Rough Set Based Approach for IMT Automatic Estimation

Carotid artery (CA) intima-media thickness (IMT) is commonly deemed as one of the risk marker for cardiovascular diseases. The automatic estimation of the IMT on ultrasound images is based on the correct identification of the lumen-intima (LI) and media-adventitia (MA) interfaces. This task is complicated by noise, vessel morphology and pathology of the carotid artery. In a previous study we applied four non-linear methods for feature selection on a set of variables extracted from ultrasound carotid images. The main aim was to select those parameters containing the highest amount of information useful to classify the image pixels in the carotid regions they belong to. In this study we present a pixel classifier based on the selected features. Once the pixels classification was correctly performed, the IMT was evaluated and compared with two sets of manual-traced profiles. The results showed that the automatic IMTs are not statistically different from the manual one

Segmentation and classification of gait cycles

Gait abnormalities can be studied by means of instrumented gait analysis. Foot-switches are useful to study the foot-floor contact and for timing the gait phases in many gait disorders, provided that a reliable foot-switch signal may be collected. Considering long walks allows reducing the intra-subject variability, but requires automatic and user-independent methods to analyze a large number of gait cycles. The aim of this work is to describe and validate an algorithm for the segmentation of the foot-switch signal and the classification of the gait cycles. The performance of the algorithm was assessed comparing its results against the manual segmentation and classification performed by a gait analysis expert on the same signal. The performance was found to be equal to 100% for healthy subjects and over 98% for pathological subjects. The algorithm allows determining the atypical cycles (cycles that do not match the standard sequence of gait phases) for many different kinds of pathological gait, since it is not based on pathology-specific template

Using Landscape Pattern Metrics to Characterize Ecoregions

Ecological regions, or ecoregions, are areas that exhibit “relative homogeneity in ecosystems”. The principal objective of this research was to determine if and how landscape structure (quantified by landscape pattern metrics) may be related to ecoregions defined using Omernik’s approach to ecoregionalization. Nine key landscape pattern metrics (number or LULC classes and the proportion of each class, number of patches, mean patch size and area-weighted fractal dimension, perimeter-area fractal dimension, contagion, mean Euclidean nearest neighbor distance and interspersion and juxtaposition index) where used to asses landscape structure in a sample of 26 Omernik Level III ecoregions located in the central United States. The results indicated that the behavior of most of the metrics (such as Number of Patches, Mean Patch Size, Mean Euclidean Nearest Neighbor, and Contagion) could only be explained when they were considered in context with the other metrics. There were significant correlations among several of the metrics used, reasserting the redundancy of information provided by some of these indices. Adviser: James Merchan

Key Aspects to Teach Medical Device Software Certification

Certification of Medical Device Software (MDS) according to the EU Medical Device Regulation 2017/745 requires demonstrating safety and effectiveness. Thus, the syllabus of a course on MDS development must provide tools for addressing these issues. To assure safety, risk analysis has to be performed using a four-step procedure. Effectiveness could be demonstrated by literature systematic review combined with meta-analysis, to compare the MDS performances with those of similar tools

A Wearable Multi-Sensor Array Enables the Recording of Heart Sounds in Homecare

The home monitoring of patients affected by chronic heart failure (CHF) is of key importance in preventing acute episodes. Nevertheless, no wearable technological solution exists to date. A possibility could be offered by Cardiac Time Intervals extracted from simultaneous recordings of electrocardiographic (ECG) and phonocardiographic (PCG) signals. Nevertheless, the recording of a good-quality PCG signal requires accurate positioning of the stethoscope over the chest, which is unfeasible for a naïve user as the patient. In this work, we propose a solution based on multi-source PCG. We designed a flexible multi-sensor array to enable the recording of heart sounds by inexperienced users. The multi-sensor array is based on a flexible Printed Circuit Board mounting 48 microphones with a high spatial resolution, three electrodes to record an ECG and a Magneto-Inertial Measurement Unit. We validated the usability over a sample population of 42 inexperienced volunteers and found that all subjects could record signals of good to excellent quality. Moreover, we found that the multi-sensor array is suitable for use on a wide population of at-risk patients regardless of their body characteristics. Based on the promising findings of this study, we believe that the described device could enable the home monitoring of CHF patients soon

