Novel Fourier Quadrature Transforms and Analytic Signal Representations for Nonlinear and Non-stationary Time Series Analysis

The Hilbert transform (HT) and associated Gabor analytic signal (GAS) representation are well-known and widely used mathematical formulations for modeling and analysis of signals in various applications. In this study, like the HT, to obtain quadrature component of a signal, we propose the novel discrete Fourier cosine quadrature transforms (FCQTs) and discrete Fourier sine quadrature transforms (FSQTs), designated as Fourier quadrature transforms (FQTs). Using these FQTs, we propose sixteen Fourier-Singh analytic signal (FSAS) representations with following properties: (1) real part of eight FSAS representations is the original signal and imaginary part is the FCQT of the real part, (2) imaginary part of eight FSAS representations is the original signal and real part is the FSQT of the real part, (3) like the GAS, Fourier spectrum of the all FSAS representations has only positive frequencies, however unlike the GAS, the real and imaginary parts of the proposed FSAS representations are not orthogonal to each other. The Fourier decomposition method (FDM) is an adaptive data analysis approach to decompose a signal into a set of small number of Fourier intrinsic band functions which are AM-FM components. This study also proposes a new formulation of the FDM using the discrete cosine transform (DCT) with the GAS and FSAS representations, and demonstrate its efficacy for improved time-frequency-energy representation and analysis of nonlinear and non-stationary time series.Comment: 22 pages, 13 figure

Boosting the Battery Life of Wearables for Health Monitoring Through the Compression of Biosignals

Modern wearable Internet of Things (IoT) devices enable the monitoring of vital parameters such as heart or respiratory (RESP) rates, electrocardiography (ECG), photo-plethysmographic (PPG) signals within e-health applications. A common issue of wearable technology is that signal transmission is power-demanding and, as such, devices require frequent battery charges and this poses serious limitations to the continuous monitoring of vitals. To ameliorate this, we advocate the use of lossy signal compression as a means to decrease the data size of the gathered biosignals and, in turn, boost the battery life of wearables and allow for fine-grained and long-term monitoring. Considering 1-D biosignals such as ECG, RESP, and PPG, which are often available from commercial wearable IoT devices, we provide a thorough review of existing biosignal compression algorithms. Besides, we present novel approaches based on online dictionaries, elucidating their operating principles and providing a quantitative assessment of compression, reconstruction and energy consumption performance of all schemes. As we quantify, the most efficient schemes allow reductions in the signal size of up to 100 times, which entail similar reductions in the energy demand, by still keeping the reconstruction error within 4% of the peak-to-peak signal amplitude. Finally, avenues for future research are discussed. © 2014 IEEE

Design and Implementation of a Motif-based Compression Algorithm for Biometric Signals

Wearable devices are becoming a natural and economic means to gather biometric data from users: this thesis is centered around lossy data compression techniques, whose aim is to minimize the amount of information that is to be stored on their onboard memory and subsequently transmitted over wireless interfaces. A new class of codebook based (CB) compression algorithms is proposed, designed to be energy efficient, online and amenable to any type of signal exhibiting recurrent patternsope

Rate-Distortion Classification for Self-Tuning IoT Networks

Many future wireless sensor networks and the Internet of Things are expected to follow a software defined paradigm, where protocol parameters and behaviors will be dynamically tuned as a function of the signal statistics. New protocols will be then injected as a software as certain events occur. For instance, new data compressors could be (re)programmed on-the-fly as the monitored signal type or its statistical properties change. We consider a lossy compression scenario, where the application tolerates some distortion of the gathered signal in return for improved energy efficiency. To reap the full benefits of this paradigm, we discuss an automatic sensor profiling approach where the signal class, and in particular the corresponding rate-distortion curve, is automatically assessed using machine learning tools (namely, support vector machines and neural networks). We show that this curve can be reliably estimated on-the-fly through the computation of a small number (from ten to twenty) of statistical features on time windows of a few hundreds samples

ECG data compression using a neural network model based on multi-objective optimization

