Statistical-based HRV Feature Importance Evaluation for Arrhythmia and Atrial Fibrillation Classification

Tihak, Amina; Bošković, Dušanka

doi:10.1109/meco58584.2023.10154938

Cited by 1 publication

(2 citation statements)

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“…According to the derivation and ignoring some items with relatively small values, the expression of the IF signal can finally be obtained, as shown in Equation (6).…”

Section: Signal Modelmentioning

confidence: 99%

“…HRV holds diverse clinical implications, particularly in conditions like coronary heart disease [1], hypertension [2], and diabetic neuropathy, which manifest as neurological impairments [3] in the cardiac system. Moreover, HRV serves as a valuable tool for diagnosing different types of arrhythmias [4][5][6], guiding pharmacological interventions and effectiveness judgment [7]. As a non-invasive physiological indicator, HRV has attracted significant attention, showcasing its potential for application in clinical settings.…”

Section: Introductionmentioning

confidence: 99%

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Heart Rate Variability Monitoring Based on Doppler Radar Using Deep Learning

Yuan,

Fan,

Deng

et al. 2024

Sensors

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The potential of microwave Doppler radar in non-contact vital sign detection is significant; however, prevailing radar-based heart rate (HR) and heart rate variability (HRV) monitoring technologies often necessitate data lengths surpassing 10 s, leading to increased detection latency and inaccurate HRV estimates. To address this problem, this paper introduces a novel network integrating a frequency representation module and a residual in residual module for the precise estimation and tracking of HR from concise time series, followed by HRV monitoring. The network adeptly transforms radar signals from the time domain to the frequency domain, yielding high-resolution spectrum representation within specified frequency intervals. This significantly reduces latency and improves HRV estimation accuracy by using data that are only 4 s in length. This study uses simulation data, Frequency-Modulated Continuous-Wave radar-measured data, and Continuous-Wave radar data to validate the model. Experimental results show that despite the shortened data length, the average heart rate measurement accuracy of the algorithm remains above 95% with no loss of estimation accuracy. This study contributes an efficient heart rate variability estimation algorithm to the domain of non-contact vital sign detection, offering significant practical application value.

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“…According to the derivation and ignoring some items with relatively small values, the expression of the IF signal can finally be obtained, as shown in Equation (6).…”

Section: Signal Modelmentioning

confidence: 99%