Sparse time-frequency data analysis : a multi-scale approach

In this work, we further extend the recently developed adaptive data analysis method, the Sparse Time-Frequency Representation (STFR) method. This method is based on the assumption that many physical signals inherently contain AM-FM representations. We propose a sparse optimization method to extract the AM-FM representations of such signals. We prove the convergence of the method for periodic signals under certain assumptions and provide practical algorithms specifically for the non-periodic STFR, which extends the method to tackle problems that former STFR methods could not handle, including stability to noise and non-periodic data analysis. This is a significant improvement since many adaptive and non-adaptive signal processing methods are not fully capable of handling non-periodic signals. Moreover, we propose a new STFR algorithm to study intrawave signals with strong frequency modulation and analyze the convergence of this new algorithm for periodic signals. Such signals have previously remained a bottleneck for all signal processing methods. Furthermore, we propose a modified version of STFR that facilitates the extraction of intrawaves that have overlaping frequency content. We show that the STFR methods can be applied to the realm of dynamical systems and cardiovascular signals. In particular, we present a simplified and modified version of the STFR algorithm that is potentially useful for the diagnosis of some cardiovascular diseases. We further explain some preliminary work on the nature of Intrinsic Mode Functions (IMFs) and how they can have different representations in different phase coordinates. This analysis shows that the uncertainty principle is fundamental to all oscillating signals.

Improving the Scalability of Reduct Determination in Rough Sets

Rough Set Data Analysis (RSDA) is a non-invasive data analysis approach that solely relies on the data to find patterns and decision rules. Despite its noninvasive approach and ability to generate human readable rules, classical RSDA has not been successfully used in commercial data mining and rule generating engines. The reason is its scalability. Classical RSDA slows down a great deal with the larger data sets and takes much longer times to generate the rules. This research is aimed to address the issue of scalability in rough sets by improving the performance of the attribute reduction step of the classical RSDA - which is the root cause of its slow performance. We propose to move the entire attribute reduction process into the database. We defined a new schema to store the initial data set. We then defined SOL queries on this new schema to find the attribute reducts correctly and faster than the traditional RSDA approach. We tested our technique on two typical data sets and compared our results with the traditional RSDA approach for attribute reduction. In the end we also highlighted some of the issues with our proposed approach which could lead to future research.

Multispectral retinal image analysis:a novel non-invasive tool for retinal imaging

Purpose-To develop a non-invasive method for quantification of blood and pigment distributions across the posterior pole of the fundus from multispectral images using a computer-generated reflectance model of the fundus. Methods - A computer model was developed to simulate light interaction with the fundus at different wavelengths. The distribution of macular pigment (MP) and retinal haemoglobins in the fundus was obtained by comparing the model predictions with multispectral image data at each pixel. Fundus images were acquired from 16 healthy subjects from various ethnic backgrounds and parametric maps showing the distribution of MP and of retinal haemoglobins throughout the posterior pole were computed. Results - The relative distributions of MP and retinal haemoglobins in the subjects were successfully derived from multispectral images acquired at wavelengths 507, 525, 552, 585, 596, and 611?nm, providing certain conditions were met and eye movement between exposures was minimal. Recovery of other fundus pigments was not feasible and further development of the imaging technique and refinement of the software are necessary to understand the full potential of multispectral retinal image analysis. Conclusion - The distributions of MP and retinal haemoglobins obtained in this preliminary investigation are in good agreement with published data on normal subjects. The ongoing development of the imaging system should allow for absolute parameter values to be computed. A further study will investigate subjects with known pathologies to determine the effectiveness of the method as a screening and diagnostic tool.

Non invasive ventilation during exercise in COPD patients: a systematic review with meta-analysis

COPD is a major cause of morbidity and mortality worldwide, representing a major public health problem due to the high health and economic resource consumption. Pulmonary rehabilitation is a standard care recommendation for these patients, in order to control the symptoms and optimize the functional capacity, reducing health care costs associated with exacerbations and activity limitations and participation. However, in patients with severe COPD exercise performance can be difficult, due to extreme dyspnea, decreased muscle strength and fatigue. In addition, hypoxemia and dyspnea during efforts and daily activities may occur, limiting their quality of life. Thus, NIV have been used as adjunct to exercise, in order to improve exercise capacity in these patients. However, there is no consensus for this technique recommendation. Our objective was to verify whether the use of NIV during exercise is effective than exercise without NIV in dyspnea, walked distance, blood gases and health status in COPD patients, through a systematic review and meta-analysis.

