ECG monitoring leads and special leads

Dose-dependent response of prefrontal transcranial direct current stimulation on the heart rate variability: An electric field modeling study

Transcranial direct current stimulation (tDCS) of the prefrontal cortex (PFC) modulates the autonomic nervous system by activating deeper brain areas via top-down pathway. However, effects on the nervous system are heterogeneous and may depend on the amount of current that penetrates. Therefore, we aimed to investigate the variable effects of tDCS on heart rate variability (HRV), a measure of the functional state of the autonomic nervous system. Using three prefrontal tDCS protocols (1.5, 3 mA and sham), we associated the simulated individual electric field (E-field) magnitude in brain regions of interest with the HRV effects. This was a randomized, double-blinded, sham-controlled and within-subject trial, in which healthy young-adult participants received tDCS sessions separated by 2 weeks. The brain regions of interest were the dorsolateral PFC (DLPFC), anterior cingulate cortex, insula and amygdala. Overall, 37 participants were investigated, corresponding to a total of 111 tDCS sessions. The findings suggested that HRV, measured by root mean squared of successive differences (RMSSD) and high-frequency HRV (HF-HRV), were significantly increased by the 3.0 mA tDCS when compared to sham and 1.5 mA. No difference was found between sham and 1.5 mA. E-field analysis showed that all brain regions of interest were associated with the HRV outcomes. However, this significance was associated with the protocol intensity, rather than inter-individual brain structural variability. To conclude, our results suggest a dose-dependent effect of tDCS for HRV. Therefore, further research is warranted to investigate the optimal current dose to modulate HRV.

Wireless continuous single-lead ST-segment monitoring to detect new-onset myocardial injury at the general ward-An exploratory subanalysis

Patients admitted for acute medical conditions and major noncardiac surgery are at risk of myocardial injury. This is frequently asymptomatic, especially in the context of concomitant pain and analgesics, and detection thus relies on cardiac biomarkers. Continuous single-lead ST-segment monitoring from wireless electrocardiogram (ECG) may enable more timely intervention, but criteria for alerts need to be defined to reduce false alerts. This study aimed to determine optimal ST-deviation thresholds from wireless single-lead ECG for detection of myocardial injury following major abdominal cancer surgery and during acute exacerbation of chronic obstructive pulmonary disease. Patients were monitored with a wireless single-lead ECG patch for up to 4 days and had daily troponin measurements. Single-lead ST-segment deviations of <0.255 mV and/or >0.245 mV (based on previous study comparison with 0.1 mV 12-lead ECG and variation in single-lead ECG) were analyzed for relation to myocardial injury defined as hsTnT elevation of 20-64 ng/L with an absolute change of ≥5 ng/L, or a hsTnT level ≥ 65 ng/L. In total, 528 patients were included for analysis, of which 15.5% had myocardial injury. For corrected ST-thresholds lasting ≥10 and ≥ 20 min, we found specificities of 91% and 94% and sensitivities of 17% and 13% with odds ratios of 2.0 (95% CI: 1.1; 3.9) and 2.4 (95% CI: 1.1; 5.1) for myocardial injury. In conclusion, wireless single-lead ECG monitoring with corrected ST thresholds detected patients developing myocardial injury with specificities >90% and sensitivities <20%, suggesting increased focus on sensitivity improvement.

Arrhythmia detection by the graph convolution network and a proposed structure for communication between cardiac leads

