ECG classification using an optimal temporal convolutional network for remote health monitoring

Increased life expectancy in most countries is a result of continuous improvements at all levels, starting from medicine and public health services, environmental and personal hygiene to the use of the most advanced technologies by healthcare providers. Despite these significant improvements, especially at the technological level in the last few decades, the overall access to healthcare services and medical facilities worldwide is not equally distributed. Indeed, the end beneficiary of these most advanced healthcare services and technologies on a daily basis are mostly residents of big cities, whereas the residents of rural areas, even in developed countries, have major difficulties accessing even basic medical services. This may lead to huge deficiencies in timely medical advice and assistance and may even cause death in some cases. Remote healthcare is considered a serious candidate for facilitating access to health services for all; thus, by using the most advanced technologies, providing at the same time high quality diagnosis and ease of implementation and use. ECG analysis and related cardiac diagnosis techniques are the basic healthcare methods providing rapid insights in potential health issues through simple visualization and interpretation by clinicians or by automatic detection of potential cardiac anomalies. In this paper, we propose a novel machine learning (ML) architecture for the ECG classification regarding five heart diseases based on temporal convolution networks (TCN). The proposed design, which implements a dilated causal one-dimensional convolution on the input heartbeat signals, seems to be outperforming all existing ML methods with an accuracy of 96.12% and an F1 score of 84.13%, using a reduced number of parameters (10.2 K). Such results make the proposed TCN architecture a good candidate for low power consumption hardware platforms, and thus its potential use in low cost embedded devices for remote health monitoring

Berpizteko bihotz erritmoak sailkatzeko ikasketa automatikoan oinarritutako algoritmo baten garapena

