Does the liver facilitate aging-related cognitive impairment: Conversation between liver and brain during exercise?

Liver, an important regulator of metabolic homeostasis, is critical for healthy brain function. In particular, age-related neurodegenerative diseases seriously reduce the quality of life for the elderly. As population aging progresses rapidly, unraveling the mechanisms that effectively delay aging has become critical. Appropriate exercise is reported to improve aging-related cognitive impairment. Whereas current studies focused on exploring the effect of exercise on the aging brain itself, ignoring the persistent effects of peripheral organs on the brain through the blood circulation. The aim of this paper is to summarize the communication and aging processes of the liver and brain and to emphasize the metabolic mechanisms of the liver-brain axis about exercise ameliorating aging-related neurodegenerative diseases. A comprehensive understanding of the potential mechanisms about exercise ameliorating aging is critical for improving adaptation to age-related brain changes and formulating effective interventions against age-related cognitive decline.

Meta-analysis of the Correlation between Helicobacter Pylori Infection and the risk of Colorectal Neoplasia.

Objective: To study the association of H. pylori infection with colorectal adenomas. Methods: Web searches of PubMed, Embase, and Scopus databases for randomized controlled trials, class-experimental studies, and cohort studies on the association between H. pylori and colorectal adenomas were performed from May 2000 to May 2023. Literature was screened based on inclusion and exclusion criteria, data were extracted and evaluated for quality, and statistical analyses were performed using RevMan 5.2 software. Results: A total of 15 studies were included, and meta-analysis showed a statistically significant difference between colorectal neoplastic polyp cases in the H. pylori-positive and H. pylori-negative groups [OR=1.80, 95%CI: (1.31, 2.47), P < .01, I2 = 95%]. Analysis based on subgroups of different H. pylori detection methods showed that the correlation between H. pylori infection and colorectal polyp incidence is not affected by their detection methods, with serological detection subgroup: [OR=0.13, 95%CI: (0.05, 0.21), P < .01, I2 = 88%], and non-serological detection subgroup: [OR=0.13, 95%CI: (0.04, 0.22), P < .01, I2 = 95%]. Subgroup analysis of pathological types showed that H. pylori infection is not significantly associated with the development of non-neoplastic polyps [OR=1.47, 95%CI: 0.98-2.22, P = .06], whereas it is correlated with the development of neoplastic polyps [95%CI: 1.69-3.22, P < .01]. In the subgroup analysis of geographic differences in the population, H. pylori infection was correlated with the development of colorectal polyps in different geographic populations (P < .01). Conclusion: H. pylori infection is a risk factor for colorectal polyp neoplasia, its infection is associated with colorectal neoplasia, and the correlation is not affected by the different methods of H. pylori detection and the different geographic regions of the population.

Testosterone attenuates senile cavernous fibrosis by regulating TGFßR1 and galectin-1 signaling pathways through miR-22-3p.

Erectile dysfunction (ED) is a major health problem affecting a large proportion of the general population. Testosterone also plays a key role in sexual dysfunction. In this study, we found that testosterone can inhibit cavernous fibrosis by affecting the expression of miR-22-3p, providing a new basis for research and treatment of ED. Old and young rats were used to study the effects of testosterone on cavernous fibrosis. Hematoxylin and eosin (HE) and Masson's staining were used to observe the cavernous tissue. A luciferase assay was used to analyze the relationship between the miR-22-3p, TGFßR1, and Galectin-1 signaling pathways. CCK-8 and flow cytometry were used to detect the proliferation and apoptosis rates of cavernosum smooth muscle cells (CSMCs) following testosterone intervention. Immunohistochemical analysis was performed to examine the positive rate of caspase 3 and Ki67. IF was used to analyze the expression of collagen IV, MMP2, and α-SMA. The levels of GnRH, tT, LH, and F-TESTO in old rats increased after testosterone intervention. miR-22-3p inhibits the expression of TGFßR1 and Galectin-1. The protein expression of TGFßR1, Galectin-1, SMAD2, and p-SMAD2 was reduced by testosterone. The expression levels of α-SMA, collagen I, collagen IV, FN, and MMP2 in the cavernous tissues of old rats treated with testosterone were significantly reduced. The levels of caspase 3 and collagen IV decreased, and the levels of MMP2, Ki67, and α-SMA increased. Testosterone and miR-22-3p inhibit CSMC apoptosis and promote cell proliferation. Testosterone promoted the expression of miR-22-3p to interfere with the expression of the cavernous TGFßR1 and Galectin-1 signaling pathways. Testosterone can reduce cavernous fibrosis during the treatment of functional ED.

