Wearable Devices for Remote Monitoring of Heart Rate and Heart Rate Variability-What We Know and What Is Coming

Heart rate at rest and exercise may predict cardiovascular risk. Heart rate variability is a measure of variation in time between each heartbeat, representing the balance between the parasympathetic and sympathetic nervous system and may predict adverse cardiovascular events. With advances in technology and increasing commercial interest, the scope of remote monitoring health systems has expanded. In this review, we discuss the concepts behind cardiac signal generation and recording, wearable devices, pros and cons focusing on accuracy, ease of application of commercial and medical grade diagnostic devices, which showed promising results in terms of reliability and value. Incorporation of artificial intelligence and cloud based remote monitoring have been evolving to facilitate timely data processing, improve patient convenience and ensure data security.

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

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.

Analysis and postprocessing of ECG or heart rate data from wearable devices beyond the proprietary cloud and app infrastructure of the vendors

Background

The impact of medical-grade wearable electrocardiographic (ECG) recording technology is increasing rapidly. A wide range of different portable smartphone-connected ECG and heart rate trackers is available on the market. Smart ECG devices are especially valuable to monitor either supraventricular arrhythmias or prolonged QT intervals to avoid drug-induced life-threatening arrhythmias. However, frequent false alarms or false-positive arrhythmia results from wearable devices are unwanted. Therefore, for clinical evaluation, it should be possible to measure and evaluate the biosignals of the wearables independent of the manufacturer.

Objective

Unlike radiological devices that do support the universal digital imaging and communications in medicine standard, these medical-grade devices do not yet support a secure standardized exchange pathway between sensors, smartphones/smartwatches, and end services such as cloud storage or universal Web-based application programming interface (API) access. Consequently, postprocessing of recorded ECGs or heart rate interval data requires a whole toolbox of customized software technologies.

Methods/Results

Various methods for measuring and analyzing nonstandardized ECG and heart rate data are proposed, including online measurement of ECG waveforms within a PDF, access to data using manufacturer-specific software development kits, and access to biosignals using modern Web APIs.

Conclusion

With the appropriate workaround, modern software technologies such as JavaScript and PHP allow health care providers and researchers to easily and instantly access necessary and important signal measurements on demand.

The History and Challenges of SCP-ECG: The Standard Communication Protocol for Computer-Assisted Electrocardiography

Ever since the first publication of the standard communication protocol for computer-assisted electrocardiography (SCP-ECG), prENV 1064, in 1993, by the European Committee for Standardization (CEN), SCP-ECG has become a leading example in health informatics, enabling open, secure, and well-documented digital data exchange at a low cost, for quick and efficient cardiovascular disease detection and management. Based on the experiences gained, since the 1970s, in computerized electrocardiology, and on the results achieved by the pioneering, international cooperative research on common standards for quantitative electrocardiography (CSE), SCP-ECG was designed, from the beginning, to empower personalized medicine, thanks to serial ECG analysis. The fundamental concept behind SCP-ECG is to convey the necessary information for ECG re-analysis, serial comparison, and interpretation, and to structure the ECG data and metadata in sections that are mostly optional in order to fit all use cases. SCP-ECG is open to the storage of the ECG signal and ECG measurement data, whatever the ECG recording modality or computation method, and can store the over-reading trails and ECG annotations, as well as any computerized or medical interpretation reports. Only the encoding syntax and the semantics of the ECG descriptors and of the diagnosis codes are standardized. We present all of the landmarks in the development and publication of SCP-ECG, from the early 1990s to the 2009 International Organization for Standardization (ISO) SCP-ECG standards, including the latest version published by CEN in 2020, which now encompasses rest and stress ECGs, Holter recordings, and protocol-based trials.

Combining Optical Character Recognition With Paper ECG Digitization

Objective: We propose a MATLAB-based tool to convert electrocardiography (ECG) waveforms from paper-based ECG records into digitized ECG signals that is vendor-agnostic. The tool is packaged as an open source standalone graphical user interface (GUI) based application. Methods and procedures: To reach this objective we: (1) preprocess the ECG records, which includes skew correction, background grid removal and linear filtering; (2) segment ECG signals using Connected Components Analysis (CCA); (3) implement Optical Character Recognition (OCR) for removal of overlapping ECG lead characters and for interfacing of patients’ demographic information with their research records or their electronic medical record (EMR). The ECG digitization results are validated through a reader study where clinically salient features, such as intervals of QRST complex, between the paper ECG records and the digitized ECG records are compared. Results: Comparison of clinically important features between the paper-based ECG records and the digitized ECG signals, reveals intra- and inter-observer correlations of 0.86–0.99 and 0.79–0.94, respectively. The kappa statistic was found to average at 0.86 and 0.72 for intra- and inter-observer correlations, respectively. Conclusion: The clinically salient features of the ECG waveforms such as the intervals of QRST complex, are preserved during the digitization procedure. Clinical and Healthcare Impact: This open-source digitization tool can be used as a research resource to digitize paper ECG records thereby enabling development of new prediction algorithms to risk stratify individuals with cardiovascular disease, and/or allow for development of ECG-based cardiovascular diagnoses relying upon automated digital algorithms.

