Exploiting Prior Knowledge in Compressed Sensing Wireless ECG Systems

Recent results in telecardiology show that compressed sensing (CS) is a promising tool to lower energy consumption in wireless body area networks for electrocardiogram (ECG) monitoring. However, the performance of current CS-based algorithms, in terms of compression rate and reconstruction quality of the ECG, still falls short of the performance attained by state-of-the-art wavelet based algorithms. In this paper, we propose to exploit the structure of the wavelet representation of the ECG signal to boost the performance of CS-based methods for compression and reconstruction of ECG signals. More precisely, we incorporate prior information about the wavelet dependencies across scales into the reconstruction algorithms and exploit the high fraction of common support of the wavelet coefficients of consecutive ECG segments. Experimental results utilizing the MIT-BIH Arrhythmia Database show that significant performance gains, in terms of compression rate and reconstruction quality, can be obtained by the proposed algorithms compared to current CS-based methods.Comment: Accepted for publication at IEEE Journal of Biomedical and Health Informatic

On power line positioning systems

Power line infrastructure is available almost everywhere. Positioning systems aim to estimate where a device or target is. Consequently, there may be an opportunity to use power lines for positioning purposes. This survey article reports the different efforts, working principles, and possibilities for implementing positioning systems relying on power line infrastructure for power line positioning systems (PLPS). Since Power Line Communication (PLC) systems of different characteristics have been deployed to provide communication services using the existing mains, we also address how PLC systems may be employed to build positioning systems. Although some efforts exist, PLPS are still prospective and thus open to research and development, and we try to indicate the possible directions and potential applications for PLPS.European Commissio

Analysis performance of wavelet OFDM in mobility platforms

Wavelet orthogonal frequency division multiplexing (OFDM) is one of the medium access techniques recommended by the IEEE 1901 working group for broadband communications over electrical networks, and is under consideration for IoT applications. This standard provides a flexible architecture supporting integrated access, smart grid, building, in-home, and mobility platform (vehicle) applications. Wavelet OFDM is a filter bank multicarrier system based on the extended lapped transform, in which the transmitting and receiving filters are obtained from a waveform provided by the standard. In this paper, we explore system performance when other waveforms are employed, studying the trade-off between stopband attenuation and transition band width. Furthermore, an alternative and more efficient way of obtaining the theoretical expressions of the achievable data rate is shown, assuming realistic power line communication noise other than additive white Gaussian noise. To demonstrate the capabilities of wavelet OFDM, the results of simulation of the symbol error rate and the data rate in several systems in platform scenarios (in-vehicle and in-aircraft) are shown.Comunidad de MadridUniversidad de Alcal

Characterization of noise in long-term ECG monitoring with machine learning based on clinical criteria

Noise and artifacts affect strongly the quality of the electrocardiogram (ECG) in long-term ECG monitoring (LTM), making some of its parts impractical for diagnosis. The clinical severity of noise defines a qualitative quality score according to the manner clinicians make the interpretation of the ECG, in contrast to assess noise from a quantitative standpoint. So clinical noise refers to a scale of different levels of qualitative severity of noise which aims at elucidating which ECG fragments are valid to achieve diagnosis from a clinical point of view, unlike the traditional approach, which assesses noise in terms of quantitative severity. This work proposes the use of machine learning (ML) techniques to categorize different qualitative noise severity using a database annotated according to a clinical noise taxonomy as gold standard. A comparative study is carried out using five representative ML methods, namely, K neareast neighbors, decision trees, support vector machine, single-layer perceptron, and random forest. The models are fed by signal quality indexes characterizing the waveform in time and frequency domains, as well as from a statistical viewpoint, to distinguish between clinically valid ECG segments from invalid ones. A solid methodology to prevent overfitting to both the dataset and the patient is developed, taking into account balance of classes, patient separation, and patient rotation in the test set. All the proposed learning systems have demonstrated good classification performance, attaining a recall, precision, and F1 score up to 0.78, 0.80, and 0.77, respectively, in the test set by a single-layer perceptron approach. These systems provide a classification solution for assessing the clinical quality of the ECG taken from LTM recordings.Universidad de Alcal