One of the most common causes of death worldwide is heart disease, including arrhythmia. Today, sciences such as artificial intelligence and medical statistics are looking for methods and models for correct and automatic diagnosis of cardiac arrhythmia. In pursuit of increasing the accuracy of automated methods, many studies have been conducted. However, in none of the previous articles, the relationship and structure between the heart leads have not been included in the model. It seems that the structure of ECG data can help develop the accuracy of arrhythmia detection. Therefore, in this study, a new structure of Electrocardiogram (ECG) data was introduced, and the Graph Convolution Network (GCN), which has the possibility of learning the structure, was used to develop the accuracy of cardiac arrhythmia diagnosis. Considering the relationship between the heart leads and clusters based on different ECG poles, a new structure was introduced. In this structure, the Mutual Information(MI) index was used to evaluate the relationship between the leads, and weight was given based on the poles of the leads. Weighted Mutual Information (WMI) matrices (new structure) were formed by R software. Finally, the 15-layer GCN network was adjusted by this structure and the arrhythmia of people was detected and classified by it. To evaluate the performance of the proposed new network, sensitivity, precision, specificity, accuracy, and confusion matrix indices were used. Also, the accuracy of GCN networks was compared by three different structures, including WMI, MI, and Identity. Chapman's 12-lead ECG Dataset was used in this study. The results showed that the values of sensitivity, precision, specificity, and accuracy of the GCN-WMI network with 15 intermediate layers were equal to 98.74%, 99.08%, 99.97% & 99.82%, respectively. This new proposed network was more accurate than the Graph Convolution Network-Mutual Information (GCN-MI) with an accuracy equal to 99.71% and GCN-Id with an accuracy equal to 92.68%. Therefore, utilizing this network, the types of arrhythmia were recognized and classified. Also, the new network proposed by the Graph Convolution Network-Weighted Mutual Information (GCN-WMI) was more accurate than those conducted in other studies on the same data set (Chapman). Based on the obtained results, the structure proposed in this study increased the accuracy of cardiac arrhythmia diagnosis and classification on the Chapman data set. Achieving such accuracy for arrhythmia diagnosis is a great achievement in clinical sciences.

Real-time arrhythmia detection using convolutional neural networks

Cardiovascular diseases, such as heart attack and congestive heart failure, are the leading cause of death both in the United States and worldwide. The current medical practice for diagnosing cardiovascular diseases is not suitable for long-term, out-of-hospital use. A key to long-term monitoring is the ability to detect abnormal cardiac rhythms, i.e., arrhythmia, in real-time. Most existing studies only focus on the accuracy of arrhythmia classification, instead of runtime performance of the workflow. In this paper, we present our work on supporting real-time arrhythmic detection using convolutional neural networks, which take images of electrocardiogram (ECG) segments as input, and classify the arrhythmia conditions. To support real-time processing, we have carried out extensive experiments and evaluated the computational cost of each step of the classification workflow. Our results show that it is feasible to achieve real-time arrhythmic detection using convolutional neural networks. To further demonstrate the generalizability of this approach, we used the trained model with processed data collected by a customized wearable sensor from a lab setting, and the results shown that our approach is highly accurate and efficient. This research provides the potentials to enable in-home real-time heart monitoring based on 2D image data, which opens up opportunities for integrating both machine learning and traditional diagnostic approaches.

Heartbeat classification based on single lead-II ECG using deep learning

The analysis and processing of electrocardiogram (ECG) signals is a vital step in the diagnosis of cardiovascular disease. ECG offers a non-invasive and risk-free method for monitoring the electrical activity of the heart that can assist in predicting and diagnosing heart diseases. The manual interpretation of the ECG signals, however, can be challenging and time-consuming even for experts. Machine learning techniques are increasingly being utilized to support the research and development of automatic ECG classification, which has emerged as a prominent area of study. In this paper, we propose a deep neural network model with residual blocks (DNN-RB) to classify cardiac cycles into six ECG beat classes. The MIT-BIH dataset was used to validate the model resulting in a test accuracy of 99.51%, average sensitivity of 99.7%, and average specificity of 98.2%. The DNN-RB method has achieved higher accuracy than other state-of-the-art algorithms tested on the same dataset. The proposed method is effective in the automatic classification of ECG signals and can be used for both clinical and out-of-hospital monitoring and classification combined with a single-lead mobile ECG device. The method has also been integrated into a web application designed to accept digital ECG beats as input for analyses and to display diagnostic results.

