scholarly journals Faster Detection of Abnormal Electrocardiogram (ECG) Signals Using Fewer Features of Heart Rate Variability (HRV)

2018 ◽  
Vol 12 (01) ◽  
Author(s):  
Gong X
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Ecg Signals ◽  
Electrocardiogram Ecg

scholarly journals Differential effects of the blood pressure state on pulse rate variability and heart rate variability in critically ill patients

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Elisa Mejía-Mejía ◽  
James M. May ◽  
Mohamed Elgendi ◽  
Panayiotis A. Kyriacou
Keyword(s):  
Blood Pressure ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Pulse Rate ◽  
Pulse Transit Time ◽  
Pulse Rate Variability ◽  
Physiological Factors ◽  
Ecg Signals ◽  
Non Invasive ◽  
Electrocardiogram Ecg

AbstractHeart rate variability (HRV) utilizes the electrocardiogram (ECG) and has been widely studied as a non-invasive indicator of cardiac autonomic activity. Pulse rate variability (PRV) utilizes photoplethysmography (PPG) and recently has been used as a surrogate for HRV. Several studies have found that PRV is not entirely valid as an estimation of HRV and that several physiological factors, including the pulse transit time (PTT) and blood pressure (BP) changes, may affect PRV differently than HRV. This study aimed to assess the relationship between PRV and HRV under different BP states: hypotension, normotension, and hypertension. Using the MIMIC III database, 5 min segments of PPG and ECG signals were used to extract PRV and HRV, respectively. Several time-domain, frequency-domain, and nonlinear indices were obtained from these signals. Bland–Altman analysis, correlation analysis, and Friedman rank sum tests were used to compare HRV and PRV in each state, and PRV and HRV indices were compared among BP states using Kruskal–Wallis tests. The findings indicated that there were differences between PRV and HRV, especially in short-term and nonlinear indices, and although PRV and HRV were altered in a similar manner when there was a change in BP, PRV seemed to be more sensitive to these changes.

scholarly journals Multiscale Distribution Entropy Analysis of Short-Term Heart Rate Variability

Entropy ◽  
2018 ◽  
Vol 20 (12) ◽  
pp. 952 ◽  
Author(s):  
Dae-Young Lee ◽  
Young-Seok Choi
Keyword(s):  
Heart Rate ◽  
Time Series ◽  
Heart Rate Variability ◽  
Coarse Grained ◽  
Multiscale Entropy ◽  
Short Term ◽  
Ecg Signals ◽  
Improved Stability ◽  
Moving Averaging ◽  
Electrocardiogram Ecg

Electrocardiogram (ECG) signal has been commonly used to analyze the complexity of heart rate variability (HRV). For this, various entropy methods have been considerably of interest. The multiscale entropy (MSE) method, which makes use of the sample entropy (SampEn) calculation of coarse-grained time series, has attracted attention for analysis of HRV. However, the SampEn computation may fail to be defined when the length of a time series is not enough long. Recently, distribution entropy (DistEn) with improved stability for a short-term time series has been proposed. Here, we propose a novel multiscale DistEn (MDE) for analysis of the complexity of short-term HRV by utilizing a moving-averaging multiscale process and the DistEn computation of each moving-averaged time series. Thus, it provides an improved stability of entropy evaluation for short-term HRV extracted from ECG. To verify the performance of MDE, we employ the analysis of synthetic signals and confirm the superiority of MDE over MSE. Then, we evaluate the complexity of short-term HRV extracted from ECG signals of congestive heart failure (CHF) patients and healthy subjects. The experimental results exhibit that MDE is capable of quantifying the decreased complexity of HRV with aging and CHF disease with short-term HRV time series.

scholarly journals Musical Rhythms Affect Heart Rate Variability: Algorithm and Models

2014 ◽  
Vol 2014 ◽  
pp. 1-14 ◽  
Author(s):  
Hui-Min Wang ◽  
Sheng-Chieh Huang
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
High Frequency ◽  
Physiological Model ◽  
Psychological Model ◽  
Two Measures ◽  
Electrocardiogram Ecg ◽  
Musical Rhythms ◽  
Increased Valence ◽  
Affect Heart Rate

