Discrimination power of long-term heart rate variability measures for Chronic Heart Failure detection

The aim of this study was to investigate the discrimination power of standard long-term Heart Rate Variability (HRV) measures for the diagnosis of Chronic Heart Failure (CHF). We performed a retrospective analysis on 4 public Holter databases, analyzing the data of 72 normal subjects and 44 patients suffering from CHF. To assess the discrimination power of HRV measures, we adopted an exhaustive search of all possible combinations of HRV measures and we developed classifiers based on Classification and Regression Tree (CART) method, which is a non-parametric statistical technique. We found that the best combination of features is: Total spectral power of all NN intervals up to 0.4 Hz (TOTPWR), square Root of the Mean of the Sum of the Squares of Differences between adjacent NN intervals (RMSSD) and Standard Deviation of the Averages of NN intervals in all 5-minute segments of a 24-hour recording (SDANN). The classifiers based on this combination achieved a specificity rate and a sensitivity rate of 100.00% and 89.74% respectively. Our results are comparable with other similar studies, but the method we used is particularly valuable because it provides an easy to understand description of classification procedures, in terms of intelligible “if … then …” rules. Finally, the rules obtained by CART are consistent with previous clinical studies

Algorithms for automated diagnosis of cardiovascular diseases based on ECG data: A comprehensive systematic review

The prevalence of cardiovascular diseases is increasing around the world. However, the technology is evolving and can be monitored with low-cost sensors anywhere at any time. This subject is being researched, and different methods can automatically identify these diseases, helping patients and healthcare professionals with the treatments. This paper presents a systematic review of disease identification, classification, and recognition with ECG sensors. The review was focused on studies published between 2017 and 2022 in different scientific databases, including PubMed Central, Springer, Elsevier, Multidisciplinary Digital Publishing Institute (MDPI), IEEE Xplore, and Frontiers. It results in the quantitative and qualitative analysis of 103 scientific papers. The study demonstrated that different datasets are available online with data related to various diseases. Several ML/DP-based models were identified in the research, where Convolutional Neural Network and Support Vector Machine were the most applied algorithms. This review can allow us to identify the techniques that can be used in a system that promotes the patient’s autonomy.N/

Investigation of the relevance of heart rate variability changes after heart transplantation

Heart transplantation has become an established treatment for end-stage heart disease. However, the shortage of donor organs is a major problem and long-term results are limited by allograft rejection. Heart rate variability (HRV) has emerged as a popular noninvasive research tool in cardiology. Analysis of HRV is regarded as a valid technique to assess the sympathovagal balance of the heart. The primary goal of this study was to investigate the relevance of heart rate variability changes after heart transplantation. It was found that spectral analysis of HRV is useful in detecting rejection episodes. Heart transplantation leaves the donor heart denervated. Spectral analysis of HRV was found appropriate to detect functional autonomous reinnervation. Extensive literature review was done to validate the findings. The paper is divided into two parts. The first part of the paper deals mainly with the techniques and current status of heart transplantation. The second part, deals with the relevance of heart rate variability and reinnervation after heart transplantation. The results of the study suggest that heart rate variability analysis is a valuable tool in assessing the cardiovascular status after heart transplantation

Chaotic Signatures of Heart Rate Variability and Its Power Spectrum in Health, Aging and Heart Failure

A paradox regarding the classic power spectral analysis of heart rate variability (HRV) is whether the characteristic high- (HF) and low-frequency (LF) spectral peaks represent stochastic or chaotic phenomena. Resolution of this fundamental issue is key to unraveling the mechanisms of HRV, which is critical to its proper use as a noninvasive marker for cardiac mortality risk assessment and stratification in congestive heart failure (CHF) and other cardiac dysfunctions. However, conventional techniques of nonlinear time series analysis generally lack sufficient sensitivity, specificity and robustness to discriminate chaos from random noise, much less quantify the chaos level. Here, we apply a ‘litmus test’ for heartbeat chaos based on a novel noise titration assay which affords a robust, specific, time-resolved and quantitative measure of the relative chaos level. Noise titration of running short-segment Holter tachograms from healthy subjects revealed circadian-dependent (or sleep/wake-dependent) heartbeat chaos that was linked to the HF component (respiratory sinus arrhythmia). The relative ‘HF chaos’ levels were similar in young and elderly subjects despite proportional age-related decreases in HF and LF power. In contrast, the near-regular heartbeat in CHF patients was primarily nonchaotic except punctuated by undetected ectopic beats and other abnormal beats, causing transient chaos. Such profound circadian-, age- and CHF-dependent changes in the chaotic and spectral characteristics of HRV were accompanied by little changes in approximate entropy, a measure of signal irregularity. The salient chaotic signatures of HRV in these subject groups reveal distinct autonomic, cardiac, respiratory and circadian/sleep-wake mechanisms that distinguish health and aging from CHF