Importance of Electrode Selection and Number in Reconstructing Standard Twelve Lead Electrocardiograms

Many clinical and consumer electrocardiogram (ECG) devices collect fewer electrodes than the standard twelve-lead ECG and either report less information or employ algorithms to reconstruct a full twelve-lead signal. We assessed the optimal electrode selection and number that minimizes redundant information collection while maximizing reconstruction accuracy. We employed a validated deep learning model to reconstruct ECG signals from 250 different patients in the PTB database. Different numbers and combinations of electrodes were removed from the ECG before reconstruction to measure the effect of electrode inclusion on reconstruction accuracy. The Left Leg (LL) electrode registered the largest drop in average reconstruction accuracy, from an R2 of 0.836 when the LL was included to 0.737 when excluded. Additionally, we conducted a correlation analysis to identify leads that behave similarly. We demonstrate that there exists a high correlation between leads I, II, aVL, aVF, V4, V5, and V6, which all occupy the bottom right quadrant in an ECG axis interpretation, and likely contain redundant information. Based on our analysis, we recommend the prioritization of electrodes RA, LA, LL, and V3 in any future lead collection devices, as they appear most important for full ECG reconstruction.

Effects of non-invasive vagus nerve stimulation on cognitive and autonomic correlates of perseverative cognition

Perseverative cognitions can provoke psychophysiological stress in the absence of an actual stressor and are considered important transdiagnostic vulnerability factors for several (mental) health issues. These stress-related cognitive processes are reflected by both cognitive (assessed by self-reports) and autonomic inflexibility (assessed by heart rate variability; HRV), with a key role attributed to the vagus nerve. Interestingly, modulation of the afferent branches of the vagus can be achieved with transcutaneous auricular vagus nerve stimulation (taVNS), a non-invasive technique that employs a low-intensity electrical current applied to the ear. In a sample of healthy individuals, we investigated the effects of taVNS of the left concha, compared to sham (earlobe) stimulation, on the cognitive and autonomic correlates of perseverative cognition following a psychosocial stress task. Interestingly, taVNS significantly reduced cognitive rigidity, reflected by reduced subjective perseverative thinking after psychosocial stress. Although there were no direct effects on autonomic correlates of perseverative cognition, individual differences in perseverative thinking after the stressor significantly affected the effects of taVNS on HRV. Specifically, more autonomic inflexibility during the stress task (i.e., reduced HRV) was associated with increases in perseverative thinking afterward for the sham condition, but not the active taVNS condition. Additional exploratory analyses revealed no significant moderation of stimulation intensity. Overall, the study findings endorse the association between perseverative cognitions and vagus nerve functioning.

Non-Contact Monitoring of ECG in the Home Environment-Selecting Optimal Electrode Configuration

Capacitive electrocardiography (cECG) is most often used in wearable or embedded measurement systems. The latter is considered in the paper. An optimal electrocardiographic lead, as an individual feature, was determined based on model studies. It was defined as the possibly highest value of the R-wave amplitude measured on the back of the examined person. The lead configuration was also analyzed in terms of minimizing its susceptibility to creating motion artifacts. It was found that the direction of the optimal lead coincides with the electrical axis of the heart. Moreover, the electrodes should be placed in the areas preserving the greatest voltage and at the same time characterized by the lowest gradient of the potential. Experimental studies were conducted using the developed measurement system on a group of 14 people. The ratio of the R-wave amplitude (as measured on the back and chest, using optimal leads) was less than 1 while the SNR reached at least 20 dB. These parameters allowed for high-quality QRS complex detection with a PPV of 97%. For the "worst" configurations of the leads, the signals measured were practically uninterpretable.

ECG Patient Simulator Based on Mathematical Models

In this work, we propose a versatile, low-cost, and tunable electronic device to generate realistic electrocardiogram (ECG) waveforms, capable of simulating ECG of patients within a wide range of possibilities. A visual analysis of the clinical ECG register provides the cardiologist with vital physiological information to determine the patient's heart condition. Because of its clinical significance, there is a strong interest in algorithms and medical ECG measuring devices that acquire, preserve, and process ECG recordings with high fidelity. Bearing this in mind, the proposed electronic device is based on four different mathematical models describing macroscopic heartbeat dynamics with ordinary differential equations. Firstly, we produce full 12-lead ECG profiles by implementing a model comprising a network of heterogeneous oscillators. Then, we implement a discretized reaction-diffusion model in our electronic device to reproduce ECG waveforms from various rhythm disorders. Finally, in order to show the versatility and capabilities of our system, we include two additional models, a ring of three coupled oscillators and a model based on a quasiperiodic motion, which can reproduce a wide range of pathological conditions. With this, the proposed device can reproduce around thirty-two cardiac rhythms with the possibility of exploring different parameter values to simulate new arrhythmias with the same hardware. Our system, which is a hybrid analog-digital circuit, generates realistic ECG signals through digital-to-analog converters whose amplitudes and waveforms are controlled through an interactive and friendly graphic interface. Our ECG patient simulator arises as a promising platform for assessing the performance of electrocardiograph equipment and ECG signal processing software in clinical trials. Additionally the produced 12-lead profiles can be tested in patient monitoring systems.

