Utility of Quantitative 99mTc-MAA SPECT/CT for 90yttrium-Labelled Microsphere Treatment Planning: Calculating Vascularized Hepatic Volume and Dosimetric Approach

Objectives. The aim of this study was to assess the effectiveness of SPECT/CT for volume measurements and to report a case illustrating the major impact of SPECT/CT in calculating the vascularized liver volume and dosimetry prior to injecting radiolabelled yttrium-90 microspheres (Therasphere). Materials and Methods. This was a phantom study, involving volume measurements carried out by two operators using SPECT and SPECT/CT images. The percentage of error for each method was calculated, and interobserver reproducibility was evaluated. A treatment using Therasphere was planned in a patient with three hepatic arteries, and the quantitative analysis of SPECT/CT for this patient is provided. Results. SPECT/CT volume measurements proved to be accurate (mean error <6% for volumes ≥16 cm3) and reproductive (interobserver agreement = 0.9). In the case report, 99mTc-MAA SPECT/CT identified a large liver volume, not previously identified with angiography, which was shown to be vascularized after selective MAA injection into an arterial branch, resulting in a large modification in the activity of Therasphere used. Conclusions. MAA SPECT/CT is accurate for vascularized liver volume measurements, providing a valuable contribution to the therapeutic planning of patients with complex hepatic vascularization

The BANCA Database and Evaluation Protocol

In this paper we describe the acquisition and content of a new large, realistic and challenging multi-modal database intended for training and testing multi-modal verification systems. The BANCA database was captured in four European languages in two modalities (face and voice). For recording, both high and low quality microphones and cameras were used. The subjects were recorded in three different scenarios, controlled, degraded and adverse over a period of three months. In total 208 people were captured, half men and half women. In this paper we also describe a protocol for evaluating verification algorithms on the database. The database will be made available to the research community through http://www.ee.surrey.ac.uk/Research/VSSP/banca

Modulation of transforming growth factor beta signalling pathway genes by transforming growth factor beta in human osteoarthritic chondrocytes: involvement of Sp1 in both early and late response cells to transforming growth factor beta

International audienc

Detection of Levodopa Induced Dyskinesia in Parkinson's Disease Patients Based on Activity Classification

International audienceIn this paper, we present an activity classification-based algorithm for the automatic detection of Levodopa Induced Dyskinesia in Parkinson's Disease (PD) patients. Two PD patients experiencing motor fluctuations related to chronic Levodopa therapy performed a protocol of simple daily life activities on at least two different occasions. A Random Forest classifier was able to classify the performed activities by the patients with an overall accuracy of 86%. Based on the detected activity, a K Nearest Neighbor classifier detected the presence of dyskinesia with accuracy ranging from 75% to 88

Feature Selection for Activity Classification and Dyskinesia Detection in Parkinson's Disease Patients

International audienceRecent advances in wearable sensing technologies have favored the search for reliable and objective methods of estimating motor symptoms and complications of Parkinson's disease (PD). In this paper, we present a complete system of motor assessment composed of Shimmer3 inertial measurement modules aimed to classify a series of daily life activities performed by PD patients and detect the occurrence of Levodopa Induced Dyskinesia (LID). Feature selection methods are implemented on datasets collected from nine healthy individuals and 2 PD patients in order to determine the most relevant module positions with respect to activity classification and detection of LID. Classifying activities resulted in an overall accuracy of 88.05% in healthy individuals and 85.87% in PD patients, while detection of dyskinesia yielded 83.89%. The lowered performance is likely to be caused by the difficulty of classifying PD patients' activities due to presence of motor dysfunction

Activity Recognition Using Complex Network Analysis

International audienceIn this paper, we perform complex network analysis on a connectivity dataset retrieved from a monitoring system in order to classify simple daily activities. The monitoring system is composed of a set of wearable sensing modules positioned on the subject's body and the connectivity data consists of the correlation between each pair of modules. A number of network measures are then computed followed by the application of statistical significance and feature selection methods. These methods were implemented for the purpose of reducing the total number of modules in the monitoring system required to provide accurate activity classification. The obtained results show that an overall accuracy of 84.6% for activity classification is achieved, using a Random Forest (RF) classifier, and when considering a monitoring system composed of only two modules positioned at the Neck and Thigh of the subject's body

Activity Recognition Using Multiple Inertial Measurement Units

International audienceObjectives: This paper addresses the design of an ambulatory monitoring system based on a set of wearable, wireless inertial measurementunits able to perform activity recognition for healthy individuals and Parkinson’s disease patients, as well as analyze and assess the severity oflevodopa induced dyskinesia.Material and methods: The monitoring system is composed of six Shimmer3 modules placed at different positions of the individual’s body.Both healthy individuals and one patient performed a protocol of simple daily life activities while wearing the Shimmer3 modules. As an initialstep, validity of the monitoring system in identifying healthy individuals’ activities is assessed. Data corresponding to the activities was separatedand features in both time and frequency domains were extracted. Multiple factor analysis was used to evaluate and infer the relationships betweenthe different module positions. A method of feature selection was implemented to determine the most important features, positions and sensorsincluded in the different modules. The classification of activities was done using a KNN classifier.Results: Promising results were obtained in classifying the activities of healthy individuals, with a global accuracy of 77.6%. However, certainadaptation is required for the application on Parkinson’s disease patients.Conclusion: While activity recognition for healthy individuals using this system was successful, further evaluation of the contribution of eachmodule needs to be done in order to determine optimal module positions. To validate the obtained results on Parkinson’s disease patients, a largerstudy based on more patient acquisitions is envisioned

Surface electrocardiogram reconstruction from intracardiac electrograms using a dynamic time delay artificial neural network.

International audienceThis study proposes a method to facilitate the remote follow up of patients suffering from cardiac pathologies and treated with an implantable device, by synthesizing a 12-lead surface ECG from the intracardiac electrograms (EGM) recorded by the device. Two methods (direct and indirect), based on dynamic time-delay artificial neural networks (TDNNs) are proposed and compared with classical linear approaches. The direct method aims to estimate 12 different transfer functions between the EGM and each surface ECG signal. The indirect method is based on a preliminary orthogonalization phase of the available EGM and ECG signals, and the application of the TDNN between these orthogonalized signals, using only three transfer functions. These methods are evaluated on a dataset issued from 15 patients. Correlation coefficients calculated between the synthesized and the real ECG show that the proposed TDNN methods represent an efficient way to synthesize 12-lead ECG, from two or four EGM and perform better than the linear ones. We also evaluate the results as a function of the EGM configuration. Results are also supported by the comparison of extracted features and a qualitative analysis performed by a cardiologist