Improved Cardiac Arrhythmia Prediction Based on Heart Rate Variability Analysis

Parsi, Ashkan

doi:10.13140/rg.2.2.15748.40322

Cited by 1 publication

(3 citation statements)

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“…It serves as a measurable indicator of cardiovascular integrity. HRV reflects both the sympathetic and the parasympathetic components of the autonomic nervous system which could be affected by stress and it has been proven that HRV decreases as stress increases [6], [15]. The IHR signal is calculated from the R-R intervals recorded using the following equation:…”

Section: Hrv Features Extractionmentioning

confidence: 99%

“…Where RRI (R-R intervals) is the time in milliseconds between instantaneous heartbeats as measured by any ECG wearable sensor, or the period between adjacent QRS complexes resulting from sinus node depolarizations, which is termed the normal to normal (NN) interval [6]. The unit of IHR is beats/minute (bpm).…”

Section: Hrv Features Extractionmentioning

confidence: 99%

“…Heart rate variability (HRV) as a measure of the balance between sympathetic mediators of heart rate (HR) (i.e. the effect of epinephrine and norepinephrine, released from sympathetic nerve fibers [6]) is one of these physiological measurements [7]. HRV operates on different time scales to help us adapt to environmental and psychological challenges.…”

Section: Introductionmentioning

confidence: 99%

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A feature selection method for driver stress detection using heart rate variability and breathing rate

Parsi

O’Callaghan

Lemley

2023

Fifteenth International Conference on Machine Vision (ICMV 2022)

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Driver stress is a major cause of car accidents and death worldwide. Furthermore, persistent stress is a health problem, contributing to hypertension and other diseases of the cardiovascular system. Stress has a measurable impact on heart and breathing rates and stress levels can be inferred from such measurements. Galvanic skin response is a common test to measure the perspiration caused by both physiological and psychological stress, as well as extreme emotions. In this paper, galvanic skin response is used to estimate the ground truth stress levels. A feature selection technique based on the minimal redundancy-maximal relevance method is then applied to multiple heart rate variability and breathing rate metrics to identify a novel and optimal combination for use in detecting stress. The support vector machine algorithm with a radial basis function kernel was used along with these features to reliably predict stress. The proposed method has achieved a high level of accuracy on the target dataset.

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Section: Hrv Features Extractionmentioning

confidence: 99%

Section: Hrv Features Extractionmentioning

confidence: 99%