Multiple electrocardiogram generator with single-lead electrocardiogram

BACKGROUND AND OBJECTIVE

Electrocardiogram (ECG) is measured in various ways. The three main ECG measurement methods include resting ECG, Holter monitoring, and treadmill method. In standard ECG measurement methods, multiple electrodes are attached to the limb and chest. Limb and chest leads measure the frontal and sagittal planes of the heart, respectively. In this case, ECG signals are measured briefly up to 10 seconds. To measure ECG signals based on a single lead, wearable devices have been developed that could measure long-term ECG signals daily. ECG signals are vectors in the heart, which is a three-dimensional structure. Therefore, a single-lead measurement lacks detailed information. The objective of this study was to synthesize multiple ECGs from a single-lead ECG using a generative adversarial network (GAN).

METHODS

We trained our model with two independent datasets and one combined dataset. For experiment 1, the PTB-XL dataset was used as the training set, and the China dataset was used as the test set. For experiment 2, the China dataset was used as the training set, and the PTB-XL was used as the test set. Optimized GAN models were obtained for each experiment and evaluated.

RESULTS

The Fréchet distance (FD) score and mean squared error (MSE) were used for evaluation. The FD and MSE scores for experiments 1 and 2 were 7.237 and 0.024, and 8.055 and 0.011, respectively.

CONCLUSION

We proposed a method to overcome the limitations of modern ECG measurement methods. Low FD and MSE scores not only indicate the possibility but also the similarity between synthesized ECG and reference ECG when compared in ECG paper format. This indicates that the proposed method can be applied to wearable devices that measure single-lead ECG.

Effects of Caffeine Intake on Cardiopulmonary Variables and QT Interval after a Moderate-Intensity Aerobic Exercise in Healthy Adults: A Randomized Controlled Trial

Caffeine is considered a widely consumed natural and legal psychoactive stimulant with several effects on the body. The present study attempted to investigate the effects of caffeine consumed before and after a physical exercise on cardiovascular and cardiorespiratory functions in healthy adults. 36 healthy adult males were recruited and randomly allocated to one of the three (3) groups: group I (exercise without caffeine consumption), group II (caffeine beverage intake before exercise), and group III (caffeine beverage intake immediately after exercise). The heart rate (HR), QTc interval, blood pressure (BP), respiratory rate (RR), oxygen consumption (VO₂), and carbon dioxide emission (VCO₂) were measured at 0, 5, 10, and 15 min after the exercise. We observed a significant difference in all measured outcomes during the different recovery times in all the groups (p < 0.05). HR, RR, SBP, VO2, and VCO2 gradually decreased with time, DBP contrarily increased with time, and the QTc showed an irregular pattern. We can affirm that ingestion of caffeine before and after moderate aerobic exercise slows down the parasympathetic stimulation, heart rate recovery, and the recovery of HR and QTc with no major effects on BP, RR, VO₂, and VCO₂ in healthy adult men.

Lead Reconstruction Using Artificial Neural Networks for Ambulatory ECG Acquisition

One of the most powerful techniques to diagnose cardiovascular diseases is to analyze the electrocardiogram (ECG). To increase diagnostic sensitivity, the ECG might need to be acquired using an ambulatory system, as symptoms may occur during a patient’s daily life. In this paper, we propose using an ambulatory ECG (aECG) recording device with a low number of leads and then estimating the views that would have been obtained with a standard ECG location, reconstructing the complete Standard 12-Lead System, the most widely used system for diagnosis by cardiologists. Four approaches have been explored, including Linear Regression with ECG segmentation and Artificial Neural Networks (ANN). The best reconstruction algorithm is based on ANN, which reconstructs the actual ECG signal with high precision, as the results bring a high accuracy (RMS Error < 13 μV and CC > 99.7%) for the set of patients analyzed in this paper. This study supports the hypothesis that it is possible to reconstruct the Standard 12-Lead System using an aECG recording device with less leads.

