Clinical correlates of frequency analyses of cardiovascular control after spinal cord injury

Spinal cord injury (SCI) has profound effects on cardiovascular autonomic function due to injury to descending autonomic pathways, and cardiovascular diseases are the leading causes of morbidity and mortality after SCI. Evaluation of cardiovascular autonomic dysfunction after SCI and appraisal of simple noninvasive autonomic assessments that are clinically meaningful would be useful to SCI clinicians and researchers. We aimed to assess supine and upright cardiovascular autonomic function from frequency analyses of heart rate and blood pressure variability (HRV and BPV) after SCI. We studied 26 subjects with chronic cervical or thoracic SCI and 17 able-bodied controls. We continuously recorded R-R interval (RRI, by ECG) and beat-to-beat blood pressure (by Finometer) in supine and seated positions. Cardiovascular control was assessed from spectral analysis of RRI and blood pressure time series. Cardiac baroreflex control was assessed from cross-spectral analyses of low-frequency spectra. Supine and upright low-frequency HRV and BPV were reduced in cervical SCI subjects, as were total BPV and HRV. Supine high-frequency HRV was reduced in thoracic SCI subjects. Cardiac baroreflex delay was increased in cervical SCI subjects. Supine frequency domain indexes were correlated with sympathetic skin responses, orthostatic cardiovascular responses, and plasma catecholamine levels. SCI results in reduced sympathetic drive to the heart and vasculature and increased baroreflex delay in cervical SCI subjects and reduced cardiac vagal tone in thoracic SCI subjects. Frequency analyses of autonomic function are related to clinical measures of autonomic control after SCI and provide useful noninvasive clinical tools with which to assess autonomic completeness of injury following SCI.     

Identifying physiological measurements for medical monitoring: implications for autonomous health care in austere environments

In a patient who has lost a significant amount of blood, avoiding cardiovascular collapse and impending circulatory shock depends on the ability to maintain adequate arterial blood pressure in the presence of significant central hypovolemia. Our analysis of hemodynamic, autonomic, and metabolic data obtained from healthy human subjects exposed to progressive reduction in central blood volume and supported by data from trauma patients provide evidence to support the following conclusions: 1. Because of autonomically-mediated compensatory mechanisms, standard vital signs can remain unchanged or change too late, when cardiovascular collapse is imminent. 2. Currently proposed closed-loop resuscitation and oxygen delivery systems controlled by arterial blood pressure and SpO2 may prove inadequate for early intervention decision-support. 3. Continuous capture of PP, ECG R-wave amplitude, indices of HRV, cardiac BRS, and/or muscle PO2 could improve the sensitivity of closed-loop resuscitation and oxygen delivery by providing earlier indications of clinical status.     

Heart rate variability as a marker of cardiovascular dysautonomia in post-COVID-19 syndrome using artificial intelligence

IntroductionCardiovascular dysautonomia comprising postural orthostatic tachycardia syndrome (POTS) and orthostatic hypotension (OH) is one of the presentations in COVID-19 recovered subjects. We aim to determine the prevalence of cardiovascular dysautonomia in post COVID-19 patients and to evaluate an Artificial Intelligence (AI) model to identify time domain heart rate variability (HRV) measures most suitable for short term ECG in these subjects.MethodsThis observational study enrolled 92 recently COVID-19 recovered subjects who underwent measurement of heart rate and blood pressure response to standing up from supine position and a 12-lead ECG recording for 60 s period during supine paced breathing. Using feature extraction, ECG features including those of HRV (RMSSD and SDNN) were obtained. An AI model was constructed with ShAP AI interpretability to determine time domain HRV features representing post COVID-19 recovered state. In addition, 120 healthy volunteers were enrolled as controls.ResultsCardiovascular dysautonomia was present in 15.21% (OH:13.04%; POTS:2.17%). Patients with OH had significantly lower HRV and higher inflammatory markers. HRV (RMSSD) was significantly lower in post COVID-19 patients compared to healthy controls (13.9 ± 11.8 ms vs 19.9 ± 19.5 ms; P = 0.01) with inverse correlation between HRV and inflammatory markers. Multiple perceptron was best performing AI model with HRV(RMSSD) being the top time domain HRV feature distinguishing between COVID-19 recovered patients and healthy controls.ConclusionPresent study showed that cardiovascular dysautonomia is common in COVID-19 recovered subjects with a significantly lower HRV compared to healthy controls. The AI model was able to distinguish between COVID-19 recovered patients and healthy controls.     