Laburpena Dokumentu hau berpizteko bihotz erritmoak sailkatzeko teknika ez-inbaditzaileen erabileran eta garapenean oinarrituta dago; zehatzago, ikasketa automatikoan oinarritutako algoritmo baten garapena dugu helburu. Ikasketa automatikoaren erabilera, ingelesez Machine Learning (ML) gisa ezaguna, datu multzo batetik abiatuta ondorio esanguratsuak lortzeko aurkezten duen gaitasuna dela medio, asko nagusitu da azken urteotan. Kasu honetan, seinaleen elektrokardiogramek (EKG) aurkezten dituzten ezaugarri ezberdinez baliatuz, Supervised learning (Ikasketa gainbegiratua) deritzon ikasketan funtsatutako Random Forest (RF) izeneko sailkatzailea erabili da. Bihotz-biriketako gaixotasunak nabarmen hazi dira mundu mailako heriotza-tasa kausarik altuenak izatera ailegatuz. Izatez, Munduko Osasun Erakundearen esanetan, herrialde garatuen heriotzen %50a eta garapen bidean dauden herrialde askoren heriotza kausa nagusietako bat gaixotasun kardiobaskularrak dira [1]. Horrenbestez, biomedikuntza esparru zientifiko honekin loturiko ikerkuntza oro sustatzeak berebiziko garrantzia hartu du. Proiektu honek, egoera hauei aurre egiteko egun existitzen diren terapiek pazienteekiko duten eragina ebaluatzeko bai eta tratamenduen kalitatea hobetzeko ezinbestekoa den EKG seinaleen analisia eta azterketa errazteko berpizte bihotz erritmoen anotatze automatikoa ahalbidetzen du; era horretan, adituak diren medikuentzako euskarri edota laguntza izanik, diagnostiko objektibo zuzenak eta egokiak emateko ziurtasun handiagoa eskaintzen da. Ez hori bakarrik, proiektu honek etengabeko kontrolean egon behar duten pazienteen monitorizaziorako, zein larrialdi egoera baten aurrean hartu beharreko erabaki kritikoak azkarrak eta egokiak izateko ere laguntza handia ematen du. Proiektuaren xedea erdiesteko, 857 pazientez osaturiko datu-base batekin egin da lan. Garaturiko interfaze grafikoan oinarriturik, paziente bakoitzaren seinaleen analisia, anotazioa eta segmentazioa burutu da; eta, azkenik, 10-20 segundo bitarteko 3803 segmentuz osaturiko datu-basea lortu da. Hortik abiaturik, algoritmoaren prozesaketarako 4 eta 8 segundotako EKG seinale segmentuak erabili dira. Segmentu bakoitzeko 33 ezaugarri kalkulatu dira; eta, ondoren, ML algoritmoa elikatu da eta balioztatze gurutzatuarekin ebaluatu da. RF algoritmoak eskuragarri dituen parametro ezberdinen araberako algoritmoak sorturik, hauen errendimendua eta eraginkortasuna ebaluatzeko klase anitzeko sailkatzaileetan balio orokorra eskaintzen duten UMS (Unweighted Mean of Sensitivities) eta MulAcc (Multiway Accuracy) metrikak erabili dira. Seinale segmentu iraupen ezberdinen artean errendimendu aldeak ikusi dira, emaitza hobeak 8 segundotako seinale segmentuekin lortu direlarik. Orokorrean, sailkatzailearen zehaztasun maila %96.5koa izan da eta sentikortasun maila %88.0koa izan da, batez beste; gure emaitzek, beraz, berpizte bihotz erritmoak automatikoki identifikatzea eta sailkatzea posiblea dela baieztatu dute.Abstract: This document is based on the use and development of a non-invasive technique for the classification of cardiac resuscitation rhythms; specifically, its objective is the development of an algorithm based on automatic learning. In recent years, the use of Machine Learning (ML) has been prevailed because of the ability to achieve meaningful conclusions from a set of data. In this case, a classifier based on Supervised learning and known as Random Forest (RF) has been used, taking advantage of the different characteristics offered by the electrocardiogram (ECG) signals. Cardiovascular diseases have grown significantly becoming one of the leading causes of mortality worldwide. In fact, the World Health Organization reports that cardiovascular diseases represent half of all deaths in developed countries and that it is one of the leading causes of death in many developing countries [1]. Therefore, the promotion of research related to this biomedical scientific area is very important. This project allows automatic recording of cardiac resuscitation rhythms to facilitate the necessary study and analysis of ECG signals, with the aim of achieving improvements in the quality of treatments, as well as to pursue the evaluation of therapies on patients that are in the presence of the mentioned cardiovascular arrest situations; in this way, being a support and help for expert doctors, this project offers greater reliability on the correct and appropriate objective diagnoses that are given. Not only that, both in the continuous monitoring of patients and when making critical decisions in an emergency situation, this project offers a great help. To achieve the project’s