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Regulation of Heart Rates01:31

Regulation of Heart Rates

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The regulation of heart rate is a complex process controlled by the autonomic nervous system (ANS), hormonal influences, and intrinsic cardiac mechanisms. The ANS has two main components: the sympathetic nervous system (SNS) and the parasympathetic nervous system (PNS).
The SNS increases heart rate through the release of norepinephrine and epinephrine, which act on beta-1 adrenergic receptors in the heart. This action increases the rate of depolarization in the sinoatrial (SA) node, the heart's...
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Factors Influencing Heart Rate01:30

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The heart rate, or pulse rate, is a vital indicator of cardiovascular health. It reflects the number of times the heart beats per minute. Various physiological and environmental factors influence heart rate, increasing or decreasing cardiac output. Understanding these factors is crucial for assessing heart function and identifying potential health issues.
Let us explore the significant factors affecting heart rate, including age, body temperature, posture, acute pain, chemical influences,...
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Drug Dosing in Renal Diseases: Estimation of Glomerular Filtration Rate Based on Serum Creatinine Concentration01:28

Drug Dosing in Renal Diseases: Estimation of Glomerular Filtration Rate Based on Serum Creatinine Concentration

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Glomerular filtration rate (GFR) can be estimated from serum creatinine using the modification of diet in renal disease (MDRD) formula or the chronic kidney disease–epidemiology collaboration (CKD–EPI) equation. Both methods are widely used in clinical practice to assess kidney function and guide treatment decisions.The MDRD equation does not require weight or height measurements and is normalized to the body surface area of 1.73 m², considered the average adult surface area.
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Cardiac Output I:Effect of Heart Rate on Cardiac Output01:19

Cardiac Output I:Effect of Heart Rate on Cardiac Output

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Cardiac Output
Cardiac output (CO) refers to the total amount of blood ejected by one of the ventricles in liters per minute (L/min). In a resting adult, CO ranges from 5 to 6 L/min, adjusting according to the body's metabolic requirements.
Effect of Heart Rate on Cardiac Output
Cardiac output adapts to metabolic demands during stress, physical activity, or illness. The autonomic nervous system regulates heart rate via the sinoatrial node. The parasympathetic nervous system decreases heart...
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Muscles for Facial Expressions01:14

Muscles for Facial Expressions

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The craniofacial muscles are a collection of approximately 20 thin skeletal muscles situated beneath the skin of the face and scalp. These muscles, primarily responsible for the vast array of human facial expressions, originate from the bones or fibrous structures of the skull and extend outwards to connect with the skin. While most skeletal muscles in the body are enveloped in thick fascia, facial muscles generally have a more delicate fascial covering, with the buccinator muscle being a...
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What are Estimates?01:06

What are Estimates?

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It isn't easy to measure a parameter such as the mean height or the mean weight of a population. So, we draw samples from the population and calculate the mean height or mean weight of the individuals in the sample. This sample data acts as a representative measure of the population parameter. These sample statistics are known as estimates. 
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Measuring Microbial Mutation Rates with the Fluctuation Assay
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Head-motion Robust Video-based Heart Rate Estimation Using Facial Feature Point Fluctuations.

Terumi Umematsu, Masanori Tsujikawa

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |November 17, 2018
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    Summary

    This study introduces a new video-based method for accurate heart rate (HR) estimation. It effectively removes noise from head movements, improving non-contact health monitoring.

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    Area of Science:

    • Biomedical Engineering
    • Computer Vision
    • Physiological Signal Processing

    Background:

    • Growing demand for non-contact methods to measure heart rates (HRs) for stress detection and healthcare.
    • Existing video-based HR estimation methods are often susceptible to noise, particularly from head movements.
    • The need for robust algorithms that can isolate subtle cardiac signals from motion artifacts.

    Purpose of the Study:

    • To develop a head-motion robust video-based heart rate estimation technique.
    • To accurately estimate and remove noise components caused by head motion.
    • To extract reliable heart rate signals from facial feature point fluctuations.

    Main Methods:

    • Utilized facial feature point fluctuations to detect and quantify rigid-noise components, such as horizontal head motion.
    • Developed an adaptive algorithm to estimate and remove these noise components.
    • Leveraged the dominance of facial feature point changes over noise signals compared to RGB luminance signals for accurate extraction.

    Main Results:

    • The proposed method demonstrated superior accuracy in heart rate estimation compared to existing state-of-the-art techniques.
    • Successfully achieved head-motion robust performance in evaluation experiments on a benchmark dataset.
    • Effectively isolated and extracted subtle cardiac signals despite the presence of significant motion artifacts.

    Conclusions:

    • The proposed video-based approach offers a highly accurate and robust solution for non-contact heart rate monitoring.
    • Facial feature point analysis provides a viable mechanism for mitigating motion-related noise in physiological measurements.
    • This method holds significant potential for applications in remote health assessment and stress monitoring.