Heartbeat Interval Research Articles

Effects of Ballistocardiogram Peak Detection Jitters on the Quality of Heart Rate Variability Features: A Simulation-Based Case Study in the Context of Sleep Staging

Heart rate variability (HRV) features support several clinical applications, including sleep staging, and ballistocardiograms (BCGs) can be used to unobtrusively estimate these features. Electrocardiography is the traditional clinical standard for HRV estimation, but BCGs and electrocardiograms (ECGs) yield different estimates for heartbeat intervals (HBIs), leading to differences in calculated HRV parameters. This study examines the viability of using BCG-based HRV features for sleep staging by quantifying the impact of these timing differences on the resulting parameters of interest. We introduced a range of synthetic time offsets to simulate the differences between BCG- and ECG-based heartbeat intervals, and the resulting HRV features are used to perform sleep staging. Subsequently, we draw a relationship between the mean absolute error in HBIs and the resulting sleep-staging performances. We also extend our previous work in heartbeat interval identification algorithms to demonstrate that our simulated timing jitters are close representatives of errors between heartbeat interval measurements. This work indicates that BCG-based sleep staging can produce accuracies comparable to ECG-based techniques such that at an HBI error range of up to 60 ms, the sleep-scoring error could increase from 17% to 25% based on one of the scenarios we examined.

Root mean square of successive differences is not a valid measure of parasympathetic reactivity during slow deep breathing.

Deep breathing exercises are the second most used complementary health approach in the United States. Two heart rate variability (HRV) parameters, the root mean square of successive differences (RMSSD) and the respiratory sinus arrhythmia (RSA), are used to assess parasympathetic reactivity to deep breathing, but they are often not in agreement. Our purpose was to determine the cause of the disagreement. We investigated HRV parameters in 38 subjects during baseline, deep breathing, and recovery. Here we show that RMSSD as a measure of parasympathetic reactivity is unreliable; it does not reflect the increase in HRV during deep breathing as determined by RSA. We observed a decrease in RMSSD values despite a marked increase in HRV as determined by RSA and the standard deviation of normal heartbeat interval (SDNN) in healthy subjects and patients with functional bowel disorders. We show that RSA captures all aspects of HRV, whereas successive differences in heart rate intervals are only a small part of HRV, with decreasing variability during deep breathing in most subjects. We present a new measure of calculating RSA during deep breathing that may become an essential tool for researchers and clinicians. We also provide a unique visualization of the increased heart rate variability during deep breathing. Hence, RMSSD cannot be used to assess parasympathetic reactivity during deep breathing; using RSA is recommended. The use of RMSSD in previous influential studies may have led to erroneous conclusions about parasympathetic reactivity during deep breathing. Its continued use may undervalue the effects of the autonomic nervous system in slow deep breathing.

A concept for emotion recognition systems for children with profound intellectual and multiple disabilities based on artificial intelligence using physiological and motion signals

Purpose This study proposes a concept for emotion recognition systems for children with profound intellectual and multiple disabilities (PIMD) based on artificial intelligence (AI) using physiological and motion signals. Methods First, the heartbeat interval (R–R interval, RRI) of a child with PIMD was measured, and the correlation between the RRI and emotion was briefly tested in a preliminary experiment. Then, a concept based on AI for emotion recognition systems for children with PIMD was created using physiological and motion signals, and an emotion recognition system based on the proposed concept was developed using a random forest classifier taking as inputs the RRI, eye gaze, and other data acquired using low physical burden sensors. Subsequently, the developed emotion recognition system was evaluated, validating the proposed concept. Finally, we proposed a validated concept for emotion recognition systems. Results A correlation was found between the RRI and emotion. The emotion recognition system was created based on the proposed concept and tested. According to the results, the recognition rate of “negative” and “not negative” of 70.4% ± 6.1% (Mean ± S.D.) of the developed emotion recognition system was higher than 48.5% ± 5.0% of an unfamiliar person used as a control. Conclusion The results indicate that the proposed concept for emotion recognition systems is useful for communicating with children with PIMD.

An assessment of heart rate and blood pressure asymmetry in the diagnosis of vasovagal syncope in females.

