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

Design and Implementation of Complexity Reduced Digital Signal Processors for Low Power Biomedical Applications

Wearable health monitoring systems can provide remote care with supervised, inde-pendent living which are capable of signal sensing, acquisition, local processing and transmission. A generic biopotential signal (such as Electrocardiogram (ECG), and Electroencephalogram (EEG)) processing platform consists of four main functional components. The signals acquired by the electrodes are amplified and preconditioned by the (1) Analog-Front-End (AFE) which are then digitized via the (2) Analog-to-Digital Converter (ADC) for further processing. The local digital signal processing is usually handled by a custom designed (3) Digital Signal Processor (DSP) which is responsible for either anyone or combination of signal processing algorithms such as noise detection, noise/artefact removal, feature extraction, classification and compres-sion. The digitally processed data is then transmitted via the (4) transmitter which is renown as the most power hungry block in the complete platform. All the afore-mentioned components of the wearable systems are required to be designed and fitted into an integrated system where the area and the power requirements are stringent. Therefore, hardware complexity and power dissipation of each functional component are crucial aspects while designing and implementing a wearable monitoring platform. The work undertaken focuses on reducing the hardware complexity of a biosignal DSP and presents low hardware complexity solutions that can be employed in the aforemen-tioned wearable platforms. A typical state-of-the-art system utilizes Sigma Delta (Σ∆) ADCs incorporating a Σ∆ modulator and a decimation filter whereas the state-of-the-art decimation filters employ linear phase Finite-Impulse-Response (FIR) filters with high orders that in-crease the hardware complexity [1–5]. In this thesis, the novel use of minimum phase Infinite-Impulse-Response (IIR) decimators is proposed where the hardware complexity is massively reduced compared to the conventional FIR decimators. In addition, the non-linear phase effects of these filters are also investigated since phase non-linearity may distort the time domain representation of the signal being filtered which is un-desirable effect for biopotential signals especially when the fiducial characteristics carry diagnostic importance. In the case of ECG monitoring systems the effect of the IIR filter phase non-linearity is minimal which does not affect the diagnostic accuracy of the signals. The work undertaken also proposes two methods for reducing the hardware complexity of the popular biosignal processing tool, Discrete Wavelet Transform (DWT). General purpose multipliers are known to be hardware and power hungry in terms of the number of addition operations or their underlying building blocks like full adders or half adders required. Higher number of adders leads to an increase in the power consumption which is directly proportional to the clock frequency, supply voltage, switching activity and the resources utilized. A typical Field-Programmable-Gate-Array’s (FPGA) resources are Look-up Tables (LUTs) whereas a custom Digital Signal Processor’s (DSP) are gate-level cells of standard cell libraries that are used to build adders [6]. One of the proposed methods is the replacement of the hardware and power hungry general pur-pose multipliers and the coefficient memories with reconfigurable multiplier blocks that are composed of simple shift-add networks and multiplexers. This method substantially reduces the resource utilization as well as the power consumption of the system. The second proposed method is the design and implementation of the DWT filter banks using IIR filters which employ less number of arithmetic operations compared to the state-of-the-art FIR wavelets. This reduces the hardware complexity of the analysis filter bank of the DWT and can be employed in applications where the reconstruction is not required. However, the synthesis filter bank for the IIR wavelet transform has a higher computational complexity compared to the conventional FIR wavelet synthesis filter banks since re-indexing of the filtered data sequence is required that can only be achieved via the use of extra registers. Therefore, this led to the proposal of a novel design which replaces the complex IIR based synthesis filter banks with FIR fil-ters which are the approximations of the associated IIR filters. Finally, a comparative study is presented where the hybrid IIR/FIR and FIR/FIR wavelet filter banks are de-ployed in a typical noise reduction scenario using the wavelet thresholding techniques. It is concluded that the proposed hybrid IIR/FIR wavelet filter banks provide better denoising performance, reduced computational complexity and power consumption in comparison to their IIR/IIR and FIR/FIR counterparts

Wavelets and multirate filter banks : theory, structure, design, and applications

