ALZUMERIC: a decision support system for diagnosis and monitoring of cognitive impairment

ALZUMERIC: Un sistema de apoyo a la decisión para el diagnóstico y la monitorización del deterioro cognitivo. – La Internet de las cosas o de las ciudades inteligentes se está convirtiendo en una realidad. Cada vez más dispositivos están interconectados y, para hacer frente a esta nueva situación, las velocidades de procesamiento de datos se incrementan. Los dispositivos inteligentes, como las tabletas y los teléfonos, son accesibles para una gran parte de la sociedad en los países desarrollados, y las mejoras en las conexiones a Internet para el intercambio de datos hacen posible manejar grandes volúmenes de información en menos tiempo. Esta nueva realidad ha abierto la posibilidad de desarrollar arquitecturas cliente-servidor centradas en el diagnóstico clínico en tiempo real y a un coste muy bajo. Este trabajo ilustra la concepción del sistema ALZUMERIC orientado al diagnóstico de la enfermedad de Alzheimer. Es una plataforma a partir de la cual el médico especialista puede tomar muestras de voz a través de métodos no invasivos a pacientes con y sin deterioro cognitivo leve (MCI), y en la cual se parametriza la señal de entrada automáticamente para posteriormente avanzar una propuesta de diagnóstico. El MCI es un tipo de deterioro neurológico que produce una pérdida cognitiva no lo suficientemente grave como para interferir en la vida cotidiana. El presente estudio está enfocado hacia la descripción de las patologías del habla con respecto a los siguientes perfiles: fonación, articulación, calidad del habla, análisis de la respuesta emocional, percepción del lenguaje y dinámica de sistemas complejos. También se consideran aspectos relativos a la privacidad, la confidencialidad y la seguridad de la información frente a las posibles amenazas que pudiera sufrir el sistema, por lo que este primer prototipo de servicios ofrecidos por ALZUMERIC se ha dirigido a un número predeterminado de médicos especialistas. ----------ABSTRACT---------- Internet of things and smart cities are becoming a reality. Nowadays, more and more devices are interconnected and in order to deal with this new situation, data processing speeds are increasing to keep the pace. Smart devices like tablets and smartphones are accessible to a wide part of society in developed countries, and Internet connections for data exchange make it possible to handle large volumes of information in less time. This new reality has opened up the possibility of developing client-server architectures focused on clinical diagnosis in real time and at a very low cost. This paper illustrates the design and implementation of the ALZUMERIC system that is oriented to the diagnosis of Alzheimer’s disease (AD). It is a platform where the medical specialist can gather voice samples through non-invasive methods from patients with and without mild cognitive impairment (MCI), and the system automatically parameterizes the input signal to make a diagnose proposal. Although this type of impairment produces a cognitive loss, it is not severe enough to interfere with daily life. The present approach is based on the description of speech pathologies with regard to the following profiles: phonation, articulation, speech quality, analysis of the emotional response, language perception, and complex dynamics of the system. Privacy, confidentiality and information security have also been taken into consideration, as well as possible threats that the system could suffer, so this first prototype of services offered by ALZUMERIC has been targeted to a predetermined number of medical specialists

EMD-based data augmentation method applied to handwriting data for the diagnosis of Essential Tremor using LSTM networks.

The increasing capacity of today's technology represents great advances in diagnosing diseases using standard procedures supported by computer science. Deep learning techniques are able to extract the characteristics of temporal signals to study their patterns and diagnose diseases such as essential tremor. However, these techniques require a large amount of data to train the neural network and achieve good results, and the more data the network has, the more accurate the final model implemented. In this work we propose the use of a data augmentation technique to improve the accuracy of a Long short-term memory system in the diagnosis of essential tremor. For this purpose, the multivariate Empirical Mode Decomposition method will be used to decompose the original temporal signals collected from control subjects and patients with essential tremor. The time series obtained from the decomposition, covering different frequency ranges, will be randomly shuffled and combined to generate new artificial samples for each group. Then, both the generated artificial samples and part of the real samples will be used to train the LSTM network, and the remaining original samples will be used to test the model. The experimental results demonstrate the capability of the proposed method, which is compared to a set of 10 different data augmentation methods, and in all cases outperforms all other methods. In the best case, the proposed method increases the accuracy of the classifier from 83.20% to almost 93% when artificial samples are generated, which is a promising result when only small databases are available