Noninvasively determined muscle oxygen saturation is an early indicator of central hypovolemia in humans.

Ten healthy human volunteers were subjected to progressive lower body negative pressure (LBNP) to the onset of cardiovascular collapse to compare the response of noninvasively determined skin and fat corrected deep muscle oxygen saturation (SmO2) and pH to standard hemodynamic parameters for early detection of imminent hemodynamic instability. Muscle SmO2 and pH were determined with a novel near infrared spectroscopic (NIRS) technique. Heart rate (HR) was measured continuously via ECG, and arterial blood pressure (BP) and stroke volume (SV) were obtained noninvasively via Finometer and impedance cardiography on a beat-to-beat basis. SmO2 and SV were significantly decreased during the first LBNP level (-15 mmHg), whereas HR and BP were late indicators of impending cardiovascular collapse. SmO2 declined in parallel with SV and inversely with total peripheral resistance, suggesting, in this model, that SmO2 is an early indicator of a reduction in oxygen delivery through vasoconstriction. Muscle pH decreased later, suggesting an imbalance between delivery and demand. Spectroscopic determination of SmO2 is noninvasive and continuous, providing an early indication of impending cardiovascular collapse resulting from progressive reduction in central blood volume.     

The functional state of microcirculation and autonomic regulation in young subjects with different levels of subjectively experienced psychological stress

A comprehensive comparative analysis of hemodynamics, microcirculation (the method of laser Doppler flowmetry with an occlusion test and the optical tissue oxymetry), blood circulation neurohumoral regulation (analysis of heart rate variability, HRV) in apparently healthy young subjects with different levels of subjectively experienced emotional stress has been performed. Depending on the degree of everyday stress (acute and/or chronic), the character of the autonomic regulation of blood circulation, as well as the state of the microcirculation and its regulation substantially vary. Moderate stress is accompanied by coactivation of sympathetic-parasympathetic regulatory mechanisms with augmented HRV baroreflex regulation circuits, which compensates for hemodynamic changes and is not accompanied by hypotensive reactions. An increase in the activity of neurogenic and myogenic tones of microhemodynamics in the subjects with moderate stress, which determines a high probability of blood shunting in tested tissue and decrease in relative oxygen extraction, has been discovered. Moderate levels of stress are also associated with an increase in the latency of postocclusive vasoreactive hyperemia, which is considered to be an early sign of the endothelium-mediated dysfunction of microcirculation.     

Detection of pathogens in water: from phylochips to qPCR to pyrosequencing

Waterborne pathogens pose a significant threat to human health and a proper assessment of microbial water quality is important for decision making regarding water infrastructure and treatment investments and eventually to provide early warning of disease, particularly given increasing global disasters associated with severe public health risks. Microbial water quality monitoring has undergone tremendous transition in recent years, with novel molecular tools beginning to offer rapid, high-throughput, sensitive and specific detection of a wide spectrum of microbial pathogens that challenge traditional culture-based techniques. High-density microarrays, quantitative real-time PCR (qPCR) and pyrosequencing which are considered to be breakthrough technologies borne out of the 'molecular revolution' are at present emerging rapidly as tools of pathogen detection and discovery. Future challenges lie in integrating these molecular tools with concentration techniques and bioinformatics platforms for unbiased guide of pathogen surveillance in water and developing standardized protocols.     