Comparison of different similarity measures in hierarchical clustering

The management of datasets containing heterogeneous types of data is a crucial point in the context of precision medicine, where genetic, environmental, and life-style information of each individual has to be analyzed simultaneously. Clustering represents a powerful method, used in data mining, for extracting new useful knowledge from unlabeled datasets. Clustering methods are essentially distance-based, since they measure the similarity (or the distance) between two elements or one element and the cluster centroid. However, the selection of the distance metric is not a trivial task: it could influence the clustering results and, thus, the extracted information. In this study we analyze the impact of four similarity measures (Manhattan or L1 distance, Euclidean or L2 distance, Chebyshev or L∞ distance and Gower distance) on the clustering results obtained for datasets containing different types of variables. We applied hierarchical clustering combined with an automatic cut point selection method to six datasets publicly available on the UCI Repository. Four different clusterizations were obtained for every dataset (one for each distance) and were analyzed in terms of number of clusters, number of elements in each cluster, and cluster centroids. Our results showed that changing the distance metric produces substantial modifications in the obtained clusters. This behavior is particularly evident for datasets containing heterogeneous variables. Thus, the choice of the distance measure should not be done a-priori but evaluated according to the set of data to be analyzed and the task to be accomplished

Gait Impairment Score: A Fuzzy Logic-Based Index for Gait Assessment

The objective assessment of subject’s gait impairment is a complicated task. For this reason, several indices have been proposed in literature for achieving this purpose, taking into account different gait parameters. All of them were essentially based on the identification of “normality ranges” for the gait parameters of interest or of a “normal population”. However, it is not trivial to obtain a unique definition of “normal gait”. In this study we proposed the Gait Impairment Score (GIS) that is a novel index to evaluate the subject’s gait impairment level based on fuzzy logic. This index was obtained combining two Fuzzy Inference Systems (FISs), based on gait phases (GP) and knee joint kinematics (JK) parameters, respectively. Eight GP parameters and ten JK parameters were extracted from the basographic and knee kinematic signals, respectively. Those signals were acquired, for each subject’s lower limb, using a set of wearable sensors connected to a commercial system for gait analysis. Each parameter was used as input variable of the corresponding FIS. The output variable of the two FISs represented the impairment level from the GP and JK point of view. GP-FIS and JK-FIS were applied separately to both right and left leg parameters. Then, the fuzzy outputs of the two FISs were aggregated, independently for each side, to obtain the leg fuzzy output. The final subject’s GIS was obtained aggregating the fuzzy outputs of the two legs. The score was validated against two gait analysis experts on a population of 12 subjects both with and without walking pathologies. The Analytic Hierarchy Process (AHP) pairwise comparisons were used to obtain the subjects’ ranking from the two experts. The same population was scored using the GIS and ordered in ascending order. Comparing the three rankings (from our system and from the two human experts) it emerged that our system gives the same “judgment” of a human expert

A machine learning approach for stride speed estimation based on a head-mounted IMU

Walking speed in real-life conditions is typically estimated through wearable inertial sensors mounted on waist, lower limbs, or wrists. Very recently, head-mounted inertial sensors are emerging for gait assessment. The present study explores the feasibility of measuring the stride speed with a head-mounted inertial sensor in both laboratory and real-world settings. The developed algorithm exploits a Temporal Convolutional Network for the detection of the gait events and a Gaussian Process Regression for the stride speed estimation. The experimental evaluation was carried out on healthy young participants during both standardised indoor and real-world walking trials. For indoor trials, errors were smaller than previous studies (0.05 m/s). As expected, errors increased at lower speed regimes due to a reduced signals amplitude. During 2.5-hours real-world evaluation, errors were slightly larger but acceptable (0.1 m/s). Reported results are encouraging and show the feasibility of estimating gait speed with a single head-worn inertial sensor

Surface Electromyography Applied to Gait Analysis: How to Improve Its Impact in Clinics?

Surface electromyography (sEMG) is the main non-invasive tool used to record the electrical activity of muscles during dynamic tasks. In clinical gait analysis, a number of techniques have been developed to obtain and interpret the muscle activation patterns of patients showing altered locomotion. However, the body of knowledge described in these studies is very seldom translated into routine clinical practice. The aim of this work is to analyze critically the key factors limiting the extensive use of these powerful techniques among clinicians. A thorough understanding of these limiting factors will provide an important opportunity to overcome limitations through specific actions, and advance toward an evidence-based approach to rehabilitation based on objective findings and measurements