9,113 research outputs found
Compression algorithms for biomedical signals and nanopore sequencing data
The massive generation of biological digital information creates various computing
challenges such as its storage and transmission. For example, biomedical
signals, such as electroencephalograms (EEG), are recorded by multiple sensors over
long periods of time, resulting in large volumes of data. Another example is genome
DNA sequencing data, where the amount of data generated globally is seeing explosive
growth, leading to increasing needs for processing, storage, and transmission
resources. In this thesis we investigate the use of data compression techniques for
this problem, in two different scenarios where computational efficiency is crucial.
First we study the compression of multi-channel biomedical signals. We present
a new lossless data compressor for multi-channel signals, GSC, which achieves compression
performance similar to the state of the art, while being more computationally
efficient than other available alternatives. The compressor uses two novel
integer-based implementations of the predictive coding and expert advice schemes
for multi-channel signals. We also develop a version of GSC optimized for EEG
data. This version manages to significantly lower compression times while attaining
similar compression performance for that specic type of signal.
In a second scenario we study the compression of DNA sequencing data produced
by nanopore sequencing technologies. We present two novel lossless compression algorithms
specifically tailored to nanopore FASTQ files. ENANO is a reference-free
compressor, which mainly focuses on the compression of quality scores. It achieves
state of the art compression performance, while being fast and with low memory
consumption when compared to other popular FASTQ compression tools. On the
other hand, RENANO is a reference-based compressor, which improves on ENANO,
by providing a more efficient base call sequence compression component. For RENANO
two algorithms are introduced, corresponding to the following scenarios: a
reference genome is available without cost to both the compressor and the decompressor;
and the reference genome is available only on the compressor side, and a
compacted version of the reference is included in the compressed le. Both algorithms
of RENANO significantly improve the compression performance of ENANO,
with similar compression times, and higher memory requirements.La generación masiva de información digital biológica da lugar a múltiples desafíos informáticos, como su almacenamiento y transmisión. Por ejemplo, las señales biomédicas, como los electroencefalogramas (EEG), son generadas por múltiples sensores registrando medidas en simultaneo durante largos períodos de tiempo,
generando grandes volúmenes de datos. Otro ejemplo son los datos de secuenciación de ADN, en donde la cantidad de datos a nivel mundial esta creciendo de forma explosiva, lo que da lugar a una gran necesidad de recursos de procesamiento, almacenamiento y transmisión. En esta tesis investigamos como aplicar técnicas de compresión de datos para atacar este problema, en dos escenarios diferentes donde
la eficiencia computacional juega un rol importante.
Primero estudiamos la compresión de señales biomédicas multicanal. Comenzamos presentando un nuevo compresor de datos sin perdida para señales multicanal, GSC, que logra obtener niveles de compresión en el estado del arte y que al mismo tiempo es mas eficiente computacionalmente que otras alternativas disponibles. El compresor utiliza dos nuevas implementaciones de los esquemas de codificación predictiva
y de asesoramiento de expertos para señales multicanal, basadas en aritmética
de enteros. También presentamos una versión de GSC optimizada para datos de
EEG. Esta versión logra reducir significativamente los tiempos de compresión, sin
deteriorar significativamente los niveles de compresión para datos de EEG.
En un segundo escenario estudiamos la compresión de datos de secuenciación
de ADN generados por tecnologías de secuenciación por nanoporos. En este sentido,
presentamos dos nuevos algoritmos de compresión sin perdida, específicamente
diseñados para archivos FASTQ generados por tecnología de nanoporos. ENANO
es un compresor libre de referencia, enfocado principalmente en la compresión de
los valores de calidad de las bases. ENANO alcanza niveles de compresión en el
estado del arte, siendo a la vez mas eficiente computacionalmente que otras herramientas
populares de compresión de archivos FASTQ. Por otro lado, RENANO es
un compresor basado en la utilización de una referencia, que mejora el rendimiento
de ENANO, a partir de un nuevo esquema de compresión de las secuencias de bases.
