The Sasso Fratino Integral Natural Reserve (Central Italy), a rare example of climax Mediterranean forest, provides an extraordinary opportunity to create an important soundscape reference of old-growth forest. In this study, we describe the soundscape of three localities (Lama, Sasso 950, Sasso 1400) representative of a gradient of variety and complexity of habitats, recorded during the period 10 May to 9 June 2017. Our results reveal temporal partitioning into acoustically homogeneous periods across 24 h suggesting that soniferous species (mainly birds) adopt ecological routines in which their acoustic activity is organized according to specific transient physiological needs. We processed multi-temporal aggregates of 1, 5, 10, and 15 s recordings and calculated the Acoustic Signature (AS) with four new indices: Ecoacoustic Events (EE), Acoustic Signature Dissimilarity (ASD), and their fractal dimensions (DEE and DASD), derived from the Acoustic Complexity Index (ACI). The use of the EE and ASD greatly improved the AS interpretation, adding further details such as the emergence of a clear sequence of patterns consistent with the daily evolution of the overall soundscape. DEE and DASD confirm the patterns observed using the AS, but provide more clarity and detail about the great acoustic complexity that exists across temporal scales in this old-growth forest. The temporal turnover of different acoustic communities occurs as a result of a gradual shift of different homogenous acoustic properties. We conclude that soniferous species use distinct, species-specific temporal resolutions according to their physiological and ecological needs and that the fractal approach used here provides a novel tool to overcome the difficulties associated with describing multi-temporal acoustic patterns.
BackgroundSpontaneous heart rate fluctuations contain rich information related to health and illness in terms of physiological complexity, an accepted indicator of plasticity and adaptability. However, it is challenging to make inferences on complexity from shorter, more practical epochs of data. Distribution entropy (DistEn) is a recently introduced complexity measure that is designed specifically for shorter duration heartbeat recordings. We hypothesized that reduced DistEn predicted increased mortality in a large population cohort.Method and ResultsThe prognostic value of DistEn was examined in 7631 middle‐older–aged UK Biobank participants who had 2‐minute resting ECGs conducted (mean age, 59.5 years; 60.4% women). During a median follow‐up period of 7.8 years, 451 (5.9%) participants died. In Cox proportional hazards models with adjustment for demographics, lifestyle factors, physical activity, cardiovascular risks, and comorbidities, for each 1‐SD decrease in DistEn, the risk increased by 36%, 56%, and 73% for all‐cause, cardiovascular, and respiratory disease–related mortality, respectively. These effect sizes were equivalent to the risk of death from being \textgreater5 years older, having been a former smoker, or having diabetes mellitus. Lower DistEn was most predictive of death in those \textless55 years with a prior myocardial infarction, representing an additional 56% risk for mortality compared with older participants without prior myocardial infarction. These observations remained after controlling for traditional mortality predictors, resting heart rate, and heart rate variability.ConclusionsResting heartbeat complexity from short, resting ECGs was independently associated with mortality in middle‐ to older‐aged adults. These risks appear most pronounced in middle‐aged participants with prior MI, and may uniquely contribute to mortality risk screening.
QT interval variability (QTV) and heart rate variability (HRV) are both accepted biomarkers for cardiovascular events. QTV characterizes the variations in ventricular depolarization and repolarization. It is a predominant element of HRV. However, QTV is also believed to accept direct inputs from upstream control system. How QTV varies along with HRV is yet to be elucidated. We studied the dynamic relationship of QTV and HRV during different physiological conditions from resting, to cycling, and to recovering. We applied several entropy-based measures to examine their bivariate relationships, including cross sample entropy (XSampEn), cross fuzzy entropy (XFuzzyEn), cross conditional entropy (XCE), and joint distribution entropy (JDistEn). Results showed no statistically significant differences in XSampEn, XFuzzyEn, and XCE across different physiological states. Interestingly, JDistEn demonstrated significant decreases during cycling as compared with that during the resting state. Besides, JDistEn also showed a progressively recovering trend from cycling to the first 3 min during recovering, and further to the second 3 min during recovering. It appeared to be fully recovered to its level in the resting state during the second 3 min during the recovering phase. The results suggest that there is certain nonlinear temporal relationship between QTV and HRV, and that the JDistEn could help unravel this nuanced property.