There were a lot of psychological music experiments and models but there were few psychological rhythm experiments and models. There were a lot of physiological music experiments but there were few physiological music models. There were few physiological rhythm experiments but there was no physiological rhythm model. We proposed a physiological rhythm model to fill this gap. Twenty-two participants, 4 drum loops as stimuli, and electrocardiogram (ECG) were employed in this work. We designed an algorithm to map tempo, complexity, and energy into two heart rate variability (HRV) measures, the standard deviation of normal-to-normal heartbeats (SDNN) and the ratio of low- and high-frequency powers (LF/HF); these two measures form the physiological valence/arousal plane. There were four major findings. Initially, simple and loud rhythms enhanced arousal. Secondly, the removal of fast and loud rhythms decreased arousal. Thirdly, fast rhythms increased valence. Finally, the removal of fast and quiet rhythms increased valence. Our work extended the psychological model to the physiological model and deepened the musical model into the rhythmic model. Moreover, this model could be the rules of automatic music generating systems.

scholarly journals Sex Differences in Time-Domain and Frequency-Domain Heart Rate Variability Measures of Fatigued Drivers

2020 ◽  
Vol 17 (22) ◽  
pp. 8499
Author(s):  
Chao Zeng ◽  
Wenjun Wang ◽  
Chaoyang Chen ◽  
Chaofei Zhang ◽  
Bo Cheng
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Sex Differences ◽  
Frequency Domain ◽  
Time Domain ◽  
Mental States ◽  
Male And Female ◽  
Frequency Domain Methods ◽  
The Difference ◽  
Electrocardiogram Ecg

The effects of fatigue on a driver’s autonomic nervous system (ANS) were investigated through heart rate variability (HRV) measures considering the difference of sex. Electrocardiogram (ECG) data from 18 drivers were recorded during a simulator-based driving experiment. Thirteen short-term HRV measures were extracted through time-domain and frequency-domain methods. First, differences in HRV measures related to mental state (alert or fatigued) were analyzed in all subjects. Then, sex-specific changes between alert and fatigued states were investigated. Finally, sex differences between alert and fatigued states were compared. For all subjects, ten measures showed significant differences (Mann-Whitney U test, p < 0.01) between different mental states. In male and female drivers, eight and four measures, respectively, showed significant differences between different mental states. Six measures showed significant differences between males and females in an alert state, while ten measures showed significant sex differences in a fatigued state. In conclusion, fatigue impacts drivers’ ANS activity, and this impact differs by sex; more differences exist between male and female drivers’ ANS activity in a fatigued state than in an alert state.

scholarly journals Heart Rate Variability Time Analyses of the Streptozotocin- Diabetic Rat

2007 ◽  
Vol 4 (1) ◽  
pp. 64
Author(s):  
M. Jacobson ◽  
F.C. Howarth ◽  
E. Adeghate ◽  
K. Fatima-Shad
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Diabetic Rat ◽  
Physiological Effects ◽  
Short Term ◽  
The World ◽  
Electrocardiogram Ecg ◽  
Prevalence Of Diabetes Mellitus ◽  
Streptozotocin Diabetic Rat

As the world prevalence of diabetes mellitus (DM) increases, animal models of the disease's progression are required for researching effective treatment. The streptozotocin (STZ) treated rat is known to cause hyperglycaemia. This study confirms that this animal model also displays DM physiological effects in the animal heart rate (HR) and heart rate variability (HRV). In particular, 5 minutes of rat (n=13) electrocardiogram (ECG) is acquired hourly for 30 days. At day 10, the animal (n=7) is dosed with STZ and the ECG is analyzed in order to determine the HR and HRV. The HRV is indexed using two time-based analyses, based on long-term (24hr) and short-term (5min) analyses. All analyses are compared to control non-STZ dosed animals (n=6) and display significant DM effects. 

