Privacy-Preserving Electrocardiogram Monitoring for Intelligent Arrhythmia Detection

Son, Junggab; Park, Ju‐Young; Oh, Heekuck; Bhuiyan, Zakirul Alam; Hur, Junbeom; Kang, Kyungtae

doi:10.3390/s17061360

Cited by 22 publications

(7 citation statements)

References 36 publications

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“…Very recent alternatives are being proposed in the very last generation of works in the literature, which point towards an inflection point in this field. Works in short-term monitoring (duration of some minutes) and new public databases [ 50 ] can be found, as the intelligent heart-monitoring public cryptosystem, which involves a patient-worn ECG sensor and a remote monitoring station, using PT and classification trees as heartbeat detection and classification algorithms, respectively. More advanced methods, such as sample entropy, fast Fourier transform, and ANN, were recently selected for the integrated detection algorithms [ 21 ], in order to validate an arrhythmia detection method for supra and ventricular ectopic beats and atrial/ventricular fibrillation, when using the ANSI/AAMI EC57:2012 standard.…”

Section: Discussionmentioning

confidence: 99%

On the Beat Detection Performance in Long-Term ECG Monitoring Scenarios

Melgarejo-Meseguer

Everss

Gimeno-Blanes

et al. 2018

Sensors

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Despite the wide literature on R-wave detection algorithms for ECG Holter recordings, the long-term monitoring applications are bringing new requirements, and it is not clear that the existing methods can be straightforwardly used in those scenarios. Our aim in this work was twofold: First, we scrutinized the scope and limitations of existing methods for Holter monitoring when moving to long-term monitoring; Second, we proposed and benchmarked a beat detection method with adequate accuracy and usefulness in long-term scenarios. A longitudinal study was made with the most widely used waveform analysis algorithms, which allowed us to tune the free parameters of the required blocks, and a transversal study analyzed how these parameters change when moving to different databases. With all the above, the extension to long-term monitoring in a database of 7-day Holter monitoring was proposed and analyzed, by using an optimized simultaneous-multilead processing. We considered both own and public databases. In this new scenario, the noise-avoid mechanisms are more important due to the amount of noise that exists in these recordings, moreover, the computational efficiency is a key parameter in order to export the algorithm to the clinical practice. The method based on a Polling function outperformed the others in terms of accuracy and computational efficiency, yielding 99.48% sensitivity, 99.54% specificity, 99.69% positive predictive value, 99.46% accuracy, and 0.85% error for MIT-BIH arrhythmia database. We conclude that the method can be used in long-term Holter monitoring systems.

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Section: Discussionmentioning

confidence: 99%

On the Beat Detection Performance in Long-Term ECG Monitoring Scenarios

Melgarejo-Meseguer

Everss

Gimeno-Blanes

et al. 2018

Sensors

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“…The system protects sensing data and user privacy, which is an essential attribute of dependability, by adopting signal scrambling and anonymous identity schemes. They also employ a public key cryptosystem to enable secure communication between the entities [5].…”

Section: Literature Surveymentioning

confidence: 99%

Location Privacy Protection for IoT-based WBANs using Anonymous Authentication and Improved AES Encryption

S.V¹

2022

IJSREM

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Advances in wireless communications, embedded systems, and integrated circuit technologies have enabled the wireless body area network (WBAN) to become a promising networking paradigm. Over the last decade, as an important part of the Internet of Things, we have witnessed WBANs playing an increasing role in modern medical systems because of its capabilities to collect real-time biomedical data through intelligent medical sensors in or around the patients' body and send the collected data to remote medical personnel for clinical diagnostics. WBANs not only bring us conveniences but also bring along the challenge of keeping data's confidentiality and preserving patients' privacy. In the previous, anonymous authentication (AA) schemes for WBANs were proposed to enhance security by protecting patients' identities and by encrypting medical data. However, many of these schemes are not secure enough. The AA scheme for WBANs and point out that it is not secure for medical applications by proposing an impersonation attack. In this project, propose an Improved AES with secure anonymous authentication framework for WBANs and prove that it is provably secure. The comprehensive analysis section shows that the proposed scheme overcomes the security weaknesses in the existing schemes and also provides low computation cost during anonymous authentication. Keywords—: Cross-modality, contrast enhancement, 2D histogram specification (HS), SSIM gradient, tumor segmentation.

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“…Hacker close to the patient can intercept the ECG data transmission between sensors and the patient's smartphone. Alternatively, the hacker could manipulate the recorded ECG signals at the decision support server, thereby changing the health status of the patient [160]. Another issue is the added usage of the Internet could further increase the number of security and privacy threats while also resulting in data integrity issues [161].…”

Section: Privacy Preservation Techniquesmentioning

confidence: 99%

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

et al. 2021

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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.

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Privacy-Preserving Electrocardiogram Monitoring for Intelligent Arrhythmia Detection

Cited by 22 publications

References 36 publications

On the Beat Detection Performance in Long-Term ECG Monitoring Scenarios

On the Beat Detection Performance in Long-Term ECG Monitoring Scenarios

Location Privacy Protection for IoT-based WBANs using Anonymous Authentication and Improved AES Encryption

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

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