Non-invasive vagal nerve stimulation enhances cognitive emotion regulation

Transcutaneous auricular vagus nerve stimulation (taVNS) has been proposed as a potential new tool in the treatment of major depressive disorder. Prior studies have demonstrated that taVNS enhances cognitive control and is able to modulate brain activity in key regions involved in cognitive emotion regulation, such as the anterior cingulate and medial prefrontal cortex, which is known to be impaired in depressed patients. Preclinical studies are lacking but may provide important insights into the working mechanisms of taVNS on cognitive emotion regulatory processes. In this between-subject study, 83 healthy subjects underwent a single-session of active taVNS or sham stimulation, after which cognitive reappraisal was examined using a computer-based cognitive emotion regulation task. Our results indicate that participants receiving active taVNS, compared to sham, were better at using cognitive reappraisal and rated their response to emotion-eliciting pictures as less intense. Yet, even though we found significant differences in behavioral measures of cognitive emotion regulation, no differences between groups were found in terms of physiological responses to the emotional stimuli. Overall, these findings suggest a positive effect of taVNS on the cognitive reappraisal of emotions, but future studies assessing objective measures of neural activity during cognitive emotion regulation following taVNS are warranted.

Non-invasive Neurophysiology in Learning and Training: Mechanisms and a SWOT Analysis

Although many scholars deem non-invasive measures of neurophysiology to have promise in assessing learning, these measures are currently not widely applied, neither in educational settings nor in training. How can non-invasive neurophysiology provide insight into learning and how should research on this topic move forward to ensure valid applications? The current article addresses these questions by discussing the mechanisms underlying neurophysiological changes during learning followed by a SWOT (strengths, weaknesses, opportunities, and threats) analysis of non-invasive neurophysiology in learning and training. This type of analysis can provide a structured examination of factors relevant to the current state and future of a field. The findings of the SWOT analysis indicate that the field of neurophysiology in learning and training is developing rapidly. By leveraging the opportunities of neurophysiology in learning and training (while bearing in mind weaknesses, threats, and strengths) the field can move forward in promising directions. Suggestions for opportunities for future work are provided to ensure valid and effective application of non-invasive neurophysiology in a wide range of learning and training settings.

Perianesthesia Implications and Considerations for Drug-Induced QT Interval Prolongation

Prolongation of the QT interval can predispose patients to fatal arrhythmias such as torsade de pointes. While arrhythmias can occur spontaneously in patients with a genetic predisposition, drugs such as ondansetron and droperidol, which are frequently used in the perioperative period, have been implicated in the prolongation of the QT interval. As the list of medications that cause QT prolongation grows, anesthesia providers and perioperative nurses must be informed regarding the importance of the QT interval. This article reviews the physiology and measurement of the QT interval, the risk factors of QT prolongation, the mechanism of drug-induced QT prolongation, and perioperative considerations for patient care.

A high-performance 8 nV/√Hz 8-channel wearable and wireless system for real-time monitoring of bioelectrical signals

BACKGROUND

It is widely accepted by the scientific community that bioelectrical signals, which can be used for the identification of neurophysiological biomarkers indicative of a diseased or pathological state, could direct patient treatment towards more effective therapeutic strategies. However, the design and realisation of an instrument that can precisely record weak bioelectrical signals in the presence of strong interference stemming from a noisy clinical environment is one of the most difficult challenges associated with the strategy of monitoring bioelectrical signals for diagnostic purposes. Moreover, since patients often have to cope with the problem of limited mobility being connected to bulky and mains-powered instruments, there is a growing demand for small-sized, high-performance and ambulatory biopotential acquisition systems in the Intensive Care Unit (ICU) and in High-dependency wards. Finally, to the best of our knowledge, there are no commercial, small, battery-powered, wearable and wireless recording-only instruments that claim the capability of recording electrocorticographic (ECoG) signals.