Introduction: Heart Rate Asymmetry (HRA) describes a phenomenon of differences between accelerations and decelerations in human heart rate. Methods used for HRA assessment can be further implemented in the evaluation of asymmetry in blood pressure variations (Blood Pressure Asymmetry-BPA). Methods: We have analyzed retrospectively the series of heartbeat intervals extracted from ECG and beat-to-beat blood pressure signals from 16 vasovagal patients (age: 32.1 ± 13.3; BMI: 21.6 ± 3.8; all female) and 19 healthy subjects (age: 34.6 ± 7.6; BMI: 22.1 ± 3.4; all female) who have undergone tilt test (70°). Asymmetry was evaluated with Poincaré plot-based methods for 5min recordings from supine and tilt stages of the test. The analyzed biosignals were heart rate (RR), diastolic (dBP) and systolic Blood Pressure (sBP) and Pulse Pressure (PP). In the paper we explored the differences between healthy and vasovagal women. Results: The changes of HRA indicators between supine and tilt were observed only in the control group (Porta Index p = 0.026 and Guzik Index p = 0.005). No significant differences in beat-to-beat variability (i.e. spread of points across the line of identity in Poincaré plot-SD1) of dBP was noted between supine and tilt in the vasovagal group (p = 0.433 in comparison to p = 0.014 in healthy females). Moreover, in vasovagal patients the PP was significantly different (supine: 41.47; tilt: 39.27mmHg) comparing to healthy subjects (supine: 35.87; tilt: 33.50mmHg) in supine (p = 0.019) and in tilt (p = 0.014). Discussion: Analysis of HRA and BPA represents a promising method for the evaluation of cardiovascular response to orthostatic stressors, however currently it is difficult to determine a subject's underlying health condition based only on these parameters.

Are infrared thermography, feeding behavior, and heart rate variability measures capable of characterizing group-housed sow social hierarchies?

Group gestation housing is quickly becoming standard practice in commercial swine production. However, poor performance and welfare in group housed sows may result from the formation and maintenance of the social hierarchy within the pen. In the future, the ability to quickly characterize the social hierarchy via precision technologies could be beneficial to producers for identifying animals at risk of poor welfare outcomes. Therefore, the objective of this study was to investigate the use of infrared thermography (IRT), automated electronic sow feeding systems, and heart rate monitors as potential technologies for detecting the social hierarchy within five groups of sows. Behavioral data collection occurred for 12 h after introducing five sow groups (1-5; n = 14, 12, 15, 15, and 17, respectively) to group gestation housing to determine the social hierarchy and allocate individual sows to 1 of 4 rank quartiles (RQ 1-4). Sows within RQ1 were ranked highest while RQ4 sows were ranked lowest within the hierarchy. Infrared thermal images were taken behind the neck at the base of the ear of each sow on days 3, 15, 30, 45, 60, 75, 90, and 105 of the experiment. Two electronic sow feeders tracked feeding behavior throughout the gestation period. Heart rate monitors were worn by 10 randomly selected sows per repetition for 1 h prior to and 4 h after reintroduction to group gestation housing to collect heart rate variability (HRV). No differences were found between RQ for any IRT characteristic. Sows within RQ3 and RQ4 had the greatest number of visits to the electronic sow feeders overall (P < 0.04) but spent shorter time per visit in feeders (P < 0.05) than RQ1 and RQ2 sows. There was an interaction of RQ with hour for feed offered (P = 0.0003), with differences between RQ occurring in hour 0, 1, 2, and 8. Higher-ranked sows (RQ1 and RQ2) occupied the feeder for longer during the first hour than lower ranking sows (RQ3 and RQ4; P < 0.04), while RQ3 sows occupied the feeder longer than RQ1 sows during hour 6, 7, and 8 (P < 0.02). Heart beat interval (RR) collected prior to group housing introduction differed between RQ (P < 0.02 for all), with RQ3 sows exhibiting the lowest RR, followed by RQ4, RQ1, and RQ2. Rank quartile also affected standard deviation of RR (P = 0.0043), with RQ4 sows having the lowest, followed by RQ1, RQ3, and RQ2 sows. Overall, these results indicate that feeding behavior and HRV measures may be capable of characterizing social hierarchy in a group housing system.

Fetal Arrhythmia Detection Based on Labeling Considering Heartbeat Interval.

