NONLINEAR HEART RATE VARIABILITY-BASED ANALYSIS AND PREDICTION OF PERFORMANCE STATUS IN PULMONARY METASTASES PATIENTS

2018 ◽  
Vol 30 (06) ◽  
pp. 1850043
Author(s):  
Reema Shyamsunder Shukla ◽  
Yogender Aggarwal
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Pulmonary Metastases ◽  
Performance Status ◽  
Eastern Cooperative Oncology Group ◽  
Approximate Entropy ◽  
Support Vector ◽  
Fluctuation Analysis ◽  
Nonlinear Features ◽  
Hrv Analysis

Cancer causes chronic stress and is associated with impaired autonomic nervous system (ANS). Heart rate variability (HRV) has been suggested to be an important tool in the identification and prediction of performance status (PS) in cancer. Lead II surface electrocardiogram (ECG) was recorded from 24 pulmonary metastases (PM) subjects and 30 healthy controls for nonlinear HRV analysis. Artificial neural network (ANN) and support vector machine (SVM) were applied for the prediction analysis. Analysis of variance (ANOVA) along with post-hoc Tukey’s HSD test was conducted using statistical R, 64-bit, v.3.3.2, at [Formula: see text]. The obtained results suggested lower HRV that increases with cancer severity from the Eastern Cooperative Oncology Group (ECOG)1 PS to ECOG4 PS. ANOVA results stated that approximate entropy (ApEn) ([Formula: see text]-[Formula: see text], [Formula: see text]), detrended fluctuation analysis (DFA) [Formula: see text] ([Formula: see text]-[Formula: see text], [Formula: see text]) and correlation dimension (CD) ([Formula: see text]-[Formula: see text], [Formula: see text]) were significant. The 13 nonlinear features were fed to ANN and SVM to obtain 82.25% and 100% accuracies, respectively. Nonlinear HRV analysis has given promising results in the prediction of diagnosis of PS in PM patients. These inputs would be very useful for clinicians to diagnose PS in their cancer patients and improve their quality of living.

Detecting Pedestrian Situation Awareness in Real-Time: Algorithm Development Using Heart Rate Variability and Phone Position

2018 ◽  
Vol 62 (1) ◽  
pp. 1579-1583
Author(s):  
Yourui Tong ◽  
Bochen Jia ◽  
Yi Wang ◽  
Si Yang
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Real Time ◽  
Situation Awareness ◽  
Mental State ◽  
Time Algorithm ◽  
Support Vector ◽  
Vehicle Design ◽  
Automated Vehicle ◽  
Hrv Analysis

To help automated vehicles learn surrounding environments via V2X communications, it is important to detect and transfer pedestrian situation awareness to the related vehicles. Based on the characteristics of pedestrians, a real-time algorithm was developed to detect pedestrian situation awareness. In the study, the heart rate variability (HRV) and phone position were used to understand the mental state and distractions of pedestrians. The HRV analysis was used to detect the fatigue and alert state of the pedestrian, and the phone position was used to define the phone distractions of the pedestrian. A Support Vector Machine algorithm was used to classify the pedestrian’s mental state. The results indicated a good performance with 86% prediction accuracy. The developed algorithm shows high applicability to detect the pedestrian’s situation awareness in real-time, which would further extend our understanding on V2X employment and automated vehicle design.

NOVEL CLASSIFICATION OF CORONARY ARTERY DISEASE USING HEART RATE VARIABILITY ANALYSIS

2012 ◽  
Vol 12 (04) ◽  
pp. 1240017 ◽  
Author(s):  
SUMEET DUA ◽  
XIAN DU ◽  
S. VINITHA SREE ◽  
THAJUDIN AHAMED V. I.
Keyword(s):  
Coronary Artery Disease ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Coronary Artery ◽  
Principal Component ◽  
Heart Rate Variability Analysis ◽  
Fluctuation Analysis ◽  
Non Invasive ◽  
Nonlinear Features ◽  
Artery Disease

Coronary artery disease (CAD) is a leading cause of death worldwide. Heart rate variability (HRV) has been proven to be a non-invasive marker of the autonomic modulation of the heart. Nonlinear analyses of HRV signals have shown that the HRV is reduced significantly in patients with CAD. Therefore, in this work, we extracted nonlinear features from the HRV signals using the following techniques: recurrence plots (RP), Poincare plots, and detrended fluctuation analysis (DFA). We also extracted three types of entropy, namely, Shannon entropy (ShanEn), approximation entropy (ApEn), and sample entropy (SampEn). These features were subjected to principal component analysis (PCA). The significant principal components were evaluated using eight classification techniques, and the performances of these techniques were evaluated to determine which presented the highest accuracy in classifying normal and CAD classes. We observed that the multilayer perceptron (MLP) method resulted in the highest classification accuracy (89.5%) using our proposed technique.

