Fog Computing in Medical Internet-of-Things: Architecture, Implementation, and Applications

In the era when the market segment of Internet of Things (IoT) tops the chart in various business reports, it is apparently envisioned that the field of medicine expects to gain a large benefit from the explosion of wearables and internet-connected sensors that surround us to acquire and communicate unprecedented data on symptoms, medication, food intake, and daily-life activities impacting one's health and wellness. However, IoT-driven healthcare would have to overcome many barriers, such as: 1) There is an increasing demand for data storage on cloud servers where the analysis of the medical big data becomes increasingly complex, 2) The data, when communicated, are vulnerable to security and privacy issues, 3) The communication of the continuously collected data is not only costly but also energy hungry, 4) Operating and maintaining the sensors directly from the cloud servers are non-trial tasks. This book chapter defined Fog Computing in the context of medical IoT. Conceptually, Fog Computing is a service-oriented intermediate layer in IoT, providing the interfaces between the sensors and cloud servers for facilitating connectivity, data transfer, and queryable local database. The centerpiece of Fog computing is a low-power, intelligent, wireless, embedded computing node that carries out signal conditioning and data analytics on raw data collected from wearables or other medical sensors and offers efficient means to serve telehealth interventions. We implemented and tested an fog computing system using the Intel Edison and Raspberry Pi that allows acquisition, computing, storage and communication of the various medical data such as pathological speech data of individuals with speech disorders, Phonocardiogram (PCG) signal for heart rate estimation, and Electrocardiogram (ECG)-based Q, R, S detection.Comment: 29 pages, 30 figures, 5 tables. Keywords: Big Data, Body Area Network, Body Sensor Network, Edge Computing, Fog Computing, Medical Cyberphysical Systems, Medical Internet-of-Things, Telecare, Tele-treatment, Wearable Devices, Chapter in Handbook of Large-Scale Distributed Computing in Smart Healthcare (2017), Springe

Técnicas de Adquisición y Procesamiento de Señales Electrocardiográficas en la Detección de Arritmias Cardíacas

The development of ambulatory monitoring systems and its electrocardiographic (ECG) signal processing techniques has become an important field of investigation, due to its relevance in the early detection of cardiovascular diseases such as the arrhythmias. The current trend of this technology is oriented to the use of portable equipment and mobile devices such as Smartphones, which have been widely accepted due to the technical characteristics and common integration in daily life. A fundamental characteristic of these systems is their ability to reduce the most common types of noise by means of digital signal processing techniques.  Among the most used techniques are the adaptive filters and the Discrete Wavelet Transform (DWT) which have been successfully implemented in several studies. There are systems that integrate classification stages based on artificial intelligence, which increases the performance in the process of arrhythmias detection. These techniques are not only evaluated for their functionality but for their computational cost, since they will be used in real-time applications, and implemented in embedded systems. This paper shows a review of each of the stages in the construction of a standard ambulatory monitoring system, for the contextualization of the reader in this type of technology.El desarrollo de sistemas de  monitoreo  ambulatorio  y  sus  técnicas  de  procesamiento  de  la  señal  electrocardiográfica (ECG) se han convertido en un importante campo de investigación, debido a su relevancia en la detección temprana de enfermedades cardiovasculares, tales como arritmias. La tendencia actual de esta tecnología está orientada al uso de equipos portátiles y dispositivos móviles como los Smartphones, que han sido ampliamente aceptados debido a sus características técnicas y a su integración, cada vez más común, en la vida diaria. Una característica fundamental de estos sistemas es su capacidad de reducir los tipos más comunes de ruido mediante técnicas de procesamiento de señales digitales. Entre las técnicas más utilizadas se encuentran los filtros adaptativos y la Transformada Discreta Wavelet (DWT, por sus siglas en inglés), los cuales han sido implementados exitosamente en diversos estudios. Así mismo, se reportan sistemas que integran etapas de clasificación basadas en inteligencia artificial, con lo cual se aumenta el rendimiento en el proceso de detección de arritmias. En este sentido, estas técnicas no solo son evaluadas por su funcionalidad, sino por su costo computacional, debido a que deben ser utilizadas en aplicaciones en tiempo real, e implementadas en sistemas embebidos. Este documento presenta una revisión del estado del arte de cada una de las etapas en la construcción de un sistema de monitoreo ambulatorio estándar, para la contextualización del lector en este tipo de tecnologías