Erratum: Sandeep P., et al. An Efficient Biometric-Based Algorithm Using Heart Rate Variability for Securing Body Sensor Networks. Sensors 2015, 15, 15067-15089.

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Erratum: Sandeep P., et al. A Novel Secure IoT-Based Smart Home Automation System Using a Wireless Sensor Network. Sensors 2017, 17, 69.

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A Novel Secure IoT-Based Smart Home Automation System Using a Wireless Sensor Network.

Wireless sensor networks (WSNs) provide noteworthy benefits over traditional approaches for several applications, including smart homes, healthcare, environmental monitoring, and homeland security. WSNs are integrated with the Internet Protocol (IP) to develop the Internet of Things (IoT) for connecting everyday life objects to the internet. Hence, major challenges of WSNs include: (i) how to efficiently utilize small size and low-power nodes to implement security during data transmission among several sensor nodes; (ii) how to resolve security issues associated with the harsh and complex environmental conditions during data transmission over a long coverage range. In this study, a secure IoT-based smart home automation system was developed. To facilitate energy-efficient data encryption, a method namely Triangle Based Security Algorithm (TBSA) based on efficient key generation mechanism was proposed. The proposed TBSA in integration of the low power Wi-Fi were included in WSNs with the Internet to develop a novel IoT-based smart home which could provide secure data transmission among several associated sensor nodes in the network over a long converge range. The developed IoT based system has outstanding performance by fulfilling all the necessary security requirements. The experimental results showed that the proposed TBSA algorithm consumed less energy in comparison with some existing methods.

Quantification of three-dimensional computed tomography angiography for evaluating coronary luminal stenosis using digital subtraction angiography as the standard of reference.

OBJECTIVE: We sought to evaluate the accuracy of quantitative three-dimensional (3D) CT angiography (CTA) for the assessment of coronary luminal stenosis using digital subtraction angiography (DSA) as the standard of reference. METHOD: Twenty-three patients with 54 lesions were referred for CTA followed by DSA. The CTA scans were performed with 256-slice spiral CT. 3D CTA were reconstructed from two-dimensional CTA imaging sequences in order to extract the following quantitative indices: minimal lumen diameter, percent diameter stenosis (%DS), minimal lumen area, and percent area stenosis (%AS). Correlation and limits of agreement were calculated using Pearson correlation and Bland-Altman analysis, respectively. The diagnostic performance and the diagnostic concordance of 3D CTA-derived anatomic parameters (%DS, %AS) for the detection of severe coronary arterial stenosis (as assessed by DSA) were presented as sensitivity, specificity, diagnostic accuracy, and Kappa statistics. Of which vessels with %DS >50% or with %AS >75% were identified as severe coronary arterial lesions. RESULT: The correlations of the anatomic parameters between 3D CTA and DSA were significant (r = 0.51-0.74, P < 0.001). Bland-Altman analysis confirmed that the mean differences were small (from -1.11 to 27.39%), whereas the limits of agreement were relatively wide (from ±28.07 to ±138.64%). Otherwise, the diagnostic accuracy (74.1% with 58.3% sensitivity and 86.7% specificity for DS%; 74.1% with 45.8% sensitivity and 96.7% specificity for %AS) and the diagnostic concordance (k = 0.46 for DS%; 0.45 for %AS) of 3D CTA-derived anatomic parameters for the detection of severe stenosis were moderate. CONCLUSION: 3D advanced imaging reconstruction technique is a helpful tool to promote the use of CTA as an alternative to assess luminal stenosis in clinical practice.

Cuffless and Continuous Blood Pressure Estimation from the Heart Sound Signals.

Cardiovascular disease, like hypertension, is one of the top killers of human life and early detection of cardiovascular disease is of great importance. However, traditional medical devices are often bulky and expensive, and unsuitable for home healthcare. In this paper, we proposed an easy and inexpensive technique to estimate continuous blood pressure from the heart sound signals acquired by the microphone of a smartphone. A cold-pressor experiment was performed in 32 healthy subjects, with a smartphone to acquire heart sound signals and with a commercial device to measure continuous blood pressure. The Fourier spectrum of the second heart sound and the blood pressure were regressed using a support vector machine, and the accuracy of the regression was evaluated using 10-fold cross-validation. Statistical analysis showed that the mean correlation coefficients between the predicted values from the regression model and the measured values from the commercial device were 0.707, 0.712, and 0.748 for systolic, diastolic, and mean blood pressure, respectively, and that the mean errors were less than 5 mmHg, with standard deviations less than 8 mmHg. These results suggest that this technique is of potential use for cuffless and continuous blood pressure monitoring and it has promising application in home healthcare services.

