China
Africa
Explore chapters and articles related to this topic
A Review on Biomedical Signals with Fundamentals of Digital Signal Processing
Published in Mitul Kumar Ahirwal, Narendra D. Londhe, Anil Kumar, Artificial Intelligence Applications for Health Care, 2022
Mangesh Ramaji Kose, Mitul Kumar Ahirwal, Mithilesh Atulkar
An ECG signal carries information of electrical activities/functioning of heart. There are various signal-processing techniques applied on ECG signal to extract the information. Different stages of ECG signal processing techniques include signal preprocessing, analysis of heart rate variability, and ECG signal compression [10,11]. ECG signals get contaminated with various noise signals during ECG recording process. Signal preprocessing includes elimination of noise to obtain ECG signals of interest. Noise in ECG signal may be of different types, such as low or high frequency unwanted signals. Therefore, a band pass filter is frequently used. After the filtering process of ECG signal, generally, heart rate variability (HRV) analysis is performed. HRV analysis gives information about the various heart disorders and diseases.
A Framework for Emergency Remote Care and Monitoring Using Internet of Things
Published in Sourav Banerjee, Chinmay Chakraborty, Kousik Dasgupta, Green Computing and Predictive Analytics for Healthcare, 2020
An IoT-based secluded HRV monitoring system for hypertensive patients has been proposed [31] where HRV is a quantification of variation in the time interval between consecutive heartbeats. HRV analysis is of utmost importance in recent times, as it is linked with cardiovascular disease, diabetes mellitus and disease states associated with autonomic dysrhythmia, such as hypertension and a large array of chronic degenerative medical conditions. The architecture in brief is scheduled as the wearable sensor will send data to the Arduino module (with Zigbee), that will be sent to database storage or the MQTT server. When comparison and checking has been done, an SMS will be sent to caregivers if an emergency case arises or any urgency is detected. The observation concludes that there is shrinking in HRV time domain parameters beneath the normal range for hypertensive patients compared to normotensive persons, and there is much deviation from normal seen in the rendered graphs of hypertensive patients, indicating increased risk for cardiac mortality and stroke mortality. Thus, the proposed system successfully functions to monitor and provide insights regarding the hypertension condition. As a look into future enhancements of the system, the web application could also be hosted in a cloud environment with storage and the MQTT broker implemented with the same cloud environment.
Measurements and Assessment of Lighting Parameters and Measures of Non-Visual Effects of Light
Published in Agnieszka Wolska, Dariusz Sawicki, Małgorzata Tafil-Klawe, Visual and Non-Visual Effects of Light, 2020
Agnieszka Wolska, Dariusz Sawicki, Małgorzata Tafil-Klawe
There are several types of psychophysiological responses of the human body to light that can be measured objectively or subjectively in order to evaluate lighting and select appropriate values for light parameters producing particular effects. These include methods of measuring brain activity, electrical heartbeat activity, tracking and recording eye movements, measuring hormone levels in human biological material, and vigilant performance and sleepiness assessment tests. Electrical heart beat (heart rate variability – HRV) activity is usually assessed using the electrocardiography (ECG) method of monitoring and registering the electric current of a heartbeat.
The Effects of Degrees of Freedom and Field of View on Motion Sickness in a Virtual Reality Context
Published in International Journal of Human–Computer Interaction, 2023
Different methods have been suggested to measure MS symptoms. First, physiological measurements are widely accepted because MS is a biological phenomenon, for example, electrocardiogram (ECG) (Keshavarz et al., 2022; Park et al., 2022), electroencephalogram (EEG) (Liao et al., 2020; Lin et al., 2007), and electrodermal activity (EDA) (Chardonnet et al., 2017; Keshavarz et al., 2022). Among various physiological data, heart rate variability (HRV) collected using the ECG method is a representative indicator that can measure MS symptoms quickly and precisely (Doweck et al., 1997). HRV is a measure of variation in time between successive heartbeats, which is caused by the effect of the autonomic nervous system on the sinoatrial node of the heart (Arlt et al., 2003). It is known that we can notice the activity and balance of the autonomic nervous system (parasympathetic and sympathetic) from HRV (Appelhans & Luecken, 2008). Therefore, HRV has been used to observe MS symptoms (Bahit et al., 2016; Park et al., 2014; 2022; Zhang et al., 2016), because there are physiological and morphological connections between the automatic nervous system and vestibular systems (Money, 1970; Previc, 1993).
Synthesis of Harvard Environmental Protection Agency (EPA) Center studies on traffic-related particulate pollution and cardiovascular outcomes in the Greater Boston Area
Published in Journal of the Air & Waste Management Association, 2019
Iny Jhun, Jina Kim, Bennet Cho, Diane R. Gold, Joel Schwartz, Brent A. Coull, Antonella Zanobetti, Mary B. Rice, Murray A. Mittleman, Eric Garshick, Pantel Vokonas, Marie-Abele Bind, Elissa H. Wilker, Francesca Dominici, Helen Suh, Petros Koutrakis
HRV is a physiological measure for cardiac autonomic tone that describes the variation of the time interval between heart beats, and there are a number of HRV indices that have been utilized by air pollution epidemiologists. HRV indices can be broadly divided into those that primarily indicate parasympathetic activity (e.g., high frequency [HF], root mean squared differences between adjacent RR intervals [r-MSSD], proportion of adjacent normal-to-normal heartbeat intervals differing by more than 50 ms [PNN50]), and those that predominantly reflect sympathetic activity (e.g., total power [TPow], standard deviation of normal-to-normal intervals [SDNN], low frequency [LF]). The predominance of sympathetic and/or diminished parasympathetic activity is believed to drive the association between HRV and adverse cardiovascular outcomes.
Towards Patient-centered Diagnosis of Pediatric Obstructive Sleep Apnea—A Review of Biomedical Engineering Strategies
Published in Expert Review of Medical Devices, 2019
Chronic upper airway obstruction and intermittent hypoxemia may have detrimental effects on autonomic control of cardiac function [34]. Heart rate variability (HRV) parameters represent the beat-to-beat variability of cardiac rhythm and an absolute decrease in HRV is considered unfavorable. HRV is therefore used as an indicator of overall cardiac health. Nisbet et al. [35] demonstrated that OSA is associated with predictable changes in HRV parameters among children attending preschool. Aljadeff et al. [36] examined HRV variability from PSG recordings of seven children and found significantly increased variation at slow rates in children with OSA. Based on these results, the authors suggested that HRV could be adopted as a screening tool for OSA in children. Baharav et al. [37] investigated two spectral regions of the autonomic nervous system in 20 children. In this study, the low-frequency component represented sympathetic activity and the high-frequency component represented parasympathetic activity. They demonstrated increased low-frequency dominance in children with OSA during all sleep stages relative to normal controls. Promising results in these relatively small datasets necessitate validation using larger studies.