Fully Analogue ECG Front-end Applicable in Remote Patient Monitoring

Iliev, Ivo; Badarov, Dimiter; Tabakov, Serafim; Ganev, Borislav; Kanev, Ivan

doi:10.1109/et50336.2020.9238247

Cited by 3 publications

(8 citation statements)

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“…To elaborate, this includes remote patient monitoring through IoHT devices as IoT technologies have implemented secure and accurate representations of collecting, storing, and interacting with sensitive health data from various environments such as a physician interacting with their patients in their workplaces, at home, or on the go. Patient monitoring as a field considers the way medical or health care equipment can display or represent the data of a patient such as displaying their vital signs through screens, displays, or other wearable or mobile devices such as ECGs [ 43 ]. To contextualize the data, it is crucial to comprehend the heterogeneous data generated by these IoHT devices in health care monitoring.…”

Section: Research Design and Resultsmentioning

confidence: 99%

A Data Taxonomy for Adaptive Multifactor Authentication in the Internet of Health Care Things

Suleski¹,

Ahmed²

2023

J Med Internet Res

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The health care industry has faced various challenges over the past decade as we move toward a digital future where services and data are available on demand. The systems of interconnected devices, users, data, and working environments are referred to as the Internet of Health Care Things (IoHT). IoHT devices have emerged in the past decade as cost-effective solutions with large scalability capabilities to address the constraints on limited resources. These devices cater to the need for remote health care services outside of physical interactions. However, IoHT security is often overlooked because the devices are quickly deployed and configured as solutions to meet the demands of a heavily saturated industry. During the COVID-19 pandemic, studies have shown that cybercriminals are exploiting the health care industry, and data breaches are targeting user credentials through authentication vulnerabilities. Poor password use and management and the lack of multifactor authentication security posture within IoHT cause a loss of millions according to the IBM reports. Therefore, it is important that health care authentication security moves toward adaptive multifactor authentication (AMFA) to replace the traditional approaches to authentication. We identified a lack of taxonomy for data models that particularly focus on IoHT data architecture to improve the feasibility of AMFA. This viewpoint focuses on identifying key cybersecurity challenges in a theoretical framework for a data model that summarizes the main components of IoHT data. The data are to be used in modalities that are suited for health care users in modern IoHT environments and in response to the COVID-19 pandemic. To establish the data taxonomy, a review of recent IoHT papers was conducted to discuss the related work in IoHT data management and use in next-generation authentication systems. Reports, journal articles, conferences, and white papers were reviewed for IoHT authentication data technologies in relation to the problem statement of remote authentication and user management systems. Only publications written in English from the last decade were included (2012-2022) to identify key issues within the current health care practices and their management of IoHT devices. We discuss the components of the IoHT architecture from the perspective of data management and sensitivity to ensure privacy for all users. The data model addresses the security requirements of IoHT users, environments, and devices toward the automation of AMFA in health care. We found that in health care authentication, the significant threats occurring were related to data breaches owing to weak security options and poor user configuration of IoHT devices. The security requirements of IoHT data architecture and identified impactful methods of cybersecurity for health care devices, data, and their respective attacks are discussed. Data taxonomy provides better understanding, solutions, and improvements of user authentication in remote working environments for security features.

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Section: Research Design and Resultsmentioning

confidence: 99%

A Data Taxonomy for Adaptive Multifactor Authentication in the Internet of Health Care Things

Suleski¹,

Ahmed²

2023

J Med Internet Res

View full text Add to dashboard Cite

show abstract

“…Our team co-authored five of six studies we could find using a similar wireless interface that generates a sonified ECG stream and captures it with a GSM microphone [ 41 , 42 , 43 , 44 , 45 , 46 ], where all developments were designed for single-channel ECG processing: Remote recording of ECG between fingers with a commercial AliveCor device, found in a public database for screening of atrial fibrillation, including >12 k ECG recordings up to 60 s in duration, published for the PhysioNet/Computing in Cardiology Challenge 2017 [ 42 ]. Specifically, after the analogue-to-audio conversion of the ECG signal, the patient module transmitted acoustic data to a smartphone or tablet microphone, using a 19 kHz carrier frequency and a 200 Hz/mV modulation index.…”

Section: Methodsmentioning

confidence: 99%

“…According to the attending cardiologist, the waveform of the remotely recovered ECGs was sufficient for monitoring of the patients’ condition. Fully analogue hardware solution for sonification that could convert analogue ECG signals to an audio stream using a voltage-controlled oscillator [ 44 ]. Study of the audio characteristics of mobile phones [ 45 ] and development of a GSM modem [ 46 ] in the context of transmission of biosignals converted to sound [ 45 ].…”

Section: Methodsmentioning

confidence: 99%

“…Fully analogue hardware solution for sonification that could convert analogue ECG signals to an audio stream using a voltage-controlled oscillator [ 44 ].…”

Section: Methodsmentioning

confidence: 99%

“…It is worth noting a limitation in the real-life use of sonified ECG as an acoustic interface, related to the short distances that must be maintained between the patient module and the GSM microphone (<20 cm in previous experimental studies [ 41 , 43 , 44 , 45 , 46 ]). Nevertheless, keeping such short distances is not difficult to ensure, given that the patient is in close contact during routine GSM use.…”

