2006 compared to expected new cases of Rb using our algorithm. Results: A total of 134 families with sporadic Rb were enrolled; testing was performed in 570 individuals and 99 patients younger than 4 years old were identified. We observed one new case of Rb. Using our algorithm, the cumulated total a posteriori risk of recurrence was 1.77. Conclusions: This is the first time that linkage analysis has been validated to monitor the risk of recurrence in sporadic Rb. This should be a useful tool in genetic counseling, especially when direct RBI screening for mutations leaves a negative result or is unavailable.

Multi-modal Change Detection, Application to the Detection of Flooded Areas: Outcome of the 2009-2010 data fusion contest

The 2009-2010 Data Fusion Contest organized by the Data Fusion Technical Committee of the IEEE Geoscience and Remote Sensing Society was focused on the detection of flooded areas using multi-temporal and multi-modal images. Both high spatial resolution optical and synthetic aperture radar data were provided. The goal was not only to identify the best algorithms (in terms of accuracy), but also to investigate the further improvement derived from decision fusion. This paper presents the four awarded algorithms and the conclusions of the contest, investigating both supervised and unsupervised methods and the use of multi-modal data for flood detection. Interestingly, a simple unsupervised change detection method provided similar accuracy as supervised approaches, and a digital elevation model-based predictive method yielded a comparable projected change detection map without using post-event data.

Risk assessment of recurrence in sporadic retinoblastoma using a molecular-based algorithm.

PURPOSE: Most RB1 mutations are unique and distributed throughout the RB1 gene. Their detection can be time-consuming and the yield especially low in cases of conservatively-treated sporadic unilateral retinoblastoma (Rb) patients. In order to identify patients with true risk of developing Rb, and to reduce the number of unnecessary examinations under anesthesia in all other cases, we developed a universal sensitive, efficient and cost-effective strategy based on intragenic haplotype analysis. METHODS: This algorithm allows the calculation of the a posteriori risk of developing Rb and takes into account (a) RB1 loss of heterozygosity in tumors, (b) preferential paternal origin of new germline mutations, (c) a priori risk derived from empirical data by Vogel, and (d) disease penetrance of 90% in most cases. We report the occurrence of Rb in first degree relatives of patients with sporadic Rb who visited the Jules Gonin Eye Hospital, Lausanne, Switzerland, from January 1994 to December 2006 compared to expected new cases of Rb using our algorithm. RESULTS: A total of 134 families with sporadic Rb were enrolled; testing was performed in 570 individuals and 99 patients younger than 4 years old were identified. We observed one new case of Rb. Using our algorithm, the cumulated total a posteriori risk of recurrence was 1.77. CONCLUSIONS: This is the first time that linkage analysis has been validated to monitor the risk of recurrence in sporadic Rb. This should be a useful tool in genetic counseling, especially when direct RB1 screening for mutations leaves a negative result or is unavailable.

Post-detection analysis for grating-based ultra-small angle X-ray scattering.

Until recently, the hard X-ray, phase-sensitive imaging technique called grating interferometry was thought to provide information only in real space. However, by utilizing an alternative approach to data analysis we demonstrated that the angular resolved ultra-small angle X-ray scattering distribution can be retrieved from experimental data. Thus, reciprocal space information is accessible by grating interferometry in addition to real space. Naturally, the quality of the retrieved data strongly depends on the performance of the employed analysis procedure, which involves deconvolution of periodic and noisy data in this context. The aim of this article is to compare several deconvolution algorithms to retrieve the ultra-small angle X-ray scattering distribution in grating interferometry. We quantitatively compare the performance of three deconvolution procedures (i.e., Wiener, iterative Wiener and Lucy-Richardson) in case of realistically modeled, noisy and periodic input data. The simulations showed that the algorithm of Lucy-Richardson is the more reliable and more efficient as a function of the characteristics of the signals in the given context. The availability of a reliable data analysis procedure is essential for future developments in grating interferometry.

Endoleak and in-stent thrombus detection with CT angiography in a thoracic aortic aneurysm phantom at different tube energies using filtered back projection and iterative algorithms.