Presentamos dos variantes de RENANO, correspondientes a los siguientes escenarios:
(i) se tiene a disposición un genoma de referencia, tanto del lado del compresor
como del descompresor, y (ii) se tiene un genoma de referencia disponible solo del
lado del compresor, y se incluye una versión compacta de la referencia en el archivo
comprimido. Ambas variantes de RENANO mejoran significativamente los niveles
compresión de ENANO, alcanzando tiempos de compresión similares y un mayor
consumo de memoria
Non-invasive Detection and Compression of Fetal Electrocardiogram
Noninvasive detection of fetal electrocardiogram (FECG) from abdominal ECG recordings is highly dependent on typical statistical signal processing techniques such as independent component analysis (ICA), adaptive noise filtering, and multichannel blind deconvolution. In contrast to the previous multichannel FECG extraction methods, several recent schemes for single‐channel FECG extraction such as the extended Kalman filter (EKF), extended Kalman smoother (EKS), template subtraction (TS), and support vector regression (SVR) for detecting R waves on ECG, are evaluated via the quantitative metrics such as sensitivity (SE), positive predictive value (PPV), F‐score, detection error rate (DER), and range of accuracy. A correlation predictor that combines with multivariable gray model (GM) is also proposed for sequential ECG data compression, which displays better percent root mean-square difference (PRD) than those of Sabah’s scheme for fixed and predicted compression ratio (CR). Automatic calculation on fetal heart rate (FHR) on the reconstructed FECG from mixed signals of abdominal ECG recordings is also experimented with sample synthetic ECG data. Sample data on FHR and T/QRS for both physiological case and pathological case are simulated in a 10-min time sequence
Tensor Decompositions for Signal Processing Applications From Two-way to Multiway Component Analysis
The widespread use of multi-sensor technology and the emergence of big
datasets has highlighted the limitations of standard flat-view matrix models
and the necessity to move towards more versatile data analysis tools. We show
that higher-order tensors (i.e., multiway arrays) enable such a fundamental
paradigm shift towards models that are essentially polynomial and whose
uniqueness, unlike the matrix methods, is guaranteed under verymild and natural
conditions. Benefiting fromthe power ofmultilinear algebra as theirmathematical
backbone, data analysis techniques using tensor decompositions are shown to
have great flexibility in the choice of constraints that match data properties,
and to find more general latent components in the data than matrix-based
methods. A comprehensive introduction to tensor decompositions is provided from
a signal processing perspective, starting from the algebraic foundations, via
basic Canonical Polyadic and Tucker models, through to advanced cause-effect
and multi-view data analysis schemes. We show that tensor decompositions enable
natural generalizations of some commonly used signal processing paradigms, such
as canonical correlation and subspace techniques, signal separation, linear
regression, feature extraction and classification. We also cover computational
aspects, and point out how ideas from compressed sensing and scientific
computing may be used for addressing the otherwise unmanageable storage and
manipulation problems associated with big datasets. The concepts are supported
by illustrative real world case studies illuminating the benefits of the tensor
framework, as efficient and promising tools for modern signal processing, data
analysis and machine learning applications; these benefits also extend to
vector/matrix data through tensorization. Keywords: ICA, NMF, CPD, Tucker
decomposition, HOSVD, tensor networks, Tensor Train
A Deep Learning Approach for Vital Signs Compression and Energy Efficient Delivery in mhealth Systems
© 2013 IEEE. Due to the increasing number of chronic disease patients, continuous health monitoring has become the top priority for health-care providers and has posed a major stimulus for the development of scalable and energy efficient mobile health systems. Collected data in such systems are highly critical and can be affected by wireless network conditions, which in return, motivates the need for a preprocessing stage that optimizes data delivery in an adaptive manner with respect to network dynamics. We present in this paper adaptive single and multiple modality data compression schemes based on deep learning approach, which consider acquired data characteristics and network dynamics for providing energy efficient data delivery. Results indicate that: 1) the proposed adaptive single modality compression scheme outperforms conventional compression methods by 13.24% and 43.75% reductions in distortion and processing time, respectively; 2) the proposed adaptive multiple modality compression further decreases the distortion by 3.71% and 72.37% when compared with the proposed single modality scheme and conventional methods through leveraging inter-modality correlations; and 3) adaptive multiple modality compression demonstrates its efficiency in terms of energy consumption, computational complexity, and responding to different network states. Hence, our approach is suitable for mobile health applications (mHealth), where the smart preprocessing of vital signs can enhance energy consumption, reduce storage, and cut down transmission delays to the mHealth cloud.This work was supported by NPRP through the Qatar National Research Fund (a member of the Qatar Foundation) under Grant 7-684-1-127
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