Arrhythmia is one of the causes of sudden infant death, and it is very important to detect fetal arrhythmia for fetal well-being. Fetal electrocardiogram (FECG) is one of the methods to detect a heartbeat. Fetal arrhythmia can be detected based on the heartbeat detection results from FECG signals such as heartbeat intervals. However, the accuracy of arrhythmia detection easily degrades depending on the accuracy of heartbeat detection. In this paper, we propose a deep learning-based fetal arrhythmia detection method using FECG signals. Recently, arrhythmia detection methods using adult ECG signals have achieved a high arrhythmia detection accuracy based on deep learning. Motivated by this fact, in the proposed method, the acquired FECG signals are segmented, and the segments are input into a deep learning model that classifies them into normal or arrhythmia ones. Based on the classification results of multiple segments, a subject is judged as a healthy or arrhythmia subject. Each segment of the training data is divided into three categories based on the estimated heartbeat interval: (i) normal, (ii) arrhythmia, and (iii) a segment that could be both normal and arrhythmic. Only segments labeled as normal or arrhythmia are used for training a deep learning model to achieve a higher classification accuracy of the model. Through these procedures, the proposed method detects fetal arrhythmia with fewer effects of heartbeat detection results. The experimental results show that the proposed method achieves 96.2% accuracy, 100% specificity, and 100% recall, improving the values of conventional methods based on heartbeat detection and feature detection.

Age-dependent cardiorespiratory directional coupling in wake-resting state

Objective. Cooperation in the cardiorespiratory system helps maintain internal stability. Various types of system interactions have been investigated; however, the characteristics of the interactions have mostly been studied using data collected in well-defined physiological states, such as sleep. Furthermore, most analyses provided general information about the interaction, making it difficult to quantify how the systems influenced one another. Approach. Cardiorespiratory directional coupling was investigated in different age groups (20 young and 19 elderly subjects) in a wake-resting state. The directionality index (DI) was calculated using instantaneous phases from the heartbeat interval and respiratory signal to provide information about the strength and direction of interaction between the systems. Statistical analysis was performed between the groups on the DI and independent measures of directionality (ncr: influence from cardiac system to respiratory system, and ncc: influence from the respiratory system to the cardiac system). Main results. The values of DI were −0.52 and −0.17 in the young and elderly groups, respectively (p < 0.001). Furthermore, the values of ncr and ncc were found to be significantly different between the groups (p < 0.001), respectively. Significance. Changes in both directions between the systems influence different aspects of cardiorespiratory coupling between the groups. This observation could be linked to different levels of autonomic modulation associated with ageing. Our approach could aid in quantitatively tracking and comprehending how systems interact in response to physiological and environmental changes. It could also be used to understand how abnormal interaction characteristics influence physiological system dysfunctions and disorders.

Pulse respiration quotient as a measure sensitive to changes in dynamic behavior of cardiorespiratory coupling such as body posture and breathing regime.

Objective: In this research we explored the (homeo)dynamic character of cardiorespiratory coupling (CRC) under the influence of different body posture and breathing regimes. Our tool for it was the pulse respiration quotient (PRQ), representing the number of heartbeat intervals per breathing cycle. We obtained non-integer PRQ values using our advanced Matlab® algorithm and applied it on the signals of 20 healthy subjects in four conditions: supine position with spontaneous breathing (Supin), standing with spontaneous breathing (Stand), supine position with slow (0.1Hz) breathing (Supin01) and standing with slow (0.1Hz) breathing (Stand01). Main results: Linear features of CRC (in PRQ signals) were dynamically very sensitive to posture and breathing rhythm perturbations. There are obvious increases in PRQ mean level and variability under the separated and joined influence of orthostasis and slow (0.1Hz) breathing. This increase was most pronounced in Stand01 as the state of joint influences. Importantly, PRQ dynamic modification showed greater sensitivity to body posture and breathing regime changes than mean value and standard deviation of heart rhythm and breathing rhythm. In addition, as a consequence of prolonged supine position, we noticed the tendency to integer quantization of PRQ (especially after 14min), in which the most common quantization number was 4:1 (demonstrated in other research reports as well). In orthostasis and slow breathing, quantization can also be observed, but shifted to other values. We postulate that these results manifest resonance effects induced by coupling patterns from sympathetic and parasympathetic adjustments (with the second as dominant factor). Significance: Our research confirms that cardiorespiratory coupling adaptability could be profoundly explored by precisely calculated PRQ parameter since cardiorespiratory regulation in healthy subjects is characterized by a high level of autonomic adaptability (responsiveness) to posture and breathing regime, although comparisons with pathological states has yet to be performed. We found Stand01 to be the most provoking state for the dynamic modification of PRQ (cardiorespiratory inducement). As such, Stand01 has the potential of using for PRQ tuning by conditioning the cardiorespiratory autonomic neural networks, e.g., in the cases where PRQ is disturbed by environmental (i.e., microgravity) or pathologic conditions.