Online estimation of respiratory mechanics in non-invasive pressure support ventilation: a bench model study.

An online algorithm for determining respiratory mechanics in patients using non-invasive ventilation (NIV) in pressure support mode was developed and embedded in a ventilator system. Based on multiple linear regression (MLR) of respiratory data, the algorithm was tested on a patient bench model under conditions with and without leak and simulating a variety of mechanics. Bland-Altman analysis indicates reliable measures of compliance across the clinical range of interest (± 11-18% limits of agreement). Resistance measures showed large quantitative errors (30-50%), however, it was still possible to qualitatively distinguish between normal and obstructive resistances. This outcome provides clinically significant information for ventilator titration and patient management.

Patient-ventilator asynchrony during non-invasive ventilation for acute respiratory failure: a multicenter study.

OBJECTIVE : To determine the prevalence of patient-ventilator asynchrony in patients receiving non-invasive ventilation (NIV) for acute respiratory failure. DESIGN : Prospective multicenter observation study. SETTING : Intensive care units in three university hospitals. METHODS: Patients consecutively admitted to ICU were included. NIV, performed with an ICU ventilator, was set by the clinician. Airway pressure, flow, and surface diaphragmatic electromyography were recorded continuously for 30 min. Asynchrony events and the asynchrony index (AI) were determined from visual inspection of the recordings and clinical observation. RESULTS: A total of 60 patients were included, 55% of whom were hypercapnic. Auto-triggering was present in 8 (13%) patients, double triggering in 9 (15%), ineffective breaths in 8 (13%), premature cycling 7 (12%) and late cycling in 14 (23%). An AI > 10%, indicating severe asynchrony, was present in 26 patients (43%), whose median (25-75 IQR) AI was 26 (15-54%). A significant correlation was found between the magnitude of leaks and the number of ineffective breaths and severity of delayed cycling. Multivariate analysis indicated that the level of pressure support and the magnitude of leaks were weakly, albeit significantly, associated with an AI > 10%. Patient comfort scale was higher in pts with an AI < 10%. CONCLUSION: Patient-ventilator asynchrony is common in patients receiving NIV for acute respiratory failure. Our results suggest that leaks play a major role in generating patient-ventilator asynchrony and discomfort, and point the way to further research to determine if ventilator functions designed to cope with leaks can reduce asynchrony in the clinical setting.

Clinical characteristics and outcome of patients admitted to a medico- surgical ICU requiring non invasive ventilation (NIV) for hypercapnic respiratory failure

Introduction Because it decreases intubation rate and mortality, NIV has become first-line treatment in case of hypercapnic acute respiratory failure (HARF). Whether this approach is equally successful for all categories of HARF patients is however debated. We assessed if any clinical characteristics of HARF patients were associated with NIV intensity, success, and outcome, in order to identify prognostic factors. Methods Retrospective analysis of the clinical database (clinical information system and MDSi) of patients consecutively admitted to our medico-surgical ICU, presenting with HARF (defined as PaCO2 > 50 mmHg), and receiving NIV between May 2008 and December 2010. Demographic data, medical diagnoses (including documented chronic lung disease), reason for ICU hospitalization, recent surgical interventions, SAPS II and McCabe scores were extracted from the database. Total duration of NIV and the need for tracheal intubation during the 5 days following the first hypercapnia documentation, as well as ICU, hospital and one year mortality were recorded. Results are reported as median [IQR]. Comparisons were carried out with Chi2 or Kruskal-Wallis tests, p<0.05 (*). Results Two hundred and twenty patients were included. NIV successful patients received 16 [9-31] hours of NIV for up to 5 days. Fifty patients (22.7%) were intubated 11 [2-34] hours after HARF occurence, after having receiving 10 [5-21] hours of NIV. Intubation was correlated with increased ICU (18% vs. 6%, p<0.05) and hospital (42% vs. 31%, p>0.05) mortality. SAPS II score was related to increasing ICU (51 [29-74] vs. 23 [12-41]%, p<0.05), hospital (37% [20-59] vs 20% [12-37], p<0.05) and one year mortality (35% vs 20%, p<0.05). Surgical patients were less frequent among hospital fatalities (28.8% vs. 46.3%, p<0.05, RR 0.8 [0-6-0.9]). Nineteen patients (8.6%) died in the ICU, 73 (33.2%) during their hospital stay and 108 (49.1%) were dead one year after HARF. Conclusion The practice to start NIV in all suitable patients suffering from HARF is appropriate. NIV can safely and appropriately be used in patients suffering from HARF from an origin different from COPD exacerbation. Beside usual predictors of severity such as severity score (SAPS II) appear to be associated with increased mortality. Although ICU mortality was low in our patients, hospital and one year mortality were substantial. Surgical patients, although undergoing a similar ICU course, had a better hospital and one year outcome.

