Tensor decomposition of multi-channel wearable sensors for Parkinson’s disease assessment

The cure for Parkinson’s disease is considered as one of the greatest challenges in chronic neurological disorder therapy, motivating efforts to provide information to guide therapy adjustments. This disease affects the patients day to day tasks which may vary from drinking water to a more complex task like folding laundry. Postural instability and rigidity of motion can be defined as some of the main symptoms for Parkinson’s disease. In order to better understand and analyze the patients suffering from this disease, the patients were asked to maintain records in a diary of times when they felt an unusual behavior while doing a particular task. Due to the difficulty in maintaining such records, each patient is asked to wear inertial sensors that monitor various movements of the patient. With the help of mathematical tools like Tensors, data fusion is carried out on the signal received from the sensors in order to determine the severity of Parkinson’s Disease. Using machine learning algorithms, it is possible to determine the accuracy with which the developed algorithm manages to determine the extent by which each patient is affected by the Parkinson’s disease

Uber-Claws : unsupervised pattern classification for multi-unit extracellular neuronal burst extraction

To further an understanding of how a neuronal population generates patterns of rhythmic activity, the temporal dynamics of the group of neurons must be formalized. Essential to this pursuit, is the ability to reliably detect and separate the classes of single-unit neuronal activity from multi-unit extracellular signals recorded in a single channel. This study proposes a unified approach to automatically detect and classify single-unit bursts, and to observe the precise onset and offset of burst activity. Existing approaches to the problem fundamentally depend on the statistics of spike waveform variability, both extrinsic and intrinsic to the neuron. In contrast, the proposed approach depends on statistics that characterize the burst variability. An unsupervised learning procedure is implemented using hierarchical clustering to derive a complete and natural description of the variability in terms of clusters of bursts that possess strong internal similarities. Redundant solution vectors are used to parameterize each cluster, and a fuzzy classification approach assigns each burst to a class. Accuracy of the technique is demonstrated on in vivo and in vitro recordings of the triphasic pyloric rhythm in stomatogastric ganglion of crab Cancer borealis. The results, evaluated against a widely used manual classification approach, show that the technique performs detection and classification with comparable accuracy and quantifiable certainty, and is robust to background activity and noise

An automatic feature extraction technique from the images of granular parakeratosis disease

The largest and most vital part of the human body is skin and any change in the features of skin is termed as a skin lesion. The paper considers granular parakeratosis lesion that is an epidermal reaction occurring due to the disorder of keratinization, and mainly seen in intertriginous areas. The manual inspection of the lesion features is a bit cumbersome due to which an automated system is proposed in this paper. The main goal is to determine the size and depth of granular parakeratosis lesions using the proposed ensemble algorithm, partition clustering and region properties method. As a flow of the proposed model, segmentation is done using U-net with binary cross entropy, features are extracted using partition clustering and region properties method, and classification is done using SVM 10-fold model. The proposed feature extraction method estimates the depth and absolute size of K lesions in each image by predicting the absolute height and width of the lesion in terms of pixel square. After extracting the features, classification is done, thereby obtaining an accuracy of 95%, sensitivity and specificity of 100%. The proposed model provides better performance compared to state-of-the-art models. The main application of this automated system is in dermatology field where some skin lesions have same features which makes the experts to diagnose the disease incorrectly. If the proposed system is incorporated, diagnosis can be done in an effective manner considering all the relevant features

Weighted Mahalanobis Distance for Hyper-Ellipsoidal Clustering

Cluster analysis is widely used in many applications, ranging from image and speech coding to pattern recognition. A new method that uses the weighted Mahalanobis distance (WMD) via the covariance matrix of the individual clusters as the basis for grouping is presented in this thesis. In this algorithm, the Mahalanobis distance is used as a measure of similarity between the samples in each cluster. This thesis discusses some difficulties associated with using the Mahalanobis distance in clustering. The proposed method provides solutions to these problems. The new algorithm is an approximation to the well-known expectation maximization (EM) procedure used to find the maximum likelihood estimates in a Gaussian mixture model. Unlike the EM procedure, WMD eliminates the requirement of having initial parameters such as the cluster means and variances as it starts from the raw data set. Properties of the new clustering method are presented by examining the clustering quality for codebooks designed with the proposed method and competing methods on a variety of data sets. The competing methods are the Linde-Buzo-Gray (LBG) algorithm and the Fuzzy c-means (FCM) algorithm, both of them use the Euclidean distance. The neural network for hyperellipsoidal clustering (HEC) that uses the Mahalnobis distance is also studied and compared to the WMD method and the other techniques as well. The new method provides better results than the competing methods. Thus, this method becomes another useful tool for use in clustering