The promising prognostic value of vagal nerve activity at the initial management of ovarian cancer.

Identifying new modifiable prognostic markers is important for ovarian cancer (OC). Low parasympathic activity is associated with inflammation, oxidative stress and sympathetic nervous system activation. Previous studies reported that low vagal nerve activity, measured by low heart rate variability (HRV), may predict poor cancer prognosis. We aimed to examine the prognostic value of HRV in OC. This bicentric retrospective study included patients diagnosed with serous OC FIGO stage ≥IIB, between January 2015 and August 2019, with electrocardiograms (ECG) available around diagnosis. HRV was measured from ECG using the time domain parameter of standard deviation of all normal-to-normal heartbeat intervals (SDNN). Optimal SDNN cut-off was determined using the Youden index criteria of time-dependent ROC curves. We used multivariate cox proportional hazard models to investigate the association between HRV and overall survival (OS), while adjusting for well-known OC prognostic factors. The 202 patients included were 65.7 years-old on average, 93% had stage FIGO IIIC/IV, 56% had complete surgical resection. Median OS was 38.6 months [95%CI:34.4-47.4]. The median SDNN was 11.1ms, with an optimal cut-off of 10ms to predict OS. OS was shorter for patients with low HRV compared to high HRV (26.4 vs 45.1 months; p<0.001). In multivariate analysis, HRV remained an independent prognostic factor with a two-fold higher risk of death among patients with low SDNN compared to those with high SDNN (HR=2.03, 95%CI=1.35-3.06, p<0.001). Low HRV, was associated with worse OS in OC patients, supporting previous studies on the prognostic role of HRV in cancer. If replicated in prospective studies, vagal nerve activity may be a new therapeutic target in OC.

Robust Method for Screening Sleep Apnea With Single-Lead ECG Using Deep Residual Network: Evaluation With Open Database and Patch-Type Wearable Device Data

This paper proposes a robust method to screen patients with sleep apnea syndrome (SAS) using a single-lead electrocardiogram (ECG). This method consists of minute-by-minute abnormal breathing detection and apnea-hypopnea index (AHI) estimation. Heartbeat interval and ECG-derived respiration (EDR) are calculated using the single-lead ECG and used to train the models, including ResNet18, ResNet34, and ResNet50. The proposed method, using data from 1232 subjects, was developed with two open datasets and experimental data and evaluated using two additional open datasets and data acquired from an abdomen-attached wearable device (in total, data from 189 subjects). ResNet18 showed the best results, having an average Cohen's kappa coefficient of 0.57, in the abnormal breathing detection. Moreover, SAS patient classification, with 15 as the AHI threshold, yielded an average Cohen's kappa coefficient of 0.71. The results of patient classification were biased toward data from the wearable patch-type device, which may be influenced by different ECG waveforms. The proposed method is tuned with a sample of the data from the device, and the performance result of Cohen's kappa increased from 0.54 to 0.91 for SAS patient classification. Our method, proposed in this paper, achieved equivalent performance results with data recorded using an abdomen-attached wearable device and two open datasets used in previous studies, although the method had not used those data during model training. The proposed method could reduce the development costs of commercial software, as it was developed using open datasets, has robust performance throughout all datasets.