ECG denoising and fiducial point extraction using an extended Kalman filtering framework with linear and nonlinear phase observations

International audienceIn this paper we propose an efficient method for denoising and extracting fiducial point (FP) of ECG signals. The method is based on a nonlinear dynamic model which uses Gaussian functions to model ECG waveforms. For estimating the model parameters, we use an extended Kalman filter (EKF). In this framework called EKF25, all the parameters of Gaussian functions as well as the ECG waveforms (P-wave, QRS complex and T-wave) in the ECG dynamical model, are considered as state variables. In this paper, the dynamic time warping method is used to estimate the nonlinear ECG phase observation. We compare this new approach with linear phase observation models. Using linear and nonlinear EKF25 for ECG denoising and nonlinear EKF25 for fiducial point extraction and ECG interval analysis are the main contributions of this paper. Performance comparison with other EKF-based techniques shows that the proposed method results in higher output SNR with an average SNR improvement of 12 dB for an input SNR of-8 dB. To evaluate the FP extraction performance, we compare the proposed method with a method based on partially collapsed Gibbs sampler and an established EKF-based method. The mean absolute error and the root mean square error of all FPs, across all databases are 14 msec and 22 msec, respectively, for our proposed method, with an advantage when using a nonlinear phase observation. These errors are significantly smaller than errors obtained with other methods. For ECG interval analysis, with an absolute mean error and a root mean square error of about 22 msec and 29 msec, the proposed method achieves better accuracy and smaller variability with respect to other methods. Keywords: Electrocardiogram (ECG), Extended Kalman Filter (EKF), Dynamic Time Warping (DTW), Fiducial Point Extraction, Denoising

A method for context-based adaptive QRS clustering in real-time

Continuous follow-up of heart condition through long-term electrocardiogram monitoring is an invaluable tool for diagnosing some cardiac arrhythmias. In such context, providing tools for fast locating alterations of normal conduction patterns is mandatory and still remains an open issue. This work presents a real-time method for adaptive clustering QRS complexes from multilead ECG signals that provides the set of QRS morphologies that appear during an ECG recording. The method processes the QRS complexes sequentially, grouping them into a dynamic set of clusters based on the information content of the temporal context. The clusters are represented by templates which evolve over time and adapt to the QRS morphology changes. Rules to create, merge and remove clusters are defined along with techniques for noise detection in order to avoid their proliferation. To cope with beat misalignment, Derivative Dynamic Time Warping is used. The proposed method has been validated against the MIT-BIH Arrhythmia Database and the AHA ECG Database showing a global purity of 98.56% and 99.56%, respectively. Results show that our proposal not only provides better results than previous offline solutions but also fulfills real-time requirements.Comment: 12 pages, 6 figure