Effects of breathing patterns and light exercise on linear and nonlinear heart rate variability

2015 ◽  
Vol 40 (8) ◽  
pp. 762-768 ◽  
Author(s):  
Matthias Weippert ◽  
Kristin Behrens ◽  
Annika Rieger ◽  
Mohit Kumar ◽  
Martin Behrens
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Minute Ventilation ◽  
Load Condition ◽  
Approximate Entropy ◽  
Cardiac Risk ◽  
Fluctuation Analysis ◽  
Breathing Frequency ◽  
Breathing Patterns ◽  
Slow Breathing

Despite their use in cardiac risk stratification, the physiological meaning of nonlinear heart rate variability (HRV) measures is not well understood. The aim of this study was to elucidate effects of breathing frequency, tidal volume, and light exercise on nonlinear HRV and to determine associations with traditional HRV indices. R–R intervals, blood pressure, minute ventilation, breathing frequency, and respiratory gas concentrations were measured in 24 healthy male volunteers during 7 conditions: voluntary breathing at rest, and metronome guided breathing (0.1, 0.2 and 0.4 Hz) during rest, and cycling, respectively. The effect of physical load was significant for heart rate (HR; p < 0.001) and traditional HRV indices SDNN, RMSSD, lnLFP, and lnHFP (p < 0.01 for all). It approached significance for sample entropy (SampEn) and correlation dimension (D2) (p < 0.1 for both), while HRV detrended fluctuation analysis (DFA) measures DFAα1 and DFAα2 were not affected by load condition. Breathing did not affect HR but affected all traditional HRV measures. D2 was not affected by breathing; DFAα1 was moderately affected by breathing; and DFAα2, approximate entropy (ApEn), and SampEn were strongly affected by breathing. DFAα1 was strongly increased, whereas DFAα2, ApEn, and SampEn were decreased by slow breathing. No interaction effect of load and breathing pattern was evident. Correlations to traditional HRV indices were modest (r from –0.14 to –0.67, p < 0.05 to <0.01). In conclusion, while light exercise does not significantly affect short-time HRV nonlinear indices, respiratory activity has to be considered as a potential contributor at rest and during light dynamic exercise.

scholarly journals Heart Rate Variability Assessment by the Lyapunov Exponent

2020 ◽  
Vol 9 (8) ◽  
pp. 724-728
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Lyapunov Exponent ◽  
Low Frequency ◽  
Approximate Entropy ◽  
Mean Value ◽  
Spectral Estimates ◽  
Power Spectral ◽  
Hrv Analysis ◽  
Entropy Estimates

Heart rate variability (HRV) is a measure that evaluates cardiac autonomic activity according to the complexity or irregularity of an HRV dataset. At present, among various entropy estimates, the Lyapunov exponent (LE) is not as well described as approximate entropy (ApEn) and sample entropy (SampEn). Therefore, in this study, we investigated the characteristics of the parameters associated with the LE to evaluate whether the LE parameters can replace the frequency-domain parameters for HRV analysis. For the LE analysis in this study, two-dimensional factors were adjusted: length, which determines the size of the dimension vectors and is known as time delay embedding, varied over a range of 1 to 7, and the interval, which determines the distance between two successive embedding vectors, varied over a range of 1 to 3. A new parameter similar to the LA, the accumulation of the LE, was developed along with the LE to characterize the HRV parameters. The high frequency (HF) components dominated when the mean value of the LA was largest for interval 2, with 2.89 ms2 at the low frequency (LF) and 4.32 ms2 at the HF. The root mean square of the successive difference (RMSSD) in the LE decreased with increasing length in interval 1 from 2.6056 for length 1 to 0.2666 for length 7, resulting in a low HRV. The results suggest that the Lyapunov exponent methodology could be used in characterizing HRV analysis and replace power spectral estimates, specifically, HF components.

scholarly journals An Effective Deep Learning Model for Automated Detection of Myocardial Infarction Based on Ultrashort-Term Heart Rate Variability Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Muhammad Bilal Shahnawaz ◽  
Hassan Dawood
Keyword(s):  
Myocardial Infarction ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Deep Learning ◽  
Blood Supply ◽  
Heart Attack ◽  
Learning Model ◽  
Fluctuation Analysis ◽  
Hrv Analysis ◽  
Deep Learning Model