Objective: Coronary artery disease (CAD) is a common fatal disease. At present, an accurate method to screen CAD is urgently needed. This study aims to provide optimal detection models for suspected CAD detection according to the differences in medical conditions, so as to assist physicians to make accurate judgments on suspected CAD patients. Approach: Electrocardiogram (ECG) and phonocardiogram (PCG) signals of 32 CAD patients and 30 patients with chest pain and normal coronary angiograms (CPNCA) were simultaneously collected for this paper. For each subject, the ECG and PCG multi-domain features were extracted, and the results of Holter monitoring, echocardiography (ECHO), and biomarker levels (BIO) were obtained to construct a multi-modal feature set. Then, a hybrid feature selection (HFS) method was developed using mutual information, recursive feature elimination, random forest, and weight of support vector machine to obtain the optimal feature subset. A support vector machine with nested cross-validation was used for classification. Main results: Results showed that the Holter model achieved the best performance as a single-modal feature model with an accuracy of 82.67%. In terms of multi-modal feature models, PCG-Holter, PCG-Holter-ECHO, PCG-Holter-ECHO-BIO, and ECG-PCG-Holter-ECHO-BIO were the optimal bimodal, three-modal, four-modal, and five-modal models, with accuracies of 90.38%, 91.92%, 95.25%, and 96.67%, respectively. Among them, the ECG-PCG-Holter-ECHO-BIO model, which was constructed by combining ECG and PCG signals features with Holter, ECHO, and BIO examination results, achieved the best classification results with an average accuracy, sensitivity, specificity, and F1-measure of 96.67%, 96.67%, 96.67%, and 96.64%, respectively. Significance: The study indicated that multi-modal feature fusion and HFS can obtain more effective information for CAD detection and provide a reference for physicians to diagnose CAD patients.
There is a growing interest in automated diagnosis of coronary artery disease (CAD) with the application of machine learning (ML) methods to the body surface electrocardiograph (ECG). Although prior studies have documented associations of CAD with increased QT variability and ST-T segment abnormalities such as T-wave inversion and ST-segment elevation or depression, their efficacy in automated CAD detection has not been fully investigated. To validate their usefulness, a dataset containing related clinical characteristics and 5-min single-lead ECGs of 107 healthy controls and 93 CAD patients was first constructed. Based on this dataset, simultaneous analyses were then conducted in five scenarios, in which different ML algorithms were applied to classify the two groups with various features derived from the RR and QT interval time-series and ST-T segment waveforms. Compared with utilizing features obtained from the RR interval time-series, better classification results were achieved utilizing that obtained from the QT interval time-series. The classification results were elevated with combining utilization of features derived from both the RR and QT interval time-series. By further fusing features extracted from ST-T segment waveforms, the best performance was achieved with 96.16% accuracy, 95.75% sensitivity, and 96.40% specificity. Based the best performance, an automated CAD detection system was developed with extreme gradient boosting, an ensemble ML algorithm, and the residual neural network, namely, a deep learning method. The results of this study support the potential of information derived from the QT interval time-series and ST-T segment waveforms in ECG-based automated CAD detection.
The complexity of a heart rate variability (HRV) signal is considered an important nonlinear feature to detect cardiac abnormalities. This work aims at explaining the physiological meaning of a recently developed complexity measurement method, namely, distribution entropy (DistEn), in the context of HRV signal analysis. We thereby propose modified distribution entropy (mDistEn) to remove the physiological discrepancy involved in the computation of DistEn. The proposed method generates a distance matrix that is devoid of over-exerted multi-lag signal changes. Restricted element selection in the distance matrix makes “mDistEn” a computationally inexpensive and physiologically more relevant complexity measure in comparison to DistEn.
Inflammation, nutrition, and coagulation play significant roles in cancer prognosis. Autonomic function is also actively involved in tumorigenesis. Previous studies have shown that an elevated C-reactive protein (CRP) level, a serum marker for inflammation, is associated with low heart rate variability (HRV), a common clinical tool for the assessment of autonomic function. It is yet to be investigated whether HRV links to these prognostic factors in cancer patients. Sixty-one patients who were first diagnosed with gastric cancer (GC) were enrolled in this study. Fasting blood samples were collected in the morning seven days before surgery. Blood CRP, prealbumin (PA), and fibrinogen (FIB) were used to assess the inflammation level, nutritional status, and coagulation function respectively. Five-minute resting electrocardiogram (ECG) signals were collected one day before surgical treatment. Short-term HRV time-series were extracted from ECG recordings and were analyzed using commonly-used time- and frequency-domain parameters including standard deviation of normal-to-normal intervals (SDNN), root mean square of successive heartbeat interval differences (RMSSD), very-low-frequency power (VLF), low-frequency power (LF), high-frequency power (HF), total power (TP), LF power in normalized units (LF n.u.), HF power in normalized units (HF n.u.), and ratio of LF to HF (LF/HF). After adjusted for sex, age, body mass index, alcohol consumption, history of diabetes, left ventricular ejection fraction, and hemoglobin levels, our results demonstrated negative associations of HRV with levels of CRP and FIB, while positive associations between HRV and PA level, with effect sizes of as high as 35%–52% standard deviations (SD) changes in CRP, FIB, or PA per 1-SD change in HRV parameters. Therefore, decreased HRV in patients with GC predicts increased burdens of inflammation and coagulation and perturbed nutrition, suggesting that short-term HRV measurement can potentially be a noninvasive biomarker for GC prognosis.
