Heart rate variability (HRV) is a crucial metric that quantifies the variation between consecutive heartbeats, serving as a significant indicator of autonomic nervous system (ANS) activity. It has found widespread applications in clinical diagnosis, treatment, and prevention of cardiovascular diseases. In this study, we proposed an optical model for defocused speckle imaging, to simultaneously incorporate out-of-plane translation and rotation-induced motion for highly-sensitive non-contact seismocardiogram (SCG) measurement. Using electrocardiogram (ECG) signals as the gold standard, we evaluated the performance of photoplethysmogram (PPG) signals and speckle-based SCG signals in assessing HRV. The results indicated that the HRV parameters measured from SCG signals extracted from laser speckle videos showed higher consistency with the results obtained from the ECG signals compared to PPG signals. Additionally, we confirmed that even when clothing obstructed the measurement site, the efficacy of SCG signals extracted from the motion of laser speckle patterns persisted in assessing the HRV levels. This demonstrates the robustness of camera-based non-contact SCG in monitoring HRV, highlighting its potential as a reliable, non-contact alternative to traditional contact-PPG sensors.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1109/JBHI.2024.3370394 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Spent coffee ground disrupts Listeria monocytogenes biofilm formation through inhibition of motility and adhesion via quorum sensing regulation.
Int J Food Microbiol
January 2025
Department of Food Science and Biotechnology, Dongguk University, Goyang 10326, Republic of Korea. Electronic address:
Spent coffee grounds (SCGs) have been explored for use as various bioresources, such as biofuels, and are known to possess biological functions, including antioxidant activity. However, the antibiofilm properties of SCGs against pathogenic bacteria have not been fully investigated. Therefore, this study aimed to highlight the inhibitory effects of SCG extract (SCGE) on biofilm formation in Listeria monocytogenes and investigated the underlying mechanisms.
View Article and Find Full Text PDFContinuous Estimation of Blood Pressure by Utilizing Seismocardiogram Signal Features in Relation to Electrocardiogram.
Biosensors (Basel)
December 2024
Optoelectronics and Measurement Techniques Research Unit, University of Oulu, 90570 Oulu, Finland.
There is an ongoing search for a reliable and continuous method of noninvasive blood pressure (BP) tracking. In this study, we investigate the feasibility of utilizing seismocardiogram (SCG) signals, i.e.
View Article and Find Full Text PDFImpact of Weekly Community-Based Dance Training Over 8 Months on Depression and Blood Oxygen Level-Dependent Signals in the Subcallosal Cingulate Gyrus for People With Parkinson Disease: Observational Study.
JMIRx Med
December 2024
Connected Minds: Neural and Machine Systems for a Healthy, Just Society, York University, Toronto, ON, Canada.
Background: Dance has emerged as a complementary treatment that may promote adaptive neural plasticity while improving symptoms of Parkinson disease (PD), such as balance, gait, posture, and walking. Understanding brain changes that arise from participation in dance interventions is important as these neural plastic changes play an important role in protecting and healing the brain. Although dance has been shown to improve PD motor and nonmotor symptoms, the neural mechanisms underlying these changes, specifically depression and mood, remain elusive.
View Article and Find Full Text PDFEffect of respiration and exercise on seismocardiographic signals.
Comput Biol Med
February 2025
Department of Mechanical and Aerospace Engineering, University of Central Florida, and the Biomedical Acoustics Research Company, 32816, Orlando, FL, USA.
Background: Seismocardiographic signals (SCG) are chest wall vibrations induced by mechanical cardiac activities. This study investigated the morphological changes in the SCG signal due to respiration and exercise.
Methods: Fifteen healthy subjects were recruited, and SCG was acquired before and after exercise.
Classification of Aortic Stenosis Patients via ECG-Independent Multi-Site Measurements of Cardiac-Induced Accelerations and Angular Velocities at the Skin Level.
IEEE Open J Eng Med Biol
May 2024
Research Unit of Measurements and Biomedical instrumentation, Department of EngineeringUniversità Campus Bio-Medico di Roma 00128 Rome Italy.
Article Synopsis
- The study aims to evaluate the effectiveness of seismocardiogram (SCG) and gyrocardiogram (GCG) in differentiating aortic stenosis (AS) patients from healthy individuals, discovering the best sensor placement for accurate classification.
- SCG and GCG data were collected from 15 participants and analyzed using both machine learning and deep learning techniques, yielding the highest accuracy from the Support Vector Machine (SVM) at specific heart locations.
- By combining SCG and GCG signals, accuracy improved significantly, reaching up to 97.2% with a single sensor, demonstrating a reliable method for classifying AS patients.
Want AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!