A Noncontact Ballistocardiography-Based IoMT System for Cardiopulmonary Health Monitoring of Discharged COVID-19 Patients

Liu, Jikui; Miao, Fen; Yin, Liyan; Pang, Zhiqiang; Li, Ye

doi:10.1109/jiot.2021.3063549

Cited by 28 publications

(21 citation statements)

References 35 publications

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“…The last one was excluded because the study did not report HRV parameters [ 36 ]. Finally, 11 studies in 12 documents [ 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 ] were included in this review, of which, Mekhael et al [ 45 ] (journal article) and Dagher et al [ 48 ] (conference abstract) reported different outcomes for the same study. In dealing with these two documents, we described characteristics and assessed methodological quality based on Mekhael et al [ 45 ], but we extracted HRV parameters from Dagher et al [ 48 ].…”

Section: Resultsmentioning

confidence: 99%

Long-Term Impact of COVID-19 on Heart Rate Variability: A Systematic Review of Observational Studies

2023

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Coronavirus disease 2019 (COVID-19) sequelae (or long COVID) has become a clinically significant concern. Several studies have reported the relationship between heart rate variability (HRV) parameters and COVID-19. This review investigates the long-term association between COVID-19 and HRV parameters. Four electronic databases were searched up to 29 July 2022. We included observational studies comparing HRV parameters (measurement durations: 1 min or more) in participants with and without a history of COVID-19. We used assessment tools developed by the National Heart, Lung, and Blood Institute group to evaluate the methodological quality of included studies. Eleven cross-sectional studies compared HRV parameters in individuals who recovered from acute COVID-19 infection to controls (n = 2197). Most studies reported standard deviation of normal-to-normal intervals (SDNN) and root mean square of the successive differences. The methodological quality of the included studies was not optimal. The included studies generally found decreased SDNN and parasympathetic activity in post-COVID-19 individuals. Compared to controls, decreases in SDNN were observed in individuals who recovered from COVID-19 or had long COVID. Most of the included studies emphasized parasympathetic inhibition in post-COVID-19 conditions. Due to the methodological limitations of measuring HRV parameters, the findings should be further validated by robust prospective longitudinal studies.

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Section: Resultsmentioning

confidence: 99%

Long-Term Impact of COVID-19 on Heart Rate Variability: A Systematic Review of Observational Studies

2023

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“…Before and during the 10 min period of the vital signs collection, all subjects did not have any exercise and kept the fixed positions. The recorded data are involved the non-contact sensing device and the echocardiography, where the collected vial signs contain the information of both the BCG and the respiratory effort from the head and necks of the subjects ( Liu et al., 2021 ). After the data acquisition, some professional cardiologists made the HF diagnosis by the echocardiography signals, and other recorded data was selected by the above results.…”

Section: Methodsmentioning

confidence: 99%

“…Among them, recordings were evenly distributed from each subject to ensure no bias in the data and furthermore the universality of the experimental results. In the traditional studies, the HF diseases can be easy detected by the typical waveforms of the BCG signals ( Carlson et al., 2020 ; Liu et al., 2021 ). However, the corresponding BCG signals are usually irregular in rhythm and morphology due to disordered cardiac movement (HF patients with LVEF

49

and GLS

20

) ( Siniorakis et al., 2018 ; Hamazaki et al., 2019 ; McDonagh et al., 2021 ).…”

Section: Methodsmentioning

confidence: 99%

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Machine learning-aided detection of heart failure (LVEF ≤ 49%) by using ballistocardiography and respiratory effort signals

Feng

Wu²,

Bao³

et al. 2023

Front. Physiol.

