FPGA-based denoising and beat detection of the ECG signal

Alhelal, Dheyaa; Aboalayon, Khald A. I.; Daneshzand, Mohammad; Faezipour, Miad

doi:10.1109/lisat.2015.7160184

Cited by 19 publications

(9 citation statements)

References 11 publications

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“…The performance of the implemented "ECG_AD_module" was compared with other arrhythmia detection system. The total number of registers utilized for the proposed design was 77.57 improved than the previous design [24].…”

Section: Discussionmentioning

confidence: 88%

“…The proposed arrhythmia detection system designed based on FPGA from the results showed that it utilized less number of resources in terms of total number of pins, registers and memory bits when compared to previous design found from the literature. The proposed design when compared with most proven design [24] proved to be more efficient in terms of resource utilization, the results showed that the total registers are 792 when compared to that of the previous design [24] to be 3532. Total pins and memory bits are 43 and 51177, that in case of previous design [24] are 128, 131072 respectively.…”

Section: Fig62 Proposed Ecg-ad-module When Reset =0 Using Chip-smentioning

confidence: 92%

“…The proposed design when compared with most proven design [24] proved to be more efficient in terms of resource utilization, the results showed that the total registers are 792 when compared to that of the previous design [24] to be 3532. Total pins and memory bits are 43 and 51177, that in case of previous design [24] are 128, 131072 respectively. The comparison overhead in terms of total registers is found to be 78% and in terms of total memory bits is found to be 61%.…”

Section: Fig62 Proposed Ecg-ad-module When Reset =0 Using Chip-smentioning

confidence: 92%

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An Optimized FPGA Based System Design for the Arrhythmia Detection using ECG

C*,

S V

2019

IJITEE

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Electrocardiogram signals are highly susceptible to interferences caused due to various kinds of noises including artefacts’, disruptions in power lines attained from the human interferences and device disturbances. These noise signals tend to lower the quality of signals that result in crucial environment for detecting and diagnosing different types of arrhythmia. In order to avoid this issue, multiple filtering techniques are being incorporated out of all Gaussian filters with Haar DWT portray better outcomes in noise elimination and smoothening of signal. The process of ECG signal filtering allows performing the testing and validation of in the actual world emulation. Enhancement in PSNR ratio is observed by using the ECG signal filters along the reconstructed signal. For a given input ECG signal, the levels of the signal peak decide if the patient is suffering from arrhythmia or not. If peak is low, patient is detected with the arrhythmia disease, if high patient is normal. The results can be observed in simulation. FPGA prototyping of the design is carried out along the hardware debugging in chip scope pro tool. The design is realized using Verilog coding with the technique of morphological filtering. For the purpose of debugging the hardware device used is Artix-7. The FPGA methodology is success full in a position to detect arrhythmia. The framework based on FPGA is structured and executed in the paper which can detect a type of arrhythmia which indicates Atrio Ventricular block along with all the noises removed. The simulation results are obtained by taking ECG signals from MIT-BIH arrhythmia database. The proposed FPGA based system design is proven to be optimized as it showed very less utilization of resources when compared to previous arrhythmia detection system designs.

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Section: Discussionmentioning

confidence: 88%

Section: Fig62 Proposed Ecg-ad-module When Reset =0 Using Chip-smentioning

confidence: 92%

Section: Fig62 Proposed Ecg-ad-module When Reset =0 Using Chip-smentioning

confidence: 92%

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An Optimized FPGA Based System Design for the Arrhythmia Detection using ECG

C*,

S V

2019

IJITEE

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“…Recently, deep learning methods have been widely utilized in face recognition and other classification problems [32,26,8,34,35,16,37] instead of classical methods [6,2]. These methods, can also be employed for the task of sketch-photo recognition problem by learning the relationship between the two modalities.…”