PURPOSE: To determine the lower limit of dose reduction with hybrid and fully iterative reconstruction algorithms in detection of endoleaks and in-stent thrombus of thoracic aorta with computed tomographic (CT) angiography by applying protocols with different tube energies and automated tube current modulation. MATERIALS AND METHODS: The calcification insert of an anthropomorphic cardiac phantom was replaced with an aortic aneurysm model containing a stent, simulated endoleaks, and an intraluminal thrombus. CT was performed at tube energies of 120, 100, and 80 kVp with incrementally increasing noise indexes (NIs) of 16, 25, 34, 43, 52, 61, and 70 and a 2.5-mm section thickness. NI directly controls radiation exposure; a higher NI allows for greater image noise and decreases radiation. Images were reconstructed with filtered back projection (FBP) and hybrid and fully iterative algorithms. Five radiologists independently analyzed lesion conspicuity to assess sensitivity and specificity. Mean attenuation (in Hounsfield units) and standard deviation were measured in the aorta to calculate signal-to-noise ratio (SNR). Attenuation and SNR of different protocols and algorithms were analyzed with analysis of variance or Welch test depending on data distribution. RESULTS: Both sensitivity and specificity were 100% for simulated lesions on images with 2.5-mm section thickness and an NI of 25 (3.45 mGy), 34 (1.83 mGy), or 43 (1.16 mGy) at 120 kVp; an NI of 34 (1.98 mGy), 43 (1.23 mGy), or 61 (0.61 mGy) at 100 kVp; and an NI of 43 (1.46 mGy) or 70 (0.54 mGy) at 80 kVp. SNR values showed similar results. With the fully iterative algorithm, mean attenuation of the aorta decreased significantly in reduced-dose protocols in comparison with control protocols at 100 kVp (311 HU at 16 NI vs 290 HU at 70 NI, P ≤ .0011) and 80 kVp (400 HU at 16 NI vs 369 HU at 70 NI, P ≤ .0007). CONCLUSION: Endoleaks and in-stent thrombus of thoracic aorta were detectable to 1.46 mGy (80 kVp) with FBP, 1.23 mGy (100 kVp) with the hybrid algorithm, and 0.54 mGy (80 kVp) with the fully iterative algorithm.

Event-related potentials and changes of brain rhythm oscillations during working memory activation in patients with first-episode psychosis.

Background: Earlier contributions have documented significant changes in sensory, attention-related endogenous event-related potential (ERP) components and θ band oscillatory responses during working memory activation in patients with schizophrenia. In patients with first-episode psychosis, such studies are still scarce and mostly focused on auditory sensory processing. The present study aimed to explore whether subtle deficits of cortical activation are present in these patients before the decline of working memory performance. Methods: We assessed exogenous and endogenous ERPs and frontal θ event-related synchronization (ERS) in patients with first-episode psychosis and healthy controls who successfully performed an adapted 2-back working memory task, including 2 visual n-backworking memory tasks as well as oddball detection and passive fixation tasks. Results: We included 15 patients with first-episode psychosis and 18 controls in this study. Compared with controls, patients with first-episode psychosis displayed increased latencies of early visual ERPs and phasic θ ERS culmination peak in all conditions. However, they also showed a rapid recruitment of working memory-related neural generators, even in pure attention tasks, as indicated by the decreased N200 latency and increased amplitude of sustained θ ERS in detection compared with controls. Limitations: Owing to the limited sample size, no distinction was made between patients with first-episode psychosis with positive and negative symptoms. Although we controlled for the global load of neuroleptics, medication effect cannot be totally ruled out. Conclusion: The present findings support the concept of a blunted electroencephalographic response in patients with first-episode psychosis who recruit the maximum neural generators in simple attention conditions without being able to modulate their brain activation with increased complexity of working memory tasks.

Seizure detection with automated EEG analysis: a validation study focusing on periodic patterns.

OBJECTIVE: To evaluate an automated seizure detection (ASD) algorithm in EEGs with periodic and other challenging patterns. METHODS: Selected EEGs recorded in patients over 1year old were classified into four groups: A. Periodic lateralized epileptiform discharges (PLEDs) with intermixed electrical seizures. B. PLEDs without seizures. C. Electrical seizures and no PLEDs. D. No PLEDs or seizures. Recordings were analyzed by the Persyst P12 software, and compared to the raw EEG, interpreted by two experienced neurophysiologists; Positive percent agreement (PPA) and false-positive rates/hour (FPR) were calculated. RESULTS: We assessed 98 recordings (Group A=21 patients; B=29, C=17, D=31). Total duration was 82.7h (median: 1h); containing 268 seizures. The software detected 204 (=76.1%) seizures; all ictal events were captured in 29/38 (76.3%) patients; in only in 3 (7.7%) no seizures were detected. Median PPA was 100% (range 0-100; interquartile range 50-100), and the median FPR 0/h (range 0-75.8; interquartile range 0-4.5); however, lower performances were seen in the groups containing periodic discharges. CONCLUSION: This analysis provides data regarding the yield of the ASD in a particularly difficult subset of EEG recordings, showing that periodic discharges may bias the results. SIGNIFICANCE: Ongoing refinements in this technique might enhance its utility and lead to a more extensive application.