PÉEK: A cloud-based application for automatic electrocardiogram pre-diagnosis

Zermeño-Campos, Nestor Alexander; Cuevas-González, Daniel; García-Vázquez, Juan Pablo; Avitia, Roberto L.; Bravo-Zanoguera, Miguel E.; Reyna, Marco A.; Díaz-Ramírez, Arnoldo

doi:10.1016/j.softx.2022.101124

Cited by 3 publications

(1 citation statement)

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“…However, there are other conditions that add value to the selection of a format and this depends on the type of development envisaged, since efficiency is not only based on interoperability, but also on the management of computational resources such as RAM, CPU usage, transfer rate and query speed, which play an important role in the selection of the format [97,98]. This is because formats such as XML are shown as inflated files by placing start and end tags on each of their elements and increasing the size of their final file and JSON, despite being a format that requires less storage space due to its hierarchical structure, still uses a greater number of characters to identify its elements compared to the CSV format [19,[99][100][101].…”

Section: Discussionmentioning

confidence: 99%

ECG Standards and Formats for Interoperability between mHealth and Healthcare Information Systems: A Scoping Review

Cuevas-González

García-Vázquez

Bravo-Zanoguera

et al. 2022

IJERPH

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Interoperability is defined as the ability of a system or device to communicate between different technologies and software applications. This allows the exchange and use of data in an efficient, precise, and robust way. The present article gives researchers and healthcare information systems developers a qualitative and quantitative synthesis of the state of knowledge related to data formats and data standards proposed for mHealth devices interoperability in healthcare information systems that retrieve and store ECG data. We carry out a scoping review to answer to following questions: (1) What digital data formats or data standards have been proposed for the interoperability of electrocardiograph data between traditional healthcare information systems and mobile healthcare information systems? (2) What are the advantages and disadvantages of these data formats or data standards? The scoping review was conducted in four databases in accordance with the JBI methodology for scoping reviews, and in line with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR). A total of 4018 studies were identified of which 30 studies met the inclusion criteria. Based on our findings, we identify four standards and nine formats for capturing and storing streaming ECG data in mobile health applications. The standards used were HL7, SCP-ECG, x73-PHD, and PDF/A. Formats include CSV, PDF-ECG, and seven XML-based formats. These are ECG-XML, HL7-XML, mPCG-XML, mECGML, JSON, SaECG, and CDA R2.

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Section: Discussionmentioning

confidence: 99%

ECG Standards and Formats for Interoperability between mHealth and Healthcare Information Systems: A Scoping Review

Cuevas-González

García-Vázquez

Bravo-Zanoguera

et al. 2022

IJERPH

Self Cite

View full text Add to dashboard Cite

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TrustML: A Python package for computing the trustworthiness of ML models

Manzano,

Ayala,

Gómez

2024

SoftwareX

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Improved R-Peak Detection in Long-Term ECGs: Leveraging Hybrid Linearization and LSTM with Grey Wolf Optimization

VELUSAMY,

MURUGAN

2024

Preprint

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The precise interpretation of the ECG signal can reveal the condition of the heart. ECG signal analysis can assist in identifying any abnormalities or arrhythmias in the heart. Premature Ventricular Contractions (PVCs) are irregular heartbeats that may signal the presence of a heart ailment. Long-term ECGs are commonly utilized in clinical practice to diagnose PVCs. However, analyzing these long-term ECGs is time-consuming for cardiologists and requires human involvement. This research proposes a robust approach for detecting R peaks in QRS complexes using a recurrent neural network. Our proposed methodology was applied to the well-known MIT-BIH Arrhythmia Database (MIT-DB) dataset and the China Physiological Signal Challenge (2020) database, which contains over a million beats. The hybrid linearization technique uses an adaptive filter and discrete wavelet transform (DWT) to remove noise from the ECG signal. The next step is to use principle component analysis (PCA) to extract characteristics from the ECG data. Lastly, the R peak signals are classified using long short-term memory (LSTM) to improve accuracy through optimization techniques like Grey Wolf optimization (GWO). The algorithm's performance was also evaluated using the MIT-BIH Arrhythmia database and the China Physiological Signal Challenge (2020). The suggested formal technique yields the best results for R-peak detection on CPSC-DB, with F1-score of 95.3%, recall of 96.8%, accuracy of 99.5%, and precision of 95.3%. The F1-score, recall, and precision of the algorithms on MIT-DB are all equivalent to, or better than, those of the competing methods.

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PÉEK: A cloud-based application for automatic electrocardiogram pre-diagnosis

Cited by 3 publications

References 22 publications

ECG Standards and Formats for Interoperability between mHealth and Healthcare Information Systems: A Scoping Review

ECG Standards and Formats for Interoperability between mHealth and Healthcare Information Systems: A Scoping Review

TrustML: A Python package for computing the trustworthiness of ML models

Improved R-Peak Detection in Long-Term ECGs: Leveraging Hybrid Linearization and LSTM with Grey Wolf Optimization

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