Advances of ECG Sensors from Hardware, Software and Format Interoperability Perspectives

It is well-known that cardiovascular disease is one of the major causes of death worldwide nowadays. Electrocardiogram (ECG) sensor is one of the tools commonly used by cardiologists to diagnose and detect signs of heart disease with their patients. Since fast, prompt and accurate interpretation and decision is important in saving the life of patients from sudden heart attack or cardiac arrest, many innovations have been made to ECG sensors. However, the use of traditional ECG sensors is still prevalent in the clinical settings of many medical institutions. This article provides a comprehensive survey on ECG sensors from hardware, software and data format interoperability perspectives. The hardware perspective outlines a general hardware architecture of an ECG sensor along with the description of its hardware components. The software perspective describes various techniques (denoising, machine learning, deep learning, and privacy preservation) and other computer paradigms used in the software development and deployment for ECG sensors. Finally, the format interoperability perspective offers a detailed taxonomy of current ECG formats and the relationship among these formats. The intention is to help researchers towards the development of modern ECG sensors that are suitable and approved for adoption in real clinical settings.

A practical approach to storage and retrieval of high-frequency physiological signals

OBJECTIVE

Storage of physiological waveform data for retrospective analysis presents significant challenges. Resultant data can be very large, and therefore becomes expensive to store and complicated to manage. Traditional database approaches are not appropriate for large scale storage of physiological waveforms. Our goal was to apply modern time series compression and indexing techniques to the problem of physiological waveform storage and retrieval.

APPROACH

We deployed a vendor-agnostic data collection system and developed domain-specific compression approaches that allowed long term storage of physiological waveform data and other associated clinical and medical device data. The database (called AtriumDB) also facilitates rapid retrieval of retrospective data for high-performance computing and machine learning applications.

MAIN RESULTS

A prototype system has been recording data in a 42-bed pediatric critical care unit at The Hospital for Sick Children in Toronto, Ontario since February 2016. As of December 2019, the database contains over 720,000 patient-hours of data collected from over 5300 patients, all with complete waveform capture. One year of full resolution physiological waveform storage from this 42-bed unit can be losslessly compressed and stored in less than 300 GB of disk space. Retrospective data can be delivered to analytical applications at a rate of up to 50 million time-value pairs per second.

SIGNIFICANCE

Stored data are not pre-processed or filtered. Having access to a large retrospective dataset with realistic artefacts lends itself to the process of anomaly discovery and understanding. Retrospective data can be replayed to simulate a realistic streaming data environment where analytical tools can be rapidly tested at scale.

Archiving and exchange of digital ECGs: A review of existing data formats

Digital ECG is today a common practice but a universal format for its storage and exchange has never been widely implemented. The reason is linked on one side to the need of the manufacturing industry to (rightly) protect intellectual propriety and technology, but on the other to an inadequate effort of the research community to sufficiently enforce the use of digital ECG data. To some degree, and at least from a practical point of view, the problem is also linked to other factors, such as the need in some instances to protect patient-sensitive information, and whether digital exchanged data should also include annotations and measurements from an algorithm or by human intervention. As a result, after more than 30 years it is still common that the full ECG acquired information is not preserved, but only partially stored or saved as a PDF report. Paradoxically, the modern era of hospital information technology and the advent and large diffusion of electronic health record systems did not bring expected improvements: the process of digital ECG retrieval and management remains extremely complicated and cumbersome. The ultimate risk is that the ECG may end up being considered "just" an image rather than a voltage-versus-time signal as it has always been. A critical review of the most commonly used formats for digital ECG will be given, focusing in particular to those linked with DICOM, HL7 and SCP-ECG standards, and highlighting the respective advantages and limitations with special emphasis to the needs typically encountered by the research community. The goal is to provide a snapshot of the present, and to discuss mid- and long-term potential directions and changes, emphasizing what digital ECG organizations could do to "save" ECG information and facilitate its widespread exchange.

