Estimation of the Heart Rate Variability Features via Recurrent Neural Networks

Objective To investigate the characteristics of heart rate variability (HRV) of ambulatory electrocardiogram in patients with acute stroke, and evaluate the predictive value of HRV in stroke prognosis. Methods Eighty-three patients acute stroke (study group) and 83 cases of healthy subjects (control group) from October 2016 to October 2017 in Dalian Municipal Central Hospital Affiliated of Dalian Medical University were selected. The 24 h ambulatory electrocardiogram was performed to determine HRV in 2 groups, including standard deviation of NN intervals (SDNN), standard deviation of the 5 min mean cycle lengths (SDANN), root-mean-square successive difference (RMSSD) and percentage value of NN50 count (PNN50). Results The SDNN, SDANN, RMSSD and PNN50 in study group were significantly lower than those in control group: (80.83 ± 10.52) ms vs. (148.11 ± 22.59) ms, (79.98 ± 8.89) ms vs. (129.35 ± 5.34) ms, (19.28 ± 4.25) ms vs. (39.57 ± 2.38) ms and (5.91 ± 2.12)% vs. (19.35 ± 12.15)%, and there were statistical differences (P 0.05). The SDNN, SDANN, RMSSD and PNN50 in right stroke (43 cases) were significantly lower than those in left stroke (40 cases):(75.18 ± 2.32) ms vs. (88.12 ± 3.58) ms, (73.36 ± 2.18) ms vs. (85.69 ± 7.29) ms, (17.57 ± 1.67) ms vs. (20.58 ± 4.23) ms and (4.39 ± 1.57)% vs. (8.61 ± 1.12)%, and there were statistical differences (P<0.05). Patients were followed up for 1 year, 24 died and 59 survived. The SDNN, SDANN, RMSSD and PNN50 in dead patients were significantly lower than those in survived patients: (92.35 ± 4.58) ms vs. (154.37 ± 4.65) ms, (76.23 ± 4.03) ms vs. (143.95 ± 4.34) ms, (7.43 ± 2.12) ms vs. (31.65 ± 1.52) ms and (2.35 ± 0.46)% vs. (11.65 ± 0.48)%, and there were statistical differences (P<0.05). Conclusions The autonomic nervous function of patients with acute stroke is seriously unbalanced, with increased sympathetic excitability and decreased vagus excitability. The decrease of HRV can easily induce cardiac events and seriously affect the prognosis. Key words: Stroke; Prognosis; Heart rate viability; Autonomic nervous function

Physiological stress is human body's response to a challenge by multiple systems in the body, in which ANS (Autonomic Nervous System) plays key roles. This paper proposes a stress level evaluation method based on time-frequency domain analysis of HRV (Heart Rate Variability) features combining statistical optimization. The most reliable and efficient indicator to ANS is HRV features obtained from time and frequency domain analysis of R-R intervals recorded during the modified Stroop test. A BP Neural network is utilized to train HRV features selected by t-test and one-way Anova test and classify new samples into four stress level categories i.e. relaxed state, low stress, medium stress and high stress. Weighing factors in classification and number of neurons in hidden layer are considered for optimized network. The proposed method has been validated by experimental results.

Electrocardiogram analysis using a combination of statistical, geometric, and nonlinear heart rate variability features

Early and noninvasive identification of heart failure progression is an important adjunct to successful and timely intervention. Severity of heart failure (HF) was assessed by Left Ventricular Ejection Fraction (LVEF). In this paper, we explore the circadian (24-hour) heart rate variability (HRV) features from ''normal" (EF >50%), "at-risk" (EF <40%), and "border-line" (40% ≤ EF ≤ 50%) patient data to determine whether HRV features can predict the stage of heart failure. All coronary artery disease (CAD) 24-hour circadian heart rate data were fitted by a cosinor analysis algorithm. Hourly HRV features from time- and frequency-domains were then extracted from all 24-hour patient data. A one-way ANOVA test was performed followed by a Tukey post-hoc multiple comparison test to investigate the differences among the three groups. The results showed a statistically significant difference between the three groups when using the normalized high frequency (HF Norm), low frequency peak (LF Peak), and the normalized very-low frequency (VLF Norm) for the 05:00-06:00 and 18:00-19:00 time periods. These results highlight a possible link between the circadian variation of sympathetic and parasympathetic nervous system activity and LVEF for CAD patients. The results could be useful in differentiating the various degrees of LVEF by using only noninvasive HRV features derived over a 24-hour period.Clinical relevance- The proposed method could be clinically useful to estimate the extent of LVEF associated with the severity of heart failure by recording the circadian variation of the heart rate in CAD patients. However, further clinical trials on a larger cohort of patients and controls are required.

