Voice pathologies : the most comum features and classification tools

Speech pathologies are quite common in society, however the exams that exist are invasive, making them uncomfortable for patients and depending on the experience of the clinician who performs the assessment. Hence the need to develop non-invasive methods, which allow objective and efficient analysis. Taking this need into account in this work, the most promising list of features and classifiers was identified. As features, jitter, shimmer, HNR, LPC, PLP, and MFCC were identified and as classifiers CNN, RNN and LSTM. This study intends to develop a device to support medical decision, however this article already presents the system interface.info:eu-repo/semantics/publishedVersio

Applications of Artificial Intelligence in the Treatment of Behavioral and Mental Health Conditions

Introduction Artificial intelligence (AI) is the branch of science that studies and designs intelligent devices. For individuals unfamiliar with artificial intelligence, the concept of intelligent machines may bring up visions of attractive human-like computers or robots, like those described in science fiction. Others may consider AI technology to be mysterious machines limited to research facilities or a technical triumph that will come in the far future. Popular media accounts on the deployment of aerial drones, autonomous autos, or the potential dangers of developing super-intelligent technologies may have raised some broad awareness of the subject

Knowledge Base for MENTAL AI, in Data Science Context

Globally, 1 in 7 people has some kind of mental or substance use disorder that affects their thinking, feelings, and behaviour in everyday life. Mental well-being is vital for physical health. No Health Without Mental Health! People with mental health disorders can carry on with normal life if they get the proper treatment and support. Mental disorders are complex to diagnose due to similar and common symptoms for numerous types of mental illnesses, with a minute difference among them. In the era of big, the challenge stays to make sense of the huge amount of health research and care data. Computational methods hold significant potential to enable superior patient stratification approaches to the established clinical practice, which in turn are a pre-requirement for the development of effective personalized medicine approaches. Personalized psychiatry also plays a vital role in predicting mental disorders and improving diagnosis and optimized treatment. The use of intelligent systems is expected to grow in the medical field, and it will continue to pose abundant opportunities for solutions that can help save patients’ lives. As it does for many industries, Artificial Intelligence (AI) systems can support mental health specialists in their jobs. Machine learning algorithms can be applied to find different patterns in the most diverse sets of data. This work aims to examine and compare different machine learning classification methodologies to predict different mental disorders and, from that, extract knowledge that can help mental health professionals in their tasks. Our algorithms were trained using a total dataset of 3353 patients from different hospital units. These data are divided into three subsets of data, mainly by the characteristics that the pathologies present. We evaluate the performance of the algorithms using different metrics. Among the metrics applied, we chose the F1 score to compare and analyze the algorithms, as it is the most suitable for the data we have since they found themselves imbalances. In the first evaluation, we trained our models, using all the patient’s symptoms and diagnoses. In the second evaluation, we trained our models, using only the symptoms that were somehow related to each other and that influenced the other pathologies.Milhões de pessoas em todo o mundo são afetadas por transtornos mentais que influenciam o seu pensamento, sentimento ou comportamento. A saúde mental é um pré-requisito essencial para a saúde física e geral. Pessoas com transtornos mentais geralmente precisam de tratamento e apoio adequados para levar uma vida normal. A saúde mental é uma condição de bem-estar em que um indivíduo reconhece as suas habilidades, pode lidar com as tensões quotidianas da vida, trabalhar de forma produtiva e pode contribuir para a sua comunidade. A saúde mental afeta a vida das pessoas com transtorno mental, as suas profissões e a produtividade da comunidade. Boa saúde mental e resiliência são essenciais para a nossa saúde biológica, conexões humanas, educação, trabalho e alcançar o nosso potencial. A pandemia do covid-19 impactou significativamente a saúde mental das pessoas, em particular grupos como saúde e outros trabalhadores da linha de frente, estudantes, pessoas que moram sozinhas e pessoas com condições de saúde mental pré-existentes. Além disso, os serviços para transtornos mentais, neurológicos e por uso de substâncias foram significativamente interrompidos. Os transtornos mentais são classificados como de diagnóstico complexo devido à semelhança dos sintomas. Consultas regulares de saúde de pessoas com transtornos mentais graves podem impedir a morte prematura. A dificuldade dos especialistas em diagnosticar é geralmente causada pela semelhança dos sintomas nos transtornos mentais, como por exemplo, transtorno de bordeline e bipolar. Os algoritmos de aprendizado de máquina podem ser aplicados para encontrar diferentes padrões nos mais diversos conjuntos de dados. Este trabalho, visa examinar e comparar diferentes metodologias de classificação de aprendizado de máquina para prever difentes transtornos mentais e disso, extrair conhecimento que possam auxiliar os profissionais da area de saude mental, nas suas tarefas. Os nossos algoritmos, foram treinados utilizando um conjunto total de dados de 3353 pacientes, provenientes de diferentes unidades hospitalares. Esses dados, estão repartidos em três subconjuntos de dados, principalmente, pelas características que as patologias apresentam. Avaliamos o desempenho dos algoritmos usando diferentes métricas. Dentre as métricas aplicadas, escolhemos o F1 score para comparar e analisar os algoritmos, pois é o mais adequado para os dados que possuímos. Visto que eles se encontravam desequilíbrios. Na primeira avaliação, treinamos os nossos modelos, utilizando todos os sintomas e diagnósticos dos pacientes. Na segunda avaliação, treinamos os nossos modelos, utilizando apenas os sintomas que apresentavam alguma relação entre si e que influenciavam nas outras patologias