Production of reliability proven physiological data for use in automated monitoring and diagnostic systems

In recent years manufacturers of intensive care monitoring systems have introduced complex digital processing architectures that theoretically have enormous processing power. This power should allow the realization of many useful processing methodologies that up to now have only been research tools, e.g. the generation of reliable alarms, the implementation of predictive monitoring strategies and reliable diagnostic and treatment guidance to the clinical staff. However, before any of these methodologies can be successfully initiated, each must have accurate and relaible derived physiological data available to them, e.g. beat-by-beat heart rate and blood pressure. From the very nature of monitoring physiological quantities there will be much misinformation or ‘noise’ superimposed on the raw signal obtained from the patient. The major source of noise (as far as electocardiogram (ECG) monitoring is concerned) is internal to the body and is electromyographic noise. This results from the contraction of skeletal muscles producing action potentials of similar magnitude and frequency to that of the ECG. Fortunately, nursing staff are very good at ‘filtering out’ any misinformation before recording any data (on a ward chart for instance). However, in completely automated systems, if this noise is not detected and eliminated or compensated for at an early stage in the processing chain, misinformation will result with potentially serious consequences. The recognition and elimination of such noise cannot be readily achieved using standard filtering techniques without serious degradation of information. This paper discusses the potential of modern digital system architectures developed for ECG monitoring. It analyses the noise that occurs on this physiological variable and demonstrates a novel method of eliminating such noise.     

Proteomics-driven cancer biomarker discovery: looking to the future

Availability of a suite of biomarkers for early detection, stratification into distinct subtypes, and monitoring progression or response to therapy promises significant improvements in clinical outcomes for cancer patients. However, despite the recent progress in proteomics technologies based on mass spectrometry (MS), discovery of novel clinical assessment tools has been slow. This is, partly due to the inherent difficulties in working with blood as the biospecimen for candidate discovery. A better understanding of the limitations of blood for comparative protein profiling and a better appreciation of the advantages of cancer tissue or cancer cell secretomes have the potential to greatly enhance the progress.     

Hemodynamic monitoring in acute myocardial infarction.

The main cause of in-hospital death in patients with acute myocardial infarction is the "power failure syndrome". Hemodynamic monitoring provides precise and current data on the filling and output status of the left ventricle and, when indicated, the right ventricle. The information obtained is used to determine the hemodynamic status more precisely than is possible from conventional clinical assessment. It permits categorization of patients by hemodynamic status; the hemodynamic subset classification of Forrester, Diamond and Swan is a powerful tool in guiding therapy and establishing prognosis in individual patients. In addition to guiding the initiation of therapy, hemodynamic monitoring is useful in the continuing assessment of potent and complex treatment. This therapy is directed at resolving hemodynamic derangements without unfavourably altering the myocardial oxygen supply-demand relationship. Specific clinical indications for hemodynamic monitoring may include confusing or complicated clinical situations in which diagnostic problems exist, complicating mechanical derangements, severe congestive heart failure, cardiogenic shock and clinical research in acute myocardial infarction.     

An approach to remote monitoring of heart rate variability (HRV) using microwave radar during a calculation task

Recently, nonrestrictive and noninvasive sensing techniques to measure vital signs have been actively researched and developed. This study aimed to develop a prototype system to monitor cardiac activity using microwave radar without making contact with the body and without removing clothing--namely, a completely noncontact, remote monitoring system. In addition, heart rate and changes in heart rate variability (HRV) during simple mental arithmetic tasks were observed with the prototype system. The prototype system has a microwave Doppler radar antenna with 24 GHz frequency and approximately 7 mW output power. The experiments were conducted with seven subjects (23.00±0.82 years). We found that the prototype system captured heart rate and HRV precisely. The strong relationship between the heart rates during tasks (r=0.96), LF (cross-correlation=0.76), and LF/HF (cross-correlation=0.73) of HRV calculated from the prototype system and from electrocardiograph (ECG) measurements were confirmed. The proposed completely noncontact, remote method appears promising for future monitoring of cardiac activity as an indicator of changes in mental workload in workplaces.     

