Use of neural networks in classification of heart diseases

Práce je zaměřená na návrh a využití umělých neuronových sítí jako klasifikátoru srdečních onemocnění z EKG signálu se zaměřením na ischemickou chorobu srdeční. Změny ST-T komplexů jsou významným ukazatelem ischemie v EKG signálu. Různe typy ischemické choroby srdeční se projevují zejména elevací nebo depresí ST segmentů a změnami T vlny v analyzovaném signálu. První část této práce obsahuje teoretický úvod popisující jednotlivé typy ischemické choroby srdeční a na ně vázané změny EKG signálu. Druhá část je věnována popisu předzpracování EKG signálu ke klasifikaci neuronovými sítěmi. Obsahuje filtraci EKG, QRS detekci, detekci ST-T komplexů a popis analýzy hlavních komponent a její využítí k popisu analyzovaného signálu. V poslední části práce je popsán návrh a způsob detekce možných příznaků ischemické choroby srdeční v EKG pomocí dvou typů umělých neuronových sítí: Back-propagation, SOM. Dále jsou zde uvedeny výsledky navržených algoritmů. Přílohy obsahují popis navrženého programu pro klasifikaci srdečních onemocnění, popis jednotlivých jeho funkcí, dále zde najdeme podrobný popis všech použitých neuronových sítí a tabulky obsahující detailní výsledky klasifikace EKG signálu. Samotný program byl vytvořen v programovacím prostředí Matlab R2007b.This thesis discusses the design and the utilization of the artificial neural networks as ECG classifiers and the detectors of heart diseases in ECG signal especially myocardial ischaemia. The changes of ST-T complexes are the important indicator of ischaemia in ECG signal. Different types of ischaemia are expressed particularly by depression or elevation of ST segments and changes of T wave. The first part of this thesis is orientated towards the theoretical knowledges and describes changes in the ECG signal rising close to different types of ischaemia. The second part deals with to the ECG signal pre-processing for the classification by neural network, filtration, QRS detection, ST-T detection, principal component analysis. In the last part there is described design of detector of myocardial ischaemia based on artificial neural networks with utilisation of two types of neural networks back – propagation and self-organizing map and the results of used algorithms. The appendix contains detailed description of each neural networks, description of the programme for classification of ECG signals by ANN and description of functions of programme. The programme was developed in Matlab R2007b.

Architectures and Algorithms for Intrinsic Computation with Memristive Devices

Neuromorphic engineering is the research field dedicated to the study and design of brain-inspired hardware and software tools. Recent advances in emerging nanoelectronics promote the implementation of synaptic connections based on memristive devices. Their non-volatile modifiable conductance was shown to exhibit the synaptic properties often used in connecting and training neural layers. With their nanoscale size and non-volatile memory property, they promise a next step in designing more area and energy efficient neuromorphic hardware. My research deals with the challenges of harnessing memristive device properties that go beyond the behaviors utilized for synaptic weight storage. Based on devices that exhibit non-linear state changes and volatility, I present novel architectures and algorithms that can harness such features for computation. The crossbar architecture is a dense array of memristive devices placed in-between horizontal and vertical nanowires. The regularity of this structure does not inherently provide the means for nonlinear computation of applied input signals. Introducing a modulation scheme that relies on nonlinear memristive device properties, heterogeneous state patterns of applied spatiotemporal input data can be created within the crossbar. In this setup, the untrained and dynamically changing states of the memristive devices offer a useful platform for information processing. Based on the MNIST data set I\u27ll demonstrate how the temporal aspect of memristive state volatility can be utilized to reduce system size and training complexity for high dimensional input data. With 3 times less neurons and 15 times less synapses to train as compared to other memristor-based implementations, I achieve comparable classification rates of up to 93%. Exploiting dynamic state changes rather than precisely tuned stable states, this approach can tolerate device variation up to 6 times higher than reported levels. Random assemblies of memristive networks are analyzed as a substrate for intrinsic computation in connection with reservoir computing; a computational framework that harnesses observations of inherent dynamics within complex networks. Architectural and device level considerations lead to new levels of task complexity, which random memristive networks are now able to solve. A hierarchical design composed of independent random networks benefits from a diverse set of topologies and achieves prediction errors (NRMSE) on the time-series prediction task NARMA-10 as low as 0.15 as compared to 0.35 for an echo state network. Physically plausible network modeling is performed to investigate the relationship between network dynamics and energy consumption. Generally, increased network activity comes at the cost of exponentially increasing energy consumption due to nonlinear voltage-current characteristics of memristive devices. A trade-off, that allows linear scaling of energy consumption, is provided by the hierarchical approach. Rather than designing individual memristive networks with high switching activity, a collection of less dynamic, but independent networks can provide more diverse network activity per unit of energy. My research extends the possibilities of including emerging nanoelectronics into neuromorphic hardware. It establishes memristive devices beyond storage and motivates future research to further embrace memristive device properties that can be linked to different synaptic functions. Pursuing to exploit the functional diversity of memristive devices will lead to novel architectures and algorithms that study rather than dictate the behavior of such devices, with the benefit of creating robust and efficient neuromorphic hardware

