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Adaptation of the Segmented Beat Modulation Method to support diagnosis of cardiovascular disorders using electrocardiographic tracings acquired by wearable sensors
Full text linkLo scopo di questa tesi è adattare il Segmented Beat Modulation Method (SBMM), un metodo per il filtraggio di segnali electrocardiografici (ECG), per tenere conto sia dei ritmi cardiaci sinusali che non e per aumentare la sua usabilità includendo i moderni sensori indossabili oltre ai tradizionali dispositivi clinici per la diagnosi di patologie cardiovascolari. Infatti, SBMM non è attualmente in grado di funzionare in presenza di battiti cardiaci anormali o aritmici (eventi critici che potrebbero portare allaa morte cardiaca improvvisa), il che limita enormemente la sua applicabilità alla diagnosi di malattie cardiovascolari in uno scenario reale. A questo scopo, questo lavoro presenta il Extended Segmented Beat Modulation Method (ESBMM) con una funzione di classificazione del battito cardiaco utilizzando la convolutional neural network (CNN) che separa prima i battiti cardiaci normali da quelli sopraventricolari (S) e ventricolari (V), e in secondo luogo utilizza modelli rappresentativi mediani separati per filtrare e ricostruire la registrazione ECG pulita. Nel complesso, l’accuratezza (Ac) della classificazione CNN era del 91,5% mentre i valori di predizione positive erano del 92,8%, 95,6% e 83,6%, rispettivamente per le classi di battito N, S e V. Alla fine, il miglioramento del rapporto segnale-rumore (SNR) è stato inferiore a 2 dB in presenza di livelli di rumore trascurabile, ma è aumentato in presenza di rumore fino a superare i 5 dB in presenza di artefatti da movimento degli elettrodi. Pertanto, ESBMM si è dimostrato uno strumento affidabile per classificare i battiti cardiaci in classi N, S e V e per filtraggio di tracciati ECG caratterizzati da ritmi sia sinusali che non sinusali mantenendo la variabilità morfologica nel segnale ECG pseudo-periodico. Altri miglioramenti proposti a SBMM sono un test di compressione preliminare che utilizza la trasformata coseno discreta. Il metodo viene valutato utilizzando SNR e il rapport di compressione (CR) considerando diversi livelli di energia del segnale ECG ricostruito. Per il filtraggio, è stato raggiunto un SNR medio di 4,56 dB che rappresenta un calo complessivo medio di 1,68 dB (37,9%) rispetto all'elaborazione del segnale non compresso mentre il 95% dell'energia del segnale è intatto e quantizzato a 6 bit per la memorizzazione del segnale (CR=2) rispetto ai 12 bit originali, con conseguente riduzione del 50% delle dimensioni di archiviazione. Un altro miglioramento è l’adattamento dell'SBMM alla frequenza cardiaca in modo dinamico ogni 20 secondi, particolarmente indicato per l'acquisizione di dati ECG a lungo termine. Un altro miglioramento presentato adatta SBMM al moderno hardware veloce utilizzando la tecnica di vettorizzazione e le unità di elaborazione grafica chiamate GPU-SBMM. L'applicazione GPU-SBMM ha prodotto un aumento significativo dell'SNR (da 1±5 dB a 19±5 dB; p<10-10). Inoltre, è stata raggiunta una notevole velocità nel runtime dell'algoritmo (3,56x volte GPU NVIDIA GeForce). In aggiunta, viene presentato un sistema automatico di rilevamento dell'aritmia progettato per produrre la massima precisione diagnostica con una quantità minima di dati utilizzando differential evolution (DE) e una probabilistic neural network (PNN) meno pesante dal punto di vista computazionale. Tutti i test sono stati eseguiti su ECG ambulatoriali e a lungo termine acquisiti utilizzando sensoristica indossabile. Lo schema DE-PNN proposto ha fornito una migliore accuratezza di classificazione considerando 8 classi con solo 41 caratteristiche ottimizzate da un insieme di 253 elementi che hanno causato una riduzione dell'83,7% delle caratteristiche di ampiezza diretta. In conclusione, questo lavoro si è dimostrato utile per migliorare la qualità e l'efficienza del sistema di diagnosi automatica delle malattie cardiovascolari su una piattaforma di monitoraggio della salute cardiovascolare moderna e in evoluzione, ovvero sensori ECG indossabiliAbstract
Designing automatic cardiovascular disease (CVD) diagnostic systems specifically for signals acquired using wearable electrocardiogram (ECG) sensors becomes a challenge specifically requiring solutions for signal distortions caused by high level of motion artifacts and efficient CVD diagnosis. Hence the aim of this thesis is to develop an adaptation of Segmented Beat Modulation Method (SBMM, a template-based method for denoising of ECG signals) using wearable ECG data to additionally account for non-sinus rhythms and to increase the usability of modern wearable sensors in comparison to traditional in-clinic machines for CVD diagnosis. SBMM has currently failed to work with abnormal or arrhythmic (rare but critical events often leading to sudden cardiac death) heartbeats which hugely limits its applicability to cardiovascular disease diagnosis in a real-world scenario. To this aim, this work presents Extended Segmented Beat Modulation Method with a heartbeat classification function using convolutional neural network (CNN) that first separates the normal (N) from supraventricular (S) and ventricular (V) heartbeats and secondly uses separate median representative templates to denoise and reconstruct the clean ECG recording. Overall, the CNN classification accuracy (Ac) was 91.5% while the positive predictive (PP) values were 92.8%, 95.6%, and 83.6%, for N, S, and V beat classes, respectively. Eventually, signal-to-noise (SNR) improvement was less than 2 dB in the absence