Aproximação inteligente baseada no design de sistemas integrados para aplicativos de telemedicina

Introduction: The present research was conducted at Sikha ‘O’ Anusandan (deemed to be University) in 2017. Telemedicine application in the field of medicine creates a new age. Accordingly, it requires technology to be compatible. Easy access and fast processing are the major focuses in different applications. In this paper, an approach has been considered to diagnose heart diseases. Methods: The model is designed using fuzzy logic in which the rule-based principle is applied to satisfy the objective. The model is developed keeping a view over the multi-agent system. The diagnosis of the patient is performed using Fuzzy Inference System (fis). Results: The pathological test results will help to form the rules of the model and can work for the diagnosis in a convenient way. Furthermore, the results of detection are communicated through Internet and sms for monitoring and post care purpose of supporting IoT application. Conclusion: The simulated result shows its performance can be helpful to physicians as well as patients from remote places. Originality: The model is proposed for disease detection and monitoring patients on remote locations. Also, distributed agents are proposed to act on a common platform using Internet for the benefit of society. This will save time for physicians and travelling costs for the patient. Limitations: The research results can be practically implemented in new medical equipment for hospitals with earlier equipment.Introducción: la presente investigación se realizó en Sikha ‘O’ Anusandan (la cual se considera una universidad) en 2017. La aplicación de la telemedicina en el campo de la medicina genera una nueva era. En consecuencia, requiere que la tecnología sea compatible. Las características principales que se demandan de dichas aplicaciones son el fácil acceso y el procesamiento rápido. Este estudio se aproxima a la telemedicina para el caso de diagnosis de enfermedades cardíacas. Métodos: el modelo se diseña mediante una lógica difusa en la que se aplica el principio basado en reglas para satisfacer el objetivo. El modelo se desarrolla teniendo en cuenta el sistema de agentes múltiples. El diagnóstico del paciente se realiza con el sistema de inferencia difusa (fis). Resultados: los resultados de la prueba patológica ayudarán a formar las reglas del modelo y pueden aportar para el diagnóstico de manera conveniente. Además, los resultados de la detección se comunican a través de Internet y sms para fines de seguimiento y cuidado posterior de la aplicación IoT. Conclusión: el resultado simulado muestra que su desempeño puede ser útil tanto para médicos como para pacientes en lugares remotos. Originalidad: se propone el modelo para detectar enfermedades y monitorear pacientes situados en locaciones remotas. Además, se propone que agentes distribuidos en una zona actúen sobre una plataforma común utilizando internet para el beneficio de la sociedad, esto ahorrará tiempo a los médicos y costos de traslado o transporte del paciente. Limitaciones: los resultados de la investigación se pueden implementar de forma práctica en nuevos equipos médicos para hospitales con equipos ya existentes.Introdução: a presente pesquisa foi realizada na Universidade de Sikha ‘O’ Anusandan, em 2017. O aplicativo de telemedicina no campo da medicina gera uma nova era. Em consequência, requer que a tecnologia seja compatível. O acesso fácil e o processamento rápido são as principais características esperadas dos diferentes aplicativos. Neste estudo foi considerada uma aproximação para diagnosticar as doenças cardíacas.Métodos: o design do modelo é feito através de uma lógica difusa, na qual o princípio baseado em regras para satisfazer o objetivo é utilizado. O modelo é desenvolvido tendo em conta o sistema de agentes múltiplos. O diagnóstico do paciente é realizado utilizando o sistema de inferência difusa (fis).Resultados: os resultados do exame patológico ajudarão a formar as regras do modelo e podem contribuir para o diagnóstico de forma conveniente. Além disso, os resultados do exame são comunicados, por internet e sms, para fins de seguimento e cuidado posterior do aplicativo IoT.Conclusão: o resultado simulado mostra que seu desempenho pode ser útil tanto para médicos quanto para pacientes em lugares remotos.Originalidade: é proposto o modelo para detectar doenças e monitorar pacientes situados em lugares remotos. Além disso, é proposto que agentes distribuídos em determinadas zonas utilizem uma plataforma comum, fazendo uso da internet para beneficiar a sociedade, o que economizará tempo para os médicos e custos de traslado e/ou transporte do paciente.Limitações: os resultados da pesquisa podem ser inseridos de forma prática em novas equipes médicas para hospitais com equipes já existentes

