Analysis of HRV signal for disease diagnosis

Gautam, Desh Deepak; Giri, V. K.

doi:10.1109/iciinfs.2016.8263017

Cited by 3 publications

(2 citation statements)

References 3 publications

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“…The important clinical information which are helpful clinicians can be obtained from the early measurements of HRV during Acute Myocardial Infarction (AMI). Basically HRV decreases under the emotional stress or physical stress situations and it increases with the rest [1][2][3][4][5]. Pulse rate, temperature of the skin and level of skin conductance are the three parameters which are used for stress management as they are the marker of sympathetic activation in response to stress reaction.…”

Section: Hrv and Stressmentioning

confidence: 99%

Heart Rate Variability-A Review

Vyasaraj¹,

Hegde²

2021

JUSST

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Information about the health can be obtained by measuring various physiological parameters such as Heart Rate, Heart Rate Variability (HRV), Nerve conduction, brain activity, blood oxygen saturation level etc. The useful information resulted from the systematic analysis of these physical parameters are helpful for clinicians to make better decisions. HRV reflects the state of the Autonomic Nervous System (ANS) defined as the variance in the time between successive heartbeats expressed in milliseconds. The various factors that affects HRV are diet, nutrition, age, alcohol, gender, cardiac rhythm, sleep habits, genetics etc. The analysis of HRV is helpful in stress assessment and also in identifying the diseases at the early stage. This paper discusses the fundamentals of HRV, analysis of HRV, and the role of HRV in stress detection.

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Section: Hrv and Stressmentioning

confidence: 99%

Heart Rate Variability-A Review

Vyasaraj¹,

Hegde²

2021

JUSST

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“…It is an essential tool for diagnosing various cardiac diseases. It is also commonly applied to derive another signal-heart rate variability (HRV), whose high diagnostic ability has been demonstrated in recent years [1][2][3]. In particular, the analysis of frequency components, including high (0.15-0.4 Hz), low (0.04-0.15 Hz), very low (0.004-0.04 Hz), and ultra-low (<0.004 Hz) bands, is of actual interest, as they reflect, among others, sympathetic and the parasympathetic activity, blood pressure, thermoregulation, renin-angiotensin mechanism, or circadian rhythms [4].…”

Section: Introductionmentioning

confidence: 99%

Processing Photoplethysmograms Recorded by Smartwatches to Improve the Quality of Derived Pulse Rate Variability

Polak

Klich

Saganowski

et al. 2022

Sensors

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Cardiac monitoring based on wearable photoplethysmography (PPG) is widespread because of its usability and low cost. Unfortunately, PPG is negatively affected by various types of disruptions, which could introduce errors to the algorithm that extracts pulse rate variability (PRV). This study aims to identify the nature of such artifacts caused by various types of factors under the conditions of precisely planned experiments. We also propose methods for their reduction based solely on the PPG signal while preserving the frequency content of PRV. The accuracy of PRV derived from PPG was compared to heart rate variability (HRV) derived from the accompanying ECG. The results indicate that filtering PPG signals using the discrete wavelet transform and its inverse (DWT/IDWT) is suitable for removing slow components and high-frequency noise. Moreover, the main benefit of amplitude demodulation is better preparation of the PPG to determine the duration of pulse cycles and reduce the impact of some other artifacts. Post-processing applied to HRV and PRV indicates that the correction of outliers based on local statistical measures of signals and the autoregressive (AR) model is only important when the PPG is of low quality and has no effect under good signal quality. The main conclusion is that the DWT/IDWT, followed by amplitude demodulation, enables the proper preparation of the PPG signal for the subsequent use of PRV extraction algorithms, particularly at rest. However, post-processing in the proposed form should be applied more in the situations of observed strong artifacts than in motionless laboratory experiments.

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Reducing Pulse Rate Variability Computational Error from a 30 Hz Photoplethysmography Recording

Panganiban

Leon

2019

2019 International Symposium on Multimedia and Communication Technology (ISMAC)

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Analysis of HRV signal for disease diagnosis

Cited by 3 publications

References 3 publications

Heart Rate Variability-A Review

Heart Rate Variability-A Review

Processing Photoplethysmograms Recorded by Smartwatches to Improve the Quality of Derived Pulse Rate Variability

Reducing Pulse Rate Variability Computational Error from a 30 Hz Photoplethysmography Recording

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