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Purpose: Under the influence of COVID-19 and the in-hospital cost, the in-home detection of cardiovascular disease with smart sensing devices is becoming more popular recently. In the presence of the qualified signals, ballistocardiography (BCG) can not only reflect the cardiac mechanical movements, but also detect the HF in a non-contact manner. However, for the potential HF patients, the additional quality assessment with ECG-aided requires more procedures and brings the inconvenience to their in-home HF diagnosis. To enable the HF detection in many real applications, we proposed a machine learning-aided scheme for the HF detection in this paper, where the BCG signals recorded from the force sensor were employed without the heartbeat location, and the respiratory effort signals separated from force sensors provided more HF features due to the connection between the heart and the lung systems. Finally, the effectiveness of the proposed HF detection scheme was verified in comparative experiments.Methods: First, a piezoelectric sensor was used to record a signal sequences of the two-dimensional vital sign, which includes the BCG and the respiratory effort. Then, the linear and the non-linear features w.r.t. BCG and respiratory effort signals were extracted to serve the HF detection. Finally, the improved HF detection performance was verified through the LOO and the LOSO cross-validation settings with different machine learning classifiers.Results: The proposed machine learning-aided scheme achieved the robust performance in the HF detection by using 4 different classifiers, and yielded an accuracy of 94.97% and 87.00% in the LOO and the LOSO experiments, respectively. In addition, experimental results demonstrated that the designed respiratory and cardiopulmonary features are beneficial to the HF detection (LVEF ≤49%).Conclusion: This study proposed a machine learning-aided HF diagnostic scheme. Experimental results demonstrated that the proposed scheme can fully exploit the relationship between the heart and the lung systems to potentially improve the in-home HF detection performance by using both the BCG, the respiratory and the cardiopulmonary-related features.

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“…Especially in COVID-19 pandemic since 2020, the urgent and widespread need for care, coupled with the challenge of physical distancing, has accelerated the creation and adoption of new digital technologies, as well as new processes to support their adoption and implementation across healthcare [10][11][12]. The potential benefits of IoMT can be seen within a hospital setting, where monitoring COVID-19 patients is costly in terms of time and PPE (personal protective equipment) consumption [13]. IoMT technologies enable medical devices to send data to medical practitioners who can monitor a patient's condition without having to take readings at the bedside.…”

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confidence: 99%

A review of security issues and solutions for precision health in Internet-of-Medical-Things systems

et al. 2023

Security and Safety

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Precision medicine provides a holistic view of a person’s health that combines genes, environment and lifestyle, aiming at realizing the individualized therapy. With the developing of Internet of Things (IOT) devices, widespread emergence of Electronic Medical Records (EMR), booming of cloud computing and artificial intelligence, it provides an opportunity to collect the healthcare big data throughout the lifespan and analyze the disease risk at all stages of life. Thus, precision medicine is shifting to the focus from treatment to prediction and prevention, namely precision health. To achieve this goal, different types of data, such as omics, imaging, EMR, continuous physiological monitoring, lifestyle, and environmental information need to be collected, tracked, managed and shared. For this purpose, Internet-of-Medical Things (IoMT) is playing a vital role in bringing together the health systems, applications, services and devices, that can improve the speed and accuracy of diagnosis and treatments, and monitor and modify patient behaviour and health status in real time. However, due to the proliferation of IoMT devices, security has become a growing concern. The increasing interconnectivity of IoMT-enabled devices with the health data reception, transmission, and processing significantly increases the number of potential vulnerabilities within a system. To address the security issues for precision health in IoMT systems, in this article, we review the state-of-the-art techniques and schemes from the perspective of a hierarchical system architecture. We present an IoMT system model consisting of three layers: the sensing layer, the network layer and the cloud infrastructure layer. In each layer, we discuss the security vulnerabilities and threats, and review the existing security techniques and schemes corresponding to the system components and their functionalities. Due to the unique nature of biometric features in medical and health services, we highlight the biometrics-based technologies applied in IoMT systems, which makes a great difference from the security solutions in other existing IoT systems. Finally, we summarize the challenges and future research directions in IoMT systems for a better and more secure future of precision health.

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A Noncontact Ballistocardiography-Based IoMT System for Cardiopulmonary Health Monitoring of Discharged COVID-19 Patients

Cited by 28 publications

References 35 publications

Long-Term Impact of COVID-19 on Heart Rate Variability: A Systematic Review of Observational Studies

Long-Term Impact of COVID-19 on Heart Rate Variability: A Systematic Review of Observational Studies

Machine learning-aided detection of heart failure (LVEF ≤ 49%) by using ballistocardiography and respiratory effort signals

A review of security issues and solutions for precision health in Internet-of-Medical-Things systems

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