Predictive maintenance: a novel framework for a data-driven, semi-supervised, and partially online prognostic health management application in industries

Prognostic Health Management (PHM) is a predictive maintenance strategy, which is based on Condition Monitoring (CM) data and aims to predict the future states of machinery. The existing literature reports the PHM at two levels: methodological and applicative. From the methodological point of view, there are many publications and standards of a PHM system design. From the applicative point of view, many papers address the improvement of techniques adopted for realizing PHM tasks without covering the whole process. In these cases, most applications rely on a large amount of historical data to train models for diagnostic and prognostic purposes. Industries, very often, are not able to obtain these data. Thus, the most adopted approaches, based on batch and off-line analysis, cannot be adopted. In this paper, we present a novel framework and architecture that support the initial application of PHM from the machinery producers’ perspective. The proposed framework is based on an edge-cloud infrastructure that allows performing streaming analysis at the edge to reduce the quantity of the data to store in permanent memory, to know the health status of the machinery at any point in time, and to discover novel and anomalous behaviors. The collection of the data from multiple machines into a cloud server allows training more accurate diagnostic and prognostic models using a higher amount of data, whose results will serve to predict the health status in real-time at the edge. The so-built PHM system would allow industries to monitor and supervise a machinery network placed in different locations and can thus bring several benefits to both machinery producers and users. After a brief literature review of signal processing, feature extraction, diagnostics, and prognostics, including incremental and semi-supervised approaches for anomaly and novelty detection applied to data streams, a case study is presented. It was conducted on data collected from a test rig and shows the potential of the proposed framework in terms of the ability to detect changes in the operating conditions and abrupt faults and storage memory saving. The outcomes of our work, as well as its major novel aspect, is the design of a framework for a PHM system based on specific requirements that directly originate from the industrial field, together with indications on which techniques can be adopted to achieve such goals

Bright lesion detection in retinal images

Master'sMASTER OF SCIENC

Automatic Assessment of Medication States of Patients with Parkinson’s Disease using Wearable Sensors

Motor fluctuations are a major focus of clinical managements in patients with mid-stage and advance Parkinson\u27s disease (PD). In this thesis, an automated algorithm is developed to identify those fluctuations (i.e., medication OFF and ON) using wearable sensors while PD patients are engaging in a variety of daily living activities. Four different methods are proposed which are supervised learning using Support Vector machine (SVM) with fuzzy classification, semi-supervised learning using k-means or using Self-organizing Tree Map Algorithm (SOTM) with fuzzy classification, and supervised classification using Long short-term memory (LSTM) as a deep learning method. A set of temporal and spectral features are extracted from the ambulatory signals of triaxial gyroscope sensors. After performing dimensionality reduction, the features are introduced to SVM or clustering methods using k-means or SOTM. Signals of the gyroscope sensors are passed directly to LSTM network. The developed methods were evaluated on two datasets that included recordings of 19 PD patients. Two scenarios were considered: general training/classification and patient-specific where the former trains and tests the algorithm using subject-based leave-one-out cross-validation, and the latter trains and tests the algorithm for each patient individually. In addition, for patient-specific scenarios, the number and placement of sensors is selected for each patient and this selection is based on the average change in UPDRS score between ON and OFF medication states and the presence of tremor for that patient. Overall, patient-specific algorithm resulted in a higher classification performance when it based on SVM with fuzzy classification (i.e., 80%, 82% and 78% for accuracy, sensitivity, and specificity of the OFF state, respectively). This algorithm was able to classify the medication states with high confidence (i.e., accuracy 94.86%, sensitivity 91.94% and specificity 96.83%) for the group of patients with a change of more than 15 in their UPDRS score between the OFF and ON medication states. This results are promising and thus this algorithm can be potentially used in routine clinical practice to improve the quality of this group of PD patients. In addition to these results, when only one sensor mounted on the ankle was used in the general training scenario, the algorithm based on LSTM performed better than SVM with 74.91%, 69.42%, and 80.55% for accuracy, sensitivity, and specificity, respectively. The promising results of LSTM show the potential outcome of developing deep learning methods in this field