Flexible Time Series Matching for Clinical and Behavioral Data

Time Series data became broadly applied by the research community in the last decades after a massive explosion of its availability. Nonetheless, this rise required an improvement in the existing analysis techniques which, in the medical domain, would help specialists to evaluate their patients condition. One of the key tasks in time series analysis is pattern recognition (segmentation and classification). Traditional methods typically perform subsequence matching, making use of a pattern template and a similarity metric to search for similar sequences throughout time series. However, real-world data is noisy and variable (morphological distortions), making a template-based exact matching an elementary approach. Intending to increase flexibility and generalize the pattern searching tasks across domains, this dissertation proposes two Deep Learning-based frameworks to solve pattern segmentation and anomaly detection problems. Regarding pattern segmentation, a Convolution/Deconvolution Neural Network is proposed, learning to distinguish, point-by-point, desired sub-patterns from background content within a time series. The proposed framework was validated in two use-cases: electrocardiogram (ECG) and inertial sensor-based human activity (IMU) signals. It outperformed two conventional matching techniques, being capable of notably detecting the targeted cycles even in noise-corrupted or extremely distorted signals, without using any reference template nor hand-coded similarity scores. Concerning anomaly detection, the proposed unsupervised framework uses the reconstruction ability of Variational Autoencoders and a local similarity score to identify non-labeled abnormalities. The proposal was validated in two public ECG datasets (MITBIH Arrhythmia and ECG5000), performing cardiac arrhythmia identification. Results indicated competitiveness relative to recent techniques, achieving detection AUC scores of 98.84% (ECG5000) and 93.32% (MIT-BIH Arrhythmia).Dados de séries temporais tornaram-se largamente aplicados pela comunidade científica nas últimas decadas após um aumento massivo da sua disponibilidade. Contudo, este aumento exigiu uma melhoria das atuais técnicas de análise que, no domínio clínico, auxiliaria os especialistas na avaliação da condição dos seus pacientes. Um dos principais tipos de análise em séries temporais é o reconhecimento de padrões (segmentação e classificação). Métodos tradicionais assentam, tipicamente, em técnicas de correspondência em subsequências, fazendo uso de um padrão de referência e uma métrica de similaridade para procurar por subsequências similares ao longo de séries temporais. Todavia, dados do mundo real são ruidosos e variáveis (morfologicamente), tornando uma correspondência exata baseada num padrão de referência uma abordagem rudimentar. Pretendendo aumentar a flexibilidade da análise de séries temporais e generalizar tarefas de procura de padrões entre domínios, esta dissertação propõe duas abordagens baseadas em Deep Learning para solucionar problemas de segmentação de padrões e deteção de anomalias. Acerca da segmentação de padrões, a rede neuronal de Convolução/Deconvolução proposta aprende a distinguir, ponto a ponto, sub-padrões pretendidos de conteúdo de fundo numa série temporal. O modelo proposto foi validado em dois casos de uso: sinais eletrocardiográficos (ECG) e de sensores inerciais em atividade humana (IMU). Este superou duas técnicas convencionais, sendo capaz de detetar os ciclos-alvo notavelmente, mesmo em sinais corrompidos por ruído ou extremamente distorcidos, sem o uso de nenhum padrão de referência nem métricas de similaridade codificadas manualmente. A respeito da deteção de anomalias, a técnica não supervisionada proposta usa a capacidade de reconstrução dos Variational Autoencoders e uma métrica de similaridade local para identificar anomalias desconhecidas. A proposta foi validada na identificação de arritmias cardíacas em duas bases de dados públicas de ECG (MIT-BIH Arrhythmia e ECG5000). Os resultados revelam competitividade face a técnicas recentes, alcançando métricas AUC de deteção de 93.32% (MIT-BIH Arrhythmia) e 98.84% (ECG5000)

Unsupervised Similarity-Based Risk Stratification for Cardiovascular Events Using Long-Term Time-Series Data

In medicine, one often bases decisions upon a comparative analysis of patient data. In this paper, we build upon this observation and describe similarity-based algorithms to risk stratify patients for major adverse cardiac events. We evolve the traditional approach of comparing patient data in two ways. First, we propose similarity-based algorithms that compare patients in terms of their long-term physiological monitoring data. Symbolic mismatch identifies functional units in long-term signals and measures changes in the morphology and frequency of these units across patients. Second, we describe similarity-based algorithms that are unsupervised and do not require comparisons to patients with known outcomes for risk stratification. This is achieved by using an anomaly detection framework to identify patients who are unlike other patients in a population and may potentially be at an elevated risk. We demonstrate the potential utility of our approach by showing how symbolic mismatch-based algorithms can be used to classify patients as being at high or low risk of major adverse cardiac events by comparing their long-term electrocardiograms to that of a large population. We describe how symbolic mismatch can be used in three different existing methods: one-class support vector machines, nearest neighbor analysis, and hierarchical clustering. When evaluated on a population of 686 patients with available long-term electrocardiographic data, symbolic mismatch-based comparative approaches were able to identify patients at roughly a two-fold increased risk of major adverse cardiac events in the 90 days following acute coronary syndrome. These results were consistent even after adjusting for other clinical risk variables.National Science Foundation (U.S.) (CAREER award 1054419

Classification of Human Ventricular Arrhythmia in High Dimensional Representation Spaces

We studied classification of human ECGs labelled as normal sinus rhythm, ventricular fibrillation and ventricular tachycardia by means of support vector machines in different representation spaces, using different observation lengths. ECG waveform segments of duration 0.5-4 s, their Fourier magnitude spectra, and lower dimensional projections of Fourier magnitude spectra were used for classification. All considered representations were of much higher dimension than in published studies. Classification accuracy improved with segment duration up to 2 s, with 4 s providing little improvement. We found that it is possible to discriminate between ventricular tachycardia and ventricular fibrillation by the present approach with much shorter runs of ECG (2 s, minimum 86% sensitivity per class) than previously imagined. Ensembles of classifiers acting on 1 s segments taken over 5 s observation windows gave best results, with sensitivities of detection for all classes exceeding 93%.Comment: 9 pages, 2 tables, 5 figure