© 2017 Zhang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Electrocardiogram (ECG) data analysis is of great significance to the diagnosis of cardiovascular disease. ECG compression should be processed in real time, and the data should be based on lossless compression and have high predictability. In terms of the real time aspect, short-time Fourier transformation is applied to the processing of signal wave for reducing computational time. For the lossless compression requirement, wavelet-transformation that is a coding algorithm can be used to avoid loss of data. In practice, compression is required to avoid storing redundant recording data that are not useful in the diagnosis platform. The obtained data can be preprocessed to remove noise by using wavelet transform, and then a multi-objective optimize neural network model is used to extract feature information. Compared with the existing traditional methods such as direct data processing method and transform method, our proposed compression model has self-learning ability to achieve high data compression ratio at 1:19 without losing important ECG information and compromising quality. Upon testing, we demonstrated that the proposed ECG data compression method based on multi-objective optimization neural network is effective and efficient in clinical practice

Early Disease Detection by Extracting Features of Biomedical Signals

Elderly people face a lot of health problems in day to day life due to old age and so many reasons. Therefore a regular health check-up is needed for them which is much more expensive and cannot be afforded by many people. Again the diagnosis is much more complicated to understand and in many cases there is a chance of mistreatment. There is another chance of delay in the detection of disease and late treatment causing risk in their lives. So, the disease should be detected in the early stage for lower cost and lower risk in life. The present work is related to the different physiological parameters of a human being that are to be measured to accurately diagnose the related disease. Though there are numerous physiological parameters, this work emphasizes on some of the most common physiological parameters such as blood pressure, heart rate and ECG which are of primary importance to elderly people. Accurate measurement and analysis of these parameters can lead to diagnose of several lethal disease. In this work, the method of measurement and analysis of these physiological parameters are described. The simulation, processing and analyses of these signals are also done in the work. The prime objective of the research work is to analyze and extract the features of ECG signal and blood pressure signal for early diagnosis of life threatening diseases

Compressed Sensing for Elastography in Portable Ultrasound

Bonghun Shin, Soo Jeon, Jeongwon Ryu and Hyock Ju Kwon, “Compressed Sensing for Elastography in Portable Ultrasound,” Ultrasonic Imaging, 39(6), pp. 393-413, Copyright © The Author(s) 2017. Reprinted by permission of SAGE Publications. https://doi.org/10.1177/0161734617716938Portable wireless ultrasound has many advantages such as high portability, easy connectivity, strong individuality, as well as on-site diagnostic ability in real-time. Some of the modern portable ultrasound devices offer high image quality and multiple ultrasound modes comparable to console style ultrasound, however, none of them provides ultrasound elastography function that enables the diagnosis of malignant legions using elastic properties. This is mainly due to the limitations of hardware performance and wireless data transfer speed for processing the large amount of data for elastography. Therefore, reduction of the data transfer size is one of the feasible solutions to overcome these limitations. Recently compressive sensing (CS) theory has been rigorously studied as a means to break the conventional Nyquist sampling rate and thus can significantly decrease the amount of measurement signals without sacrificing signal quality. In this research, we implemented various CS reconstruction frameworks and comparatively evaluated their reconstruction performance for realizing ultrasound elastography function on portable ultrasound. Combinations of three most common model bases (FT, DCT, and WA) and two reconstruction algorithms (l_1 minimization and BSBL) were considered for CS frameworks. Two kinds of numerical phantoms, echoic and elastography phantoms, were developed to evaluate performance of CS on B-mode images and elastograms, respectively. To assess the reconstruction quality, mean absolute error (MAE), signal-to-noise (SNRe) and contrast-to-noise (CNRe) were measured on the B-mode images and elastograms from CS reconstructions. Results suggest that CS reconstruction adopting BSBL algorithm with DCT model basis can yield the best results for all the measures tested, and the maximum data reduction rate for producing readily discernable elastograms is around 60%.Natural Sciences and Engineering Research Council || RGPIN-2015-05273, RGPIN-2015-04118, RGPAS-354703-201