Many patients with severe obstructive sleep apnea (OSA) do not complain of excessive daytime sleepiness (EDS), possibly due to increased sympathetic nervous activity (SNA) and accompanying heightened alertness. We hypothesized that in patients with OSA, those without subjective EDS (Epworth Sleepiness Scale, ESS score < 11) would have higher very low frequency (VLF) heart rate variability (HRV) during sleep, reflecting greater sympathetic heart rate modulation than patients with an ESS score ≥ 11. Patients with severe OSA (AHI ≥ 30: 26 with and 65 without heart failure) were divided into those with and without EDS. Heart rate (HR) signals were acquired in stage 2 sleep during periods of recurrent apneas and hypopneas and submitted to coarse graining spectral analysis, which extracts harmonic, neurally mediated contributions to HRV from total spectral power. Because the apnea-hyperpnea cycle entrains muscle SNA at VLF (0 to 0.04 Hz), VLF power was our principal between-group comparison. Subjects without EDS had higher harmonic VLF power (944 ± 839 vs 447 ± 461 msec(2), p = 0.003) than those with EDS, irrespective of the presence or absence of heart failure (1218 ± 944 vs 426 ± 299 msec(2), p = 0.043, and 1029 ± 873 vs 503 ± 533 msec(2), p = 0.003, respectively). ESS scores correlated inversely with VLF power in all (r = -0.294, p = 0.005) and in heart failure subjects (r = -0.468, p = 0.016). Patients with severe OSA but without EDS have higher VLF-HRV than those with EDS. This finding suggests that patients with severe OSA but without EDS have greater sympathetic modulation of HRV than those with EDS that may reflect elevated adrenergically mediated alertness. Taranto Montemurro L; Floras JS; Picton P; Kasai T; Alshaer H; Gabriel JM; Bradley TD. Relationship of heart rate variability to sleepiness in patients with obstructive sleep apnea with and without heart failure.

Objective:This study aims to evaluate the features of heart rate variability (HRV) and respiratory signals as indices for a driver"s drowsiness and waking status in order to develop the classification model for a driver"s drowsiness and waking status using those features. Background: Driver"s drowsiness is one of the major causal factors for traffic accidents. This study hypothesized that the application of combined bio-signals to monitor the alertness level of drivers would improve the effectiveness of the classification techniques of driver"s drowsiness. Method: The features of three heart rate variability (HRV) measurements including low frequency (LF), high frequency (HF), and LF/HF ratio and two respiratory measurements including peak and rate were acquired by the monotonous car driving simulation experiments using the photoplethysmogram (PPG) and respiration sensors. The experiments were repeated a total of 50 times on five healthy male participants in their 20s to 50s. The classification model was developed by selecting the optimal measurements, applying a binary logistic regression method and performing 3-fold cross validation. Results: The power of LF, HF, and LF/HF ratio, and the respiration peak of drowsiness status were reduced by 38%, 22%, 31%, and 7%, compared to those of waking status, while respiration rate was increased by 3%. The classification sensitivity of the model using both HRV and respiratory features (91.4%) was improved, compared to that of the model using only HRV feature (89.8%) and that using only respiratory feature (83.6%). Conclusion: This study suggests that the classification of driver"s drowsiness and waking status may be improved by utilizing a combination of HRV and respiratory features. Application: The results of this study can be applied to the development of driver"s drowsiness prevention systems.

Biomedical signals carry important information about the behaviour of the living systems under study. A proper processing of these signals in principle enhances their physiological and clinical information. Analysis of variations in the instantaneous heart rate time series using the beat to-beat RR intervals (the RR tachogram) is known as heart rate variability (HRV) analysis. Sample entropy (SampEn), refined version of approximate entropy (ApEn), is a nonlinear complexity measure used to quantify the irregularity of a RR interval time series without biasing. An increase in SampEn is an indicator of increases in complexity. Linear HRV parameters are very sensitive to ECG sampling frequency and low sampling frequency may result in clinically misinterpretation of HRV. In this study consequences of errors in SampEn based HRV induced by ECG sampling frequency have been investigated. The error induced in SampEn based HRV was found to be a function of ECG sampling frequency and RR interval data length. The relative error in SampEn was approximately 3.5%.for medium and long term data (N=500, 1000 respectively) and less than 2% for short term data (N=200) at low ECG sampling frequency of 125 Hz with respect to reference values at 2000 Hz. Therefore the SampEn based HRV indices computed from RR interval time series with low ECG sampling should be regarded with caution. The finding of this study can be partly used as a reference for the optimal ECG sampling frequency for the SampEn-based HRV assessment.