An Efficient Biometric-Based Algorithm Using Heart Rate Variability for Securing Body Sensor Networks.

Body Sensor Network (BSN) is a network of several associated sensor nodes on, inside or around the human body to monitor vital signals, such as, Electroencephalogram (EEG), Photoplethysmography (PPG), Electrocardiogram (ECG), etc. Each sensor node in BSN delivers major information; therefore, it is very significant to provide data confidentiality and security. All existing approaches to secure BSN are based on complex cryptographic key generation procedures, which not only demands high resource utilization and computation time, but also consumes large amount of energy, power and memory during data transmission. However, it is indispensable to put forward energy efficient and computationally less complex authentication technique for BSN. In this paper, a novel biometric-based algorithm is proposed, which utilizes Heart Rate Variability (HRV) for simple key generation process to secure BSN. Our proposed algorithm is compared with three data authentication techniques, namely Physiological Signal based Key Agreement (PSKA), Data Encryption Standard (DES) and Rivest Shamir Adleman (RSA). Simulation is performed in Matlab and results suggest that proposed algorithm is quite efficient in terms of transmission time utilization, average remaining energy and total power consumption.

A novel channel selection method for multiple motion classification using high-density electromyography.

BACKGROUND: Selecting an appropriate number of surface electromyography (EMG) channels with desired classification performance and determining the optimal placement of EMG electrodes would be necessary and important in practical myoelectric control. In previous studies, several methods such as sequential forward selection (SFS) and Fisher-Markov selector (FMS) have been used to select the appropriate number of EMG channels for a control system. These exiting methods are dependent on either EMG features and/or classification algorithms, which means that when using different channel features or classification algorithm, the selected channels would be changed. In this study, a new method named multi-class common spatial pattern (MCCSP) was proposed for EMG selection in EMG pattern-recognition-based movement classification. Since MCCSP is independent on specific EMG features and classification algorithms, it would be more convenient for channel selection in developing an EMG control system than the exiting methods. METHODS: The performance of the proposed MCCSP method in selecting some optimal EMG channels (designated as a subset) was assessed with high-density EMG recordings from twelve mildly-impaired traumatic brain injury (TBI) patients. With the MCCSP method, a subset of EMG channels was selected and then used for motion classification with pattern recognition technique. In order to justify the performance of the MCCSP method against different electrode configurations, features and classification algorithms, two electrode configurations (unipolar and bipolar) as well as two EMG feature sets and two types of pattern recognition classifiers were considered in the study, respectively. And the performance of the proposed MCCSP method was compared with that of two exiting channel selection methods (SFS and FMS) in EMG control system. RESULTS: The results showed that in comparison with the previously used SFS and FMS methods, the newly proposed MCCSP method had better motion classification performance. Moreover, a fixed combination of the selected EMG channels was obtained when using MCCSP. CONCLUSIONS: The proposed MCCSP method would be a practicable means in channel selection and would facilitate the design of practical myoelectric control systems in the active rehabilitation of mildly-impaired TBI patients and in other rehabilitation applications such as the multifunctional myoelectric prostheses for limb amputees.

Automatic online detection of atrial fibrillation based on symbolic dynamics and Shannon entropy.

