Entropy Analysis of COVID-19 Cardiovascular Signals

Virtual Reality-Based Infrared Pupillometry (VIP) for Long-COVID

PURPOSE

To evaluate the use of virtual reality-based infrared pupillometry (VIP) to detect individuals with long coronavirus disease (LCVD).

DESIGN

Prospective, case-control cross-sectional study.

PARTICIPANTS

Participants 20 to 60 years of age were recruited from a community eye screening program.

METHODS

Pupillary light responses (PLRs) were recorded in response to 3 intensities of light stimuli (L6, L7, and L8) using a virtual reality head-mount display (VRHMD). Nine PLR waveform features for each stimulus were extracted by 2 masked observers and were analyzed statistically. We also used trained, validated, and tested (6:3:1) methods on the entire PLR waveform by machine learning models for 2-class and 3-class classification into LCVD, post-COVID (PCVD), or control groups.

MAIN OUTCOME MEASURES

Accuracies and areas under the receiver operating characteristic curve (AUCs) of individual or a combination of PLR features and machine learning models analyzing PLR features or whole pupillometric waveform.

RESULTS

Pupillary light responses from a total of 185 participants, including 112 in the LCVD group, 44 in the PCVD group, and 29 in the age- and sex-matched control group were analyzed. Models examined the independent effects of age and sex. Constriction time (CT) after the brightest stimulus (L8) is associated significantly with LCVD status (false discovery rate [FDR] < 0.001, 2-way analysis of variance; FDR < 0.05, multinominal logistic regression). The overall accuracy and AUC of CT after L8 alone in differentiating the LCVD group from the control or PCVD group were 0.7808 and 0.8711, respectively, and 0.8654 and 0.8140, respectively. Using cross-validated backward stepwise variable selection, CT after L8, CT after L6, and constriction velocity (CV) after L6 were most useful to detect LCVD, whereas CV after L8 was most useful for distinguishing the PCVD group from other groups. The accuracy and AUC of selected features were 0.8000 and 0.9000 (control vs. LCVD groups) and 0.9062 and 0.9710 (PCVD vs. LCVD groups), respectively, better than when all 27 pupillometric features were combined. A long short-term memory model analyzing whole pupillometric waveform achieved the highest accuracy and AUC at 0.9375 and 1.000 in differentiating the LCVD from PCVD group and a lower accuracy of 0.7838 for 3-class classification (LCVD, PCVD, and control group).

CONCLUSIONS

We report specific pupillometric signatures in differentiating LCVD from PCVD or control groups using a VRHMD. Combining statistical methods to identify specific pupillometric features and machine learning algorithms to analyze the whole pupillometric waveform further enhanced the performance of VIP as a nonintrusive, low-cost, portable, and objective method to detect LCVD.

FINANCIAL DISCLOSURE(S)

Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Enhancing Security of Telemedicine Data: A Multi-Scroll Chaotic System for ECG Signal Encryption and RF Transmission

Protecting sensitive patient data, such as electrocardiogram (ECG) signals, during RF wireless transmission is essential due to the increasing demand for secure telemedicine communications. This paper presents an innovative chaotic-based encryption system designed to enhance the security and integrity of telemedicine data transmission. The proposed system utilizes a multi-scroll chaotic system for ECG signal encryption based on master-slave synchronization. The ECG signal is encrypted by a master system and securely transmitted to a remote location, where it is decrypted by a slave system using an extended state observer. Synchronization between the master and slave is achieved through the Lyapunov criteria, which ensures system stability. The system also supports Orthogonal Frequency Division Multiplexing (OFDM) and adaptive n-quadrature amplitude modulation (n-QAM) schemes to optimize signal discretization. Experimental validations with a custom transceiver scheme confirmed the system's effectiveness in preventing channel overlap during 2.5 GHz transmissions. Additionally, a commercial RF Power Amplifier (RF-PA) for LTE applications and a development board were integrated to monitor transmission quality. The proposed encryption system ensures robust and efficient RF transmission of ECG data, addressing critical challenges in the wireless communication of sensitive medical information. This approach demonstrates the potential for broader applications in modern telemedicine environments, providing a reliable and efficient solution for the secure transmission of healthcare data.

