A Connection Between Pattern Classification by Machine Learning and Statistical Inference With the General Linear Model

A connection between the general linear model (GLM) with frequentist statistical testing and machine learning (MLE) inference is derived and illustrated. Initially, the estimation of GLM parameters is expressed as a Linear Regression Model (LRM) of an indicator matrix; that is, in terms of the inverse problem of regressing the observations. Both approaches, i.e. GLM and LRM, apply to different domains, the observation and the label domains, and are linked by a normalization value in the least-squares solution. Subsequently, we derive a more refined predictive statistical test: the linear Support Vector Machine (SVM), that maximizes the class margin of separation within a permutation analysis. This MLE-based inference employs a residual score and associated upper bound to compute a better estimation of the actual (real) error. Experimental results demonstrate how parameter estimations derived from each model result in different classification performance in the equivalent inverse problem. Moreover, using real data, the MLE-based inference including model-free estimators demonstrates an efficient trade-off between type I errors and statistical power.Ministerio de Ciencia e Innovacion (Espana)/FEDER RTI2018-098913B100Junta de AndaluciaEuropean Commission CV20-45250 A-TIC-080-UGR18 P20-00525National Health and Medical Research Council (NHMRC) of Australia 18/0490

Real Time QRS Detection Based on M-ary Likelihood Ratio Test on the DFT Coefficients

This paper shows an adaptive statistical test for QRS detection of electrocardiography (ECG) signals. The method is based on a M-ary generalized likelihood ratio test (LRT) defined over a multiple observation window in the Fourier domain. The motivations for proposing another detection algorithm based on maximum a posteriori (MAP) estimation are found in the high complexity of the signal model proposed in previous approaches which i) makes them computationally unfeasible or not intended for real time applications such as intensive care monitoring and (ii) in which the parameter selection conditions the overall performance. In this sense, we propose an alternative model based on the independent Gaussian properties of the Discrete Fourier Transform (DFT) coefficients, which allows to define a simplified MAP probability function. In addition, the proposed approach defines an adaptive MAP statistical test in which a global hypothesis is defined on particular hypotheses of the multiple observation window. In this sense, the observation interval is modeled as a discontinuous transmission discrete-time stochastic process avoiding the inclusion of parameters that constraint the morphology of the QRS complexes.This work has received research funding from the Spanish government (www.micinn.es) under project TEC2012 34306 (DiagnoSIS, Diagnosis by means of Statistical Intelligent Systems, 70K€) and projects P09-TIC-4530 (300K€) and P11-TIC-7103 (156K€) from the Andalusian government (http://www.juntadeandalucia.es/organismo​s/economiainnovacioncienciayempleo.html)

An Overview on Artificial Intelligence Techniques for Diagnosis of Schizophrenia Based on Magnetic Resonance Imaging Modalities: Methods, Challenges, and Future Works

Schizophrenia (SZ) is a mental disorder that typically emerges in late adolescence or early adulthood. It reduces the life expectancy of patients by 15 years. Abnormal behavior, perception of emotions, social relationships, and reality perception are among its most significant symptoms. Past studies have revealed the temporal and anterior lobes of hippocampus regions of brain get affected by SZ. Also, increased volume of cerebrospinal fluid (CSF) and decreased volume of white and gray matter can be observed due to this disease. The magnetic resonance imaging (MRI) is the popular neuroimaging technique used to explore structural/functional brain abnormalities in SZ disorder owing to its high spatial resolution. Various artificial intelligence (AI) techniques have been employed with advanced image/signal processing methods to obtain accurate diagnosis of SZ. This paper presents a comprehensive overview of studies conducted on automated diagnosis of SZ using MRI modalities. Main findings, various challenges, and future works in developing the automated SZ detection are described in this paper

Spatial analysis of failure sites in large area MIM capacitors using wavelets

The spatial distribution of failure sites in large area (104–105 μm2) metal-insulator-metal (MIM) capacitors with high-K dielectric (HfO2) is investigated using angular wavelets. The failure sites are the consequence of constant or ramped electrical stress applied on the capacitors. Because of the important local thermal effects that take place during stress, the failure sites become visible as a point pattern on the top metal electrode. In case of less damaged devices, the results obtained with the wavelet variance method are consistent with an isotropic distribution of breakdown spots as expected for a Poisson point process (complete spatial randomness). On the contrary, for severely damaged devices, the method shows signs of preferred directions of degradation related to the voltage probe location. In this case, the anisotropy is confirmed by alternative spatial statistics methods such as the angular point-to-event distribution and the pair correlation function

Использование преобразования Карунена-Лоэва для анализа МРТ-изображений человека

Робота присвячена використанню перетворення Карунена-Лоева для аналізу МРТ-зображень. Розглянута специфіка його використання для багатовимірних зображень. Показані можливості використання перетворення Карунена-Лоева для отримання ознак МРТ- зображень. Встановлено, що найбільший внесок у представлення зображення мають декілька перших базисних функцій. Надано рекомендації щодо одного з можливих методів отримання ознак МРТ-зображень для класифікації та діагностики хвороб, що впливають на будову мозку людини.The aim of this work is to develop new method for feature extraction from MRI images based on Karhunen-Loeve transform. Application of Karhunen-Loeve transform for multidimensional MRI images feature extraction is presented. The main result of this work is that the first basis function has the major contribution into decomposition of MRI picture, the next basis functions contributions are decreasing with their number. Recommendations for feature extraction using proposed approach for diagnosis and classification of brain diseases are given.Работа посвящена применению преобразования Каренена-Лоэва для анализа МРТ-изображений. Рассмотрена специфика его применения для многомерных изображений. Показаны возможности преобразования Карунена-Лоэва для получения признаков МРТ-изображений. Установлено, что наибольший вклад имеют первые базисные функции. Приведены рекомендации касательно возможных методов получения признаков для классификации и диагностики заболеваний, которые влияют на строение мозга человека

Failure analysis of large area pt/hfo 2 /pt capacitors using multilayer perceptrons

In this work, we investigated the spatial distribution of failure sites in large area Pt/HfO 2 /Pt capacitors using simple neural networks as classifiers. When an oxide breakdown (BD) occurs due to severe electrical stress, a mark shows up in the top metal electrode at the location where the failure event took place. The mark is the result of a microexplosion occurring inside the dielectric film. Large area devices need to be studied because the number of generated spots must be the required for statistical analysis. The obtained results using multilayer perceptrons with different number of neurons and hidden layers indicate that the largest breakdown spots tend to concentrate towards the center of the device. This observation is consistent with previous exploratory analysis carried out using spatial statistics techniques. This exercise shows the suitability of multilayer perceptrons for investigating the distribution of failure sites or defects on a given surface