Human ECG data collection, digitalization, streaming and storing

Benhamida, Abdallah; Kozlovszky, Miklós

doi:10.1109/sami48414.2020.9108765

Cited by 7 publications

(3 citation statements)

References 9 publications

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“…Type Format [17,38,42,50,[58][59][60] XML-ECG Yes Open [61] HL7-XML Yes Open [27] mPCG-XML Yes Open [62,63] Philips-XML No Propietary [64,65] ecgML No Open [14,66] mECGML Yes Open [53,67] JSON Yes Open [17] SaECG Yes Open [49,68] HL7 aECG Yes Open [52,69] CDA R2 Yes Open [67] PDF-ECG Yes Open [70][71][72] MFER No Propietary [73] EDF No Open [74] CSV Yes Open [75] ECGWARE No Open…”

Section: Studies Data Formats Mhealth Interoperabilitymentioning

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: Studies Data Formats Mhealth Interoperabilitymentioning

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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“…In the previous research presented in the literature, many problems have been detected regarding ECG monitoring, such as the problems related to the digitalization and storage of stream-like long ECG recordings that are collected from long-term ambulatory sensors or devices [32]. A framework and a real solution for all the steps involved in ECG monitoring, from data collection from single-channel ECG sensors and digitalization to data storing, were introduced in [33]. Furthermore, we have presented a new enhanced version of the Health Level Seven International (HL7) Fast Healthcare Interoperability Resources (FHIR) data format, named HL7-aECG.…”

Section: Related Workmentioning

confidence: 99%

A Novel Algorithm for Automated Human Single-Lead ECG Pre-Annotation and Beat-to-Beat Separation for Heartbeat Classification Using Autoencoders

Benhamida

Kozlovszky

2022

Electronics

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An electrocardiogram (ECG) is used to check the electrical activity of the heart over a limited short-term or long-term period. Short-term observations are often used in hospitals or clinics, whereas long-term observations (often called continuous or stream-like ECG observations) are used to monitor the heart’s electrical activity on a daily basis and during different daily activities, such as sleeping, running, eating, etc. ECG can reflect the normal sinus rhythm as well as different heart problems, which might vary from Premature Atrial Contractions (PAC) and Premature Ventricular Contractions (PVC), to Sinus Arrest and many other problems. In order to perform such monitoring on a daily basis, it is very important to implement automated solutions that perform most of the work of the daily ECG analysis and could alert the doctors in case of any problem, and could even detect the type of the problem in order for the doctors to have an immediate report about the patient’s health status. This paper aims to provide a workflow for abnormal ECG signals detection from different sources of digitized ECG signals, including ambulatory devices. We propose an algorithm for ECG pre-annotation and beat-to-beat separation for heartbeat classification using Autoencoders. The algorithm includes the training of different models for different types of abnormal ECG signals, and has shown promising results for normal sinus rhythm and PVC compared to other solutions. This solution is proposed for no-noise and noisy signals as well.

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“…This feature can effectively mine the potential information in the existing data and improve the behavior recognition rate. Researchers based on sensor behavior recognition mainly solve the problem of system energy consumption from two aspects [20][21][22]. First, by improving the system recognition architecture, reducing the energy consumption in the entire behavior recognition process, and in terms of commercial applications, improving the information transmission method to meet the demand for low power consumption.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Digital Long Jump Take‐off Wearable Sensor Monitoring System

et al. 2021

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The wearable sensor monitoring system builds a long jump take-off recognition network model based on different digital feature extraction methods (one-dimensional digital feature extraction method, two-dimensional digital feature extraction method, and feature extraction method combining one-dimensional digitization and recursion). Experimental verification and analysis are performed on the processed sample data, and the identification effects, advantages, and disadvantages of the four methods are obtained. First, the sensor behavior movement collection software is designed based on the Android system, and the collection time and frequency are specified at the same time. In addition, for the problem of multisensor behavior recognition, an effective result fusion method is proposed. In a multisensor behavior recognition system, constructing a parallel processing architecture is conducive to improving the rate of behavior recognition. To maintain or increase the rate of behavior recognition, the result fusion method plays a vital role. Finally, this paper analyzes the process of multitask behavior recognition and constructs a residual model that can effectively integrate multitask results and fully mine data information. The experimental results show that, for the monitoring of exercise volume, we use step count statistics to extract feature values that can distinguish activity types based on human motion characteristics. This paper proposes a sample autonomous learning method to find the optimal sample training set and avoid occurrence of overfitting problems. In the recognition of 11 types of long jump take-offs, the average accuracy rate reached 98.7%. The average replacement method is used to count the number of steps, which provides a data reference for the user’s daily exercise volume.

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Human ECG data collection, digitalization, streaming and storing

Cited by 7 publications

References 9 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

A Novel Algorithm for Automated Human Single-Lead ECG Pre-Annotation and Beat-to-Beat Separation for Heartbeat Classification Using Autoencoders

Analysis of Digital Long Jump Take‐off Wearable Sensor Monitoring System

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