Myocardial infarction (MI), usually termed as heart attack, is one of the main cardiovascular diseases that occur due to the blockage of coronary arteries. This blockage reduces the blood supply to heart muscles, and a prolonged deficiency of blood supply causes the death of heart muscles leading to a heart attack that may cause death. An electrocardiogram (ECG) is used to diagnose MI as it causes variations like ST-T changes in the recorded ECG. Manual inspection of these variations is a tedious task and also requires expertise as the variations produced by MI are often very short in duration with a low amplitude. Hence, these changes may be misinterpreted, leading to delayed diagnosis and appropriate treatment. Therefore, computer-aided analysis of ECG may help to detect MI automatically. In this study, a robust deep learning model is proposed to detect MI based on heart rate variability (HRV) analysis of ECG signals from a single lead. Ultrashort-term HRV analysis is performed to compute HRV analysis features from time-domain and frequency-domain parameters through power spectral density estimations. Nonlinear HRV parameters are also computed using Poincare Plot, Recurrence Analysis, and Detrended Fluctuation Analysis. A finely tuned customized artificial neural network (ANN) algorithm is applied on 23 HRV features for MI detection and classification. The K-fold validation method is used to avoid any biases in results and reported 99.1% accuracy, 100% sensitivity, 98.1% specificity, and 99.0% F1 for MI detection, whereas 98.85% accuracy, 97.40% sensitivity, 99.05% specificity, and 97.70% F1 score is achieved for classification. Furthermore, the ANN algorithm completed its execution in just 59 seconds that indicates the efficiency of the proposed ANN model. The overall performance in terms of computed evaluation matrices and execution time indicates the robustness and cost-effectiveness of the proposed methodology. Thus, the proposed model can be used for high-performance MI detection, even in wearable devices.

Non-linear analysis of heart rate variability for evaluating the acute effects of caffeinated beverages in young adults

2020 ◽  
Vol 30 (7) ◽  
pp. 1018-1023 ◽  
Author(s):  
Serife G. Caliskan ◽  
Mehmet D. Bilgin
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Young Adults ◽  
Approximate Entropy ◽  
Energy Drink ◽  
High Rate ◽  
Largest Lyapunov Exponent ◽  
Fluctuation Analysis ◽  
Electrocardiogram Signals ◽  
Non Linear

AbstractCaffeinated beverages are the most consumed substances in the world. High rate of uptake of these beverages leads to various cardiovascular disorders ranging from palpitations to coronary failure. The objective of the study is to ascertain how the complexity parameters of heart rate variability are affected by acute consumption of caffeinated beverages in young adults.Electrocardiogram measurements were performed before consuming drinks. After consuming the drinks, measurements were done again at 30 minutes and 60 minutes. Heart rate variability signals were acquired from electrocardiogram signals. Also, the signals were reconstructed in the phase space and largest Lyapunov exponent, correlation dimension, approximate entropy, and detrended fluctuation analysis values were calculated.Heart rate increased for energy drink and cola groups but not in coffee group. Non-linear parameter values of energy drink, coffee, and cola group are increased within 60 minutes after drink consumption. This change is statistically significant just for energy drink group.Energy drink consumption increases the complexity of the cardiovascular system in young adults significantly. Coffee and cola consumption have no significant effect on the non-linear parameters of heart rate variability.

scholarly journals Changes in non-linear and time-domain heart rate variability indices between critically ill COVID-19 and all-cause sepsis patients -a retrospective study

2020 ◽  
Author(s):  
Rishikesan Kamaleswaran ◽  
Ofer Sadan ◽  
Prem Kandiah ◽  
Qiao Li ◽  
James M Blum ◽  
...  
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Critically Ill ◽  
Statistical Significance ◽  
Approximate Entropy ◽  
Temporal Analysis ◽  
Potential Biomarker ◽  
Starting Point ◽  
Non Linear ◽  
Nonlinear Features

Objective: To measure heart rate variability metrics in critically ill COVID-19 patients with comparison to all-cause critically ill sepsis patients. Design and patients: Retrospective analysis of COVID-19 patients admitted to an ICU for at least 24h at any of Emory Healthcare ICUs between March and April 2020. The comparison group was a cohort of all-cause sepsis patients prior to COVID-19 pandemic. Interventions: none. Measurements: Continuous waveforms were captured from the patient monitor. The EKG was then analyzed for each patient over a 300 second (s) observational window, that was shifted by 30s in each iteration from admission till discharge. A total of 23 HRV metrics were extracted in each iteration. We use the Kruskal-Wallis and Steel-Dwass tests (p < 0.05) for statistical analysis and interpretations of HRV multiple measures. Results: A total of 141 critically-ill COVID-19 patients met inclusion criteria, who were compared to 208 patients with all-cause sepsis. Demographic parameters were similar apart from a high proportion of African-Americans in the COVID-19 cohort. Three non-linear markers, including SD1:SD2, sample entropy, approximate entropy and four linear features mode of Beat-to-Beat interval (NN), Acceleration Capacity (AC), Deceleration Capacity (DC), and pNN50, were statistical significance between more than one binary combinations of the sub-groups (comparing survivors and non-survivors in both the COVID-19 and sepsis cohorts). The three nonlinear features and AC, DC, and NN (mode) were statistically significant across all four combinations. Temporal analysis of the main markers showed low variability across the 5 days of analysis, compared with sepsis patients. Conclusions: Heart rate variability is broadly implicated across patients infected with SARS-CoV-2, and admitted to the ICU for critical illness. Comparing these metrics to patients with all-cause sepsis suggests a unique set of expressions that differentiate this viral phenotype. This finding could be investigated further as a potential biomarker to predict poor outcome in this patient population, and could also be a starting point to measure potential autonomic dysfunction in COVID-19.