BACKGROUND: Atrial fibrillation (AF) is the most common and debilitating abnormalities of the arrhythmias worldwide, with a major impact on morbidity and mortality. The detection of AF becomes crucial in preventing both acute and chronic cardiac rhythm disorders. OBJECTIVE: Our objective is to devise a method for real-time, automated detection of AF episodes in electrocardiograms (ECGs). This method utilizes RR intervals, and it involves several basic operations of nonlinear/linear integer filters, symbolic dynamics and the calculation of Shannon entropy. Using novel recursive algorithms, online analytical processing of this method can be achieved. RESULTS: Four publicly-accessible sets of clinical data (Long-Term AF, MIT-BIH AF, MIT-BIH Arrhythmia, and MIT-BIH Normal Sinus Rhythm Databases) were selected for investigation. The first database is used as a training set; in accordance with the receiver operating characteristic (ROC) curve, the best performance using this method was achieved at the discrimination threshold of 0.353: the sensitivity (Se), specificity (Sp), positive predictive value (PPV) and overall accuracy (ACC) were 96.72%, 95.07%, 96.61% and 96.05%, respectively. The other three databases are used as testing sets. Using the obtained threshold value (i.e., 0.353), for the second set, the obtained parameters were 96.89%, 98.25%, 97.62% and 97.67%, respectively; for the third database, these parameters were 97.33%, 90.78%, 55.29% and 91.46%, respectively; finally, for the fourth set, the Sp was 98.28%. The existing methods were also employed for comparison. CONCLUSIONS: Overall, in contrast to the other available techniques, the test results indicate that the newly developed approach outperforms traditional methods using these databases under assessed various experimental situations, and suggest our technique could be of practical use for clinicians in the future.

A numerical study of the effect of varied blood pressure on the stability of carotid atherosclerotic plaque.

BACKGROUND: Blood pressure (BP) is associated with early atherosclerosis and plaque rupture because the BP variability can significantly affect the blood flow velocity and shear stress over the plaque. However, the mechanical response of BP variability to the plaque remains unclear. Therefore, we investigated the correlation between different maximum systolic blood pressure (SBP) and the stress distribution on plaque, as well as the stress over the plaque and blood velocity around the plaque using different BP variations, which are the BP variability in different phases during one cardiac cycle and beat-to-beat BP variability. METHOD: We established a two-dimensional artery model with stenosis at the degree of 62.5%. Eight combinations of pulsatile pressure gradients between the inflow and outflow were implemented at the model. Three levels of fibrous cap thickness were taken into consideration to investigate the additional effect on the BP variability. Wall shear stress and stress/strain distribution over the plaque were derived as well as the oscillation shear index (OSI) to analyze the impact of the changing rate of BP. RESULT: The stresses at diastole were 2.5% ± 1.8% lower than that at systole under the same pressure drop during one cycle. It was also found that elevated SBP might cause the immediate increment of stress in the present cycle (292% ± 72.3%), but slight reduction in the successive cycle (0.48% ± 0.4%). CONCLUSION: The stress/strain distribution over the plaque is sensitive to the BP variability during one cardiac cycle, and the beat-to-beat BP variability could cause considerable impact on the progression of atherosclerosis in long-term.

Uncertainty quantification of the pressure waveform using a Windkessel model.

The Windkessel (WK) model is a simplified mathematical model used to represent the systemic arterial circulation. While the WK model is useful for studying blood flow dynamics, it suffers from inaccuracies or uncertainties that should be considered when using it to make physiological predictions. This paper aims to develop an efficient and easy-to-implement uncertainty quantification method based on a local gradient-based formulation to quantify the uncertainty of the pressure waveform resulting from aleatory uncertainties of the WK parameters and flow waveform. The proposed methodology, tested against Monte Carlo simulations, demonstrates good agreement in estimating blood pressure uncertainties due to uncertain Windkessel parameters, but less agreement considering uncertain blood-flow waveforms. To illustrate our methodology's applicability, we assessed the aortic pressure uncertainty generated by Windkessel parameters-sets from an available in silico database representing healthy adults. The results from the proposed formulation align qualitatively with those in the database and in vivo data. Furthermore, we investigated how changes in the uncertainty of the Windkessel parameters affect the uncertainty of systolic, diastolic, and pulse pressures. We found that peripheral resistance uncertainty produces the most significant change in the systolic and diastolic blood pressure uncertainties. On the other hand, compliance uncertainty considerably modifies the pulse pressure standard deviation. The presented expansion-based method is a tool for efficiently propagating the Windkessel parameters' uncertainty to the pressure waveform. The Windkessel model's clinical use depends on the reliability of the pressure in the presence of input uncertainties, which can be efficiently investigated with the proposed methodology. For instance, in wearable technology that uses sensor data and the Windkessel model to estimate systolic and diastolic blood pressures, it is important to check the confidence level in these calculations to ensure that the pressures accurately reflect the patient's cardiovascular condition.

Identification and Comprehensive Analysis of circRNA-miRNA-mRNA Regulatory Networks in A2780 Cells Treated with Resveratrol.