The allometry of non-employment. What can compositional data analysis tell us about labour market performance across the UK's regions?

The low levels of unemployment recorded in the UK in recent years are widely cited asevidence of the country’s improved economic performance, and the apparent convergence of unemployment rates across the country’s regions used to suggest that the longstanding divide in living standards between the relatively prosperous ‘south’ and the more depressed ‘north’ has been substantially narrowed. Dissenters from theseconclusions have drawn attention to the greatly increased extent of non-employment(around a quarter of the UK’s working age population are not in employment) and themarked regional dimension in its distribution across the country. Amongst these dissenters it is generally agreed that non-employment is concentrated amongst oldermales previously employed in the now very much smaller ‘heavy’ industries (e.g. coal,steel, shipbuilding).This paper uses the tools of compositiona l data analysis to provide a much richer picture of non-employment and one which challenges the conventional analysis wisdom about UK labour market performance as well as the dissenters view of the nature of theproblem. It is shown that, associated with the striking ‘north/south’ divide in nonemployment rates, there is a statistically significant relationship between the size of the non-employment rate and the composition of non-employment. Specifically, it is shown that the share of unemployment in non-employment is negatively correlated with the overall non-employment rate: in regions where the non-employment rate is high the share of unemployment is relatively low. So the unemployment rate is not a very reliable indicator of regional disparities in labour market performance. Even more importantly from a policy viewpoint, a significant positive relationship is found between the size ofthe non-employment rate and the share of those not employed through reason of sicknessor disability and it seems (contrary to the dissenters) that this connection is just as strong for women as it is for men

Non-invasive prenatal diagnosis of multiple endocrine neoplasia type 2A using COLD-PCR combined with HRM genotyping analysis from maternal serum.

The multiple endocrine neoplasia type 2A (MEN2A) is a monogenic disorder characterized by an autosomal dominant pattern of inheritance which is characterized by high risk of medullary thyroid carcinoma in all mutation carriers. Although this disorder is classified as a rare disease, the patients affected have a low life quality and a very expensive and continuous treatment. At present, MEN2A is diagnosed by gene sequencing after birth, thus trying to start an early treatment and by reduction of morbidity and mortality. We first evaluated the presence of MEN2A mutation (C634Y) in serum of 25 patients, previously diagnosed by sequencing in peripheral blood leucocytes, using HRM genotyping analysis. In a second step, we used a COLD-PCR approach followed by HRM genotyping analysis for non-invasive prenatal diagnosis of a pregnant woman carrying a fetus with a C634Y mutation. HRM analysis revealed differences in melting curve shapes that correlated with patients diagnosed for MEN2A by gene sequencing analysis with 100% accuracy. Moreover, the pregnant woman carrying the fetus with the C634Y mutation revealed a melting curve shape in agreement with the positive controls in the COLD-PCR study. The mutation was confirmed by sequencing of the COLD-PCR amplification product. In conclusion, we have established a HRM analysis in serum samples as a new primary diagnosis method suitable for the detection of C634Y mutations in MEN2A patients. Simultaneously, we have applied the increase of sensitivity of COLD-PCR assay approach combined with HRM analysis for the non-invasive prenatal diagnosis of C634Y fetal mutations using pregnant women serum.