Comparative Evaluation of Heart Rate Variability Based on the Data of ECG and Blood Pressure Measurements

The development of new approaches to the assessment of heart rate variability (HRV) is an important problem, since HRV reflects the functioning of cardiovascular control and is affected by various diseases. The purpose of this study was to evaluate the informative value of statistical and spectral HRV parameters calculated from pulse interval (PI) data of blood pressure as compared with those calculated from RR-interval data of electrocardiograms (ECG). We recorded ECG in conscious rats using skin adhesive electrodes simultaneously with blood pressure signal obtained through a catheter in the femoral artery. It has been found that the PI sequence can be used to calculate the statistical HRV indices that describe the HRV at time intervals about 1 min or longer, but statistical indices of the PI and RR intervals may differ in the analysis of beat-tobeat variations. The power spectra of the RR intervals and PI coincide in the low-frequency region, including the band of baroreflex cardiac rhythm oscillation. However, they can differ in the high-frequency region (at respiration frequency and above).     

Influence of recognition errors of computerised analysis of 24-hour electrocardiograms on the measurement of spectral components of heart rate variability

Spectral methods for the assessment of heart rate variability (HRV) in 24-h electrocardiogram (ECG) are believed to require visual verification and manual editing of the computerised recognition of the ECG. This study investigated the effect of the recognition errors of computerised ECG recognition on two methods providing spectral HRV indices: (a) Fast Fourier Transformation (FFT); and (b) peak-to-trough analysis (PTA). Both methods were used to measure HRV spectra in 24-h ECGs recorded in 557 survivors of acute myocardial infarction. Each ECG was analysed using the Marquette 8000 Holter system and spectral HRV analyses were performed both prior to and after manual verification of the automatic ECG analysis. The FFT and PTA methods were used to calculate the low (0.04-0.15 Hz), medium (0.15-0.40 Hz) and high (0.40-1.00 Hz) HRV spectral components. For each method and for each spectral component, the rank correlations between the results obtained from unedited and edited ECG recognition were calculated. The correlations between the corresponding spectral components provided by the FFT and PTA methods applied to the edited recognitions were also calculated. Both methods were substantially affected by recognition errors. The FFT method was more sensitive to the misrecognition than the PTA method. The inter-method correlations were higher for the high and medium spectral components than for the low spectral component. The study suggests that spectral HRV analysis should be performed only on carefully verified and manually corrected recognitions of long-term electrocardiograms.     

A modified Zeeman model for producing HRV signals and its application to ECG signal generation

Developing a mathematical model for the artificial generation of electrocardiogram (ECG) signals is a subject that has been widely investigated. One of the challenges is to generate ECG signals with a wide range of waveforms, power spectra and variations in heart rate variability (HRV)--all of which are important indexes of human heart functions. In this paper we present a comprehensive model for generating such artificial ECG signals. We incorporate into our model the effects of respiratory sinus arrhythmia, Mayer waves and the important very low-frequency component in the power spectrum of HRV. We use a new modified Zeeman model for generating the time series for HRV, and a single cycle of ECG is produced by using a simple neural network. The importance of the work is the model's ability to produce artificial ECG signals that resemble experimental recordings under various physiological conditions. As such the model provides a useful tool to simulate and analyse the main characteristics of ECG, such as its power spectrum and HRV under different conditions. Potential applications of this model include using the generated ECG as a flexible signal source to assess the effectiveness of a diagnostic ECG signal-processing device.     

The lightning heart: a case report and brief review of the cardiovascular complications of lightning injury

Lightning strike is a rare natural phenomenon, which carries a risk of dramatic medical complications to multiple organ systems and a high risk of fatality. The known complications include but are not limited to: myocardial infarction, arrhythmia, cardiac contusion, stroke, cutaneous burns, respiratory disorders, neurological disorders, acute kidney injury and death. We report a case of a healthy young man who suffered a lightning injury and discuss the cardiovascular complications of lightning injury, ranging from ECG changes to death. The patient in our case, a 27-year old previously healthy male, developed a syndrome of rhabdomyolysis and symptomatic cardiogenic pulmonary edema. Electrocardiographic findings included transient T-wave inversions, late transition shift and long QT. His clinical condition improved with supportive measures.Early recognition of lightning injury syndromes and anticipation of complications may help us improve outcomes for these patients. Evaluation of patients having experienced a lightning injury should include a minimum of a detailed history and physical examination, 12-lead ECG and drawing of baseline troponins. Prolonged electrocardiographical monitoring (for monitoring of ventricular arrhythmias) and assessment for signs and symptoms of hemodynamic compromise may be warranted.     