Preliminary Study on Establishing a Heart Rate Variability–Based Method for Objectively Evaluating Bone Metastasis Pain

<h3>Purpose/Objective(s)</h3> No standardized method objectively evaluates cancer pain. In this case, measuring heart rate variability (HRV) using fluctuations in heartbeat interval (statistical software) may be useful because pain activates sympathetic nerve activity. Fluctuation analysis of RRI frequency demonstrates a high frequency (HF) and low frequency (LF) component. HF represents parasympathetic nerve activity, while the LF-to-HF ratio (LF/HF) represents sympathetic nerve activity. We hypothesized that HRV measurement is an effective and objective evaluation tool for bone metastases pain. <h3>Materials/Methods</h3> This prospective study enrolled patients receiving radiotherapy for painful bone metastases. Pain was assessed using a numerical rating scale (NRS), and anxiety and depression were evaluated based on Hospital Anxiety and Depression Scale (HADS). The average, maximum, and minimum NRS scores were separately evaluated. Autonomic and physical activities were evaluated by measuring HRV through a wearable device (Union Tool Co., Tokyo, Japan). Through a three-dimensional conformal technique, radiotherapy was delivered as 20–30 Gy doses in 5–10 fractions. Alternatively, stereotactic body radiotherapy (SBRT) was delivered as doses of 24 Gy in two fractions or 35 Gy in five fractions. In principle, the values of NRS, HADS, and RRI were obtained upon starting, completing, and 3–5 weeks after radiotherapy. RRI were obtained over approximately 24 h. <h3>Results</h3> Between July 2020 and July 2021, 11 patients were enrolled. The median age was 65 years (range: 50–83 years). Seven of 11 patients were treated with SBRT. We analyzed 27 NRS scores and heart rate interval measurements and 26 HADS scores. The median, average, maximum, and minimum NRS scores were 5 (range: 2–10), 6 (range: 2–10), and 3 (range: 0–8), respectively. HADS-assessed median anxiety and depression scores were both 8 (ranges: 1–13 and 2–21 respectively), and mean HF and LF/HF values were 0.52 ms² (SD: 1.66 ms²) and 2.45 (SD: 1.20), respectively. For all samples, NRS score had no significant relationship with mean LF/HF or HF (p = 0.66 and p = 0.74, respectively). However, for samples with NRS scores ≥ 4, NRS score had a significant association with LF/HF (p = 0.03). Heart rate during physical activity was significantly higher than the resting heart rate; however, mean resting LF/HF was significantly higher than LF/HF during physical activity. During rest, mean LF/HF was not associated with NRS score across all samples, including those with NRS scores ≥ 4 (p = 0.61 and p = 0.49, respectively). In samples excluding those with NRS scores < 4 and HADS depression scores ≥ 7, NRS score had a weak significant relationship with mean LF/HF (p = 0.07). <h3>Conclusion</h3> HRV measurements could objectively evaluate bone metastases pain. However, the effects of mental status, such as depression, on LF/HF affects HRV in cancer patients with mild pain.

Influence of hypomagnetic field on the heartbeat in zebrafish embryos.

The magnetic environment may influence the functioning of the cardiovascular system. It was reported that low-frequency and static magnetic fields affect hemodynamics, heart rate, and heart rate variability in animals and humans. Moreover, recent data suggest that magnetic fields affect the circadian rhythms of physiological processes. The influence of the magnetic environment on heart functionating during early development has been studied insufficiently. We utilized transparent zebrafish embryos to evaluate the effect of the hypomagnetic field on the characteristics of cardiac function using a noninvasive optical approach based on photoplethysmographic microscopic imaging. The embryos were exposed to the geomagnetic and hypomagnetic fields from the second to the 116th hour post fertilization under a 16h light/8h dark cycle or constant illumination. The exposure of embryos to the hypomagnetic field in both lighting modes led to increased embryo mortality, the appearance of abnormal phenotypes, and a significant increase in the embryo's heartbeat rate. The difference between maximal and minimal heartbeat intervals, maximal to minimal heartbeat intervals ratio, and the coefficient of variation of heartbeat rate were increased in the embryos exposed to the hypomagnetic field under constant illumination from 96 to 116h post fertilization. The dynamics of heartbeat rate changes followed a circadian pattern in all studied groups except zebrafish exposed to the hypomagnetic field under constant illumination. The results demonstrate the importance of natural magnetic background for the early development of zebrafish. The possible mechanisms of observed effects are discussed.