Comparación de 2 tipos de preparación intestinal para la realización de colonoscopia en un hospital de tercer nivel

ResumenAntecedentesLa colonoscopia es el método para evaluar el colon. La preparación con polietilenglicol (PEG) es utilizada para la limpieza colónica. Sin embargo, la tolerabilidad y efectos adversos son frecuentes.ObjetivoComparar la eficacia mediante la escala de Boston y Harefield, y la tolerabilidad con la escala visual análoga, en 2 tipos de preparación colónica: grupo 1=PEG 4L (PEG 4L) y grupo 2=30ml de aceite de oliva (OL) más PEG en 2l de agua (30 OL+PEG 2L).MetodologíaEnsayo clínico, prospectivo, aleatorizado, unicéntrico. Los sujetos fueron aleatorizados en 2 grupos: PEG 4L, y 30 OL+PEG 2L. Se valoró la tolerancia de la preparación mediante escala visual análoga y la calidad de la limpieza con las escalas de Boston y Harefield.ResultadosSe incluyeron 42 pacientes, 22 (52.38%) se trataron con PEG 4L y 20 (47.62%) con 30 OL+PEG 2L. Veintidós (52.38%) fueron hombres y 20 (47.62%) mujeres. El resultado más frecuente de la tolerabilidad de la preparación del grupo 1 y 2 fue tolerancia parcial en 18 (42.9%) y 23 (54.76%) pacientes respectivamente, sin ser estadísticamente significativo. PEG 4L tuvo un promedio de calificación de Boston de 6.04 puntos, y la de 30 OL+PEG 2L fue de 6.65 puntos, p=0.9. La calificación de Harefield fue exitosa en 35 pacientes (83.3%).ConclusionesLa preparación colónica con 30 OL+PEG 2L al tener resultados de limpieza similares a la dosis de PEG 4L podría utilizarse en aquellos pacientes que no toleren dosis altas de líquidos.AbstractBackgroundColonoscopy is the method to evaluate the colon. The preparation with polyethylene glycol (PEG) is used for colonic cleansing. However, tolerability and side effects are common.ObjetiveTo compare effectiveness through Boston Bowel Preparation Scale (BBPS) and Harefield Cleasing Scale (HCS), and tolerability with the visual analog scale in 2 types of colonic preparation: group 1=PEG 4liters (4L PEG) and group 2=30ml olive (OL) plus PEG in 2 liters of water (30 OL+2L PEG).MethodologyClinical, prospective, randomized, single-center trial. The subjects were randomized into 2 groups: 4L PEG, and 30 OL+2L PEG. Preparation tolerance was evaluated with visual analog scale and preparation quality with the BBPS and HCS.ResultsForty two patients were included. Twenty two (52.38%) were included with 4L PEG, and 20 (47.62%), with 30 OL+2L PEG. 22 (52.38%) were men and 20 (47.62%) were women. The most frequent answer was partial tolerance in 18 (42.9%) and 23 (54.76%) patients, respectively, without statistical significance. The comparison in both preparations, 4L PEG had an average score of 6.04 points, and 30 OL+2L PEG 6.65 points by BBPS (P=.9). HCS was successful in 35 patients (83.3%).ConclusionsThe administration of 30 OL+2L PEG has similar cleansing results compared with the standard bowel preparation, which may be an alternative used in patients who are intolerable to high doses of liquids