Cardiac Autonomic Function and Functional Capacity in Post-COVID-19 Individuals with Systemic Arterial Hypertension

Individuals diagnosed with systemic arterial hypertension (SAH) are considered risk groups for COVID-19 severity. This study assessed differences in cardiac autonomic function (CAF) and functional capacity (FC) in SAH individuals without COVID-19 infection compared to SAH individuals post-COVID-19. Participants comprised 40 SAH individuals aged 31 to 80 years old, grouped as SAH with COVID-19 (G1; n = 21) and SAH without COVID-19 (G2; n = 19). CAF was assessed via heart rate variability (HRV), measuring R-R intervals during a 10-min supine period. Four HRV indices were analyzed through symbolic analysis: 0V%, 1V%, 2LV%, and 2UV%. FC assessment was performed by a 6-min walk test (6MWT). G1 and G2 showed no significant differences in terms of age, anthropometric parameters, clinical presentation, and medication use. G2 exhibited superior 6MWT performance, covering more distance (522 ± 78 vs. 465 ± 59 m, p < 0.05). Specifically, G2 demonstrated a moderate positive correlation between 6MWT and the 2LV% index (r = 0.58; p < 0.05). Shorter walking distances were observed during 6MWT in SAH individuals post-COVID-19. However, the study did not find impaired cardiac autonomic function in SAH individuals post-COVID-19 compared to those without. This suggests that while COVID-19 impacted FC, CAF remained relatively stable in this population.

Induced Relaxation Enhances the Cardiorespiratory Dynamics in COVID-19 Survivors

Most COVID-19 survivors report experiencing at least one persistent symptom after recovery, including sympathovagal imbalance. Relaxation techniques based on slow-paced breathing have proven to be beneficial for cardiovascular and respiratory dynamics in healthy subjects and patients with various diseases. Therefore, the present study aimed to explore the cardiorespiratory dynamics by linear and nonlinear analysis of photoplethysmographic and respiratory time series on COVID-19 survivors under a psychophysiological assessment that includes slow-paced breathing. We analyzed photoplethysmographic and respiratory signals of 49 COVID-19 survivors to assess breathing rate variability (BRV), pulse rate variability (PRV), and pulse-respiration quotient (PRQ) during a psychophysiological assessment. Additionally, a comorbidity-based analysis was conducted to evaluate group changes. Our results indicate that all BRV indices significantly differed when performing slow-paced breathing. Nonlinear parameters of PRV were more appropriate for identifying changes in breathing patterns than linear indices. Furthermore, the mean and standard deviation of PRQ exhibited a significant increase while sample and fuzzy entropies decreased during diaphragmatic breathing. Thus, our findings suggest that slow-paced breathing may improve the cardiorespiratory dynamics of COVID-19 survivors in the short term by enhancing cardiorespiratory coupling via increased vagal activity.

Pre-pandemic autonomic function as a predictor of the COVID clinical course in young adults

Long coronavirus disease (COVID) is emerging as a common clinical entity in the current era. Autonomic dysfunction is one of the frequently reported post-COVID complications. We hypothesize a bi-directional relationship between the autonomic function and the COVID course. This postulation has been inadequately addressed in the literature. A retrospective cohort (pre and post-comparison) study was conducted on 30 young adults whose pre-COVID autonomic function test results were available. They were divided into case and control groups based on whether they tested reverse transcription polymerase chain reaction positive for COVID-19. Autonomic function tests were performed in both the case and control groups. COVID infection in healthy young adults shifts the sympatho-vagal balance from the pre-disease state. Postural orthostatic tachycardia syndrome was present in 35% of the COVID-affected group. COVID course parameters were found to be associated with parasympathetic reactivity and the baroreflex function. Baseline autonomic function (parasympathetic reactivity represented by Δ heart rate changes during deep breathing and 30:15 ratio during lying-to-standing test) was also associated with the COVID course, the post-COVID symptoms and the post-COVID autonomic function profile. Additionally, multiple regression analysis found that the baseline parasympathetic reactivity was a very important determinant of the clinical course of COVID, the post-COVID symptoms and the post-COVID autonomic profile. Sympatho-vagal balance shifts to parasympathetic withdrawal with sympathetic predominance due to COVID infection in healthy young adults. There is a bi-directional relationship between the autonomic function and the COVID course.