objective, we have worked with a database of 857 patients. Based on a developed graphic interface, the analysis, annotation and segmentation of each of the signals corresponding to each patient was carried out, finally achieving a database of 3803 segments with an average duration of approximately 10-20 seconds. From there, segments of 4 and 8 seconds of ECG signal have been used for processing the algorithm. For each segment, 33 characteristics have been calculated, to then feed the ML algorithm and evaluate it with cross-validation. To evaluate the performance and efficiency of each of the algorithms created based on the different parameters offered by the RF algorithm, the UMS (Unweighted Mean of Sensitivities) and MulAcc (Multiway Accuracy) metrics have been used to offer generic values for multiple classes classifiers. In the performance obtained by the two different durations of signal segments employed, differences have been found differences, achieving more efficient results with the signal segments of 8 seconds in this case. In general, the accuracy and sensitivity average of the classifier have been 96.5% and 88.0%, respectively; our results show, therefore, that the identification and automatic classification of cardiac resuscitation rhythms is possible.Resumen: Este documento está basado en el uso y en el desarrollo de una técnica no invasiva para la clasificación de ritmos de resucitación cardíacos; más concretamente, tiene como objetivo el desarrollo de un algoritmo basado en el aprendizaje automático. Estos últimos años el uso del aprendizaje automático, más conocido en inglés como Machine Learning (ML), ha predominado por la capacidad de conseguir conclusiones significativas a partir de un conjunto de datos. En este caso, se ha empleado el clasificador conocido como Random Forest (RF) basado en Supervised Learning (Aprendizaje supervisado) aprovechando las diferentes características que ofrecen las señales de electrocardiograma (ECG). Las enfermedades cardiovasculares han crecido notablemente hasta el punto de ser una de las causas principales de mortalidad a nivel mundial. De hecho, la Organización Mundial de la Salud indica que las enfermedades cardiovasculares representan la mitad de todas las muertes en los países desarrollados y que es una de las principales causas de muerte en muchos países en vías de desarrollo [1]. Por consiguiente, impulsar todas las investigaciones relacionadas con esta área científica biomédica es muy importante. Este proyecto posibilita la anotación automática de ritmos de resucitación cardíacos para así facilitar el necesario estudio y análisis de las señales ECG, con el objetivo de conseguir mejoras tanto en la calidad de los tratamientos como en la evaluación de las terapias sobre pacientes ante las situaciones mencionadas de parada cardiovascular; de esa manera, siendo un apoyo y ayuda para los médicos expertos, este proyecto ofrece una mayor fiabilidad sobre los diagnósticos objetivos correctos y adecuados que se dan. No solo eso, este proyecto ofrece una gran ayuda tanto en la monitorización de pacientes con necesidad de un control continuo como a la hora de tener que tomar decisiones críticas frente a una situación de emergencia. Para alcanzar el objetivo del proyecto se ha trabajado con una base de datos de 857 pacientes. Basándose en la interfaz gráfica desarrollada, se ha realizado el análisis, la anotación y la segmentación de cada una de las señales correspondientes a cada paciente, consiguiendo finalmente una base de datos de 3803 segmentos con una duración media de 10-20 segundos aproximadamente. Partiendo de ahí, se han empleado segmentos de señal de ECG de 4 y 8 segundos para el procesamiento del algoritmo. Por cada segmento mencionado se han calculado 33 características, para después alimentar el algoritmo de ML y evaluarlo con validación cruzada. Para evaluar el rendimiento y la eficacia de cada uno de los algoritmos creados basados en los diferentes parámetros que ofrece el algoritmo RF, se han utilizado las métricas UMS (Unweighted Mean of Sensitivities) y MulAcc (Multiway Accuracy) que ofrecen valores genéricos para clasificadores de múltiples clases. En el rendimiento obtenido por las dos diferentes duraciones de segmentos de señales empleados se han observado diferencias, consiguiendo resultados más eficientes con los segmentos de señales de 8 segundos. Por lo general, la precisión y la sensibilidad media del clasificador han sido del 96.5% y 88.0%, respectivamente; nuestros resultados demuestran, por lo tanto, que es posible la identificación y clasificación automática de los ritmos de resucitación cardíacos

