Noncontact Monitoring of Heart Rate and Heart Rate Variability in Geriatric Patients Using Photoplethysmography Imaging

Yu, Xinchi; Laurentius, Thea; Bollheimer, Cornelius; Leonhardt, Steffen; Antink, Christoph Hoog

doi:10.1109/jbhi.2020.3018394

Cited by 37 publications

(31 citation statements)

References 43 publications

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“…Depth camera technology can be used to observe the chest displacements by recording video footage of the chest movements [ 13 , 14 , 54 , 55 , 56 ]. However, the camera has limitations in terms of light and privacy [ 18 , 21 , 22 , 23 , 24 , 25 , 26 ].…”

Section: Related Workmentioning

confidence: 99%

“…Radar sensor has attractive advantages in monitoring the breathing pattern [ 63 ] over camera-based systems in terms of light and privacy [ 18 , 21 , 22 , 23 , 24 , 25 , 26 ]. In [ 63 ], non-contact vital-sign detection using radar has been developed, and Lee et al [ 64 ] used radar to observe the different breathing patterns.…”

Section: Related Workmentioning

confidence: 99%

“…Radar sensor has attractive advantages over camera-based systems in terms of light and privacy [ 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 ]. Periodic linearly increasing frequency chirp, known as FMCW, is one of the radar technologies that uses a wide frequency bandwidth without requiring wideband processing.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Non-Contact Monitoring and Classification of Breathing Pattern for the Supervision of People Infected by COVID-19

Purnomo

Lin

Adiprabowo

et al. 2021

Sensors

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During the pandemic of coronavirus disease-2019 (COVID-19), medical practitioners need non-contact devices to reduce the risk of spreading the virus. People with COVID-19 usually experience fever and have difficulty breathing. Unsupervised care to patients with respiratory problems will be the main reason for the rising death rate. Periodic linearly increasing frequency chirp, known as frequency-modulated continuous wave (FMCW), is one of the radar technologies with a low-power operation and high-resolution detection which can detect any tiny movement. In this study, we use FMCW to develop a non-contact medical device that monitors and classifies the breathing pattern in real time. Patients with a breathing disorder have an unusual breathing characteristic that cannot be represented using the breathing rate. Thus, we created an Xtreme Gradient Boosting (XGBoost) classification model and adopted Mel-frequency cepstral coefficient (MFCC) feature extraction to classify the breathing pattern behavior. XGBoost is an ensemble machine-learning technique with a fast execution time and good scalability for predictions. In this study, MFCC feature extraction assists machine learning in extracting the features of the breathing signal. Based on the results, the system obtained an acceptable accuracy. Thus, our proposed system could potentially be used to detect and monitor the presence of respiratory problems in patients with COVID-19, asthma, etc.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Non-Contact Monitoring and Classification of Breathing Pattern for the Supervision of People Infected by COVID-19

Purnomo

Lin

Adiprabowo

et al. 2021

Sensors

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show abstract

“…for the evaluation use the RGB camera, the results show an error of 0.48 bpm. Figure 7 shows the proposed system [24]. And finally, the rPPG signal analysis in order to extract the frequency.…”

Section: Ecg Signal and Heart Morphologymentioning

confidence: 99%

ECG signal monitoring based on Covid-19 patients: Overview

Saddik¹,

Bella²

2021

JISIoT

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ECG signal monitoring is a very important step for patients. Especially for those infected by covid-19. This pandemic has shown that the use of artificial intelligence helps to control the propagation of this virus. Particularly the high spread of this virus influences the number of the infected population. As well as the fact that this virus attacks the respiratory system which influences the cardiac system. Therefore, an ECG signal monitoring is mandatory. Our work presents an overview based on various approaches developed for ECG signal monitoring. These techniques are based on non-contact monitoring approaches. These approaches will help to avoid frequent contact with patients and doctors. As well as non-contact ECG signal monitoring is based on low-cost techniques, which reduces the price compared to other sensors. After the revision, we can conclude that the most suitable solution for heart rate monitoring is based on image processing using RGB cameras. These solutions are accurate, low cost, and protect the doctors.

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“…In other that, the PPG signal is suitable to assess some of cardiovascular diseases (CVDs) such as diabetes [1]- [3], hypertension [4]- [6], erectile dysfunction [7], atherosclerosis [8], coronary artery disease [9], [10] and coronary heart disease [11]. It is also convenient for clinical application, for example, in the measurement and monitoring of heart rate [12]- [14] and blood pressure [15], [16]. CVDs as the most critical and the most significant contribution of death almost around the world recently are associated with smoking as well [17], [18].…”

Section: Introductionmentioning

confidence: 99%

Poincaré plots to analyze photoplethysmography signal between non-smokers and smokers

Haryadi

Chang

Akrom

et al. 2022

IJECE

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<span>An analysis of blood circulation was used to identify variations of heart rate and to create an early warning system of autonomic dysfunction. The Poincaré plot analyzed blood circulation using photoplethysmography (PPG) signals between non-smokers and smokers in three different indices: SD1, SD2, and SD1 SD2 ratio (SSR). There were twenty subjects separated into non-smoker and smoker groups with sample sizes of 10, respectively. An independent sample t-test to compare the continuous variables. Whereas, the comparison between two groups employed Fisher’s exact test for categorical variables. The result showed that SD1 was found to be considerably lower in the group of smokers (0.03±0.01) than that of the non-smokers (0.06±0.03). Similarly, SSR was recorded at 0.0012±0.0005 and 0.0023±0.0012 for smoking and non-smoking subjects, respectively. As a comparison, SD2 for non-smokers (25.7±0.5) was lower than smokers (27.3±0.4). In conclusion, we revealed that the parameters of Poincaré plots (SD1, SD2, and SSR) exert good performances to significantly differentiate the PPG signals of the group of non-smokers from those of smokers. We also supposed that the method promises to be a suitable method to distinguish the cardiovascular disease group. Therefore, this method can be applied as a part of early detection system of cardiovascular diseases.</span>

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Noncontact Monitoring of Heart Rate and Heart Rate Variability in Geriatric Patients Using Photoplethysmography Imaging

Cited by 37 publications

References 43 publications

Non-Contact Monitoring and Classification of Breathing Pattern for the Supervision of People Infected by COVID-19

Non-Contact Monitoring and Classification of Breathing Pattern for the Supervision of People Infected by COVID-19

ECG signal monitoring based on Covid-19 patients: Overview

Poincaré plots to analyze photoplethysmography signal between non-smokers and smokers

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