Computational Approaches to Explainable Artificial Intelligence:Advances in Theory, Applications and Trends

Deep Learning (DL), a groundbreaking branch of Machine Learning (ML), has emerged as a driving force in both theoretical and applied Artificial Intelligence (AI). DL algorithms, rooted in complex and non-linear artificial neural systems, excel at extracting high-level features from data. DL has demonstrated human-level performance in real-world tasks, including clinical diagnostics, and has unlocked solutions to previously intractable problems in virtual agent design, robotics, genomics, neuroimaging, computer vision, and industrial automation. In this paper, the most relevant advances from the last few years in Artificial Intelligence (AI) and several applications to neuroscience, neuroimaging, computer vision, and robotics are presented, reviewed and discussed. In this way, we summarize the state-of-the-art in AI methods, models and applications within a collection of works presented at the 9 International Conference on the Interplay between Natural and Artificial Computation (IWINAC). The works presented in this paper are excellent examples of new scientific discoveries made in laboratories that have successfully transitioned to real-life applications

Towards using Cough for Respiratory Disease Diagnosis by leveraging Artificial Intelligence: A Survey

Cough acoustics contain multitudes of vital information about pathomorphological alterations in the respiratory system. Reliable and accurate detection of cough events by investigating the underlying cough latent features and disease diagnosis can play an indispensable role in revitalizing the healthcare practices. The recent application of Artificial Intelligence (AI) and advances of ubiquitous computing for respiratory disease prediction has created an auspicious trend and myriad of future possibilities in the medical domain. In particular, there is an expeditiously emerging trend of Machine learning (ML) and Deep Learning (DL)-based diagnostic algorithms exploiting cough signatures. The enormous body of literature on cough-based AI algorithms demonstrate that these models can play a significant role for detecting the onset of a specific respiratory disease. However, it is pertinent to collect the information from all relevant studies in an exhaustive manner for the medical experts and AI scientists to analyze the decisive role of AI/ML. This survey offers a comprehensive overview of the cough data-driven ML/DL detection and preliminary diagnosis frameworks, along with a detailed list of significant features. We investigate the mechanism that causes cough and the latent cough features of the respiratory modalities. We also analyze the customized cough monitoring application, and their AI-powered recognition algorithms. Challenges and prospective future research directions to develop practical, robust, and ubiquitous solutions are also discussed in detail.Comment: 30 pages, 12 figures, 9 table

Neuro-critical multimodal Edge-AI monitoring algorithm and IoT system design and development