Ovarian cancer (OC) is one of the most commonplace gynecological malignancies. This study explored the effects of resveratrol (RES) on OC cell proliferation and apoptosis. Proliferation activity was measured for A2780 cells treated with RES for 24 h and 48 h at concentrations of 0, 10, 25, 50, 75, 100, 150, 200, and 300 µM. RNA sequencing (RNA-seq) was performed to analyze the circular RNA (circRNA), microRNA (miRNA), and messenger RNA (mRNA) expression spectrum. The differentially expressed genes included 460 circRNAs, 1988 miRNAs, and 1671 mRNAs, and they were subjected to analyses including Gene Ontology, the Kyoto Encyclopedia of Genes and Genomes (KEGG), and Reactome enrichment. We selected signaling pathways enriched in the cell processes by mRNA KEGG, comprehensively analyzed the circRNA-miRNA-mRNA regulatory network, and verified several miRNAs expressed in the regulatory network diagram using the quantitative real-time polymerase chain reaction. The data showed that the cell proliferation of A2780 cells treated with RES for 24 h or 48 h decreased with increasing concentrations of RES. The circRNA-miRNA-mRNA regulatory network that we constructed provides new insights into the ability of RES to inhibit cell proliferation and promote apoptosis in A2780 cells.

Intelligent Electrocardiogram Acquisition Via Ubiquitous Photoplethysmography Monitoring.

Recent advances in machine learning, particularly deep neural network architectures, have shown substantial promise in classifying and predicting cardiac abnormalities from electrocardiogram (ECG) data. Such data are rich in information content, typically in morphology and timing, due to the close correlation between cardiac function and the ECG. However, the ECG is usually not measured ubiquitously in a passive manner from consumer devices, and generally requires 'active' sampling whereby the user prompts a device to take an ECG measurement. Conversely, photoplethysmography (PPG) data are typically measured passively by consumer devices, and therefore available for long-period monitoring and suitable in duration for identifying transient cardiac events. However, classifying or predicting cardiac abnormalities from the PPG is very difficult, because it is a peripherally-measured signal. Hence, the use of the PPG for predictive inference is often limited to deriving physiological parameters (heart rate, breathing rate, etc.) or for obvious abnormalities in cardiac timing, such as atrial fibrillation/flutter ("palpitations"). This work aims to combine the best of both worlds: using continuously-monitored, near-ubiquitous PPG to identify periods of sufficient abnormality in the PPG such that prompting the user to take an ECG would be informative of cardiac risk. We propose a dual-convolutional-attention network (DCA-Net) to achieve this ECG-based PPG classification. With DCA-Net, we prove the plausibility of this concept on MIMIC Waveform Database with high performance level (AUROC 0.9 and AUPRC 0.7) and receive satisfactory result when testing the model on an independent dataset (AUROC 0.7 and AUPRC 0.6) which it is not perfectly-matched to the MIMIC dataset.

A Non-invasive Blood Glucose Detection System Based on Photoplethysmogram with Multiple Near-infrared Sensors.

Recent advancements in non-invasive blood glucose detection have seen progress in both photoplethysmogram and multiple near-infrared methods. While the former shows better predictability of baseline glucose levels, it lacks sensitivity to daily fluctuations. Near-infrared methods respond well to short-term changes but face challenges due to individual and environmental factors. To address this, we developed a novel fingertip blood glucose detection system combining both methods. Using multiple light sensors and a lightweight deep learning model, our system achieved promising results in oral glucose tolerance tests. A total of 10 participants were involved in the study, each providing approximately 700 data segments of about 10 seconds each. With a root mean squared error of 0.242 mmol/L and 100% accuracy in zone A of the Parkes error grid, our approach demonstrates the potential of multiple near-infrared sensors for non-invasive glucose detection.

Multi-View Cross-Fusion Transformer Based on Kinetic Features for Non-Invasive Blood Glucose Measurement Using PPG Signal.

Noninvasive blood glucose (BG) measurement could significantly improve the prevention and management of diabetes. In this paper, we present a robust novel paradigm based on analyzing photoplethysmogram (PPG) signals. The method includes signal pre-processing optimization and a multi-view cross-fusion transformer (MvCFT) network for non-invasive BG assessment. Specifically, a multi-size weighted fitting (MSWF) time-domain filtering algorithm is proposed to optimally preserve the most authentic morphological features of the original signals. Meanwhile, the spatial position encoding-based kinetics features are reconstructed and embedded as prior knowledge to discern the implicit physiological patterns. In addition, a cross-view feature fusion (CVFF) module is designed to incorporate pairwise mutual information among different views to adequately capture the potential complementary features in physiological sequences. Finally, the subject- wise 5- fold cross-validation is performed on a clinical dataset of 260 subjects. The root mean square error (RMSE) and mean absolute error (MAE) of BG measurements are 1.129 mmol/L and 0.659 mmol/L, respectively, and the optimal Zone A in the Clark error grid, representing none clinical risk, is 87.89%. The results indicate that the proposed method has great potential for homecare applications.