Time‐domain heart rate variability features for automatic congestive heart failure prediction

Aims: Heart failure is a serious condition that often goes undiagnosed in primary care due to the lack of reliable diagnostic tools and the similarity of its symptoms with other diseases. Non-invasive monitoring of heart rate variability (HRV), which reflects the activity of the autonomic nervous system, could offer a novel and accurate way to detect and manage heart failure patients. This study aimed to assess the feasibility of using machine learning techniques on HRV data as a non-invasive biomarker to classify healthy adults and those with heart failure. Methods and results: We used digitized electrocardiogram recordings from 54 adults with normal sinus rhythm and 44 adults categorized into New York Heart Association classes 1, 2, and 3, suffering from congestive heart failure. All recordings were sourced from the PhysioNet database. Following data pre-processing, we performed time-domain HRV analysis on all individual recordings, including root mean square of the successive difference in adjacent RR interval (RRi) (RMSSD), the standard deviation of RRi (SDNN, the NN stands for natural or sinus intervals), the standard deviation of the successive differences between successive RRi (SDSD), the number or percentage of RRi longer than 50 ms (NN50 and pNN50), and the average value of RRi [mean RR interval (mRRi)]. In our experimental classification performance evaluation, on the computed HRV parameters, we optimized hyperparameters and performed five-fold cross-validation using four machine learning classification algorithms: support vector machine, k-nearest neighbour (KNN), naïve Bayes, and decision tree (DT). We evaluated the prediction accuracy of these models using performance criteria, namely, precision, recall, specificity, F1 score, and overall accuracy. For added insight, we also presented receiver operating characteristic (ROC) plots and area under the ROC curve (AUC) values. The overall best performance accuracy of 77% was achieved when KNN and DT were trained on computed HRV parameters with a 5 min time window. KNN obtained an AUC of 0.77, while DT attained 0.78. Additionally, in the classification of severe congestive heart failure, KNN and DT had the best accuracy of 91%, with KNN achieving an AUC of 0.88 and DT obtaining 0.92. Conclusions: The results show that HRV can accurately predict severe congestive heart failure. The findings of this study could inform the use of machine learning approaches on non-invasive HRV, to screen congestive heart failure individuals in primary care

Quantification of long-range power law correlations among healthy and pathologic subjects using detrended fluctuation analysis and multifractal detrended fluctuation analysis

