A Survey Study of the Current Challenges and Opportunities of Deploying the ECG Biometric Authentication Method in IoT and 5G Environments

The environment prototype of the Internet of Things (IoT) has opened the horizon for researchers to utilize such environments in deploying useful new techniques and methods in different fields and areas. The deployment process takes place when numerous IoT devices are utilized in the implementation phase for new techniques and methods. With the wide use of IoT devices in our daily lives in many fields, personal identification is becoming increasingly important for our society. This survey aims to demonstrate various aspects related to the implementation of biometric authentication in healthcare monitoring systems based on acquiring vital ECG signals via designated wearable devices that are compatible with 5G technology. The nature of ECG signals and current ongoing research related to ECG authentication are investigated in this survey along with the factors that may affect the signal acquisition process. In addition, the survey addresses the psycho-physiological factors that pose a challenge to the usage of ECG signals as a biometric trait in biometric authentication systems along with other challenges that must be addressed and resolved in any future related research.

Clasificación automática de registros ECG para la detección de Fibrilación Auricular y otros ritmos cardiacos

La importancia clínica de las arritmias cardiacas está aumentando, junto con su incidencia y prevalencia, principalmente asociadas con el envejecimiento de la población. Entre estas enfermedades destaca la Fibrilación Auricular (FA) ya que es el tipo de arritmia sostenida más común en adultos con una tendencia creciente más significativa, siendo en muchas ocasiones difícil de diagnosticar debido a un comportamiento paroxístico y/o la ausencia de síntomas en algunos casos. Por otro lado, hoy en día estamos en un escenario en el que los dispositivos portátiles o ¿wearables¿ están ganando gran interés como dispositivos de monitorización, tanto en investigación como en ámbitos clínicos. Sin embargo, los métodos automáticos para proporcionar un diagnóstico fiable de la FA utilizando las señales de electrocardiograma (ECG) proporcionadas por dispositivos portátiles son todavía un reto, especialmente si también se consideran otros ritmos normales o patológicos. El objetivo de este Trabajo Final de Máster es proporcionar diversos modelos de clasificación junto con su rendimiento para discriminar registros cortos de ECG de una única derivación entre cuatro grupos: ritmo normal (N), FA (A), otros ritmos (O) y ruidoso (~). Como base de datos para este estudio se utilizaron 8.528 registros de ECG de una única derivación con duraciones entre 9 y 60 segundos, proporcionados en el contexto de la competición 2017 PhysioNet/Computing in Cardiology Challenge. La estrategia propuesta en este trabajo se basa inicialmente en la extracción automática de características derivadas de la actividad ventricular de las señales ECG. Posteriormente se realizó una selección de características utilizando dos metodologías distintas: Backward Elimination y Forward Selection. Finalmente, las características seleccionadas se utilizaron para entrenar y evaluar mediante validación cruzada el rendimiento de diferentes modelos de clasificación, principalmente redes neuronales de tipo feedforward (FFNN), así como modelos Naïve Bayes y Support Vector Machine (SVM). A cada uno de estos modelos se le realizó un ajuste de parámetros de entrenamiento mediante grid-search durante la fase de validación. Los resultados mostraron que los modelos que presentaban mejor rendimiento de clasificación fueron las redes neuronales (F1=0.75), seguidas de cerca por los modelos SVM (F1=0.73), mientras que Naïve Bayes presentó el menor rendimiento (F1=0.67). Asimismo, también se demostró que la mayor dificultad de este problema se encuentra en la identificación de otros ritmos anómalos distintos a la fibrilación auricular, así como de los registros ruidosos. Dado que las señales utilizadas comparten muchas características con las adquiridas con dispositivos móviles, los modelos de clasificación resultantes podrían ser buenos candidatos para ser implementados en sistemas de gestión de pacientes con dispositivos wearables, ya que este enfoque tiene un bajo consumo computacional durante la clasificación.The clinical importance of cardiac arrhythmias is increasing, along with its incidence and prevalence, mainly associated with the aging of the population. Among these diseases Atrial Fibrillation (AF) stands out since it is the type of sustained arrhythmia most common in adults with a more significant growing tendency, being in many cases difficult to diagnose due to a paroxysmal behavior and/or the absence of symptoms in some patients. On the other hand, today we are in a scenario in which mobile devices or ¿wearables¿ are gaining great interest as monitoring devices, both in research and in clinical settings. However, automatic methods to provide a reliable diagnosis of AF using electrocardiogram signals (ECG) provided by mobile devices are still a challenge, especially if other normal or pathological rhythms are also considered. The main objective of this Final Master's Thesis is to provide different classification models together with their performance to discriminate short ECG single-lead records among four different groups: normal rhythm (N), FA (A), other rhythms (O) and noisy (~). As database for this study, 8,528 single-lead ECG records lasting among 9 and 60 seconds were used, provided in the context of the 2017 PhysioNet/Computing in Cardiology Challenge. The proposed strategy in this work is initially based on the automatic extraction of features mainly derived from the ventricular activity of the ECG signals. Next, a selection of characteristics was made using two different methodologies: Backward Elimination and Forward Selection. Finally, the selected features were used to train and evaluate through cross-validation the performance of different classification models, mainly feedforward neural networks (FFNN), as well as Naïve Bayes and Support Vector Machine (SVM) models. The training parameters for each of these models were tuned though a grid-search validation process. Results showed that the models with the best classification performance were the neural networks (F_1=0.75), followed closely by the SVM models (F_1=0.73), while Naïve Bayes presented the lowest performance (F_1=0.67). Likewise, it was also proved that the greatest difficulty of this problem lies on the identification of other anomalous rhythms other than atrial fibrillation, as well as in the noisy registers. Since the signals used share many characteristics with those acquired with mobile devices, the resulting classification models could be good candidates to be implemented in patient management systems with wearable devices, since this approach has a low computational consumption during classification.Jiménez Serrano, S. (2018). Clasificación automática de registros ECG para la detección de Fibrilación Auricular y otros ritmos cardiacos. http://hdl.handle.net/10251/111113TFG