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 2004.Includes bibliographical references (p. 219-230) and index.Wavelets and filter banks have revolutionized signal processing with their ability to process data at multiple temporal and spatial resolutions. Fundamentally, continuous-time wavelets are governed by discrete-time filter banks with properties such as perfect reconstruction, linear phase and regularity. In this thesis, we study multi-channel filter bank factorization and parameterization strategies, which facilitate designs with specified properties that are enforced by the actual factorization structure. For M-channel filter banks (M =/> 2), we develop a complete factorization, M-channel lifting factorization, using simple ladder-like structures as predictions between channels to provide robust and efficient implementation; perfect reconstruction is structurally enforced, even under finite precision arithmetic and quantization of lifting coefficients. With lifting, optimal low-complexity integer wavelet transforms can thus be designed using a simple and fast algorithm that incorporates prescribed limits on hardware operations for power-constrained environments. As filter bank regularity is important for a variety of reasons, an aspect of particular interest is the structural imposition of regularity onto factorizations based on the dyadic form uvt. We derive the corresponding structural conditions for regularity, for which M-channel lifting factorization provides an essential parameterization. As a result, we are able to design filter banks that are exactly regular and amenable to fast implementations with perfect reconstruction, regardless of the choice of free parameters and possible finite precision effects. Further constraining u = v ensures regular orthogonal filter banks,(cont.) whereas a special dyadic form is developed that guarantees linear phase. We achieve superior coding gains within 0.1% of the optimum, and benchmarks conducted on image compression applications show clear improvements in perceptual and objective performance. We also consider the problem of completing an M-channel filter bank, given only its scaling filter. M-channel lifting factorization can efficiently complete such biorthogonal filter banks. On the other hand, an improved scheme for completing paraunitary filter banks is made possible by a novel order-one factorization which allows greater design flexibility, resulting in improved frequency selectivity and energy compaction over existing state of the art methods. In a dual setting, the technique can be applied to transmultiplexer design to achieve higher-rate data transmissions.by Ying-Jui Chen.Ph.D

On optimal design and applications of linear transforms

Linear transforms are encountered in many fields of applied science and engineering. In the past, conventional block transforms provided acceptable answers to different practical problems. But now, under increasing competitive pressures, with the growing reservoir of theory and a corresponding development of computing facilities, a real demand has been created for methods that systematically improve performance. As a result the past two decades have seen the explosive growth of a class of linear transform theory known as multiresolution signal decomposition. The goal of this work is to design and apply these advanced signal processing techniques to several different problems. The optimal design of subband filter banks is considered first. Several design examples are presented for M-band filter banks. Conventional design approaches are found to present problems when the number of constraints increases. A novel optimization method is proposed using a step-by-step design of a hierarchical subband tree. This method is shown to possess performance improvements in applications such as subband image coding. The subband tree structuring is then discussed and generalized algorithms are presented. Next, the attention is focused on the interference excision problem in direct sequence spread spectrum (DSSS) communications. The analytical and experimental performance of the DSSS receiver employing excision are presented. Different excision techniques are evaluated and ranked along with the proposed adaptive subband transform-based excises. The robustness of the considered methods is investigated for either time-localized or frequency-localized interferers. A domain switchable excision algorithm is also presented. Finally, sonic of the ideas associated with the interference excision problem are utilized in the spectral shaping of a particular biological signal, namely heart rate variability. The improvements for the spectral shaping process are shown for time-frequency analysis. In general, this dissertation demonstrates the proliferation of new tools for digital signal processing

Improvement of Speech Perception for Hearing-Impaired Listeners

Hearing impairment is becoming a prevalent health problem affecting 5% of world adult populations. Hearing aids and cochlear implant already play an essential role in helping patients over decades, but there are still several open problems that prevent them from providing the maximum benefits. Financial and discomfort reasons lead to only one of four patients choose to use hearing aids; Cochlear implant users always have trouble in understanding speech in a noisy environment. In this dissertation, we addressed the hearing aids limitations by proposing a new hearing aid signal processing system named Open-source Self-fitting Hearing Aids System (OS SF hearing aids). The proposed hearing aids system adopted the state-of-art digital signal processing technologies, combined with accurate hearing assessment and machine learning based self-fitting algorithm to further improve the speech perception and comfort for hearing aids users. Informal testing with hearing-impaired listeners showed that the testing results from the proposed system had less than 10 dB (by average) difference when compared with those results obtained from clinical audiometer. In addition, Sixteen-channel filter banks with adaptive differential microphone array provides up to six-dB SNR improvement in the noisy environment. Machine-learning based self-fitting algorithm provides more suitable hearing aids settings. To maximize cochlear implant users’ speech understanding in noise, the sequential (S) and parallel (P) coding strategies were proposed by integrating high-rate desynchronized pulse trains (DPT) in the continuous interleaved sampling (CIS) strategy. Ten participants with severe hearing loss participated in the two rounds cochlear implants testing. The testing results showed CIS-DPT-S strategy significantly improved (11%) the speech perception in background noise, while the CIS-DPT-P strategy had a significant improvement in both quiet (7%) and noisy (9%) environment

Wavelet Theory

The wavelet is a powerful mathematical tool that plays an important role in science and technology. This book looks at some of the most creative and popular applications of wavelets including biomedical signal processing, image processing, communication signal processing, Internet of Things (IoT), acoustical signal processing, financial market data analysis, energy and power management, and COVID-19 pandemic measurements and calculations. The editor’s personal interest is the application of wavelet transform to identify time domain changes on signals and corresponding frequency components and in improving power amplifier behavior

High-resolution multipath channel parameter estimation using wavelet analysis

This thesis explores the novel use of wavelet analysis as a high-resolution digital signal processing algorithm for multipath channel parameter estimation. The results obtained from this research indicate that this wavelet-based digital signal processing algorithm overcomes the resolution limitation in conventional high-resolution algorithm. This may provide a more cost-effective means of implementing channel sounding equipments for very high-resolution measurements