Nonlinear physiological signal features that reveal information content and causal flow have recently been shown to be predictors of mortality after severe traumatic brain injury (TBI). The extent to which these features interact together, and with traditional measures to describe patients in a clinically meaningful way remains unclear. In this study, we incorporated basic demographics (age and initial Glasgow Coma Scale [GCS]) with linear and non-linear signal information based features (approximate entropy [ApEn], and multivariate conditional Granger causality [GC]) to evaluate their relative contributions to mortality using cardio-cerebral monitoring data from 171 severe TBI patients admitted to a single neurocritical care center over a 10 year period. Beyond linear modelling, we employed a decision tree analysis approach to define a predictive hierarchy of features. We found ApEn (p = 0.009) and GC (p = 0.004) based features to be independent predictors of mortality at a time when mean intracranial pressure (ICP) was not. Our combined model with both signal information-based features performed the strongest (area under curve = 0.86 vs. 0.77 for linear features only). Although low "intracranial" complexity (ApEn-ICP) outranked both age and GCS as crucial drivers of mortality (fivefold increase in mortality where ApEn-ICP \textless1.56, 36.2% vs. 7.8%), decision tree analysis revealed clear subsets of patient populations using all three predictors. Patients with lower ApEn-ICP who were \textgreater60 years of age died, whereas those with higher ApEn-ICP and GCS ≥5 all survived. Yet, even with low initial intracranial complexity, as long as patients maintained robust GC and "extracranial" complexity (ApEn of mean arterial pressure), they all survived. Incorporating traditional linear and novel, non-linear signal information features, particularly in a framework such as decision trees, may provide better insight into "health" status. However, caution is required when interpreting these results in a clinical setting prior to external validation.
Background The significant association of myocardial ischemia with elevated QT interval variability (QTV) has been reported in myocardial infarction (MI) patients. However, the influence of the time course of MI on QTV has not been investigated systematically. Method Short-term QT and RR interval time series were constructed from the 5 min electrocardiograms of 49 coronary patients without MI and 26 patients with old MI (OMI). The QTV, heart rate variability (HRV), and QT–RR coupling of the two groups were analyzed using various time series analysis tools in the time- and frequency-domains, as well as nonlinear dynamics. Results Nearly all of the tested QTV indices for coronary patients with OMI were higher than those for patients without MI. However, no significant differences were found between the two groups in any of the variables employed to assess the HRV and QT–RR coupling. All of the markers that showed statistical significances in univariate analyses still possessed the capabilities of distinguishing between the two groups even after adjusting for studied baseline characteristics, including the coronary atherosclerotic burden. Conclusions The results suggested that the QTV increased in coronary patients with OMI compared to those without MI, which might reflect the influence of post-MI remodeling on the beat-to-beat temporal variability of ventricular repolarization. The non-significant differences in the HRV and QT–RR couplings could indicate that there were no differences in the modulation of the autonomic nervous system and interaction of QT with the RR intervals between the two groups.
Electrocardiogram (ECG) and phonocardiogram (PCG) signals reflect the electrical and mechanical activities of the heart, respectively. Although studies have documented that some abnormalities in ECG and PCG signals are associated with coronary artery disease (CAD), only few researches have combined the two signals for automatic CAD detection. This paper aims to differentiate between CAD and non-CAD groups using simultaneously collected ECG and PCG signals. To entirely exploit the underlying information in these signals, a novel dual-input neural network that integrates the feature extraction and deep learning methods is developed. First, the ECG and PCG features are extracted from multiple domains, and the information gain ratio is used to select important features. On the other hand, the ECG signal and the decomposed PCG signal (at four scales) are concatenated as a five-channel signal. Then, the selected features and the five-channel signal are fed into the proposed network composed of a fully connected model and a deep learning model. The results show that the classification performance of either feature extraction or deep learning is insufficient when using only ECG or PCG signal, and combining the two signals improves the performance. Further, when using the proposed network, the best result is obtained with accuracy, sensitivity, specificity, and G-mean of 95.62%, 98.48%, 89.17%, and 93.69%, respectively. Comparisons with existing studies demonstrate that the proposed network can effectively capture the combined information of ECG and PCG signals for the recognition of CAD.