Towards wearable thermal comfort assessment framework by analysis of heart rate variability

Heart Rate Variability in Idiopathic Dilated Cardiomyopathy: Characteristics and Prognostic Value

Optical heart rate monitoring (OHR) with reflective wrist photoplethysmography is a technique mainly used in the wellness application domain for monitoring heart rate levels during exercise. In the absence of motion, OHR technique is also able to estimate individual beat-to-beat intervals relatively well and can therefore also be used, for example, in monitoring of cardiac arrhythmias, stress, or sleep quality through heart rate variability (HRV) analysis. HRV analysis has also potential in monitoring the recovery of patients, e.g. after a medical intervention. However, in order to detect subtle changes, the calculated HRV parameters should be sufficiently accurate and very few studies exist that asses the accuracy of OHR derived HRV in non-healthy subjects. In this paper, we present a method to estimate beat-to-beat-intervals (BBIs) from reflective wrist PPG signal and evaluated the accuracy of the proposed method in estimating BBIs in a cross-sectional study with 29 hospitalized patients (mean age 70.6 years) in 24-h recordings performed after peripheral vascular surgery or endovascular interventions. Finally, we evaluate the accuracy of more than 30 commonly used HRV parameters and find that the accuracy of certain metrics, for example SDNN and triangular index, shown in the literature to be associated with the deterioration of the status of the patients during recovery from surgical intervention, could be adequate for patient monitoring. On the other hand, the parameters more affected by the high-frequency content of the HRV and especially the LF/HF-ratio should be used with caution.

The measurement of ECG-derived heart rate variability (HRV) provides early diagnosis and prevention of cardiac events such as coronary artery disease (CAD) and myocardial infarction (MI). The disease severity causes ECG shape deformation, leading to difficulty in R-wave detection required for heart rate variability (HRV) analysis. Therefore, the study aimed to optimize R-wave threshold filters at 50%, 60%, 70%, and 80% within the framework of HRV analysis for better diagnosis of MI and CAD. Lead-II ECG was recorded from CAD (n=15), MI (n=15), and Control (n=10) group participants using an MP45 amplifier (Biopac Systems Inc., USA). The ECG morphologic features were extracted to analyze the change in R-wave amplitude. The R-R interval time series was extracted at different threshold filters (50%, 60%, 70%, and 80%) and used for heart rate variability (HRV) analysis. The HRV features extracted using Kubios V2.0 in time, frequency, and nonlinear domains at different threshold filters were compared. The obtained findings revealed that R-wave amplitude was suppressed under the CAD and MI groups in comparison to the control subjects. The observed results also suggested 70% threshold filters for optimum detection of R-wave under CAD and MI. Further, the time and frequency domains presented the best result in comparison to the nonlinear HRV domain. The present study suggested that HRV analysis may be made more predictive for various cardiac diseases by optimizing R-wave threshold filters.

Heart rate variability (HRV) analysis is used as a marker of autonomic nervous system activity which may be related to mental and/or physical activity. HRV features can be extracted by detecting QRS complexes from an electrocardiogram (ECG) signal. The difficulties in QRS complex detection are due to the artifacts and noises that may appear in the ECG signal when subjects are performing their daily life activities such as exercise, posture changes, climbing stairs, walking, running, etc. This study describes a strong computation method for real-time QRS complex detection. The detection is improved by the prediction of the position of [Formula: see text] waves by the estimation of the RR intervals lengths. The estimation is done by computing the intensity of the electromyogram noises that appear in the ECG signals and known here in this paper as ECG Trunk Muscles Signals Amplitude (ECG-TMSA). The heart rate (HR) and ECG-TMSA increases with the movement of the subject. We use this property to estimate the lengths of the RR intervals. The method was tested using famous databases, and also with signals acquired when an experiment with 17 subjects from our laboratory. The obtained results using ECG signals from the MIT-Noise Stress Test Database show a QRS complex detection error rate (ER) of 9.06%, a sensitivity of 95.18% and a positive prediction of 95.23%. This method was also tested against MIT-BIH Arrhythmia Database, the result are 99.68% of sensitivity and 99.89% of positive predictivity, with ER of 0.40%. When applied to the signals obtained from the 17 subjects, the algorithm gave an interesting result of 0.00025% as ER, 99.97% as sensitivity and 99.99% as positive predictivity.