Open-world electrocardiogram classification via domain knowledge-driven contrastive learning.

Automatic electrocardiogram (ECG) classification provides valuable auxiliary information for assisting disease diagnosis and has received much attention in research. The success of existing classification models relies on fitting the labeled samples for every ECG type. However, in practice, well-annotated ECG datasets usually cover only limited ECG types. It thus raises an issue: conventional classification models trained with limited ECG types can only identify those ECG types that have already been observed in the training set, but fail to recognize unseen (or unknown) ECG types that exist in the wild and are not included in training data. In this work, we investigate an important problem called open-world ECG classification that can predict fine-grained observed ECG classes and identify unseen classes. Accordingly, we propose a customized method that first incorporates clinical knowledge into contrastive learning by generating "hard negative" samples to guide learning diagnostic ECG features (i.e., distinguishable representations), and then performs multi-hypersphere learning to learn compact ECG representations for classification. The experiment results on 12-lead ECG datasets (CPSC2018, PTB-XL, and Georgia) demonstrate that the proposed method outperforms the state-of-the-art methods. Specifically, our method achieves superior accuracy than the comparative methods on the unseen ECG class and certain seen classes. Overall, the investigated problem (i.e., open-world ECG classification) helps to draw attention to the reliability of automatic ECG diagnosis, and the proposed method is proven effective in tackling the challenges. The code and datasets are released at https://github.com/betterzhou/Open_World_ECG_Classification.

Semi-Supervised Learning for Multi-Label Cardiovascular Diseases Prediction: A Multi-Dataset Study.

Electrocardiography (ECG) is a non-invasive tool for predicting cardiovascular diseases (CVDs). Current ECG-based diagnosis systems show promising performance owing to the rapid development of deep learning techniques. However, the label scarcity problem, the co-occurrence of multiple CVDs and the poor performance on unseen datasets greatly hinder the widespread application of deep learning-based models. Addressing them in a unified framework remains a significant challenge. To this end, we propose a multi-label semi-supervised model (ECGMatch) to recognize multiple CVDs simultaneously with limited supervision. In the ECGMatch, an ECGAugment module is developed for weak and strong ECG data augmentation, which generates diverse samples for model training. Subsequently, a hyperparameter-efficient framework with neighbor agreement modeling and knowledge distillation is designed for pseudo-label generation and refinement, which mitigates the label scarcity problem. Finally, a label correlation alignment module is proposed to capture the co-occurrence information of different CVDs within labeled samples and propagate this information to unlabeled samples. Extensive experiments on four datasets and three protocols demonstrate the effectiveness and stability of the proposed model, especially on unseen datasets. As such, this model can pave the way for diagnostic systems that achieve robust performance on multi-label CVDs prediction with limited supervision.

Uncertainties in the Analysis of Heart Rate Variability: A Systematic Review.

Heart rate variability (HRV) is an important metric with a variety of applications in clinical situations such as cardiovascular diseases, diabetes mellitus, and mental health. HRV data can be potentially obtained from electrocardiography and photoplethysmography signals, then computational techniques such as signal filtering and data segmentation are used to process the sampled data for calculating HRV measures. However, uncertainties arising from data acquisition, computational models, and physiological factors can lead to degraded signal quality and affect HRV analysis. Therefore, it is crucial to address these uncertainties and develop advanced models for HRV analysis. Although several reviews of HRV analysis exist, they primarily focus on clinical applications, trends in HRV methods, or specific aspects of uncertainties such as measurement noise. This paper provides a comprehensive review of uncertainties in HRV analysis, quantifies their impacts, and outlines potential solutions. To the best of our knowledge, this is the first study that presents a holistic review of uncertainties in HRV methods and quantifies their impacts on HRV measures from an engineer's perspective. This review is essential for developing robust and reliable models, and could serve as a valuable future reference in the field, particularly for dealing with uncertainties in HRV analysis.