Peak annotation and data analysis software tools for mass spectrometry imaging

La metabolòmica espacial és la disciplina que estudia les imatges de les distribucions de compostos químics de baix pes (metabòlits) a la superfície dels teixits biològics per revelar interaccions entre molècules. La imatge d'espectrometria de masses (MSI) és actualment la tècnica principal per obtenir informació d'imatges moleculars per a la metabolòmica espacial. MSI és una tecnologia d'imatges moleculars sense marcador que produeix espectres de masses que conserven les estructures espacials de les mostres de teixit. Això s'aconsegueix ionitzant petites porcions d'una mostra (un píxel) en un ràster definit a través de tota la seva superfície, cosa que dona com a resultat una col·lecció d'imatges de distribució de ions (registrades com a relacions massa-càrrega (m/z)) sobre la mostra. Aquesta tesi té com a objectius desenvolupar eines computacionals per a l'anotació de pics de MSI i el disseny de fluxos de treball per a l'anàlisi estadística i multivariant de dades MSI, inclosa la segmentació espacial. El treball realitzat en aquesta tesi es pot separar clarament en dues parts. En primer lloc, el desenvolupament d'una eina d'anotació de pics d'isòtops i adductes adequada per facilitar la identificació de compostos de rang de massa baix. Ara podem trobar fàcilment ions monoisotòpics als nostres conjunts de dades MSI gràcies al paquet de programari rMSIannotation. En segon lloc, el desenvolupament de eines de programari per a l’anàlisi de dades i la segmentació espacial basades en soft clustering per a dades MSI.La metabolómica espacial es la disciplina que estudia las imágenes de las distribuciones de compuestos químicos de bajo peso (metabolitos) en la superficie de los tejidos biológicos para revelar interacciones entre moléculas. Las imágenes de espectrometría de masas (MSI) es actualmente la principal técnica para obtener información de imágenes moleculares para la metabolómica espacial. MSI es una tecnología de imágenes moleculares sin marcador que produce espectros de masas que conservan las estructuras espaciales de las muestras de tejido. Esto se logra ionizando pequeñas porciones de una muestra (un píxel) en un ráster definido a través de toda su superficie, lo que da como resultado una colección de imágenes de distribución de iones (registradas como relaciones masa-carga (m/z)) sobre la muestra. Esta tesis tiene como objetivo desarrollar herramientas computacionales para la anotación de picos en MSI y en el diseño de flujos de trabajo para el análisis estadístico y multivariado de datos MSI, incluida la segmentación espacial. El trabajo realizado en esta tesis se puede separar claramente en dos partes. En primer lugar, el desarrollo de una herramienta de anotación de picos de isótopos y aductos adecuada para facilitar la identificación de compuestos de bajo rango de masa. Ahora podemos encontrar fácilmente iones monoisotópicos en nuestros conjuntos de datos MSI gracias al paquete de software rMSIannotation.Spatial metabolomics is the discipline that studies the images of the distributions of low weight chemical compounds (metabolites) on the surface of biological tissues to unveil interactions between molecules. Mass spectrometry imaging (MSI) is currently the principal technique to get molecular imaging information for spatial metabolomics. MSI is a labelfree molecular imaging technology that produces mass spectra preserving the spatial structures of tissue samples. This is achieved by ionizing small portions of a sample (a pixel) in a defined raster through all its surface, which results in a collection of ion distribution images (registered as mass-to-charge ratios (m/z)) over the sample. This thesis is aimed to develop computational tools for peak annotation in MSI and in the design of workflows for the statistical and multivariate analysis of MSI data, including spatial segmentation. The work carried out in this thesis can be clearly separated in two parts. Firstly, the development of an isotope and adduct peak annotation tool suited to facilitate the identification of the low mass range compounds. We can now easily find monoisotopic ions in our MSI datasets thanks to the rMSIannotation software package. Secondly, the development of software tools for data analysis and spatial segmentation based on soft clustering for MSI data. In this thesis, we have developed tools and methodologies to search for significant ions (rMSIKeyIon software package) and for the soft clustering of tissues (Fuzzy c-means algorithm)