Objective To explore the heart rate variability (HRV) of dynamic electrocardiogram in children with benign or malignant ventricular premature beat (VPB) and healthy children, and analyze the changes of time and frequency domain indexes of HRV. Methods From January 2012 to December 2017, a total of 100 children with VPB diagnosed by auxiliary examinations and follow-up visits results, who were 6-14 years old and accepted dynamic electrocardiogram test in West China Second University Hospital, Sichuan University were recruited in this study. According to benign or malignant VPB, they were divided into benign VPB group (n=50) and malignant VPB group (n=50). Children in malignant VPB group were all combined with organic heart diseases, such as congenital heart defects (4 cases), dilated cardiomyopathy (DCM) (9 cases ), and viral myocarditis (37 cases) diagnosed by abnormal myocardial enzyme levels. Meanwhile, 50 cases of healthy children with the same age range who received dynamic electrocardiogram in the same hospital during the same period and the results were normal were included into control group. Measurement data with normal distribution and homogeneity such as standard deviation of all normal-to-normal intervals (SDNN), standard deviation of average normal-to-normal intervals (SDANN), root mean square of the successive difference (rMSSD), percentage normal-to-normal interval deviation greater than 50 ms (PNN50), coefficient of variance (CV), coefficient of variance (ULF), very-low frequencies (VLF), low frequencies (LF), and high frequencies (HF) were expressed by ±s. And those data among 3 groups were compared by one-way ANOVA, further comparisons between each two groups were conducted by least singnificant difference (LSD) test. Numeration data such as constituent ratio of genders were expressed by rate (%), and were compared by chi-square test among 3 groups. There were no significant differences among 3 groups in the constituent ratio of gender and age (P>0.05). This study met the requirements of the World Medical Association Declaration of Helsinki revised in 2013. Clinical informed consent was obtained from each child′s guardians. Results There were significant differences among 3 groups in HRV analysis results of time domain indicators such as SDNN, SDANN, rMSSD, PNN50, CV, and frequency domain indicators such as ULF, VLF, LF and HF (F=42.304, 14.144, 26.344, 15.362, 8.115, 18.031, 10.611, 20.928, 13.719; P 0.05), and PNN50 and HF between benign VPB group and control group (P>0.05), but there were significant differences in the others time domain indicators and frequency domain indicators of HRV between each two groups (P<0.05). Conclusions The HRV analysis of dynamic electrocardiogram is helpful in determining benign and malignant VPB in children. However, prospective randomized controlled trials with multicenter and large-sample size are needed to evaluate whether the HRV analysis can be used to determine the severity of VPB and as a prognosis marker in children with VPB. Key words: Arrhythmias, cardiac; Ventricular premature complexes; Heart rate variability; Electrocardiography; Controlled clinical trial; Child

Objective The characteristics of heart rate variability (HRV) in patients in the functional examination department of a Shanghai hospital from 2023 to 2024 were analyzed. Methods Ninety-five patients who received functional examination in our hospital from 2023 to 2024 were selected as the study objects, including 33 patients with depression, 32 patients with anxiety, and 30 patients with insomnia. Hrv-related indicators of patients with depression, anxiety and insomnia were compared respectively: The standard deviation (SDNN) of the difference of heartbeat cycles, the whole process is divided into consecutive periods by 5 minutes, the mean value of N-N interval every 5 minutes is calculated, then the standard deviation (SDNN5) of all the mean values are calculated, and the number of beats in all N-N interval periods where the difference between adjacent N-N interval periods is greater than 50ms is divided by the total number of NN interval periods. Finally, multiply by 100 (pNN50), the root-mean-square value (RMSSD) of the difference between all adjacent N-N intervals, extremely low frequency power (VLF), low frequency power (LF), high frequency power (HF), total power (TP), LF/HF. The HRV-related indexes of patients of different genders and ages were compared respectively: SDNN, SDNN5, pNN50, RMSSD, VLF, LF, HF, TP, LF/HF. Results There was no significant difference in the levels of SDNN, SDNN5, pNN50, RMSSD, VLF, LF, HF, TP and LF/HF in patients with depression, anxiety and insomnia (P &gt; 0.05). There was no significant difference in SDNN, SDNN5, pNN50, RMSSD, VLF, LF and TP between male and female patients (P &gt; 0.05). There were significant differences in HF and LF/HF levels between male and female patients (P &lt; 0.05). There were significant differences in the levels of SDNN, SDNN5, VLF, LF, TP and LF/HF among different age groups (P &lt; 0.05). There was no significant difference in pNN50, RMSSD and HF levels among different age groups (P &gt; 0.05). Conclusion HRV has a certain predictive power for patients with depression, anxiety and insomnia in the functional examination department of a Shanghai hospital from 2023 to 2024. In addition, the HF and LF/HF levels of HRV related indicators may be correlated with gender. At the same time, with the increase of age, the levels of SDNN, SDNN5, VLF, LF, TP, LF/HF showed a decreasing trend, and the autonomic nervous system function of the body was also declining.

Spectral analysis of heart rate and blood pressure variability in primary Sjögren's syndrome