Comparison of spatial temporal representations of the vectorcardiogram using digital image processing

Silva, Ittalo dos Santos; Barbosa, José Raimundo; Sousa, Rafael Duarte de; Souza, Igor Franklin Brito de; Hortegal, Renato; Regis, Carlos Danilo Miranda

doi:10.1016/j.jelectrocard.2020.02.013

Cited by 4 publications

(1 citation statement)

References 13 publications

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“…Attribute data are usually expressed by feature codes, which are digital codes defined according to classification features such as the category and level of map elements and other quality features. When building a GIS database, the attribute database must be established at the same time, and the correspondence between spatial data and attribute data is also a very tedious task [11]. e spatial object features of geographic elements include spatial features and attribute features, and spatial features are divided into spatial location and topological relationship.…”

Section: Digital Image Database Establishmentmentioning

confidence: 99%

[Retracted] Processing Methods for Digital Image Data Based on the Geographic Information System

Chen

et al. 2021

Complexity

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Digital image data processing is mainly to input digital image data into a computer to complete the conversion of a continuous spatially distributed image model into a discrete digital model so that the computer can identify, process, and store the processing process of digital image information. Geographic information system (GIS) is a computer system that integrates multiple forms of information expression, and it integrates functions such as collection, processing, transmission, storage, management, analysis, expression, and query retrieval, which can quickly discover the spatial distribution of things and their attributes and can express the results accurately and vividly in various intuitive forms. Therefore, on the basis of summarizing and analyzing previous research works, this paper expounded the research status and significance of processing methods for digital image data, elaborated the development background, current status, and future challenges of the GIS technology, introduced the methods and principles of permutation matrix algorithm and subimage averaging method, constructed the processing model for digital image data based on GIS, analyzed the data structure and its database establishment for digital image, proposed the processing methods for digital image data based on GIS, performed the enhancement processing and calculation classification of digital image data, and finally conducted a case analysis and its result discussion. The study results show that the proposed processing methods for digital image data based on GIS can perform analogue-to-digital conversion of continuous images, complete the steps of sampling, layering, and quantization, and then encode the obtained discrete digital signal into the computer to form an in-plane collection of pixels; this processing method can also organically combine spatial information and image data and identify, process, and store digital image data from both spatial and attribute aspects. The study results of this paper provide a reference for further research on the processing methods for digital image data based on GIS.

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Section: Digital Image Database Establishmentmentioning

confidence: 99%

[Retracted] Processing Methods for Digital Image Data Based on the Geographic Information System

Chen

et al. 2021

Complexity

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ECG Data Visualization: Combining the power of Grafana and InfluxDB

Aggoune

Benratem

2023

2023 International Conference on Advances in Electronics, Control and Communication Systems (ICAECCS)

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Electrocardiogram lead conversion from single-lead blindly-segmented signals

Beco

Pinto

Cardoso

2022

BMC Med Inform Decis Mak

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Background The standard configuration’s set of twelve electrocardiogram (ECG) leads is optimal for the medical diagnosis of diverse cardiac conditions. However, it requires ten electrodes on the patient’s limbs and chest, which is uncomfortable and cumbersome. Interlead conversion methods can reconstruct missing leads and enable more comfortable acquisitions, including in wearable devices, while still allowing for adequate diagnoses. Currently, methodologies for interlead ECG conversion either require multiple reference (input) leads and/or require input signals to be temporally aligned considering the ECG landmarks. Methods Unlike the methods in the literature, this paper studies the possibility of converting ECG signals into all twelve standard configuration leads using signal segments from only one reference lead, without temporal alignment (blindly-segmented). The proposed methodology is based on a deep learning encoder-decoder U-Net architecture, which is compared with adaptations based on convolutional autoencoders and label refinement networks. Moreover, the method is explored for conversion with one single shared encoder or multiple individual encoders for each lead. Results Despite the more challenging settings, the proposed methodology was able to attain state-of-the-art level performance in multiple target leads, and both lead I and lead II seem especially suitable to convert certain sets of leads. In cross-database tests, the methodology offered promising results despite acquisition setup differences. Furthermore, results show that the presence of medical conditions does not have a considerable effect on the method’s performance. Conclusions This study shows the feasibility of converting ECG signals using single-lead blindly-segmented inputs. Although the results are promising, further efforts should be devoted towards the improvement of the methodologies, especially the robustness to diverse acquisition setups, in order to be applicable to cardiac health monitoring in wearable devices and less obtrusive clinical scenarios.

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Comparison of spatial temporal representations of the vectorcardiogram using digital image processing

Cited by 4 publications

References 13 publications

[Retracted] Processing Methods for Digital Image Data Based on the Geographic Information System

[Retracted] Processing Methods for Digital Image Data Based on the Geographic Information System

ECG Data Visualization: Combining the power of Grafana and InfluxDB

Electrocardiogram lead conversion from single-lead blindly-segmented signals

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