On the accuracy of representing hearbeats with hermite basis

Traballo Fin de Máster en Tecnoloxías da Información. Curso 2012-2013Este artículo pretende analizar el impacto de elegir un cierto número de polinomios de Hermite en la exactitud de la representación del latido cardíaco. Para ello se ejecutó un cojunto de tests sobre la base de datos MIT-BIH Arrhythmia Database variando el número de polinomios utilizados entre 2 y 20. Se utilizaron tres diferentes estrategias para determinar la posición del latido y se aportan los datos de error para cada uno de los test. Basándose en los resultados obtenidos se proporcionan ciertas indicaciones acerca de cómo elegir un número de polinomios adecuado para representar el latido cardíaco según la aplicación

Personalized Health Monitoring Using Evolvable Block-based Neural Networks

This dissertation presents personalized health monitoring using evolvable block-based neural networks. Personalized health monitoring plays an increasingly important role in modern society as the population enjoys longer life. Personalization in health monitoring considers physiological variations brought by temporal, personal or environmental differences, and demands solutions capable to reconfigure and adapt to specific requirements. Block-based neural networks (BbNNs) consist of 2-D arrays of modular basic blocks that can be easily implemented using reconfigurable digital hardware such as field programmable gate arrays (FPGAs) that allow on-line partial reorganization. The modular structure of BbNNs enables easy expansion in size by adding more blocks. A computationally efficient evolutionary algorithm is developed that simultaneously optimizes structure and weights of BbNNs. This evolutionary algorithm increases optimization speed by integrating a local search operator. An adaptive rate update scheme removing manual tuning of operator rates enhances the fitness trend compared to pre-determined fixed rates. A fitness scaling with generalized disruptive pressure reduces the possibility of premature convergence. The BbNN platform promises an evolvable solution that changes structures and parameters for personalized health monitoring. A BbNN evolved with the proposed evolutionary algorithm using the Hermite transform coefficients and a time interval between two neighboring R peaks of ECG signal, provides a patient-specific ECG heartbeat classification system. Experimental results using the MIT-BIH Arrhythmia database demonstrate a potential for significant performance enhancements over other major techniques

Positive and Negative Evidence Accumulation Clustering for Sensor Fusion: An Application to Heartbeat Clustering

In this work, a new clustering algorithm especially geared towards merging data arising from multiple sensors is presented. The algorithm, called PN-EAC, is based on the ensemble clustering paradigm and it introduces the novel concept of negative evidence. PN-EAC combines both positive evidence, to gather information about the elements that should be grouped together in the final partition, and negative evidence, which has information about the elements that should not be grouped together. The algorithm has been validated in the electrocardiographic domain for heartbeat clustering, extracting positive evidence from the heartbeat morphology and negative evidence from the distances between heartbeats. The best result obtained on the MIT-BIH Arrhythmia database yielded an error of 1.44%. In the St. Petersburg Institute of Cardiological Technics 12-Lead Arrhythmia Database database (INCARTDB), an error of 0.601% was obtained when using two electrocardiogram (ECG) leads. When increasing the number of leads to 4, 6, 8, 10 and 12, the algorithm obtains better results (statistically significant) than with the previous number of leads, reaching an error of 0.338%. To the best of our knowledge, this is the first clustering algorithm that is able to process simultaneously any number of ECG leads. Our results support the use of PN-EAC to combine different sources of information and the value of the negative evidenceThis research was funded by the Ministry of Science, Innovation and Universities of Spain, and the European Regional Development Fund of the European Commission, Grant Nos. RTI2018-095324-B-I00, RTI2018-097122-A-I00, and RTI2018-099646-B-I00S

VPNet: Variable Projection Networks

In this paper, we introduce VPNet, a novel model-driven neural network architecture based on variable projections (VP). The application of VP operators in neural networks implies learnable features, interpretable parameters, and compact network structures. This paper discusses the motivation and mathematical background of VPNet as well as experiments. The concept was evaluated in the context of signal processing. We performed classification tasks on a synthetic dataset, and real electrocardiogram (ECG) signals. Compared to fully-connected and 1D convolutional networks, VPNet features fast learning ability and good accuracy at a low computational cost in both of the training and inference. Based on the promising results and mentioned advantages, we expect broader impact in signal processing, including classification, regression, and even clustering problems

Automatic identification of characteristic points related to pathologies in electrocardiograms to design expert systems

Electrocardiograms (ECG) record the electrical activity of the heart through 12 main signals called shunts. Medical experts examine certain segments of these signals in where they believe the cardiovascular disease is manifested. This fact is an important determining factor for designing expert systems for cardiac diagnosis, as it requires the direct expert opinion in order to locate these specific segments in the ECG. The main contributions of this paper are: (i) to propose a model that uses the full ECG signal to identify key characteristic points that define cardiac pathology without medical expert intervention and (ii) to present an expert system based on artificial neural networks capable of detecting bundle branch block disease using the previous approach. Cardiologists have validated the proposed model application and a comparative analysis is performed using the MIT-BIH arrhythmia database.Spanish project TSI-020302-2010-136 University of Málaga: 81434547001-

Topographic mapping for quality inspection and intelligent filtering of smart-bracelet data

Wrist-worn wearable devices equipped with heart activity sensors can provide valuable data that can be used for preventative health. However, hearth activity analysis from these devices suffers from noise introduced by motion artifacts. Methods traditionally used to remove outliers based on motion data can yield to discarding clean data, if some movement was present, and accepting noisy data, i.e., subject was still but the sensor was misplaced. This work shows that self-organizing maps (SOMs) can be used to effectively accept or reject sections of heart data collected from unreliable devices, such as wrist-worn devices. In particular, the proposed SOM-based filter can accept a larger amount of measurements (less false negatives) with an higher overall quality with respect to methods solely based on statistical analysis of motion data. We provide an empirical analysis on real-world wearable data, comprising heart and motion data of users. We show how topographic mapping can help identifying and interpreting patterns in the sensor data and help relating them to an assessment of user state. More importantly, our experimental results show the proposed approach is able to retain almost twice the amount of data while keeping samples with an error that is an order of magnitude lower with respect to a filter based on accelerometric data

Atrial fibrillation classification based on MLP networks by extracting Jitter and Shimmer parameters

Atrial fibrillation (AF) is the most common cardiac anomaly and one that potentially threatens human life. Due to its relation to a variation in cardiac rhythm during indeterminate periods, long-term observations are necessary for its diagnosis. With the increase in data volume, fatigue and the complexity of long-term features make analysis an increasingly impractical process. Most medical diagnostic aid systems based on machine learning, are designed to automatically detect, classify or predict certain behaviors. In this work, using the PhysioNet MIT-BIH Atrial Fibrillation database, a system based on MLP artificial neural network is proposed to differentiate, between AF and non-AF, segments and ECG’s features, obtaining average accuracy of 80.67% in test set, for the 10-fold cross-validation method. As a highlight, the extraction of jitter and shimmer parameters from ECG windows is presented to compose the network input sets, indicating a slight improvement in the model's performance. Added to these, Shannon's and logarithmic energy entropies are determined, also indicating an improvement in performance related to the use of fewer features.This work has been supported by FCT – Fundação para a Ciência e Tecnologia within the Project Scope: UIDB/05757/2020.info:eu-repo/semantics/publishedVersio