Entropy analysis has been attracting increasing attentions in the recent two or three decades. It assesses complexity, or irregularity, of time-series which is extraordinarily relevant to physiology and diseases as demonstrated by tremendous studies. However, the complexity can hardly be appreciated by traditional methods including time-, frequency-domain analysis, and time-frequency analysis that are the common built-in options in commercialized measurement and statistical software. To facilitate the entropy analysis of physiological time-series, a new software application, namely EZ Entropy, was developed and introduced in this article.
Fuzzy entropy (FuzEn) was introduced to alleviate limitations associated with sample entropy (SampEn) in the analysis of physiological signals. Over the past decade, FuzEn-based methods have been widely used in various real-world biomedical applications. Several fuzzy membership functions (MFs), including triangular, trapezoidal, Z-shaped, bell-shaped, Gaussian, constant-Gaussian, and exponential functions have been employed in FuzEn. However, these FuzEn-based metrics have not been systematically compared yet. Since the threshold value r used in FuzEn is not directly comparable across different MFs, we here propose to apply a defuzzification approach using a surrogate parameter called 'center of gravity' to re-enable a fair and direct comparison. To evaluate these MFs, we analyze several synthetic and three clinical datasets. FuzEn using the triangular, trapezoidal, and Z-shaped MFs may lead to undefined entropy values for short signals, thus providing a very limited advantage over SampEn. When dealing with an equal value of the center of gravity, the Gaussian MF, as the fastest algorithm, results in the highest Hedges' g effect size for long signals. Our results also indicate that the FuzEn based on exponential MF of order four better distinguishes short white, pink, and brown noises, and yields more significant differences for the short real signals based on Hedges' g effect size. The triangular, trapezoidal, and Z-shaped MFs are not recommended for short signals. We propose to use FuzEn with Gaussian and exponential MF of order four for characterization of short (around 50-400 sample points) and long data (longer than 500 sample points), respectively. We expect FuzEn with Gaussian and exponential MF as well as the concept of defuzzification to play prominent roles in the irregularity analysis of biomedical signals. The MATLAB codes for the FuzEn with different MFs are available at https://github.com/HamedAzami/FuzzyEntropy\_Matlab.
Heart rate asymmetry (HRA) could capture valuable dynamical properties from the electrocardiographic RR interval time-series that are helpful for evaluating the cardiovascular functioning. Several metrics derived from the Poincaré plot have been established for assessing HRA such as the slope index (SI) and the area index (AI). In the current study, we aimed to examine how different reference points and reference lines affect the calculations of SI and AI. To understand their performance, two case studies that were to classify subjects with (1) arrhythmias and (2) congestive heart failure, respectively, from normal controls were performed. To examine whether these effects depend on data lengths, the case studies were performed on both long-term heartbeat interval time-series and short-term segments. Our results showed that different reference points or reference lines could strongly affect the performance of both SI and AI, especially when short-term data were being analyzed. Using the minimum of data as the reference point might be a conservative solution in application but a spectrum of SI or AI measurements with multiple reference points and reference lines are highly recommended.
Many outputs from healthy neurophysiological systems including motor activity display nonrandom fluctuations with fractal scaling behavior as characterized by similar temporal fluctuation patterns across a range of time scales. Degraded fractal regulation predicts adverse consequences including Alzheimer's dementia. We examined longitudinal changes in the scaling behavior of motor activity fluctuations during the progression of Alzheimer's disease (AD) in 1068 participants in the Rush Memory and Aging Project. Motor activity of up to 10 days was recorded annually for up to 13 years. Cognitive assessments and clinical diagnoses were administered annually in the same participants. We found that fractal regulation gradually degraded over time (p \textless 0.0001) even during the stage with no cognitive impairment. The degradation rate was more than doubled after the diagnosis of mild cognitive impairment and more than doubled further after the diagnosis of Alzheimer's dementia (p's ≤ 0.0005). Besides, the longitudinal degradation of fractal regulation significantly correlated with the decline in cognitive performance throughout the progression from no cognitive impairment to mild cognitive impairment, and to AD (p \textless 0.001). All effects remained the same in subsequent sensitivity analyses that included only 255 decedents with autopsy-confirmed Alzheimer's pathology. These results indicate that the progression of AD accelerates fractal degradation and that fractal degradation may be an integral part of the process of AD.