Section: Limitations Of the Studymentioning

confidence: 99%

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Application of Convolutional Neural Network for Decoding of 12-Lead Electrocardiogram from a Frequency-Modulated Audio Stream (Sonified ECG)

Krasteva,

Iliev,

Tabakov

2024

Sensors

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Research of novel biosignal modalities with application to remote patient monitoring is a subject of state-of-the-art developments. This study is focused on sonified ECG modality, which can be transmitted as an acoustic wave and received by GSM (Global System for Mobile Communications) microphones. Thus, the wireless connection between the patient module and the cloud server can be provided over an audio channel, such as a standard telephone call or audio message. Patients, especially the elderly or visually impaired, can benefit from ECG sonification because the wireless interface is readily available, facilitating the communication and transmission of secure ECG data from the patient monitoring device to the remote server. The aim of this study is to develop an AI-driven algorithm for 12-lead ECG sonification to support diagnostic reliability in the signal processing chain of the audio ECG stream. Our methods present the design of two algorithms: (1) a transformer (ECG-to-Audio) based on the frequency modulation (FM) of eight independent ECG leads in the very low frequency band (300–2700 Hz); and (2) a transformer (Audio-to-ECG) based on a four-layer 1D convolutional neural network (CNN) to decode the audio ECG stream (10 s @ 11 kHz) to the original eight-lead ECG (10 s @ 250 Hz). The CNN model is trained in unsupervised regression mode, searching for the minimum error between the transformed and original ECG signals. The results are reported using the PTB-XL 12-lead ECG database (21,837 recordings), split 50:50 for training and test. The quality of FM-modulated ECG audio is monitored by short-time Fourier transform, and examples are illustrated in this paper and supplementary audio files. The errors of the reconstructed ECG are estimated by a popular ECG diagnostic toolbox. They are substantially low in all ECG leads: amplitude error (quartile range RMSE = 3–7 μV, PRD = 2–5.2%), QRS detector (Se, PPV > 99.7%), P-QRS-T fiducial points’ time deviation (<2 ms). Low errors generalized across diverse patients and arrhythmias are a testament to the efficacy of the developments. They support 12-lead ECG sonification as a wireless interface to provide reliable data for diagnostic measurements by automated tools or medical experts.

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A Data Taxonomy for Adaptive Multifactor Authentication in the Internet of Health Care Things (Preprint)

Suleski¹,

Ahmed²

2022

Preprint

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BACKGROUND The healthcare industry has faced various challenges over the past decade as we move towards a digital future where services and data are available at your fingertips. The systems of interconnected devices, users, data and working environments are referred to as the Internet of Healthcare Things (IoHT). IoHT devices have been emerging in the past decade as cost-effective solutions with large scalability capabilities to address the constraints on limited resources, and now to cater to the need to remote healthcare services outside of physical interactions. However, IoHT security is often overlooked as the devices are quickly deployed and configured as setup and use solutions to meet the demands of a heavily saturated industry where cyber attackers are taking advantage of the circumstances. During this pandemic, studies have shown that cybercriminals are exploiting the healthcare industry and data breaches are targeting user credentials through authentication vulnerabilities. Poor password use and management, and the lack of multi-factor authentication (MFA) security posture within IoHT, causes millions in damaged according to IBM. Therefore, it is important that healthcare authentication security moves toward adaptive multi-factor authentication (AMFA) to replace the traditional approach to authentication. OBJECTIVE At present there are no data models that particularly focus on IoHT data architecture to improve the feasibility of AMFA in healthcare. The aim of the review was to identify key cybersecurity challenges in a theoretical framework for a data model that will summarise the main components of IoHT data. The data is to be used in modalities that are suited for healthcare users in modern IoHT environments and in response to the COVID-19 pandemic. METHODS A review of recent IoHT papers was conducted to discuss the related work in IoHT data management and usage in next generation authentication systems. Reports, journal articles, conferences and white papers were included if they were relevant to the problem statement of remote authentication and user management systems. Only publications written in English from the last decade were included (2012-2022) in order to identify key issues within current healthcare practices and their management of IoHT devices. RESULTS We discuss the components of IoHT architecture from the perspective of data management and sensitivity to ensure privacy for all users. The data model will address the security requirements of IoHT users, environments and devices towards automation of AMFA in healthcare. We found that in healthcare authentication, the significant threats occurring during the pandemic were related to data breaches due to poor or weak security option and poor user configuration of IoHT devices. CONCLUSIONS This review paper of IoHT data architecture identified impactful methods of cybersecurity for healthcare devices, data and their respective attacks in the past decade. We discuss solutions and improvements of user authentication, especially when remote connections are made throughout the COVID-19 pandemic as patients and healthcare workers are working from home or accessing e-health records online. The data model will be useful in understanding the structure of the IoHT landscape during the pandemic and even in the future as new challenges arise.

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Fully Analogue ECG Front-end Applicable in Remote Patient Monitoring

Cited by 3 publications

References 3 publications

A Data Taxonomy for Adaptive Multifactor Authentication in the Internet of Health Care Things

A Data Taxonomy for Adaptive Multifactor Authentication in the Internet of Health Care Things

Application of Convolutional Neural Network for Decoding of 12-Lead Electrocardiogram from a Frequency-Modulated Audio Stream (Sonified ECG)

A Data Taxonomy for Adaptive Multifactor Authentication in the Internet of Health Care Things (Preprint)

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