An automated algorithmic approach for activity recognition and step detection in the presence of functional compromise

Wearable technology is a potential stepping stone towards personalised healthcare. It provides the opportunity to collect objective physical activity data from the users and could enable clinicians to make more informed decisions and hence provide better treatments. Current physical activity monitors generally work well in healthy populations but can be problematic when used in some patient groups with severely abnormal function. We studied healthy volunteers to assess how different algorithms might perform for those with normal and simulated-pathological conditions. Participants (n=30) were recruited from the University of Leeds to perform nine predifined activities under normal and simulated-pathological conditions using two MOX accelerometers on wrist and ankle (Maastricht Instruments, NL). Condition classification was performed using a Support Vector Machine algorithm. Activity classification was performed with five different Machine Learning algorithms: Support Vector Machine, k-Nearest Neighbour, Random Forest, Multilayer Perceptron, and Naive Bayes. A step count algorithm was developed based on pattern recognition approach, using two main techniques, Dynamic Time Warping and Dynamic Time Warping-Barycentre Averaging. Finally, synthetic acceleration signal was generated that represented walking activities since there was limited access to patient data and to refine synthetic data generation in this field. Three dynamic coupled equations were used to represent the morphology of the desired signal. Wrist and ankle locations performed similarly and the wrist location was used for further analysis. Both condition and activity classification algorithms achieved good performance metrics i.e. that the volunteer has been correctly classified in the right condition, and the activities performed have been correctly recognised. Additionally, the novel step count algorithm achieved more accurate results for both conditions in comparison to existing algorithms from the literature. Finally, the signal generation approach seems promising since the normal condition synthetic signals matched closely to their associated original signals. Algorithms developed for a specific group or even person with functional pathology, using techniques such as Dynamic Time Warping-Barycentre Averaging produce better results than traditional algorithms trained on data from a different group

Clasification Of Arrhythmic ECG Data Using Machine Learning Techniques

In this paper we proposed a automated Artificial Neural Network (ANN) based classification system for cardiac arrhythmia using multi-channel ECG recordings. In this study, we are mainly interested in producing high confident arrhythmia classification results to be applicable in diagnostic decision support systems. Neural network model with back propagation algorithm is used to classify arrhythmia cases into normal and abnormal classes. Networks models are trained and tested for MIT-BIH arrhythmia. The different structures of ANN have been trained by mixture of arrhythmic and non arrhythmic data patient. The classification performance is evaluated using measures; sensitivity, specificity, classification accuracy, mean squared error (MSE), receiver operating characteristics (ROC) and area under curve (AUC).Our experimental results gives 96.77% accuracy on MIT-BIH database and 96.21% on database prepared by including NSR database also

Separator fluid volume requirements in multi-infusion settings

INTRODUCTION. Intravenous (IV) therapy is a widely used method for the administration of medication in hospitals worldwide. ICU and surgical patients in particular often require multiple IV catheters due to incompatibility of certain drugs and the high complexity of medical therapy. This increases discomfort by painful invasive procedures, the risk of infections and costs of medication and disposable considerably. When different drugs are administered through the same lumen, it is common ICU practice to flush with a neutral fluid between the administration of two incompatible drugs in order to optimally use infusion lumens. An important constraint for delivering multiple incompatible drugs is the volume of separator fluid that is sufficient to safely separate them. OBJECTIVES. In this pilot study we investigated whether the choice of separator fluid, solvent, or administration rate affects the separator volume required in a typical ICU infusion setting. METHODS. A standard ICU IV line (2m, 2ml, 1mm internal diameter) was filled with methylene blue (40 mg/l) solution and flushed using an infusion pump with separator fluid. Independent variables were solvent for methylene blue (NaCl 0.9% vs. glucose 5%), separator fluid (NaCl 0.9% vs. glucose 5%), and administration rate (50, 100, or 200 ml/h). Samples were collected using a fraction collector until <2% of the original drug concentration remained and were analyzed using spectrophotometry. RESULTS. We did not find a significant effect of administration rate on separator fluid volume. However, NaCl/G5% (solvent/separator fluid) required significantly less separator fluid than NaCl/NaCl (3.6 ± 0.1 ml vs. 3.9 ± 0.1 ml, p <0.05). Also, G5%/G5% required significantly less separator fluid than NaCl/NaCl (3.6 ± 0.1 ml vs. 3.9 ± 0.1 ml, p <0.05). The significant decrease in required flushing volume might be due to differences in the viscosity of the solutions. However, mean differences were small and were most likely caused by human interactions with the fluid collection setup. The average required flushing volume is 3.7 ml. CONCLUSIONS. The choice of separator fluid, solvent or administration rate had no impact on the required flushing volume in the experiment. Future research should take IV line length, diameter, volume and also drug solution volumes into account in order to provide a full account of variables affecting the required separator fluid volume