METHODS

To address this problem, we designed and developed a low-noise (8 nV/√Hz), eight-channel, battery-powered, wearable and wireless instrument (55 × 80 mm2). The performance of the realised instrument was assessed by conducting both ex vivo and in vivo experiments.

RESULTS

To provide ex vivo proof-of-function, a wide variety of high-quality bioelectrical signal recordings are reported, including electroencephalographic (EEG), electromyographic (EMG), electrocardiographic (ECG), acceleration signals, and muscle fasciculations. Low-noise in vivo recordings of weak local field potentials (LFPs), which were wirelessly acquired in real time using segmented deep brain stimulation (DBS) electrodes implanted in the thalamus of a non-human primate, are also presented.

CONCLUSIONS

The combination of desirable features and capabilities of this instrument, namely its small size (~one business card), its enhanced recording capabilities, its increased processing capabilities, its manufacturability (since it was designed using discrete off-the-shelf components), the wide bandwidth it offers (0.5-500 Hz) and the plurality of bioelectrical signals it can precisely record, render it a versatile and reliable tool to be utilized in a wide range of applications and environments.

Current concepts of perioperative monitoring in high-risk surgical patients: a review

A substantial number of patients are at high-risk of intra- or post-operative complications or both. Most perioperative deaths are represented by patients who present insufficient physiological reserve to meet the demands of major surgery. Recognition and management of critical high-risk surgical patients require dedicated and effective teams, capable of preventing, recognize, start treatment with adequate support in time to refer patients to the satisfactory ICU level provision. The main task for health-care planners and managers is to identify and reduce this severe risk and to encourage patient’s safety practices. Inadequate tissue perfusion and decreased cellular oxygenation due to hypovolemia, heart dysfunction, reduced cardiovascular reserve, and concomitant diseases are the most common causes of perioperative complications. Hemodynamic, respiratory and careful sequential monitoring have become essential aspects of the clinical practice both for surgeons and intensivists. New monitoring techniques have changed significantly over the past few years and are now able to rapidly identify shock states earlier, define the etiology, and monitor the response to different therapies. Many of these techniques are now minimally invasive or non-invasive. Advanced hemodynamic and respiratory monitoring combines invasive, non-invasive monitoring skills. Non-invasive ultrasound has emerged during the last years as an essential operative and perioperative evaluation tool, and its use is now rapidly growing. Perioperative management guided by appropriate sequential clinical evaluation combined with respiratory and hemodynamic monitoring is an established tool to help clinicians to identify those patients at higher risk in the attempt to reduce the complications rate and potentially improve patient outcomes. This review aims to provide an update of currently available standard concepts and evolving technologies of the various respiratory and hemodynamic monitoring systems for the high-risk surgical patients, highlighting their potential usefulness when integrated with careful clinical evaluation.

Validation of wearable textile electrodes for ECG monitoring

A highly conductive textile was woven from nano-fibers coated with the PEDOT-PSS polymer. The aim of this study was to assess the usefulness of textile electrodes for ECG recording as a smart garment. Electrode textile pads and lead wires were sewn to the lining of sportswear and their tolerability to repeated washings were tested up to 150 times. The electrical conductivity of the textile electrode remained functional for up to 50 machine washes. To assess the level of motion artifacts or noise during the daily monitoring of ECG, a single lead ECG with conventional or textile electrodes was recorded during supine rest, seated rest, upright trunk rotation (i.e., twisting), and stepping movement in 66 healthy adults. A Holter system was used for data storage and analysis. ECG patterns of P, QRS, and T waves were comparable between the conventional and textile electrodes. However, the signal-to-artifact-and/or-noise ratio (SAR) during twisting was larger in the textile electrodes than in the conventional electrodes. No skin irritation was seen in the textile electrodes. The single lead textile electrodes embedded in an inner garment were usable for continuous and/or repeated ECG monitoring in daily life except during vigorous trunk movement.