A heuristic rule based approach for monitoring of hemodynamic data in Cardiothoracic Intensive Care Unit

Patients in Cardiothoracic Intensive Care Units (CICU) are physiologically weak and require watchful monitoring and support. Such monitoring generates massive amount of data that enable early detection of changes in the patient's condition and provide information that help medical staff to give the treatment and evaluate the response to medical interventions.The countless data gathered from monitoring systems and clinical information systems have created a challenge and are time consuming for clinicians to analyze.This paper discusses the implementation of an intelligent system that has been designed to improve interpretation of clinical data which will then increase the quality and efficiency of the working environment in CICU.The implementation is based on the description state from the cardiologist.This research work extends the cardiologist approach by providing the heuristic rules(based approach to address the treatment.The system is intended to help physicians and CICU staffs to diagnose and track the conditions of patients

Assessment of Dual-Tree Complex Wavelet Transform to improve SNR in collaboration with Neuro-Fuzzy System for Heart Sound Identification

none6siThe research paper proposes a novel denoising method to improve the outcome of heartsound (HS)-based heart-condition identification by applying the dual-tree complex wavelet transform (DTCWT) together with the adaptive neuro-fuzzy inference System (ANFIS) classifier. The method consists of three steps: first, preprocessing to eliminate 50 Hz noise; second, applying four successive levels of DTCWT to denoise and reconstruct the time-domain HS signal; third, to evaluate ANFIS on a total of 2735 HS recordings from an international dataset (PhysioNet Challenge 2016). The results show that the signal-to-noise ratio (SNR) with DTCWT was significantly improved (p < 0.001) as compared to original HS recordings. Quantitatively, there was an 11% to many decibel (dB)-fold increase in SNR after DTCWT, representing a significant improvement in denoising HS. In addition, the ANFIS, using six time-domain features, resulted in 55–86% precision, 51–98% recall, 53–86% f-score, and 54–86% MAcc compared to other attempts on the same dataset. Therefore, DTCWT is a successful technique in removing noise from biosignals such as HS recordings. The adaptive property of ANFIS exhibited capability in classifying HS recordings.Special Issue “Biomedical Signal Processing”, Section BioelectronicsopenBassam Al-Naami, Hossam Fraihat, Jamal Al-Nabulsi, Nasr Y. Gharaibeh, Paolo Visconti, Abdel-Razzak Al-HinnawiAl-Naami, Bassam; Fraihat, Hossam; Al-Nabulsi, Jamal; Gharaibeh, Nasr Y.; Visconti, Paolo; Al-Hinnawi, Abdel-Razza

Mathematical tools for identifying the fetal response to physical exercise during pregnancy

In the applied mathematics literature there exist a significant number of tools that can reveal the interaction between mother and fetus during rest and also during and after exercise. These tools are based on techniques from a number of areas such as signal processing, time series analysis, neural networks, heart rate variability as well as dynamical systems and chaos. We will briefly review here some of these methods, concentrating on a method of extracting the fetal heart rate from the mixed maternal-fetal heart rate signal, that is based on phase space reconstructio

Non-invasive hypoglycemia monitoring system using extreme learning machine for Type 1 diabetes

© 2016 ISA Hypoglycemia is a very common in type 1 diabetic persons and can occur at any age. It is always threatening to the well-being of patients with Type 1 diabetes mellitus (T1DM) since hypoglycemia leads to seizures or loss of consciousness and the possible development of permanent brain dysfunction under certain circumstances. Because of that, an accurate continuing hypoglycemia monitoring system is a very important medical device for diabetic patients. In this paper, we proposed a non-invasive hypoglycemia monitoring system using the physiological parameters of electrocardiography (ECG) signal. To enhance the detection accuracy, extreme learning machine (ELM) is developed to recognize the presence of hypoglycemia. A clinical study of 16 children with T1DM is given to illustrate the good performance of ELM