Section: Introductionmentioning

confidence: 99%

Deep Sketch-Photo Face Recognition Assisted by Facial Attributes

Iranmanesh

Kazemi

Soleymani

et al. 2018

2018 IEEE 9th International Conference on Biometrics Theory, Applications and Systems (BTAS)

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In this paper, we present a deep coupled framework to address the problem of matching sketch image against a gallery of mugshots. Face sketches have the essential information about the spatial topology and geometric details of faces while missing some important facial attributes such as ethnicity, hair, eye, and skin color. We propose a coupled deep neural network architecture which utilizes facial attributes in order to improve the sketch-photo recognition performance. The proposed Attribute-Assisted Deep Convolutional Neural Network (AADCNN) method exploits the facial attributes and leverages the loss functions from the facial attributes identification and face verification tasks in order to learn rich discriminative features in a common embedding subspace. The facial attribute identification task increases the inter-personal variations by pushing apart the embedded features extracted from individuals with different facial attributes, while the verification task reduces the intra-personal variations by pulling together all the features that are related to one person. The learned discriminative features can be well generalized to new identities not seen in the training data. The proposed architecture is able to make full use of the sketch and complementary facial attribute information to train a deep model compared to the conventional sketch-photo recognition methods. Extensive experiments are performed on composite (E-PRIP) and semi-forensic (IIIT-D semi-forensic) datasets. The results show the superiority of our method compared to the stateof-the-art models in sketch-photo recognition algorithms.

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“…Computer vision deals with acquiring, processing, and understanding images in order to solve different tasks. Computer vision has a wide range of applications including video gaming [16], in the food industry [17], robotics [18,19,20], biomedical [21,22], and many more [23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41].…”

Section: Introduction 11 Problem Definitionmentioning

confidence: 99%

Domain Adaptation and Privileged Information for Visual Recognition

Motiian¹

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Domain Adaptation and Privileged Information for Visual Recognition Saeid Motiian The automatic identification of entities like objects, people or their actions in visual data, such as images or video, has significantly improved, and is now being deployed in access control, social media, online retail, autonomous vehicles, and several other applications. This visual recognition capability leverages supervised learning techniques, which require large amounts of labeled training data from the target distribution representative of the particular task at hand. However, collecting such training data might be expensive, require too much time, or even be impossible. In this work, we introduce several novel approaches aiming at compensating for the lack of target training data. Rather than leveraging prior knowledge for building task-specific models, typically easier to train, we focus on developing general visual recognition techniques, where the notion of prior knowledge is better identified by additional information, available during training. Depending on the nature of such information, the learning problem may turn into domain adaptation (DA), domain generalization (DG), leaning using privileged information (LUPI), or domain adaptation with privileged information (DAPI). When some target data samples are available and additional information in the form of labeled data from a different source is also available, the learning problem becomes domain adaptation. Unlike previous DA work, we introduce two novel approaches for the few-shot learning scenario, which require only very few labeled target samples, and even one can be very effective. The first method exploits a Siamese deep neural network architecture for learning an embedding where visual categories from the source and target distributions are semantically aligned and yet maximally separated. The second approach instead, extends adversarial learning to simultaneously maximize the confusion between source and target domains while achieving semantic alignment. In complete absence of target data, several cheaply available source datasets related to the target distribution can be leveraged as additional information for learning a task. This is the domain generalization setting. We introduce the first deep learning approach to address the DG problem, by extending a Siamese network architecture for learning a representation of visual categories that is invariant with respect to the sources, while imposing semantic alignment and class separation to maximize generalization performance on unseen target domains. There are situations in which target data for training might come equipped with additional information that can be modeled as an auxiliary view of the data, and that unfortunately is not available during testing. This is the LUPI scenario. We introduce a novel framework based on the information bottleneck that leverages the auxiliary view to improve the performance of visual classifiers. We do so by introducing a formulation that is general, in the sense that can ...

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FPGA-based denoising and beat detection of the ECG signal

Cited by 19 publications

References 11 publications

An Optimized FPGA Based System Design for the Arrhythmia Detection using ECG

An Optimized FPGA Based System Design for the Arrhythmia Detection using ECG

Deep Sketch-Photo Face Recognition Assisted by Facial Attributes

Domain Adaptation and Privileged Information for Visual Recognition

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