Genç yetişkinlerde elektrofizyolojik ve hemodinamik parametreler üzerine kafeinli içeceklerin akut etkileri

Purpose: The consumption of caffeinated beverages has significantly increased among young people in recent years. The objective of the study is to investigate the acute effects of caffeinated beverages on electrocardiographic and hemodynamic parameters of young adults.&#x0D; Materials and Methods: Study was designed as a non-randomized, non-placebo controlled, three-arm parallel assignment. It was conducted on 56 individuals selected from a pool of students enrolled at Aydın Adnan Menderes University. Energy drink, coffee, cola and control groups were determined according to the results of the applicant acceptance questionnaire. Blood pressure and electrocardiogram were measured before and after consuming the drinks at 30 minutes and 60 minutes. Heart rate variability was detected from electrocardiogram signal and investigated by linear analysis.&#x0D; Results: All caffeinated drinks increased the blood pressure. Only, heart rate was increased by energy drink and cola. There was a small increment in root-mean square differences of successive R-R intervals and the number of times successive heartbeat intervals exceed 50ms (NN50) values in coffee group. High frequency (HF) values were increased some for coffee and cola groups. But, low frequency (LF) and LF/HF values were decreased. These alterations were statistically significant for coffee group. PR interval and QRS complex did not alter, however, QTc interval was lower in energy drink and cola groups.&#x0D; Conclusion: Caffeinated beverage consumption has the potential to induce adverse effects on cardiovascular system of young adults. Coffee appears to be more prominent than energy drink and cola.

Effect of occupational exposure to welding fumes and noise on heart rate variability: An exposed-unexposed study on welders and airport workers' population.

IntroductionWelding fumes (WF) are a complex mixture of gas and particles. Action of occupational exposure to WF on cardiovascular system has been recently studied as for noise.Research questionThe main objectives of our study are therefore to evaluate the impact of exposure to WF, noise, and combined WF and noise on autonomic nervous system as assessed by heart rate variability (HRV).MethodsThe study groups were 16 welders and eight airport workers (as a control group). All the participants underwent ambulatory electrocardiogram, personal WF, and noise exposure monitoring, respectively, with dust track and calibrated noise dosimeter during workday. Atmospheric environmental assessments at workplaces have been also performed. HRV parameters were summarized for all the workday and hourly. Correlation tests were used to examine relation between HRV parameters and levels of noise exposure in the two population. Analysis of covariance (ANCOVA) was used for mean of each HRV parameters.ResultsFor HRV parameters, we found significant higher levels for mean range of high frequency (HF), standard deviation of normal-to-normal R-R interval (SDNN), and root mean square of successive heartbeat interval difference (RMSSD) in welders which suggested an imbalance between sympathetic and parasympathetic nervous system in this population. For relation between noise and HRV parameters, we noted that levels of low frequency (LF), HF, and SDNN were significantly correlated with mean noise levels for welders (respectively, r = 0.62, r = 0.357, r = 0.48), not in control group. Using ANCOVA, we found that working as a welder significantly increases mean of HF (p = 0.01) and RMSSD (p = 0.02) and decreases in LF/HF (p = 0.008). Indeed, the interaction between exposure to WF and mean noise levels for HF (p = 0.005), LF/HF (p = 0.01), and RMSSD (p = 0.007) was significant.ConclusionThis study shows an impact of WF and noise on ANS balance. One hypothesis is WF exposure could increase sensibility to noise exposure on autonomic nervous system or there is a synergic effect.

Non-invasive prognostic biomarker of lung cancer patients with brain metastases: Recurrence quantification analysis of heart rate variability.

Background: It has previously been shown that the time-domain characteristic of heart rate variability (HRV) is an independent prognostic factor for lung cancer patients with brain metastasis (LCBM). However, it is unclear whether the nonlinear dynamic features contained in HRV are associated with prognosis in patients with LCBM. Recurrence quantification analysis (RQA) is a common nonlinear method used to characterize the complexity of heartbeat interval time series. This study was aimed to explore the association between HRV RQA parameters and prognosis in LCBM patients. Methods: Fifty-six LCBM patients from the Department of Radiation Oncology, the First Affiliated Hospital of Bengbu Medical College, were enrolled in this study. Five-minute ECG data were collected by a mini-ECG recorder before the first brain radiotherapy, and then heartbeat interval time series were extracted for RQA. The main parameters included the mean diagonal line length (Lmean), maximal diagonal line length (Lmax), percent of recurrence (REC), determinism (DET) and Shannon entropy (ShanEn). Patients were followed up (the average follow-up time was 19.2 months, a total of 37 patients died), and the relationships between the RQA parameters and survival of LCBM patients were evaluated by survival analysis. Results: The univariate analysis showed that an Lmax of >376 beats portended worse survival in LCBM patients. Multivariate Cox regression analysis revealed that the Lmax was still an independent prognostic factor for patients with LCBM after adjusting for confounders such as the Karnofsky performance status (KPS) (HR = 0.318, 95% CI: 0.151–0.669, p = 0.003). Conclusion: Reduced heartbeat complexity indicates a shorter survival time in patients with LCBM. As a non-invasive biomarker, RQA has the potential for application in evaluating the prognosis of LCBM patients.