Novel Low Complexity Biomedical Signal Processing Techniques for Online Applications

Biomedical signal processing has become a very active domain of research nowadays. With the advent of portable monitoring devices, from accelerometer-enabled bracelets and smart-phones to more advanced vital sign tracking body area networks, this field has been receiving unprecedented attention. Indeed, portable health monitoring can help uncover the underlying dynamics of human health in a way that has not been possible before. Several challenges have emerged however, as these devices present key differences in terms of signal acquisition and processing in comparison with conventional methods. Hardware constraints such as processing power and limited battery capacity make most established techniques unsuitable and therefore, the need for low-complexity yet robust signal processing methods has appeared. Another issue that needs to be addressed is the quality of the signals captured by these devices. Unlike in clinical scenarios, in portable health monitoring subjects are constantly performing their daily activities. Moreover, signals maybe captured from unconventional locations and subsequently, be prone to perturbations. In order to obtain reliable measures from these monitoring devices, one needs to acquire dependable signal quality measures, to avoid false alarms. Indeed, hardware limitations and low-quality signals can greatly influence the performance of portable monitoring devices. Nevertheless, most devices offer simultaneous acquisition of multiple physiological parameters, such as electrocardiogram (ECG) and photoplethysmogram (PPG). Through multi-modal signal processing the overall performance can be improved, for instance by deriving parameters such as heart rate estimation from the most reliable and uncontaminated source. This thesis is therefore, dedicated to propose novel low-complexity biomedical processing techniques for real-time/online applications. Throughout this dissertation, several bio-signals such as the ECG, PPG, and electroencephalogram (EEG) are investigated. %There is an emphasis on ECG processing techniques, as most of the bio-signals recorded today reflect information about the heart. The main contribution of this dissertation consists in two signal processing techniques: 1) a novel ECG QRS-complex detection and delineation technique, and 2) a short-term event extraction technique for biomedical signals. The former is based on a processing technique called mathematical morphology (MM), and adaptively uses subject QRS-complex amplitude- and morphological attributes for a robust detection and delineation. This method is generalized to intra-cardiac electrograms for atrial activation detection during atrial fibrillation. The second method, called the Relative-Energy algorithm, uses short- and long-term signal energies to highlight events of interest and discard unwanted activities. Collectively, the results obtained by these methods suggest that while presenting low-computational costs, they can efficiently and robustly extract biomedical events of interest. Using the relative energy algorithm, a continuous non-binary ECG signal quality index is presented. The ECG quality is determined by creating a cleaned-up version of the input ECG and calculating the correlation coefficient between the cleaned-up and the original ECG. The proposed quality index is fast and can be implemented online, making it suitable for portable monitoring scenarios