Sympathetic Vagal Balance and Cognitive Performance in Young Adults during the NIH Cognitive Test

Compromised cognitive function is associated with increased mortality and increased healthcare costs. Autonomic nervous system arousal, as measured by an electrocardiogram (ECG), has received recent attention because of its association with the blood perfusion of brain regions involved with cognitive function. The purposes of this study were to determine whether the ECG HR variation, as measured by the standard deviation of the heart rate N-to-N intervals (SDNN), and sympathetic vagal tone, as estimated by the low-frequency/high-frequency ratio (LF/HF), are increased with cognitive performance during the NIH Cognitive Test (Picture Sequence, Dimensional Change Card Sort, Flanker, and List Sorting). A total of 62 young people without cognitive impairment participated in this study. We discovered that the ECG LF/HF ratio was increased in the top 50% of participants who could: (1) inhibit information and stay attentive to a desired task during the Flanker Test; (U = 329, p = 0.03; R² = 0.76); and (2) promote cognitive function flexibility during the DCCS Test; (U = 55, p = 0.007; R² = 0.98). Taken together, these findings support that the arousal level influences performance during a cognitive test.

Applying Multiple Functional Connectivity Features in GCN for EEG-Based Human Identification

EEG-based human identification has gained a wide range of attention due to the further increase in demand for security. How to improve the accuracy of the human identification system is an issue worthy of attention. Using more features in the human identification system is a potential solution. However, too many features may cause overfitting, resulting in the decline of system accuracy. In this work, the graph convolutional neural network (GCN) was adopted for classification. Multiple features were combined and utilized as the structure matrix of the GCN. Because of the constant signal matrix, the training parameters would not increase as the structure matrix grows. We evaluated the classification accuracy on a classic public dataset. The results showed that utilizing multiple features of functional connectivity (FC) can improve the accuracy of the identity authentication system, the best results of which are at 98.56%. In addition, our methods showed less sensitivity to channel reduction. The method proposed in this paper combines different FCs and reaches high classification accuracy for unpreprocessed data, which inspires reducing the system cost in the actual human identification system.

Autonomic Dysfunction during Acute SARS-CoV-2 Infection: A Systematic Review

Although autonomic dysfunction (AD) after the recovery from Coronavirus disease 2019 (COVID-19) has been thoroughly described, few data are available regarding the involvement of the autonomic nervous system (ANS) during the acute phase of SARS-CoV-2 infection. The primary aim of this review was to summarize current knowledge regarding the AD occurring during acute COVID-19. Secondarily, we aimed to clarify the prognostic value of ANS involvement and the role of autonomic parameters in predicting SARS-CoV-2 infection. According to the PRISMA guidelines, we performed a systematic review across Scopus and PubMed databases, resulting in 1585 records. The records check and the analysis of included reports’ references allowed us to include 22 articles. The studies were widely heterogeneous for study population, dysautonomia assessment, and COVID-19 severity. Heart rate variability was the tool most frequently chosen to analyze autonomic parameters, followed by automated pupillometry. Most studies found ANS involvement during acute COVID-19, and AD was often related to a worse outcome. Further studies are needed to clarify the role of autonomic parameters in predicting SARS-CoV-2 infection. The evidence emerging from this review suggests that a complex autonomic nervous system imbalance is a prominent feature of acute COVID-19, often leading to a poor prognosis.

Reduction of Cardiac Autonomic Modulation and Increased Sympathetic Activity by Heart Rate Variability in Patients With Long COVID