The healthy heartbeat is traditionally thought to be regulated according to the classical principle of homeostasis whereby physiologic systems operate to reduce variability and achieve an equilibrium-like state. However recent studies reveal that under normal conditions, beat-to-beat fluctuation in heart rate display the kind of long-range correlations typically exhibited by the dynamical system far away from equilibrium. In contrast, heart rate time series from patients with severe congestive heart failure show a breakdown of this long-range correlation behavior. Two different non-linear dynamic methods namely Detrended Fluctuation Analysis (DFA) and Multifractal (MF) DFA are used for the quantification of this correlation property in non-stationary physiological time series and it revealed the presence of long-range power law correlation for the group of healthy subjects while breakdown in the long-range power law correlation for the group of subjects with cardiac heart failure. Application of DFA analysis shows evidence for a crossover phenomenon associated with a change in short(αl) and long(α2) range scaling exponents. For healthy subjects, calculated value of αl and α2 (mean value ± S.D.) are 1.31 ± 0.17 and 1.00 ± 0.07 respectively. For subjects with cardiac heart failure calculated value of ctl and a2 is 0.71 ± 0.20 and 1.24 ± 0.07 respectively i.e. only one scaling exponent is not sufficient to characterize the entire heart-rate time series which resulted into MF-DFA approach. This suggested that there is more than one exponent values needed to characterize the heart rate time series. Multifractal DFA is based on generalization of DFA and a MATLAB code is developed to implement the MF-DFA algorithm and to identify whether the given time series under analysis exhibits multifractality or not by generating more than one exponent values for multifractal signal. The value of a for q\u3eO for healthy is 1.04 ± 0.02 and for CHF is found to be 1.32 ± 0.02 and the value of a for q\u3c0 for healthy subjects is 3.01 ±0.26 and for CHF subjects is found to be 3.53 ± 0.14 (mean value ± S.D.) The student\u27s ttest suggests that p-value is 0.00001 which is less than 0.05 thus the value of a for q \u3c0 and q\u3e0 among healthy subjects and CHF subjects are statistically different. Value of a for q\u3e0 is less than that for q\u3c0. And for q =2 MF-DFA retains monofractal DFA. Thus, MF-DFA is clearly able to discriminate among the healthy and CHF for q\u3c0 as well for q\u3e0. MF-DFA also determines which fluctuations i.e. (small or large) dominate for the given interbeat interval time series because for q\u3c0 the slow fluctuations dominate whereas for q\u3e0 large fluctuations dominate. DFA and MF-DFA were able to discriminate 23 Healthy subjects out of 26 Healthy subjects data sets i.e. true positive specificity is 0.89 and false negative specificity is 0.12 and 9 CHF subjects out of 11 CHF subjects data sets i.e. true positive specificity is 0.82 and false negative specificity is 0.19. These methods may be of use in distinguishing healthy from pathologic data sets based on the difference in the scaling properties

Assessment of the state of health by the measurement of a set of biophysiological signals

The dissertation studies the estimation of the degree of self-similarity and entropy of Shannon of several real electrocardiography (ECG) signals for healthy and non-healthy humans. The goal of the dissertation is to create a starting point algorithm which allows distinguishing between healthy and non-healthy subjects and can be used as a basis for further study of a diagnosis algorithm, necessarily more complex. We used a novel Hurst parameter estimation algorithm based on the Embedded Branching Process, termed modified Embedded Branching Process algorithm. The algorithm for estimation of entropy was based on Shannon‟s entropy. Both algorithms were applied on the spatial distribution of ECG signals in a windowed manner. The studied signals were retrieved from the Physionet website, where they are diagnosed as normal or as having certain pathologies. The results presented for the Hurst parameter estimation allow us to confirm the results already published on the temporal self-similarity of ECG signals, this time for its spatial distribution. We also conclude that the non-self similar signals belong to non-healthy subjects. The results obtained for entropy estimation on the spatial distribution of ECG signals also allowed a comparison between healthy and non-healthy systems. We obtained high entropy estimates both for healthy and non-healthy subjects; nevertheless, non-healthy subjects show higher variability of Shannon‟s entropy than healthy ones.A dissertação estuda a estimativa do grau de auto-semelhança e da entropia de Shannon de vários sinais reais de electrocardiograma (ECG) obtidos em humanos saudáveis e não saudáveis. O objectivo da dissertação é criar um algoritmo inicial que permita distinguir entre indivíduos saudáveis e não saudáveis e que possa ser usado como base para o estudo de um posterior algoritmo de diagnóstico, necessariamente mais complexo. Utilizamos um algoritmo novo para estimativa do parâmetro de Hurst baseado no Embedded Branching Process, denominado algoritmo modified Embedded Branching Process. A entropia foi estimada através da entropia de Shannon. Ambos algoritmos foram aplicados sob a distribuição espacial dos sinais ECG numa forma de janela. Os sinais estudados foram retirados do website Physionet, onde estão diagnosticados como normais ou possuindo uma determinada patologia. Os resultados apresentados para a estimativa do parâmetro de Hurst permitem confirmar resultados já publicados sobre a auto-semelhança temporal dos sinais ECG, desta vez para a sua distribuição espacial. Também se concluí que os sinais não auto-semelhantes correspondem a indivíduos não saudáveis. Os resultados obtidos na estimativa da entropia para a distribuição espacial dos sinais de ECG também permitiram uma comparação entre sistemas saudáveis e não saudáveis. Obtiveram-se estimativas de entropia elevadas quer para indivíduos saudáveis quer para indivíduos não saudáveis; no entanto, os indivíduos não saudáveis mostram uma maior variabilidade da entropia de Shannon em relação aos saudáveis