In recent years, with the continuous development of neurocritical medicine, the success rate of treatment of patients with traumatic brain injury (TBI) has continued to increase, and the prognosis has also improved. TBI patients' condition is usually very complicated, and after treatment, patients often need a more extended time to recover. The degree of recovery is also related to prognosis. However, as a young discipline, neurocritical medicine still has many shortcomings. Especially in most hospitals, the condition of Neuro-intensive Care Unit (NICU) is uneven, the equipment has limited functionality, and there is no unified data specification. Most of the instruments are cumbersome and expensive, and patients often need to pay high medical expenses. Recent years have seen a rapid development of big data and artificial intelligence (AI) technology, which are advancing the medical IoT field. However, further development and a wider range of applications of these technologies are needed to achieve widespread adoption. Based on the above premises, the main contributions of this thesis are the following. First, the design and development of a multi-modal brain monitoring system including 8-channel electroencephalography (EEG) signals, dual-channel NIRS signals, and intracranial pressure (ICP) signals acquisition. Furthermore, an integrated display platform for multi-modal physiological data to display and analysis signals in real-time was designed. This thesis also introduces the use of the Qt signal and slot event processing mechanism and multi-threaded to improve the real-time performance of data processing to a higher level. In addition, multi-modal electrophysiological data storage and processing was realized on cloud server. The system also includes a custom built Django cloud server which realizes real-time transmission between server and WeChat applet. Based on WebSocket protocol, the data transmission delay is less than 10ms. The analysis platform can be equipped with deep learning models to realize the monitoring of patients with epileptic seizures and assess the level of consciousness of Disorders of Consciousness (DOC) patients. This thesis combines the standard open-source data set CHB-MIT, a clinical data set provided by Huashan Hospital, and additional data collected by the system described in this thesis. These data sets are merged to build a deep learning network model and develop related applications for automatic disease diagnosis for smart medical IoT systems. It mainly includes the use of the clinical data to analyze the characteristics of the EEG signal of DOC patients and building a CNN model to evaluate the patient's level of consciousness automatically. Also, epilepsy is a common disease in neuro-intensive care. In this regard, this thesis also analyzes the differences of various deep learning model between the CHB-MIT data set and clinical data set for epilepsy monitoring, in order to select the most appropriate model for the system being designed and developed. Finally, this thesis also verifies the AI-assisted analysis model.. The results show that the accuracy of the CNN network model based on the evaluation of consciousness disorder on the clinical data set reaches 82%. The CNN+STFT network model based on epilepsy monitoring reaches 90% of the accuracy rate in clinical data. Also, the multi-modal brain monitoring system built is fully verified. The EEG signal collected by this system has a high signal-to-noise ratio, strong anti-interference ability, and is very stable. The built brain monitoring system performs well in real-time and stability. Keywords: TBI, Neurocritical care, Multi-modal, Consciousness Assessment, seizures detection, deep learning, CNN, IoT

Decoding Neural Signals with Computational Models: A Systematic Review of Invasive BMI

There are significant milestones in modern human's civilization in which mankind stepped into a different level of life with a new spectrum of possibilities and comfort. From fire-lighting technology and wheeled wagons to writing, electricity and the Internet, each one changed our lives dramatically. In this paper, we take a deep look into the invasive Brain Machine Interface (BMI), an ambitious and cutting-edge technology which has the potential to be another important milestone in human civilization. Not only beneficial for patients with severe medical conditions, the invasive BMI technology can significantly impact different technologies and almost every aspect of human's life. We review the biological and engineering concepts that underpin the implementation of BMI applications. There are various essential techniques that are necessary for making invasive BMI applications a reality. We review these through providing an analysis of (i) possible applications of invasive BMI technology, (ii) the methods and devices for detecting and decoding brain signals, as well as (iii) possible options for stimulating signals into human's brain. Finally, we discuss the challenges and opportunities of invasive BMI for further development in the area.Comment: 51 pages, 14 figures, review articl

A Voice Disease Detection Method Based on MFCCs and Shallow CNN

The incidence rate of voice diseases is increasing year by year. The use of software for remote diagnosis is a technical development trend and has important practical value. Among voice diseases, common diseases that cause hoarseness include spasmodic dysphonia, vocal cord paralysis, vocal nodule, and vocal cord polyp. This paper presents a voice disease detection method that can be applied in a wide range of clinical. We cooperated with Xiangya Hospital of Central South University to collect voice samples from sixty-one different patients. The Mel Frequency Cepstrum Coefficient (MFCC) parameters are extracted as input features to describe the voice in the form of data. An innovative model combining MFCC parameters and single convolution layer CNN is proposed for fast calculation and classification. The highest accuracy we achieved was 92%, it is fully ahead of the original research results and internationally advanced. And we use Advanced Voice Function Assessment Databases (AVFAD) to evaluate the generalization ability of the method we proposed, which achieved an accuracy rate of 98%. Experiments on clinical and standard datasets show that for the pathological detection of voice diseases, our method has greatly improved in accuracy and computational efficiency