Although several clinical manifestations of persistent long coronavirus disease (COVID-19) have been documented, their effects on the cardiovascular and autonomic nervous system over the long term remain unclear. Thus, we examined the presence of alterations in cardiac autonomic functioning in individuals with long-term manifestations. The study was conducted from October 2020 to May 2021, and an autonomic assessment was performed to collect heart rate data for the heart rate variability (HRV) analysis. The study participants were divided into the long COVID clinical group, the intragroup, which included patients who were hospitalized, and those who were not hospitalized and were symptomatic for different periods (≤3, >3, ≤6, and >6 months), with and without dyspnoea. The control group, the intergroup, comprised of COVID-free individuals. Our results demonstrated that the long COVID clinical group showed reduced HRV compared with the COVID-19-uninfected control group. Patients aged 23–59 years developed COVID symptoms within 30 days after infection, whose diagnosis was confirmed by serologic or reverse-transcription polymerase chain reaction (swab) tests, were included in the study. A total of 155 patients with long COVID [95 women (61.29%), mean age 43.88 ± 10.88 years and 60 men (38.71%), mean age 43.93 ± 10.11 years] and 94 controls [61 women (64.89%), mean age 40.83 ± 6.31 and 33 men (35.11%), mean age 40.69 ± 6.35 years] were included. The intragroup and intergroup comparisons revealed a reduction in global HRV, increased sympathetic modulation influence, and a decrease in parasympathetic modulation in long COVID. The intragroup showed normal sympathovagal balance, while the intergroup showed reduced sympathovagal balance. Our findings indicate that long COVID leads to sympathetic excitation influence and parasympathetic reduction. The excitation can increase the heart rate and blood pressure and predispose to cardiovascular complications. Short-term HRV analysis showed good reproducibility to verify the cardiac autonomic involvement.

Predicting the number of COVID-19 infections and deaths in USA

BACKGROUND

Uncertainties surrounding the 2019 novel coronavirus (COVID-19) remain a major global health challenge and requires attention. Researchers and medical experts have made remarkable efforts to reduce the number of cases and prevent future outbreaks through vaccines and other measures. However, there is little evidence on how severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection entropy can be applied in predicting the possible number of infections and deaths. In addition, more studies on how the COVID-19 infection density contributes to the rise in infections are needed. This study demonstrates how the SARS-COV-2 daily infection entropy can be applied in predicting the number of infections within a given period. In addition, the infection density within a given population attributes to an increase in the number of COVID-19 cases and, consequently, the new variants.

RESULTS

Using the COVID-19 initial data reported by Johns Hopkins University, World Health Organization (WHO) and Global Initiative on Sharing All Influenza Data (GISAID), the result shows that the original SAR-COV-2 strain has R0<1 with an initial infection growth rate entropy of 9.11 bits for the United States (U.S.). At close proximity, the average infection time for an infected individual to infect others within a susceptible population is approximately 7 minutes. Assuming no vaccines were available, in the U.S., the number of infections could range between 41,220,199 and 82,440,398 in late March 2022 with approximately, 1,211,036 deaths. However, with the available vaccines, nearly 48 Million COVID-19 cases and 706, 437 deaths have been prevented.

CONCLUSION

The proposed technique will contribute to the ongoing investigation of the COVID-19 pandemic and a blueprint to address the uncertainties surrounding the pandemic.

Complexity of COVID-19 Dynamics

With population explosion and globalization, the spread of infectious diseases has been a major concern. In 2019, a newly identified type of Coronavirus caused an outbreak of respiratory illness, popularly known as COVID-19, and became a pandemic. Although enormous efforts have been made to understand the spread of COVID-19, our knowledge of the COVID-19 dynamics still remains limited. The present study employs the concepts of chaos theory to examine the temporal dynamic complexity of COVID-19 around the world. The false nearest neighbor (FNN) method is applied to determine the dimensionality and, hence, the complexity of the COVID-19 dynamics. The methodology involves: (1) reconstruction of a single-variable COVID-19 time series in a multi-dimensional phase space to represent the underlying dynamics; and (2) identification of “false” neighbors in the reconstructed phase space and estimation of the dimension of the COVID-19 series. For implementation, COVID-19 data from 40 countries/regions around the world are studied. Two types of COVID-19 data are analyzed: (1) daily COVID-19 cases; and (2) daily COVID-19 deaths. The results for the 40 countries/regions indicate that: (1) the dynamics of COVID-19 cases exhibit low- to medium-level complexity, with dimensionality in the range 3 to 7; and (2) the dynamics of COVID-19 deaths exhibit complexity anywhere from low to high, with dimensionality ranging from 3 to 13. The results also suggest that the complexity of the dynamics of COVID-19 deaths is greater than or at least equal to that of the dynamics of COVID-19 cases for most (three-fourths) of the countries/regions. These results have important implications for modeling and predicting the spread of COVID-19 (and other infectious diseases), especially in the identification of the appropriate complexity of models.