Dysregulation of Autonomic Control of Cardiac Function in Workers at am Broadcasting Stations (0.738–1.503 MHz)

Ninety-three adult males working at AM broadcasting stations (0.738–1.503 MHz) or radio line stations volunteered for cardiological examinations. The examinations included routine electrocardiogram (ECG) at rest, analysis of heart rate variability (HRV), Holter 24-h ECG, and 24-h ambulatory blood pressure (ABP). Results of cardiological examinations were correlated with individual exposure to EM fields (maximum exposure levels during working shift, daily exposure dose, and cumulative lifetime exposure). Of the 93 subjects qualified for the study, 71 (76.3%) experienced occupational RF exposure, while the remaining 22 (23.7%) had no history of regular EM exposure. ECG abnormalities or pathological changes were recorded quite frequently (50–70%) in both exposed and control populations. There was no correlation with exposure levels. We found measurable effects in the HRV and ABP parameters in the EM-exposed population, but none could be assigned clinical significance. The results suggest that exposure of workers to EM fields can cause slight disturbances in autonomic cardiac regulation and slight dysregulation of circadian rhythms in workers exposed to EM fields exceeding 100–150 V/m.     

Evaluation of receiver operator characteristics — optimum time of day for the assessment of heart rate variability after acute myocardial infarction

The study reports a mathematical method for deciding which clinical data are of importance for treatment decisions in a given clinical setting. The method comprises the following steps: (A) the receiver operator characteristic (ROC) functions of the compared sets of data are computed; (B) the design and aim of the clinical study is expressed as an integral measure on the space of sensitivity values (this reflects the preference of low or high sensitivity values dependent on the clinical targets); (C) the sets of data being compared are characterised by the non-linear integrals of their ROC functions. The approach has been used to compare mean heart rate (HR) and heart rate variability (HRV) data calculated in 5113 different portions of 24-h ECG recordings and assessed in 365 patients surviving acute myocardial infarction, in order to evaluate the utility of Holter recording of varying lengths and starting times for the prediction of sudden cardiac death and/or serious arrhythmic events. The results of the study show that this approach is capable of evaluating and comparing the sets of medical data used for identification of patients who are at increased risk. The experimental part of the study showed that the optimum recording interval for the assessment of HR and HRV data in patients who survived acute myocardial infarction depends on the aim of the identification of increased risk patients. The optimum interval of recording is different for an identification which requires a low number of false negative cases and permits a higher number of false positive cases, than for the situation where a low number of false positive cases are required and a higher number of false negative cases are permissible.     

On estimating intraventricular hemodynamic forces from endocardial dynamics: A comparative study with 4D flow MRI

Intraventricular pressure gradients or hemodynamic forces, which are their global measure integrated over the left ventricular volume, have a fundamental importance in ventricular function. They may help revealing a sub-optimal cardiac function that is not evident in terms of tissue motion, which is naturally heterogeneous and variable, and can influence cardiac adaptation. However, hemodynamic forces are not utilized in clinical cardiology due to the unavailability of simple non-invasive measurement tools.Hemodynamic forces depend on the intraventricular flow; nevertheless, most of them are imputable to the dynamics of the endocardial flow boundary and to the exchange of momentum across the mitral and aortic orifices. In this study, we introduce a simplified model based on first principles of fluid dynamics that allows estimating hemodynamic forces without knowing the velocity field inside the LV.The model is validated with 3D phase-contrast MRI (known as 4D flow MRI) in 15 subjects, (5 healthy and 10 patients) using the endocardial surface reconstructed from the three standard long-axis projections. Results demonstrate that the model provides consistent estimates for the base-apex component (mean correlation coefficient r = 0.77 for instantaneous values and r = 0.88 for root mean square) and good estimates of the inferolateral-anteroseptal component (r = 0.50 and 0.84, respectively).The present method represents a potential integration to the existing ones quantifying endocardial deformation in MRI and echocardiography to add a physics-based estimation of the corresponding hemodynamic forces. These could help the clinician to early detect sub-clinical diseases and differentiate between different cardiac dysfunctional states.     