Features extraction for low-power face verification

Mobile communication devices now available on the market, such as so-called smartphones, are far more advanced than the first cellular phones that became very popular one decade ago. In addition to their historical purpose, namely enabling wireless vocal communications to be established nearly everywhere, they now provide most of the functionalities offered by computers. As such, they hold an ever-increasing amount of personal information and confidential data. However, the authentication method employed to prevent unauthorized access to the device is still based on the same PIN code mechanism, which is often set to an easy-to-guess combination of digits, or even altogether disabled. Stronger security can be achieved by resorting to biometrics, which verifies the identity of a person based on intrinsic physical or behavioral characteristics. Since most mobile phones are now equipped with an image sensor to provide digital camera functionality, biometric authentication based on the face modality is very interesting as it does not require a dedicated sensor, unlike e.g. fingerprint verification. Its perceived intrusiveness is furthermore very low, and it is generally well accepted by users. The deployment of face verification on mobile devices however requires overcoming two major challenges, which are the main issues addressed in this PhD thesis. Firstly, images acquired by a handheld device in an uncontrolled environment exhibit strong variations in illumination conditions. The extracted features on which biometric identification is based must therefore be robust to such perturbations. Secondly, the amount of energy available on battery-powered mobile devices is tightly constrained, calling for algorithms with low computational complexity, and for highly optimized implementations. So as to reduce the dependency on the illumination conditions, a low-complexity normalization technique for features extraction based on mathematical morphology is introduced in this thesis, and evaluated in conjunction with the Elastic Graph Matching (EGM) algorithm. Robustness to other perturbations, such as occlusions or geometric transformations, is also assessed and several improvements are proposed. In order to minimize the power consumption, the hardware architecture of a coprocessor dedicated to features extraction is proposed and described in VHDL. This component is designed to be integrated into a System-on-Chip (SoC) implementing the complete face verification process, including image acquisition, thereby enabling biometric face authentication to be performed entirely on the mobile device. Comparison of the proposed solution with state-of-the-art academic results and recently disclosed commercial products shows that the chosen approach is indeed much more efficient energy-wise

Cell Nuclear Morphology Analysis Using 3D Shape Modeling, Machine Learning and Visual Analytics

Quantitative analysis of morphological changes in a cell nucleus is important for the understanding of nuclear architecture and its relationship with cell differentiation, development, proliferation, and disease. Changes in the nuclear form are associated with reorganization of chromatin architecture related to altered functional properties such as gene regulation and expression. Understanding these processes through quantitative analysis of morphological changes is important not only for investigating nuclear organization, but also has clinical implications, for example, in detection and treatment of pathological conditions such as cancer. While efforts have been made to characterize nuclear shapes in two or pseudo-three dimensions, several studies have demonstrated that three dimensional (3D) representations provide better nuclear shape description, in part due to the high variability of nuclear morphologies. 3D shape descriptors that permit robust morphological analysis and facilitate human interpretation are still under active investigation. A few methods have been proposed to classify nuclear morphologies in 3D, however, there is a lack of publicly available 3D data for the evaluation and comparison of such algorithms. There is a compelling need for robust 3D nuclear morphometric techniques to carry out population-wide analyses. In this work, we address a number of these existing limitations. First, we present a largest publicly available, to-date, 3D microscopy imaging dataset for cell nuclear morphology analysis and classification. We provide a detailed description of the image analysis protocol, from segmentation to baseline evaluation of a number of popular classification algorithms using 2D and 3D voxel-based morphometric measures. We proposed a specific cross-validation scheme that accounts for possible batch effects in data. Second, we propose a new technique that combines mathematical modeling, machine learning, and interpretation of morphometric characteristics of cell nuclei and nucleoli in 3D. Employing robust and smooth surface reconstruction methods to accurately approximate 3D object boundary enables the establishment of homologies between different biological shapes. Then, we compute geometric morphological measures characterizing the form of cell nuclei and nucleoli. We combine these methods into a highly parallel computational pipeline workflow for automated morphological analysis of thousands of nuclei and nucleoli in 3D. We also describe the use of visual analytics and deep learning techniques for the analysis of nuclear morphology data. Third, we evaluate proposed methods for 3D surface morphometric analysis of our data. We improved the performance of morphological classification between epithelial vs mesenchymal human prostate cancer cells compared to the previously reported results due to the more accurate shape representation and the use of combined nuclear and nucleolar morphometry. We confirmed previously reported relevant morphological characteristics, and also reported new features that can provide insight in the underlying biological mechanisms of pathology of prostate cancer. We also assessed nuclear morphology changes associated with chromatin remodeling in drug-induced cellular reprogramming. We computed temporal trajectories reflecting morphological differences in astroglial cell sub-populations administered with 2 different treatments vs controls. We described specific changes in nuclear morphology that are characteristic of chromatin re-organization under each treatment, which previously has been only tentatively hypothesized in literature. Our approach demonstrated high classification performance on each of 3 different cell lines and reported the most salient morphometric characteristics. We conclude with the discussion of the potential impact of method development in nuclear morphology analysis on clinical decision-making and fundamental investigation of 3D nuclear architecture. We consider some open problems and future trends in this field.PHDBioinformaticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/147598/1/akalinin_1.pd

Neural Network Methods for Radiation Detectors and Imaging

Recent advances in image data processing through machine learning and especially deep neural networks (DNNs) allow for new optimization and performance-enhancement schemes for radiation detectors and imaging hardware through data-endowed artificial intelligence. We give an overview of data generation at photon sources, deep learning-based methods for image processing tasks, and hardware solutions for deep learning acceleration. Most existing deep learning approaches are trained offline, typically using large amounts of computational resources. However, once trained, DNNs can achieve fast inference speeds and can be deployed to edge devices. A new trend is edge computing with less energy consumption (hundreds of watts or less) and real-time analysis potential. While popularly used for edge computing, electronic-based hardware accelerators ranging from general purpose processors such as central processing units (CPUs) to application-specific integrated circuits (ASICs) are constantly reaching performance limits in latency, energy consumption, and other physical constraints. These limits give rise to next-generation analog neuromorhpic hardware platforms, such as optical neural networks (ONNs), for high parallel, low latency, and low energy computing to boost deep learning acceleration