Parallel programming in biomedical signal processing

Dissertação para obtenção do Grau de Mestre em Engenharia BiomédicaPatients with neuromuscular and cardiorespiratory diseases need to be monitored continuously. This constant monitoring gives rise to huge amounts of multivariate data which need to be processed as soon as possible, so that their most relevant features can be extracted. The field of parallel processing, an area from the computational sciences, comes naturally as a way to provide an answer to this problem. For the parallel processing to succeed it is necessary to adapt the pre-existing signal processing algorithms to the modern architectures of computer systems with several processing units. In this work parallel processing techniques are applied to biosignals, connecting the area of computer science to the biomedical domain. Several considerations are made on how to design parallel algorithms for signal processing, following the data parallel paradigm. The emphasis is given to algorithm design, rather than the computing systems that execute these algorithms. Nonetheless, shared memory systems and distributed memory systems are mentioned in the present work. Two signal processing tools integrating some of the parallel programming concepts mentioned throughout this work were developed. These tools allow a fast and efficient analysis of long-term biosignals. The two kinds of analysis are focused on heart rate variability and breath frequency, and aim to the processing of electrocardiograms and respiratory signals, respectively. The proposed tools make use of the several processing units that most of the actual computers include in their architecture, giving the clinician a fast tool without him having to set up a system specifically meant to run parallel programs

Concept and Microarchitecture of a Streaming Processor Specialized for Biomeditronic and Adaptronic Applications

INTERNATIONNAL JOURNAL OF APPLIED BIOMEDICAL ENGINEERING, VOL.6, NO.1 2013This paper presents a streaming processor specif-\ud ically designed for adaptronic and biomedical engineering\ud applications. The main characteristics of\ud the streaming processor are the \ud exibility to implement\ud \ud oating-point-based scienti c computations\ud commonly performed in the digital signal processing\ud application. The \ud oating-point operators are connected\ud to dual-port memories through separated 3\ud operand-buses and 2 resultant-buses. Synthesized\ud with 130-nm technology, the Spectron can be clocked\ud at 480 MHz. The processor can perform 4 parallel\ud streaming/pipeline \ud oating-point operations using\ud its FPMAC and CORDIC cores, resulting in\ud a performance of about 4 485 = 1:94 GFlops\ud (Giga Floating-point operation per second), which\ud is suitable for high performance image processing in\ud biomedical electronic engineering application

Signal2Image Modules in Deep Neural Networks for EEG Classification

Deep learning has revolutionized computer vision utilizing the increased availability of big data and the power of parallel computational units such as graphical processing units. The vast majority of deep learning research is conducted using images as training data, however the biomedical domain is rich in physiological signals that are used for diagnosis and prediction problems. It is still an open research question how to best utilize signals to train deep neural networks. In this paper we define the term Signal2Image (S2Is) as trainable or non-trainable prefix modules that convert signals, such as Electroencephalography (EEG), to image-like representations making them suitable for training image-based deep neural networks defined as `base models'. We compare the accuracy and time performance of four S2Is (`signal as image', spectrogram, one and two layer Convolutional Neural Networks (CNNs)) combined with a set of `base models' (LeNet, AlexNet, VGGnet, ResNet, DenseNet) along with the depth-wise and 1D variations of the latter. We also provide empirical evidence that the one layer CNN S2I performs better in eleven out of fifteen tested models than non-trainable S2Is for classifying EEG signals and we present visual comparisons of the outputs of the S2Is.Comment: 4 pages, 2 figures, 1 table, EMBC 201