of noise but increased in the presence of noise until exceeding 5 dB in the presence of electrode motion artifacts. Hence, ESBMM proved to be a reliable tool to classify cardiac beats into N, S, and V classes and to denoise ECG tracings characterized by both sinus and non-sinus rhythms maintaining the morphological variability in the pseudo-periodic ECG signal. Other improvements proposed to SBMM are a preliminary compression test using discrete cosine transform. The method is evaluated using SNR and compression ratio (CR) considering varying levels of signal energy in the reconstructed ECG signal. For denoising, an average SNR of 4.56 dB was achieved representing an average overall decline of 1.68 dBs (37.9%) as compared to the uncompressed signal processing while 95% of signal energy is intact and quantized at 6 bits for signal storage (CR=2) compared to the original 12 bits, hence resulting in 50% reduction in storage size. Another improvement dynamic-template SBMM adapts SBMM to heart rate and generates the template in a dynamic fashion every 20 seconds and is particularly targeted and tested for long-term ECG data acquisitions. Another presented improvement adapts SBMM to modern fast hardware using vectorization technique and graphical processing units called GPU-SBMM. GPU-SBMM application yielded a significant increase of SNR (from 1±5 dB to 19±5 dB; p<10E-10). Additionally, a considerable speed up in the algorithm runtime (3.56x on average on an NVIDIA GeForce GPU) was achieved. In a secondary domain, an automated arrhythmia detection system is presented that is designed to produce maximum diagnostic accuracy with minimum amount of data (removing redundant and noisy data) using differential evolution (DE) and a less computationally intense probabilistic neural network (PNN). All tests are performed for ambulatory and long term ECG signals acquired using wearable sensing modality. The proposed DE-PNN scheme provides better classification accuracy considering 8 classes with only 41 features optimized from a 253 element feature set implying an 83.7% reduction in direct amplitude features compared to the other evolutionary and statistical schemes. In conclusion, this work has proved beneficial for improving the quality and efficiency of automatic cardiovascular disease diagnosis system on a modern and evolving cardiovascular health monitoring platform i.e. wearable ECG sensors
Solution of Linear and Non-Linear Boundary Value Problems Using Population-Distributed Parallel Differential Evolution
Full text linkCases where the derivative of a boundary value problem does not exist or is constantly changing, traditional derivative can easily get stuck in the local optima or does not factually represent a constantly changing solution. Hence the need for evolutionary algorithms becomes evident. However, evolutionary algorithms are compute-intensive since they scan the entire solution space for an optimal solution. Larger populations and smaller step sizes allow for improved quality solution but results in an increase in the complexity of the optimization process. In this research a population-distributed implementation for differential evolution algorithm is presented for solving systems of 2 nd -order, 2-point boundary value problems (BVPs). In this technique, the system is formulated as an optimization problem by the direct minimization of the overall individual residual error subject to the given constraint boundary conditions and is then solved using differential evolution in the sense that each of the derivatives is replaced by an appropriate difference quotient approximation. Four benchmark BVPs are solved using the proposed parallel framework for differential evolution to observe the speedup in the execution time. Meanwhile, the statistical analysis is provided to discover the effect of parametric changes such as an increase in population individuals and nodes representing features on the quality and behavior of the solutions found by differential evolution. The numerical results demonstrate that the algorithm is quite accurate and efficient for solving 2 nd -order, 2-point BVPs
GPU-Based Segmented-Beat Modulation Method for Denoising Athlete Electrocardiograms During Training
Full text linkSport-related sudden cardiac death (SRSCD), defined as “death occurring during sport or within one hour of cessation of training”, is the leading cause of death in athletes. SRSCD occurs in the presence of underlying cardiovascular diseases, some of which may be identified by processing electrocardiographic recordings acquired during training (TECGs). A fast and accurate processing of TECGs during or immediately after training is challenging since TECGs are typically highly corrupted by noise and interferences, which may jeopardize their interpretation and identification of abnormal morphologies. The present study evaluated the ability of GPU-based Segmented-Beat Modulation Method (GPUSBMM) to provide a noise-free estimation of TECGs, and to improve the algorithm by GPU acceleration to make it compatible with modern hardware. In this research, 19 6-
to-10 min TECGs (sampling frequency: 256 Hz), acquired from 8 subjects while performing 4 different exercise tasks (walk, run, low-resistance bike and high-resistance bike),
were analyzed. Results indicate that GPU-SBMM application yielded a significant increase of SNR(dB) (from 1±5 dB to 19±5 dB; p<10-12 ), also when stratifying by exercise tasks. Additionally, a considerable average speedup of 7.67x is achieved using NVIDIA GeForce 740M GPU processor
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
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