An Efficient Automated Algorithm for Distinguishing Normal and Abnormal ECG Signal

ObjectiveThe present study aims to simulate an alarm system for online detecting normal electrocardiogram (ECG) signals from abnormal ECG so that an individual's heart condition can be accurately and quickly monitored at any moment, and any possible serious dangers can be prevented.Materials and methodsFirst, the data from Physionet database were used to analyze the ECG signal. The data were collected equally from both males and females, and the data length varied between several seconds to several minutes. The heart rate variability (HRV) signal, which reflects heart fluctuations in different time intervals, was used due to the low spatial accuracy of ECG signal and its time constraint, as well as the similarity of this signal with the normal signal in some diseases. In this study, the proposed algorithm provided a return map as well as extracted nonlinear features of the HRV signal, in addition to the application of the statistical characteristics of the signal. Then, artificial neural networks were used in the field of ECG signal processing such as multilayer perceptron (MLP) and support vector machine (SVM), as well as optimal features, to categorize normal signals from abnormal ones.ResultsIn this paper, the area under the curve (AUC) of the ROC was used to determine the performance level of introduced classifiers. The results of simulation in MATLAB medium showed that AUC for MLP and SVM neural networks was 89.3% and 94.7%, respectively. Also, the results of the proposed method indicated that the more nonlinear features extracted from the ECG signal could classify normal signals from the patient.ConclusionThe ECG signal representing the electrical activity of the heart at different time intervals involves some important information. The signal is considered as one of the common tools used by physicians to diagnose various cardiovascular diseases, but unfortunately the proper diagnosis of disease in many cases is accompanied by an error due to limited time accuracy and hiding some important information related to this signal from the physicians' vision leading to the risks of irreparable harm for patients. Based on the results, designing the proposed alarm system can help physicians with higher speed and accuracy in the field of diagnosing normal people from patients and can be used as a complementary system in hospitals.     

Continuous cardiac output monitoring in humans by invasive and noninvasive peripheral blood pressure waveform analysis.

We present an evaluation of a novel technique for continuous (i.e., automatic) monitoring of relative cardiac output (CO) changes by long time interval analysis of a peripheral arterial blood pressure (ABP) waveform in humans. We specifically tested the mathematical analysis technique based on existing invasive and noninvasive hemodynamic data sets. With the former data set, we compared the application of the technique to peripheral ABP waveforms obtained via radial artery catheterization with simultaneous thermodilution CO measurements in 15 intensive care unit patients in which CO was changing because of disease progression and therapy. With the latter data set, we compared the application of the technique to noninvasive peripheral ABP waveforms obtained via a finger-cuff photoplethysmography system with simultaneous Doppler ultrasound CO measurements made by an expert in 10 healthy subjects during pharmacological and postural interventions. We report an overall CO root-mean-squared normalized error of 15.3% with respect to the invasive hemodynamic data set and 15.1% with respect to the noninvasive hemodynamic data set. Moreover, the CO errors from the invasive and noninvasive hemodynamic data sets were only mildly correlated with mean ABP (rho = 0.41, 0.37) and even less correlated with CO (rho = -0.14, -0.17), heart rate (rho = 0.04, 0.19), total peripheral resistance (rho = 0.38, 0.10), CO changes (rho = -0.26, -0.20), and absolute CO changes (rho = 0.03, 0.38). With further development and successful prospective testing, the technique may potentially be employed for continuous hemodynamic monitoring in the acute setting such as critical care and emergency care.