scholarly journals Field Monitoring the Effects of Overnight Shift Work on Specialist Tactical Police Training with Heart Rate Variability Analysis

2021 ◽  
Vol 13 (14) ◽  
pp. 7895
Author(s):  
Colin Tomes ◽  
Ben Schram ◽  
Robin Orr
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Shift Work ◽  
Emergency Response ◽  
Police Training ◽  
Heart Rate Variability Analysis ◽  
Response Team ◽  
Hrv Analysis ◽  
And Performance ◽  
Personnel Allocation

Police work exposes officers to high levels of stress. Special emergency response team (SERT) service exposes personnel to additional demands. Specifically, the circadian cycles of SERT operators are subject to disruption, resulting in decreased capacity to compensate in response to changing demands. Adaptive regulation loss can be measured through heart rate variability (HRV) analysis. While HRV Trends with health and performance indicators, few studies have assessed the effect of overnight shift work on HRV in specialist police. Therefore, this study aimed to determine the effects overnight shift work on HRV in specialist police. HRV was analysed in 11 SERT officers and a significant (p = 0.037) difference was found in pRR50 levels across the training day (percentage of R-R intervals varying by >50 ms) between those who were off-duty and those who were on duty the night prior. HRV may be a valuable metric for quantifying load holistically and can be incorporated into health and fitness monitoring and personnel allocation decision making.

scholarly journals Nonlinearities of heart rate variability in animal models of impaired cardiac control: contribution of different time scales

2017 ◽  
Vol 123 (2) ◽  
pp. 344-351 ◽  
Author(s):  
Luiz Eduardo Virgilio Silva ◽  
Renata Maria Lataro ◽  
Jaci Airton Castania ◽  
Carlos Alberto Aguiar Silva ◽  
Helio Cesar Salgado ◽  
...  
Keyword(s):  
Nonlinear Dynamics ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Time Scales ◽  
Multiscale Entropy ◽  
Time Frequency ◽  
Cardiac Control ◽  
Long Time ◽  
Nonlinear Features ◽  
Short Time

Heart rate variability (HRV) has been extensively explored by traditional linear approaches (e.g., spectral analysis); however, several studies have pointed to the presence of nonlinear features in HRV, suggesting that linear tools might fail to account for the complexity of the HRV dynamics. Even though the prevalent notion is that HRV is nonlinear, the actual presence of nonlinear features is rarely verified. In this study, the presence of nonlinear dynamics was checked as a function of time scales in three experimental models of rats with different impairment of the cardiac control: namely, rats with heart failure (HF), spontaneously hypertensive rats (SHRs), and sinoaortic denervated (SAD) rats. Multiscale entropy (MSE) and refined MSE (RMSE) were chosen as the discriminating statistic for the surrogate test utilized to detect nonlinearity. Nonlinear dynamics is less present in HF animals at both short and long time scales compared with controls. A similar finding was found in SHR only at short time scales. SAD increased the presence of nonlinear dynamics exclusively at short time scales. Those findings suggest that a working baroreflex contributes to linearize HRV and to reduce the likelihood to observe nonlinear components of the cardiac control at short time scales. In addition, an increased sympathetic modulation seems to be a source of nonlinear dynamics at long time scales. Testing nonlinear dynamics as a function of the time scales can provide a characterization of the cardiac control complementary to more traditional markers in time, frequency, and information domains. NEW & NOTEWORTHY Although heart rate variability (HRV) dynamics is widely assumed to be nonlinear, nonlinearity tests are rarely used to check this hypothesis. By adopting multiscale entropy (MSE) and refined MSE (RMSE) as the discriminating statistic for the nonlinearity test, we show that nonlinear dynamics varies with time scale and the type of cardiac dysfunction. Moreover, as complexity metrics and nonlinearities provide complementary information, we strongly recommend using the test for nonlinearity as an additional index to characterize HRV.

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