Predictive patterns Fractals are self-similar patterns that exist across different conditions; in medicine, changes in fractal fluctuations can indicate disease, such as degraded fractal movement fluctuations seen with dementia. Using wrist-worn activity monitors, Li et al. analyzed daily motor activity of a large cohort of elderly subjects. They found that more random fluctuations over two time scales (1 to 90 min and greater than 2 hours) predicted increased risk of frailty, disability, and death years later—independent of age, sex, chronic health conditions, and total motor activity. Results suggest that fractal analyses can help predict health outcomes in the absence of overt symptoms and support the utility of passive monitoring. Mobile healthcare increasingly relies on analytical tools that can extract meaningful information from ambulatory physiological recordings. We tested whether a nonlinear tool of fractal physiology could predict long-term health consequences in a large, elderly cohort. Fractal physiology is an emerging field that aims to study how fractal temporal structures in physiological fluctuations generated by complex physiological networks can provide important information about system adaptability. We assessed fractal temporal correlations in the spontaneous fluctuations of ambulatory motor activity of 1275 older participants at baseline, with a follow-up period of up to 13 years. We found that people with reduced temporal correlations (more random activity fluctuations) at baseline had increased risk of frailty, disability, and all-cause death during follow-up. Specifically, for 1-SD decrease in the temporal activity correlations of this studied cohort, the risk of frailty increased by 31%, the risk of disability increased by 15 to 25%, and the risk of death increased by 26%. These incidences occurred on average 4.7 years (frailty), 3 to 4.2 years (disability), and 5.8 years (death) after baseline. These observations were independent of age, sex, education, chronic health conditions, depressive symptoms, cognition, motor function, and total daily activity. The temporal structures in daily motor activity fluctuations may contain unique prognostic information regarding wellness and health in the elderly population. More random fluctuations in daily motor activity predict deteriorated quality of life and high death rate in elderly subjects. More random fluctuations in daily motor activity predict deteriorated quality of life and high death rate in elderly subjects.
Identifying prognostic factors by affordable tools is crucial for guiding gastric cancer (GC) treatments especially at earlier stages for timing interventions. The autonomic function that is clinically assessed by heart rate variability (HRV) is involved in tumorigenesis. This pilot study was aimed to examine whether nonlinear indices of HRV can be biomarkers of GC severity. Sixty-one newly-diagnosed GC patients were enrolled. Presurgical serum fibrinogen (FIB), carcinoembryonic antigen (CEA), and carbohydrate antigen 19-9 (CA199) were examined. Resting electrocardiogram (ECG) of 5-min was collected prior to surgical treatments to enable the HRV analysis. Twelve nonlinear HRV indices covering the irregularity, complexity, asymmetry, and temporal correlation of heartbeat fluctuations were obtained. Increased short-range temporal correlations, decreased asymmetry, and increased irregularity of heartbeat fluctuations were associated with higher FIB level. Increased irregularity and decreased complexity were also associated with higher CEA level. These associations were independent of age, sex, BMI, alcohol consumption, history of diabetes, left ventricular ejection fraction, and anemia. The results support the hypothesis that perturbations in nonlinear dynamical patterns of HRV predict increased GC severity. Replication in larger samples as well as the examination of longitudinal associations of HRV nonlinear features with cancer prognosis/survival are warranted.
A Poincaré plot is a return map that geometrically elucidates the progression of a time-series. It has frequently been used in heart rate variability analyses. However, algorithms for dedicatedly dissecting the shape of this geometrical plot are yet to be established. In this study, we proposed a gridded Poincaré plot by coarse-graining the original graph and using the newly proposed one, defined two novel measures, namely gridded distribution rate (GDR) and gridded distribution entropy (GDE). The GDR essentially represents the percentage of grids with points, while the GDE estimates the Shannon entropy of the grid weight; that is, the number of points in each grid. The performances of the two measures were examined using both theoretical data with known dynamics and experimental short-term RR interval time-series, and they were compared with several existing metrics. Simulation tests demonstrated that both the GDR and GDE could distinguish among different dynamics, while all the compared methods failed. The experimental results further indicated the ability of the GDR and GDE to differentiate healthy young people from healthy aged adults as well as distinguish healthy subjects from patients with coronary artery disease. Our results suggest that the proposed GDR and GDE may better characterize the Poincaré plot in terms of differentiating between varying dynamical regimes, and between human physiological or pathological conditions. Further studies are warranted to establish their feasibility in evaluating cardiovascular functions in clinical practice.