Classification techniques for arrhythmia patterns using convolutional neural networks and Internet of Things (IoT) devices

The rise of Telemedicine has revolutionized how patients are being treated, leading to several advantages such as enhanced health analysis tools, accessible remote healthcare, basic diagnostic of health parameters, etc. The advent of the Internet of Things (IoT), Artificial Intelligence (AI) and their incorporation into Telemedicine extends the potential of health benefits of Telemedicine even further. Therefore, the synergy between AI, IoT, and Telemedicine creates diverse innovative scenarios for integrating cyber-physical systems into medical health to provide remote monitoring and interactive assistance to patients. Data from World Health Organization reports that 7.4 million people died because of Atrial Fibrillation (AF), recognizing the most common arrhythmia associated with human heart rate. Causes like unhealthy diet, smoking, poor resources to go to the doctor and based on research studies, about 12 and 17.9 million of people will be suffering the AF in the USA and Europe, in 2050 and 2060, respectively. The AF as a cardiovascular disease is becoming an important public health issue to tackle. By using a systematic approach, this paper reviews recent contributions related to the acquisition of heart beats, arrhythmia detection, IoT, and visualization. In particular, by analysing the most closely related papers on Convolutional Neural Network (CNN) and IoT devices in heart disease diagnostics, we present a summary of the main research gaps with suggested directions for future research

Design and implementation of an atrial fibrillation detector based on neural networks

[EN] Atrial fibrillation (AF) is the most common sustained arrhythmia, increasing its prevalence with the age of the patients. The symptoms of AF considerably reduce the quality of life. However, the most important cause of morbid-mortality associated to AF is consequence of an augmented risk of suffering a stroke as a result of a cardiogenic thrombus. Because of this, clinical community is very interested in an early diagnosis, treatment and control of the patients with AF. The objective of this Master Thesis is to design and develop an AF detector which only uses the information contained in the inter-beat interval sequence of an electrocardiogram segment of 30 seconds. To achieve this goal, artificial neural networks are employed and several approaches to improve their learning capabilities are explored. The developed detector is integrated in a commercial software solution to analyze long-term electrocardiograms. After the integration, the detector is tested according with the standards applying to this sector, demonstrating its effectiveness.[ES] La fibrilación auricular (FA) es la arritmia sostenida más frecuente en la población, aumentando su prevalencia con la edad de los pacientes. La sintomatología de la FA reduce considerablemente la calidad de vida. No obstante, la mayor causa de morbimortalidad asociada a la FA es consecuencia del riesgo aumentado de sufrir un accidente cerebrovascular por un trombo de génesis cardiaca. Es por ello que existe un gran interés clínico en el diagnóstico precoz, tratamiento y control de los pacientes con FA. Este Trabajo Fin de Máster tiene como objetivo el diseño y desarrollo de un detector de FA que sólo utilice la información contenida en los intervalos RR de un segmento de 30 segundos de electrocardiograma. Para ello que se emplean redes neuronales y se exploran diferentes técnicas para mejorar sus capacidades de aprendizaje. El detector desarrollado se integra en una aplicación comercial de procesado de señal electrocardiográfica de larga duración. Tras la integración, el detector es posteriormente testado según los estándares de aplicación en este ámbito, demostrándose su eficacia.Ibáñez Català, X. (2015). Design and implementation of an atrial fibrillation detector based on neural networks. http://hdl.handle.net/10251/61694Archivo delegad

The Application of Computer Techniques to ECG Interpretation

This book presents some of the latest available information on automated ECG analysis written by many of the leading researchers in the field. It contains a historical introduction, an outline of the latest international standards for signal processing and communications and then an exciting variety of studies on electrophysiological modelling, ECG Imaging, artificial intelligence applied to resting and ambulatory ECGs, body surface mapping, big data in ECG based prediction, enhanced reliability of patient monitoring, and atrial abnormalities on the ECG. It provides an extremely valuable contribution to the field

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.

Quality Control in ECG-based Atrial Fibrillation Screening

This thesis comprises an introductory chapter and four papers related to quality control in ECG-based atrial fibrillation (AF) screening. Atrial fibrillation is a cardiac arrhythmia characterized by an irregular rhythm and constitutes a major risk factor for stroke. Anticoagulation therapy significantly reduces this risk, and therefore, AF screening is motivated. Atrial fibrillation screening is often done using ECGs recorded outside the clinical environment. However, the higher susceptibility of such ECGs to noise and artifacts makes the identification of patients with AF challenging. The present thesis addresses these challenges at different levels in the data analysis chain. Paper I presents a convolutional neural network (CNN)-based approach to identify transient noise and artifacts in the detected beat sequence before AF detection. The results show that by inserting a CNN, prior to the AF detector, the number of false AF detections is reduced by 22.5% without any loss in the sensitivity, suggesting that the number of recordings requiring expert review can be significantly reduced. Paper II investigates the signal quality of a novel wet electrode technology, and how the improved signal quality translates to improved beat detection and AF detection performance. The novel electrode technology is designed for reduction of motion artifacts typically present in Holter ECG recordings. The novel electrode technology shows a better signal quality and detection performance when compared to a commercially available counterpart, especially when the subject becomes more active. Thus, it has the potential to reduce the review burden and costs associated with ambulatory monitoring.Paper III introduces a detector for short-episode supraventricular tachycardia (sSVT) in AF screening recordings, which has been shown to be associated with an increased risk for future AF. Therefore, the identification of subjects with suchepisodes may increase the usefulness of AF screening. The proposed detector is based on the assumption that the beats in an sSVT episode display similar morphology, and that episodes including detections of deviating morphology should be excluded. The results show that the number of false sSVT detections can be significantly reduced (by a factor of 6) using the proposed detector.Paper IV introduces a novel ECG simulation tool, which is capable of producing ECGs with various arrhythmia patterns and with several different types of noise and artifacts. Specifically, the ECG simulator includes models to generate noise observed in ambulatory recordings, and when recording using handheld recording devices. The usefulness of the simulator is illustrated in terms of AF detection performance when the CNN training in Paper I is performed using simulated data. The results show a very similar performance when training with simulated data compared to when training with real data. Thus, the proposed simulator is a valuable tool in the development and training of automated ECG processing algorithms. Together, the four parts, in different ways, contribute to improved algorithmic efficiency in AF screening

Affective-autonomic states of domestic pigs in the context of coping and animal welfare

Gaining better insight in affective states of farm animals is of importance for understanding their welfare state. One important step in this context is to establish valid proxy measures to objectively assess and interpret an individual’s subjective perception of its environment. This thesis presents a reliable tool for the objective evaluation of affective-autonomic states in free-moving pigs and gains insight into the neurophysiological mechanisms underlying the individual processing of affective states in relation to their valence and arousal dimensions.Die Untersuchung affektiver Zustände von Nutztieren ist für das Verständnis ihres Wohlbefindens von essentieller Bedeutung. Ein wichtiger Schritt in diesem Kontext ist die Etablierung zuverlässiger Messmethoden zur objektiven Beurteilung und Interpretation individueller subjektiver Wahrnehmung. Diese Arbeit stellt eine valide Methode zur objektiven Beurteilung affektiv-autonomer Zustände bei Schweinen dar und vermittelt einen Einblick in die neurophysiologischen Mechanismen, die der individuellen Verarbeitung affektiver Zustände zugrunde liegen