Embedded filter bank-based algorithm for ECG compression

In this work, two ECG compression schemes are presented using two types of filter banks to decompose the incoming signal: wavelet packets (WP) and nearly-perfect reconstruction cosine modulated filter banks. The conventional embedded zerotree wavelet (EZW) algorithm takes advantage of the hierarchical relationship among subband coefficients of the pyramidal wavelet decomposition. Nevertheless, it performs worse when used with WP as the hierarchy becomes more complex. In order to address this problem, we propose a new technique that considers no relationship among coefficients, and is therefore suitable for use with WP. Furthermore, this new approximation makes it possible to apply the quantization method toM-channel maximally decimated filter banks. In this fashion, the proposed algorithm provides two efficient and effective ECG compressors that show better ECG compression performance than the conventional EZW algorithm

Machine Learning approach for TWA detection relying on ensemble data design

Background and objective: T-wave alternans (TWA) is a fluctuation of the ST–T complex of the surface electrocardiogram (ECG) on an every–other–beat basis. It has been shown to be clinically helpful for sudden cardiac death stratification, though the lack of a gold standard to benchmark detection methods limits its application and impairs the development of alternative techniques. In this work, a novel approach based on machine learning for TWA detection is proposed. Additionally, a complete experimental setup is presented for TWA detection methods benchmarking. Methods: The proposed experimental setup is based on the use of open-source databases to enable experiment replication and the use of real ECG signals with added TWA episodes. Also, intra-patient overfitting and class imbalance have been carefully avoided. The Spectral Method (SM), the Modified Moving Average Method (MMA), and the Time Domain Method (TM) are used to obtain input features to the Machine Learning (ML) algorithms, namely, K Nearest Neighbor, Decision Trees, Random Forest, Support Vector Machine and Multi-Layer Perceptron. Results: There were not found large differences in the performance of the different ML algorithms. Decision Trees showed the best overall performance (accuracy 0.88 ± 0.04, precision 0.89 ± 0.05, Recall 0.90± 0.05, F1 score 0.89± 0.03). Compared to the SM (accuracy 0.79, precision 0.93, Recall 0.64, F1 score 0.76) there was an improvement in every metric except for the precision. Conclusions: In this work, a realistic database to test the presence of TWA using ML algorithms was assembled. The ML algorithms overall outperformed the SM used as a gold standard. Learning from data to identify alternans elicits a substantial detection growth at the expense of a small increment of the false alarm.Universidad de Alcal

On the use of discrete cosine transforms for multicarrier communications

In this correspondence, the conditions to use any kind of discrete cosine transform (DCT) for multicarrier data transmission are derived. The symmetric convolution-multiplication property of each DCT implies that when symmetric convolution is performed in the time domain, an element-by-element multiplication is performed in the corresponding discrete trigonometric domain. Therefore, appending symmetric redun-dancy (as prefix and suffix) into each data symbol to be transmitted, and also enforcing symmetry for the equivalent channel impulse response, the linear convolution performed in the transmission channel becomes a symmetric convolution in those samples of interest. Furthermore, the channel equalization can be carried out by means of a bank of scalars in the corresponding discrete cosine transform domain. The expressions for obtaining the value of each scalar corresponding to these one-tap per subcarrier equalizers are presented. This study is completed with several computer simulations in mobile broadband wireless communication scenarios, considering the presence of carrier frequency offset (CFO). The obtained results indicate that the proposed systems outperform the standardized ones based on the DFT

Effects of audio compression in automatic detection of voice pathologies

This paper investigates the performance of an automatic system for voice pathology detection when the voice samples have been compressed in MP3 format and different binary rates (160, 96, 64, 48, 24, and 8 kb/s). The detectors employ cepstral and noise measurements, along with their derivatives, to characterize the voice signals. The classification is performed using Gaussian mixtures models and support vector machines. The results between the different proposed detectors are compared by means of detector error tradeoff (DET) and receiver operating characteristic (ROC) curves, concluding that there are no significant differences in the performance of the detector when the binary rates of the compressed data are above 64 kb/s. This has useful applications in telemedicine, reducing the storage space of voice recordings or transmitting them over narrow-band communications channels