Proposal for a Standard Format for Neurophysiology Data Recording and Exchange

The lack of interoperability between information networks is a significant source of cost in health care. Standardized data formats decrease health care cost, improve quality of care, and facilitate biomedical research. There is no common standard digital format for storing clinical neurophysiologic data. This review proposes a new standard file format for neurophysiology data (the bulk of which is video-electroencephalographic data), entitled the Multiscale Electrophysiology Format, version 3 (MEF3), which is designed to address many of the shortcomings of existing formats. MEF3 provides functionality that addresses many of the limitations of current formats. The proposed improvements include (1) hierarchical file structure with improved organization; (2) greater extensibility for big data applications requiring a large number of channels, signal types, and parallel processing; (3) efficient and flexible lossy or lossless data compression; (4) industry standard multilayered data encryption and time obfuscation that permits sharing of human data without the need for deidentification procedures; (5) resistance to file corruption; (6) facilitation of online and offline review and analysis; and (7) provision of full open source documentation. At this time, there is no other neurophysiology format that supports all of these features. MEF3 is currently gaining industry and academic community support. The authors propose the use of the MEF3 as a standard format for neurophysiology recording and data exchange. Collaboration between industry, professional organizations, research communities, and independent standards organizations is needed to move the project forward.

Implementation of integrated heterogeneous electronic electrocardiography data into Maharaj Nakorn Chiang Mai Hospital Information System

Electrocardiography is one of the most important non-invasive diagnostic tools for diagnosing coronary heart disease. The electrocardiography information system in Maharaj Nakorn Chiang Mai Hospital required a massive manual labor effort. In this article, we propose an approach toward the integration of heterogeneous electrocardiography data and the implementation of an integrated electrocardiography information system into the existing Hospital Information System. The system integrates different electrocardiography formats into a consistent electrocardiography rendering by using Java software. The interface acts as middleware to seamlessly integrate different electrocardiography formats. Instead of using a common electrocardiography protocol, we applied a central format based on Java classes for mapping different electrocardiography formats which contains a specific parser for each electrocardiography format to acquire the same information. Our observations showed that the new system improved the effectiveness of data management, work flow, and data quality; increased the availability of information; and finally improved quality of care.

Mobile, cloud, and big data computing: contributions, challenges, and new directions in telecardiology

Many studies have indicated that computing technology can enable off-site cardiologists to read patients’ electrocardiograph (ECG), echocardiography (ECHO), and relevant images via smart phones during pre-hospital, in-hospital, and post-hospital teleconsultation, which not only identifies emergency cases in need of immediate treatment, but also prevents the unnecessary re-hospitalizations. Meanwhile, several studies have combined cloud computing and mobile computing to facilitate better storage, delivery, retrieval, and management of medical files for telecardiology. In the future, the aggregated ECG and images from hospitals worldwide will become big data, which should be used to develop an e-consultation program helping on-site practitioners deliver appropriate treatment. With information technology, real-time tele-consultation and tele-diagnosis of ECG and images can be practiced via an e-platform for clinical, research, and educational purposes. While being devoted to promote the application of information technology onto telecardiology, we need to resolve several issues: (1) data confidentiality in the cloud, (2) data interoperability among hospitals, and (3) network latency and accessibility. If these challenges are overcome, tele-consultation will be ubiquitous, easy to perform, inexpensive, and beneficial. Most importantly, these services will increase global collaboration and advance clinical practice, education, and scientific research in cardiology.

Raw data extraction from electrocardiograms with Portable Document Format

During the last two decades there has been a thorough research and development of standards and protocols in order to cope with different electrocardiogram formats from heterogeneous acquisition systems. Despite the efforts of public and private consortiums on creating a standardized electrocardiogram (ECG) storage format, there is still not a single one. Indeed, there is also the necessity of access to raw data of the ECGs previously acquired. Most of these documents have been saved as Adobe PDF files, since for medical staff it is an easy format for later visualization. However, this format presents difficulties when trying to access original raw data for subsequent studies and signal analysis. In this manner, this paper presents an application that obtains plain numerical data from ECG files stored with PDF format. Data can also be exported to one of the most common file formats in existence, to be easily accessed thereafter.

A robust and simple security extension for the medical standard SCP-ECG

This paper proposes a SCP-ECG security extension after having analyzed the features of this standard, its security requirements and the current measures implemented by other medical protocols. Our approach permits SCP-ECG files to be stored safely and proper access to be granted (or denied) to users for different purposes: interpretation of the test, consultation, clinical research or teaching. The access privileges are scaled by means of role-based profiles supported by cryptographic elements (ciphering, digital certificates and digital signatures). These elements are arranged as metadata into a new section which extends the protocol and protects the remaining sections. The application built to implement this approach has been extensively tested, showing its capacity to authenticate users and to protect the integrity of files and the privacy of sensitive data, with a low impact on file size and access time. In addition, this solution is compatible with any version of the SCP-ECG and can be easily integrated into e-health platforms.