scholarly journals The Digi-NewB project for preterm infant sepsis risk and maturity analysis

2018 ◽  
Vol 10 (2-3) ◽  
Author(s):  
Alpo Värri ◽  
Antti Kallonen ◽  
Elina Helander ◽  
Andres Ledesma ◽  
Patrick Pladys
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Preterm Infant ◽  
Careful Analysis ◽  
Physiological Signals ◽  
P Value ◽  
Term Infants ◽  
Ecg Signals ◽  
Clinical Observations ◽  
Sepsis Risk

It is known from the literature that the careful analysis of the heart rate variability of a preterm infant can be used as a predictor of sepsis. The Digi-NewB project aims at collecting a database of at least 750 preterm infants including physiological signals, video and clinical observations. These data are used to design a decision support system for the early detection of sepsis and for the evaluation of the infant maturity. The preparation of the data for the exploratory analysis has turned out to be time-consuming. 190 infants have been recorded by March 2018 and of these, the R-R interval analysis of the ECG signals has been completed of 136 infants. The results of the project are still preliminary but seven heart rate variability parameters have been found to be different in preterm and full-term infants with a P value less than 0.01. The video analysis algorithm detecting the presence of personnel or relatives reached 96.8% of sensitivity and 95.1% of specificity.

scholarly journals A Novel Heart Rate Robust Method for Short-Term Electrocardiogram Biometric Identification

2019 ◽  
Vol 9 (1) ◽  
pp. 201 ◽  
Author(s):  
Di Wang ◽  
Yujuan Si ◽  
Weiyi Yang ◽  
Gong Zhang ◽  
Tong Liu
Keyword(s):  
Heart Rate ◽  
Principal Component ◽  
Identification Accuracy ◽  
Ecg Signal ◽  
Identification Task ◽  
Short Term ◽  
Ecg Signals ◽  
Subject Identification ◽  
Novel Method ◽  
Electrocardiogram Ecg

In the past decades, the electrocardiogram (ECG) has been investigated as a promising biometric by exploiting the subtle discrepancy of ECG signals between subjects. However, the heart rate (HR) for one subject may vary because of physical activities or strong emotions, leading to the problem of ECG signal variation. This variation will significantly decrease the performance of the identification task. Particularly for short-term ECG signal without many heartbeats, the hardly measured HR makes the identification task even more challenging. This study aims to propose a novel method suitable for short-term ECG signal identification. In particular, an improved HR-free resampling strategy is proposed to minimize the influence of HR variability during heartbeat processing. For feature extraction, the Principal Component Analysis Network (PCANet) is implemented to determine the potential difference between subjects. The proposed method is evaluated using a public ECG-ID database that contains various HR data for some subjects. Experimental results show that the proposed method is robust to HR change and can achieve high subject identification accuracy (94.4%) on ECG signals with only five heartbeats. Thus, the proposed method has the potential for application to systems that use short-term ECG signals for identification (e.g., wearable devices).

HRV

2020 ◽  
pp. 236-264
Author(s):  
Kirti Rawal ◽  
Gaurav Sethi ◽  
Barjinder Singh Saini ◽  
Indu Saini
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Input Signal ◽  
Medical Tourism ◽  
Ecg Signal ◽  
Rr Intervals ◽  
Experimental Protocols ◽  
Hrv Analysis ◽  
Electrocardiogram Ecg ◽  
Electrical Interference

The most important factor involved in heart rate variability (HRV) analysis is cardiac input signal, which is achieved in the form of electrocardiogram (ECG). The ECG signal is used for identifying many electrical defects associated with the heart. In this chapter, many issues involved while ECG recording such as type of the recording instrument, various sources of noise, artifacts, and electrical interference from surroundings is presented. Most importantly, this chapter comprises the details about the experimental protocols followed while ECG recording. Also, the brief overview of medical tourism as well as various interpolation methods used for pre-processing of RR intervals are presented in this chapter.

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