A non-invasive assessment of hepatic glycogen kinetics and post-absorptive gluconeogenesis in man.

A novel approach to the study of hepatic glycogen kinetics and fractional gluconeogenesis in vivo is described. Ten healthy female subjects were fed an iso-caloric diet containing 55% carbohydrate energy with a 13C abundance of 1.083 atom percent for a 3-day baseline period; then, a diet of similar composition, but providing carbohydrate with a 13C abundance of 1.093 atom percent was started and continued for 5 days. Resting respiratory gas exchanges, urinary nitrogen excretion, breath 13CO2 and plasma 13C glucose were measured every morning in the fasting state. The enrichment in 13C of hepatic glycogen was calculated from these measured data. 13C glycogen enrichment increased after switching to a 13C enriched carbohydrate diet, and was identical to the 13C enrichment of dietary carbohydrates after 3 days. The time required to renew 50% of hepatic glycogen, as determined from the kinetics of 13C glycogen enrichment, was 18.9 +/- 3.6 h. Fractional gluconeogenesis, as determined from the difference between the enrichments of glucose oxidized originating from hepatic glycogen and plasma glucose 13C was 50.8 +/- 5.3%. This non-invasive method will allow the study of hepatic glycogen metabolism in insulin-resistant patients.

Clinical characteristics and outcome of patients admitted to a medico-surgical ICU requiring non invasive ventilation (NIV) for hypercapnic respiratory failure

Introduction: Because it decreases intubation rate and mortality, NIV has become first-line treatment in case of hypercapnic respiratory failure (HRF). Whether this approach is equally successful for all categories of HRF patients is however debated. We assessed if any clinical characteristics of HRF patients were associated with NIV intensity, success, and outcome, in order to identify prognostic factors. Methods: Retrospective analysis of the clinical database (clinical information system and MDSi) of patients consecutively admitted to our medico-surgical ICU, presenting with HRF (defined as PaCO2 >50 mm Hg), and receiving NIV between January 2009 and December 2010. Demographic data, medical diagnoses (including documented chronic lung disease), reason for ICU hospitalization, recent surgical interventions, SAPS II and McCabe scores were extracted from the database. Total duration of NIV and the need for tracheal intubation during the 5 days following the first hypercapnia documentation, as well as ICU and hospital mortality were recorded. Results are reported as median [IQR]. Comparisons with Chi2 or Kruskal-Wallis tests, p <0.05 (*). Results: 164 patients were included, 45 (27.4%) of whom were intubated after 10 [2-34] hours, after having received 7 [2-19] hours of NIV. NIV successful patients received 15 [5-22] hours of NIV for up to 5 days. Intubation was correlated with increased ICU (20% vs. 3%, p <0.001) and hospital (46.7% vs. 30.2, p >0.05) mortality. Conclusions: A majority of patients requiring NIV for hypercapnic respiratory failure in our ICU have no diagnosed chronic pulmonary disease. These patients tend to have increased ICUand hospital mortality. The majority of patients were non-surgical, a feature correlated with increased hospital mortality. Beside usual predictors of severity such as age and SAPS II, absence of diagnosed chronic pulmonary disease and non-operative state appear to be associated with increased mortality. Further studies should explore whether these patients are indeed more prone to an adverse outcome and which therapeutic strategies might contribute to alter this course.

On the Selection of Non-Invasive Methods Based on Speech Analysis Oriented to Automatic Alzheimer Disease Diagnosis

The work presented here is part of a larger study to identify novel technologies and biomarkers for early Alzheimer disease (AD) detection and it focuses on evaluating the suitability of a new approach for early AD diagnosis by non-invasive methods. The purpose is to examine in a pilot study the potential of applying intelligent algorithms to speech features obtained from suspected patients in order to contribute to the improvement of diagnosis of AD and its degree of severity. In this sense, Artificial Neural Networks (ANN) have been used for the automatic classification of the two classes (AD and control subjects). Two human issues have been analyzed for feature selection: Spontaneous Speech and Emotional Response. Not only linear features but also non-linear ones, such as Fractal Dimension, have been explored. The approach is non invasive, low cost and without any side effects. Obtained experimental results were very satisfactory and promising for early diagnosis and classification of AD patients.