Deep Learning Techniques in the Classification of ECG Signals Using R-Peak Detection Based on the PTB-XL Dataset

Deep Neural Networks (DNNs) are state-of-the-art machine learning algorithms, the application of which in electrocardiographic signals is gaining importance. So far, limited studies or optimizations using DNN can be found using ECG databases. To explore and achieve effective ECG recognition, this paper presents a convolutional neural network to perform the encoding of a single QRS complex with the addition of entropy-based features. This study aims to determine what combination of signal information provides the best result for classification purposes. The analyzed information included the raw ECG signal, entropy-based features computed from raw ECG signals, extracted QRS complexes, and entropy-based features computed from extracted QRS complexes. The tests were based on the classification of 2, 5, and 20 classes of heart diseases. The research was carried out on the data contained in a PTB-XL database. An innovative method of extracting QRS complexes based on the aggregation of results from established algorithms for multi-lead signals using the k-mean method, at the same time, was presented. The obtained results prove that adding entropy-based features and extracted QRS complexes to the raw signal is beneficial. Raw signals with entropy-based features but without extracted QRS complexes performed much worse.

Heart Rate in Patients with SARS-CoV-2 Infection: Prevalence of High Values at Discharge and Relationship with Disease Severity

The most common arrhythmia associated with COronaVIrus-related Disease (COVID) infection is sinus tachycardia. It is not known if high Heart Rate (HR) in COVID is simply a marker of higher systemic response to sepsis or if its persistence could be related to a long-term autonomic dysfunction. The aim of our work is to assess the prevalence of elevated HR at discharge in patients hospitalized for COVID-19 and to evaluate the variables associated with it. We enrolled 697 cases of SARS-CoV2 infection admitted in our hospital after February 21 and discharged within 23 July 2020. We collected data on clinical history, vital signs, laboratory tests and pharmacological treatment. Severe disease was defined as the need for Intensive Care Unit (ICU) admission and/or mechanical ventilation. Median age was 59 years (first-third quartile 49, 74), and male was the prevalent gender (60.1%). 84.6% of the subjects showed a SARS-CoV-2 related pneumonia, and 13.2% resulted in a severe disease. Mean HR at admission was 90 ± 18 bpm with a mean decrease of 10 bpm to discharge. Only 5.5% of subjects presented HR > 100 bpm at discharge. Significant predictors of discharge HR at multiple linear model were admission HR (mean increase = β = 0.17 per bpm, 95% CI 0.11; 0.22, p < 0.001), haemoglobin (β = −0.64 per g/dL, 95% CI −1.19; −0.09, p = 0.023) and severe disease (β = 8.42, 95% CI 5.39; 11.45, p < 0.001). High HR at discharge in COVID-19 patients is not such a frequent consequence, but when it occurs it seems strongly related to a severe course of the disease.

Autonomic dysfunction in SARS-COV-2 infection acute and long-term implications COVID-19 editor's page series

Abstract

The autonomic nervous system (ANS) is a complex network of nerves originating in the brain, brain stem, spinal cord, heart and extracardiac organs that regulates neural and physiological responses to internal and external environments and conditions. A common observation among patients with the 2019 Coronavirus (CoV) (SARS-severe acute respiratory syndrome CoV-2) (SARS-CoV-2) or COVID-19 [CO for corona, VI for virus, D for disease and 19 for when the outbreak was first identified (31 December 2019)] in the acute and chronic phases of the disease is tachycardia, labile blood pressure, muscular fatigue and shortness of breath. Because abnormalities in the ANS can contribute to each of these symptoms, herein a review of autonomic dysfunction in SARS-COV-2 infection is provided to guide diagnostic testing, patient care and research initiatives.

Graphic abstract

The autonomic nervous system is a complex network of nerves originating in the brain, brain stem, spinal cord, heart and extracardiac organs that regulates neural and physiological responses to internal and external environments and conditions. A common collection of signs and symptoms among patients with the 2019 Coronavirus (CoV) (SARS-severe acute respiratory syndrome CoV-2) (SARS-CoV-2) or COVID-19 [CO for corona, VI for virus, D for disease and 19 for when the outbreak was first identified (31 December 2019)] is tachycardia, labile blood pressure, muscular fatigue and shortness of breath. Abnormalities in the autonomic nervous system (ANS) can contribute to each of these identifiers, potentially offering a unifying pathobiology for acute, subacute and the long-term sequelae of SARS-CoV-2 infection (PASC) and a target for intervention.