Mathematical Modeling of Stress Using Fractal Geometry; The Power Laws and Fractal Complexity of Stress

In this study, we analyze the physiological data during real-world driving tasks to determine whether driver’s relative stress is mono-fractal or multi-fractal. We use the PhysioNet database including long term ECG recordings from 15 healthy volunteers, taken while they were driving on a prescribed route including city streets and highways in and around Boston, Massachusetts. The vibration analysis such as power spectral densities (PSD) analysis has been performed to estimate the exponent from realizations of these pro- cesses and to find out if the signal of interest exhibits a power-law PSD. Multifractal dynamics of heartbeat interval signals have been assessed by multifractal spectrum analysis to explore the possibility that ECG recordings belong to class of multi-fractal process for which a large number of scaling exponents are re- quired to characterize their scaling structures. We apply Higuchi algorithm to find the fractal complexity of each cardiac rhythm for different time intervals. According to our analysis, we investigate that driver’s ECG signals under relative stress follows fractal behavior unlike control healthy signals which are multi-fractal. Our findings provide a comprehensive framework for detect stress and differentiate people who experience stress with normal people without stress which is crucial in finding the best diagnostic and controlling strat- egy in fight against many health problems due to stress, such as high blood pressure, heart disease, obesity and diabetes. Moreover, being able to recognize stress can help us to manage it.

On the fractal geometry of different heart rhythms

In this study, we explore the possibility that ECG recordings belong to class of multifractal process for which a large number of scaling exponents are required to characterize their scaling structures. We use the BIDMC Congestive Heart Failure database including long term ECG recordings from 11 men, aged 22 to 71, and 4 women, aged 54 to 63 with severe congestive heart failure and the MIT-BIH Arrhythmia database that contains 48 half-hour excerpts of two-channel ambulatory ECG recordings, obtained from 47 subjects studied by the BIH Arrhythmia Laboratory between 1975 and 1979. We compare these two chronic heart diseases with the control people in the MIT-BIH Normal Sinus Rhythm database which includes 18 long-term ECG recordings of 5 men, aged 26 to 45, and 13 women, aged 20 to 50 without significant arrhythmia. The vibration analysis such as power spectral densities (PSD) analysis has been performed for differentiating the time series. Multifractal dynamics of heartbeat interval signals have been assessed by multifractal spectrum analysis to differentiate normal signals with arrhythmia and severe congestive heart failure patients. We apply Higuchi algorithm to find the fractal complexity of each cardiac rhythm and then compare the signals for different time intervals. According to our analysis, we investigate that neither the power spectral densities nor the Higuchi algorithm to find the fractal dimension alone were sufficient to separate different classes of patients and healthy people. However, when multifractal analysis and scaling exponent were used as a classifier, the three classes were well separated. In addition, multifractal analysis revealed that we have a narrow range of exponents for arrhythmia and congestive heart failure subjects and as a result, a clear loss of multifractality for them. It was of great significance to show that we have a narrower range of exponents for arrhythmia subject compared to congestive heart failure subject which is useful to recognize these two classes of patients with heart disease. Our findings provide a comprehensive framework for diagnostic and classifying different patients with cardiac disease such as arrhythmia and congestive heart failure and differentiate them with normal people without heart disease which is crucial in finding the best diagnostic and controlling strategy in fight against chronic heart disease.

Non-Contact Heartbeat Detection Based on Ballistocardiogram Using UNet and Bidirectional Long Short-Term Memory.