Advanced intelligent control and optimization for cardiac pacemaker systems

Since cardiovascular diseases are major causes of morbidity and mortality in the developed countries and the number one cause of death in the United States, their accurate diagnosis and effective treatment via advanced cardiac pacemaker systems have become very important. Intelligent control and optimization of the pacemakers are significant research subjects. Serious but infrequently occurring arrhythmias are difficult to diagnose. The use of electrocardiogram (ECG) waveform only cannot exactly distinguish between deadly abnormalities and temporary arrhythmias. Thus, this work develops a new method based on frequency entrainment to analyze pole-zero characteristics of the phase error between abnormal ECG and entrained Yanagihara, Noma, and Irisawa (YNI)-response. The thresholds of poles and zeros to diagnose deadly bradycardia and tachycardia are derived, respectively, for the first time. For bradycardia under different states, a fuzzy proportional-integral-derivative (FPID) controller for dual- sensor cardiac pacemaker systems is designed. It can automatically control the heart rate to accurately track a desired preset profile. Through comparing with the conventional algorithm, FPID provides a more suitable control strategy for offering better adaptation of the heart rate, in order to fulfill the patient\u27s physiological needs. This novel control method improves the robustness and performance of a pacemaker system significantly. Higher delivered energy for stimulation may cause higher energy consumption in pacemakers and accelerated battery depletion. Hence, this work designs an optimal single-pulse stimulus to treat sudden cardiac arrest, while minimizing the pulse amplitude and releasing stimulus pain. Moreover, it derives the minimum pulse amplitude for successful entrainment. The simulation results confirm that the optimal single-pulse is effective to induce rapid response of sudden cardiac arrest for heartbeat recovery, while a significant reduction in the delivered energy is achieved. The study will be helpful for not only better diagnosis and treatment of cardiovascular diseases but also improving the performance of pacemaker systems

ECG-Based Arrhythmia Classification using Recurrent Neural Networks in Embedded Systems

Cardiac arrhythmia is one of the most important cardiovascular diseases (CVDs), causing million deaths every year. Moreover it is difficult to diagnose because it occurs intermittently and as such requires the analysis of large amount of data, collected during the daily life of patients. An important tool for CVD diagnosis is the analysis of electrocardiogram (ECG), because of its non-invasive nature and simplicity of acquisition. In this work we propose a classification algorithm for arrhythmia based on recurrent neural networks (RNNs) that operate directly on ECG data, exploring the effectiveness and efficiency of several variations of the general RNN, in particular using different types of layers implementing the network memory. We use the MIT-BIH arrhythmia database and the evaluation protocol recommended by the Association for the Advancement of Medical Instrumentation (AAMI). After designing and testing the effectiveness of the different networks, we then test its porting to an embedded platform, namely the STM32 microcontroller architecture from ST, using a specific framework to port a pre-built RNN to the embedded hardware, convert it to optimized code for the platform and evaluate its performance in terms of resource usage. Both in binary and multiclass classification, the basic RNN model outperforms the other architectures in terms of memory storage (∼117 KB), number of parameters (∼5 k) and inference time (∼150 ms), while the RNN LSTM-based achieved the best accuracy (∼90%)

A cyber-physical system for smart healthcare

Abstract: The increasing number of patients in hospitals is becoming a serious concern in most countries owing to the significantly associated implications for resources such as staff and budget shortages. This problem has prompted researchers to investigate low-cost alternative systems that may assist medical staff with monitoring and caring for patients. In view of the recent widespread availability of cost-effective internet of things (IoT) technologies such as ZigBee, WiFi and sensors integrated into cyber-physical systems, there is the potential for deployment as different topologies in applications such as patient diagnoses and remote patient monitoring...M.Tech. (Electrical and Electronic Engineering Technology

Automated ECG Analysis for Localizing Thrombus in Culprit Artery Using Rule Based Information Fuzzy Network

Cardio-vascular diseases are one of the foremost causes of mortality in today’s world. The prognosis for cardiovascular diseases is usually done by ECG signal, which is a simple 12-lead Electrocardiogram (ECG) that gives complete information about the function of the heart including the amplitude and time interval of P-QRST-U segment. This article recommends a novel approach to identify the location of thrombus in culprit artery using the Information Fuzzy Network (IFN). Information Fuzzy Network, being a supervised machine learning technique, takes known evidences based on rules to create a predicted classification model with thrombus location obtained from the vast input ECG data. These rules are well-defined procedures for selecting hypothesis that best fits a set of observations. Results illustrate that the recommended approach yields an accurateness of 92.30%. This novel approach is shown to be a viable ECG analysis approach for identifying the culprit artery and thus localizing the thrombus