Toward Uniform Smart Healthcare Ecosystems: A Survey on Prospects, Security, and Privacy Considerations

Habibzadeh, Hadi; Soyata, Tolga

doi:10.1007/978-3-030-27844-1_5

Cited by 19 publications

(8 citation statements)

References 135 publications

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“…Performance evaluation of the AFE revealed the following: (1) The proposed AFE in cECG measurements with 1.70 mm thick clothing reduced the BR time and RMSV resp for healthy-weight-BMI subjects, and increased the R-wave amplitude for overweight-BMI subjects. (2) The proposed AFE in cEMG measurements of biceps brachii muscle yielded stable electromyographic waveforms without a visibly biased baseline for all subjects and a significant (p < 0.01) increase in SNR. These results indicate that the proposed AFE can provide a feasible balance between sensitivity and stability in the CBM, and could be a versatile replacement for conventional voltage followers used in CBMs.…”

Section: Discussionmentioning

confidence: 78%

“…. Z in_BVF ( f ) = (R a1 + R a2 ) 2 + (2π f C a3 R a1 R a2 ) 2 R a1 + R a2 , f f ca (2πC a3 R a1 R a2 ) f , f f ca (2) Sensors 2020, 20, x FOR PEER REVIEW 3 of 17 input signal (i.e., it is frequency-dependent) and can be approximated into two states using corner frequency…”

Section: Previous and Proposed Analog Front Endmentioning

confidence: 99%

“…As population aging is accelerating globally and high medical costs are becoming increasingly problematic, the concept of smart healthcare is gaining attention as an effective solution [1,2]. Smart healthcare is a health service system that uses technology such as wearable devices, the Internet of Things (IoT), and mobile internet to dynamically access information, connect people, materials and institutions related to healthcare, and then actively manage and respond to medical ecosystem needs in an intelligent manner [3].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Novel Analog Front End with Voltage-Dependent Input Impedance and Bandpass Amplification for Capacitive Biopotential Measurements

Nakamura

Sakajiri

Ishigami

et al. 2020

Sensors

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This paper proposes a novel analogue front end (AFE) that has three features: voltage-dependent input impedance, bandpass amplification, and stray capacitance reduction. With a view to applying the AFE to capacitive biopotential measurements (CBMs), the three features were investigated separately in a schematic and mathematical manner. Capacitive electrocardiogram (cECG) or capacitive electromyogram (cEMG) measurements using the AFE were performed in low-humidity conditions (below 35% relative humidity) for a total of seven human subjects. Performance evaluation of the AFE revealed the following: (1) the proposed AFE in cECG measurement with 1.70-mm thick clothing reduced the baseline recovery time and root mean square voltage of respiratory interference in subjects with healthy-weight body mass index (BMI), and increased R-wave amplitude for overweight-BMI subjects; and (2) the proposed AFE in cEMG measurement of biceps brachii muscle yielded stable electromyographic waveforms without the marked DC component for all subjects and a significant (p < 0.01) increase in the signal-to-noise ratio. These results indicate that the proposed AFE can provide a feasible balance between sensitivity and stability in CBMs, and it could be a versatile replacement for the conventional voltage follower used in CBMs.

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Section: Discussionmentioning

confidence: 78%

Section: Previous and Proposed Analog Front Endmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Novel Analog Front End with Voltage-Dependent Input Impedance and Bandpass Amplification for Capacitive Biopotential Measurements

Nakamura

Sakajiri

Ishigami

et al. 2020

Sensors

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“…Future works must focus on the security of healthcare systems against vulnerabilities. The survey in [ 70 ] discusses threats, vulnerabilities, and consequences of cyber attacks in the healthcare system. The comparison table of accuracies with the others insecurity is being shown in Table 3 .…”

Section: Deep Learning In Secure Healthcarementioning

confidence: 99%

An Efficient CNN-Based Deep Learning Model to Detect Malware Attacks (CNN-DMA) in 5G-IoT Healthcare Applications

Anand

Rani

Anand

et al. 2021

Sensors

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The role of 5G-IoT has become indispensable in smart applications and it plays a crucial part in e-health applications. E-health applications require intelligent schemes and architectures to overcome the security threats against the sensitive data of patients. The information in e-healthcare applications is stored in the cloud which is vulnerable to security attacks. However, with deep learning techniques, these attacks can be detected, which needs hybrid models. In this article, a new deep learning model (CNN-DMA) is proposed to detect malware attacks based on a classifier—Convolution Neural Network (CNN). The model uses three layers, i.e., Dense, Dropout, and Flatten. Batch sizes of 64, 20 epoch, and 25 classes are used to train the network. An input image of 32 × 32 × 1 is used for the initial convolutional layer. Results are retrieved on the Malimg dataset where 25 families of malware are fed as input and our model has detected is Alueron.gen!J malware. The proposed model CNN-DMA is 99% accurate and it is validated with state-of-the-art techniques.

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“…et al [24] Pantelop. et al [1] Razaque et al [4] Habibzadeh et al [25] Islam et al [26] Kruse et al [27] Yaqoob et al [28] Nasiri et al [29] Our Survey on healthcare systems and summarize the impacts of these attacks based on common vulnerability metrics. In Section 6, we discuss existing approaches that have been proposed to secure healthcare systems by researchers.…”

Section: Contributionsmentioning

confidence: 99%

A Survey on Security and Privacy Issues in Modern Healthcare Systems: Attacks and Defenses

Newaz,

Sikder,

Rahman

et al. 2020

Preprint

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The recent advancements in computing systems and wireless communications have made healthcare systems more efficient than before. Modern healthcare devices can monitor and manage different health conditions of the patients automatically without any manual intervention from the medical professionals. Additionally, the use of implantable medical devices (IMDs), body area networks (BANs), and Internet of Things (IoT) technologies in healthcare systems improve the overall patient monitoring and treatment process. However, these systems are complex in software and hardware, and optimizing between security, privacy, and treatment is crucial for healthcare systems as any security or privacy violation can lead to severe effects on patients' treatments and overall health conditions. Indeed, the healthcare domain is increasingly facing security challenges and threats due to numerous design flaws and the lack of proper security measures in healthcare devices and applications. In this paper, we explore various security and privacy threats to healthcare systems and discuss the consequences of these threats. We present a detailed survey of different potential attacks and discuss their impacts. Furthermore, we review the existing security measures proposed for healthcare systems and discuss their limitations. Finally, we conclude the paper with future research directions toward securing healthcare systems against common vulnerabilities. CCS Concepts: • General and reference → Surveys and overviews; • Security and privacy → Systems security; • Applied computing → Health care information systems.

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Toward Uniform Smart Healthcare Ecosystems: A Survey on Prospects, Security, and Privacy Considerations

Cited by 19 publications

References 135 publications

A Novel Analog Front End with Voltage-Dependent Input Impedance and Bandpass Amplification for Capacitive Biopotential Measurements

A Novel Analog Front End with Voltage-Dependent Input Impedance and Bandpass Amplification for Capacitive Biopotential Measurements

An Efficient CNN-Based Deep Learning Model to Detect Malware Attacks (CNN-DMA) in 5G-IoT Healthcare Applications

A Survey on Security and Privacy Issues in Modern Healthcare Systems: Attacks and Defenses

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