Benefiting from non-invasive sensing tech- nologies, heartbeat detection from ballistocardiogram (BCG) signals is of great significance for home-care applications, such as risk prediction of cardiovascular disease (CVD) and sleep staging, etc. In this paper, we propose an effective deep learning model for automatic heartbeat detection from BCG signals based on UNet and bidirectional long short-term memory (Bi-LSTM). The developed deep learning model provides an effective solution to the existing challenges in BCG-aided heartbeat detection, especially for BCG in low signal-to-noise ratio, in which the waveforms in BCG signals are irregular due to measured postures, rhythm and artifact motion. For validations, performance of the proposed detection is evaluated by BCG recordings from 43 subjects with different measured postures and heart rate ranges. The accuracy of the detected heartbeat intervals measured in different postures and signal qualities, in comparison with the R-R interval of ECG, is promising in terms of mean absolute error and mean relative error, respectively, which is superior to the state-of-the-art methods. Numerical results demonstrate that the proposed UNet-BiLSTM model performs robust to noise and perturbations (e.g. respiratory effort and artifact motion) in BCG signals, and provides a reliable solution to long term heart rate monitoring.

Determination of the Effects of Transcutaneous Auricular Vagus Nerve Stimulation on the Heart Rate Variability Using a Machine Learning Pipeline.

Background:We are all aware of day-to-day healthy stress, but, when sustained for long periods, stress is believed to lead to serious physical and mental health issues.Materials and Methods:In this study, we investigated the potential effects of transcutaneous auricular vagus nerve stimulation (taVNS) on stress processing as reflected in the electrocardiogram (ECG)-derived biomarkers of stress adaptability. Stress reflecting biomarkers included a range of heart rate variability metrics: standard deviation of N-N intervals (SDNN), root mean squared of successive differences in heartbeat intervals (RMSSD), low-frequency component, high-frequency component and their ratio (LF, HF, and LF/HF).In addition, we created a machine learning model capable of distinguishing between the stimulated and nonstimulated conditions from the ECG-derive data from various subjects and states. The model consisted of a deep convolutional neural network, which was trained on R-R interval (RRI) data extracted from ECG and time traces of LF, HF, LF/HF, SDNN, and RMSSD.Results:Only LF/HF ratio demonstrated a statistically significant change in response to stimulation. Although the LF/HF ratio is expected to increase during exposure to stress, we have observed that stimulation during exposure to stress counteracts this increase or even reduces the LF/HF ratio. This could be an indication that the vagus nerve stimulation decreases the sympathetic activation during stress inducement.Our Machine Learning model achieved an accuracy of 70% with no significant variations across the three states (baseline, stress, and recovery). However, training an analogous neural network to identify the states (baseline, stress, and recovery) proved to be unsuccessful.Conclusion:Overall, in this study, we showed further evidence of the beneficial effect of taVNS on stress processing. Importantly we have also demonstrated the promising potential of ECG metrics as a biomarker for the development of closed-loop stimulation systems.

Sleep Staging Using Noncontact-Measured Vital Signs.

As a physiological phenomenon, sleep takes up approximately 30% of human life and significantly affects people's quality of life. To assess the quality of night sleep, polysomnography (PSG) has been recognized as the gold standard for sleep staging. The drawbacks of such a clinical device, however, are obvious, since PSG limits the patient's mobility during the night, which is inconvenient for in-home monitoring. In this paper, a noncontact vital signs monitoring system using the piezoelectric sensors is deployed. Using the so-designed noncontact sensing system, heartbeat interval (HI), respiratory interval (RI), and body movements (BM) are separated and recorded, from which a new dimension of vital signs, referred to as the coordination of heartbeat interval and respiratory interval (CHR), is obtained. By extracting both the independent features of HI, RI, and BM and the coordinated features of CHR in different timescales, Wake-REM-NREM sleep staging is performed, and a postprocessing of staging fusion algorithm is proposed to refine the accuracy of classification. A total of 17 all-night recordings of noncontact measurement simultaneous with PSG from 10 healthy subjects were examined, and the leave-one-out cross-validation was adopted to assess the performance of Wake-REM-NREM sleep staging. Taking the gold standard of PSG as reference, numerical results show that the proposed sleep staging achieves an averaged accuracy and Cohen's Kappa index of 82.42% and 0.63, respectively, and performs robust to subjects suffering from sleep-disordered breathing.