Non invasive classification system of scoliosis curve types using least-squares support vector machines

Objective To determine scoliosis curve types using non invasive surface acquisition, without prior knowledge from X-ray data. Methods Classification of scoliosis deformities according to curve type is used in the clinical management of scoliotic patients. In this work, we propose a robust system that can determine the scoliosis curve type from non invasive acquisition of the 3D back surface of the patients. The 3D image of the surface of the trunk is divided into patches and local geometric descriptors characterizing the back surface are computed from each patch and constitute the features. We reduce the dimensionality by using principal component analysis and retain 53 components using an overlap criterion combined with the total variance in the observed variables. In this work, a multi-class classifier is built with least-squares support vector machines (LS-SVM). The original LS-SVM formulation was modified by weighting the positive and negative samples differently and a new kernel was designed in order to achieve a robust classifier. The proposed system is validated using data from 165 patients with different scoliosis curve types. The results of our non invasive classification were compared with those obtained by an expert using X-ray images. Results The average rate of successful classification was computed using a leave-one-out cross-validation procedure. The overall accuracy of the system was 95%. As for the correct classification rates per class, we obtained 96%, 84% and 97% for the thoracic, double major and lumbar/thoracolumbar curve types, respectively. Conclusion This study shows that it is possible to find a relationship between the internal deformity and the back surface deformity in scoliosis with machine learning methods. The proposed system uses non invasive surface acquisition, which is safe for the patient as it involves no radiation. Also, the design of a specific kernel improved classification performance.

Towards Non Invasive Diagnosis of Scoliosis Using Semi-supervised Learning Approach

In this paper, a new methodology for the prediction of scoliosis curve types from non invasive acquisitions of the back surface of the trunk is proposed. One hundred and fifty-nine scoliosis patients had their back surface acquired in 3D using an optical digitizer. Each surface is then characterized by 45 local measurements of the back surface rotation. Using a semi-supervised algorithm, the classifier is trained with only 32 labeled and 58 unlabeled data. Tested on 69 new samples, the classifier succeeded in classifying correctly 87.0% of the data. After reducing the number of labeled training samples to 12, the behavior of the resulting classifier tends to be similar to the reference case where the classifier is trained only with the maximum number of available labeled data. Moreover, the addition of unlabeled data guided the classifier towards more generalizable boundaries between the classes. Those results provide a proof of feasibility for using a semi-supervised learning algorithm to train a classifier for the prediction of a scoliosis curve type, when only a few training data are labeled. This constitutes a promising clinical finding since it will allow the diagnosis and the follow-up of scoliotic deformities without exposing the patient to X-ray radiations.

The allometry of non-employment. What can compositional data analysis tell us about labour market performance across the UK's regions?

The low levels of unemployment recorded in the UK in recent years are widely cited as evidence of the country’s improved economic performance, and the apparent convergence of unemployment rates across the country’s regions used to suggest that the longstanding divide in living standards between the relatively prosperous ‘south’ and the more depressed ‘north’ has been substantially narrowed. Dissenters from these conclusions have drawn attention to the greatly increased extent of non-employment (around a quarter of the UK’s working age population are not in employment) and the marked regional dimension in its distribution across the country. Amongst these dissenters it is generally agreed that non-employment is concentrated amongst older males previously employed in the now very much smaller ‘heavy’ industries (e.g. coal, steel, shipbuilding). This paper uses the tools of compositiona l data analysis to provide a much richer picture of non-employment and one which challenges the conventional analysis wisdom about UK labour market performance as well as the dissenters view of the nature of the problem. It is shown that, associated with the striking ‘north/south’ divide in nonemployment rates, there is a statistically significant relationship between the size of the non-employment rate and the composition of non-employment. Specifically, it is shown that the share of unemployment in non-employment is negatively correlated with the overall non-employment rate: in regions where the non-employment rate is high the share of unemployment is relatively low. So the unemployment rate is not a very reliable indicator of regional disparities in labour market performance. Even more importantly from a policy viewpoint, a significant positive relationship is found between the size of the non-employment rate and the share of those not employed through reason of sickness or disability and it seems (contrary to the dissenters) that this connection is just as strong for women as it is for men