Simultaneous Recording Of Electrocardiogram Research Articles

Tracking Vigilance Fluctuations in Real-Time: A Sliding-Window HRV-based Machine-Learning Approach.

Heart rate variability (HRV)-based machine learning models hold promise for real-world vigilance evaluation, yet their real-time applicability is limited by lengthy feature extraction times and reliance on subjective benchmarks. This study aimed to improve the objectivity and efficiency of HRV-based vigilance evaluation by associating HRV and behavior metrics through a sliding-window approach. Forty-four healthy adults underwent psychomotor vigilance tasks under both well-rested and sleep-deprived conditions, with simultaneous electrocardiogram recording. A sliding-window approach (30s length, 10s step) was used for HRV feature extraction and behavior assessment. Repeated-measures ANOVA was used to examine how HRV related to objective vigilance levels. Stability selection technique was applied for feature selection, and the vigilance ground truth-high (fastest 40%), intermediate (middle 20%), and low (slowest 40%)-were determined based on each participant's range of performance. Four machine-learning classifiers-k-nearest neighbours, support vector machine (SVM), AdaBoost, and random forest-were trained and tested using cross-validation. Fluctuated vigilance performance indicated pronounced state instability, particularly after sleep deprivation. Temporary decrements in performance were associated with a decrease in heart rate and an increase in time-domain heart rate variability. SVM achieved the best performance, with a cross-validated accuracy of 89% for binary classification of high versus low vigilance epochs. Overall accuracy dropped to 72% for three-class classification in leave-one-participant-out cross-validation, but SVM maintained a precision of 84% in identifying low-vigilance epochs. Sliding-window-based HRV metrics would effectively capture the fluctuations in vigilance during task execution, enabling more timely and accurate detection of performance decrement.

Analysis of Electrocardiograms and Behavior in Mice from Pregnancy to Lactation Period.

Changes in the mother-offspring relationship are presumably accompanied by dynamic changes in the autonomic nervous system. Although temporal measurements of autonomic activity have been performed in human mothers and infants, the analysis of long-term changes remains unexplored. Mouse mothers can form social bonds with their pups and have a short period of pregnancy and lactation, which makes them useful for the examination of physiological changes from pregnancy to pup-rearing. Therefore, a telemetry system was used for several weeks to measure the changes in the autonomic nervous system and the behavior of mouse mothers. The current results showed that an electrocardiogram (ECG) could be stably recorded regardless of the movements of mothers and parturition. ECG analysis showed that the heart rate gradually decreased from pregnancy to lactation, and sympathetic activity sharply increased as the pups developed. Furthermore, the simultaneous recording of behavior and ECG in the home cage enabled us to understand the behavior-dependent influences on the ECG, thereby revealing the characteristics of autonomic nervous activity during each behavior. Thus, the present experimental method helps to understand how the physiological characteristics of mothers change from pregnancy through pup rearing, supporting the healthy development of pups.

Validation of a novel smartphone-based photoplethysmographic method for ambulatory heart rhythm diagnostics: the SMARTBEATS study.

In the current guidelines, smartphone photoplethysmography (PPG) is not recommended for diagnosis of atrial fibrillation (AF), without a confirmatory electrocardiogram (ECG) recording. Previous validation studies have been performed under supervision in healthcare settings, with limited generalizability of the results. We aim to investigate the diagnostic performance of a smartphone-PPG method in a real-world setting, with ambulatory unsupervised smartphone-PPG recordings, compared with simultaneous ECG recordings and including patients with atrial flutter (AFL). Unselected patients undergoing direct current cardioversion for treatment of AF or AFL were asked to perform 1-min heart rhythm recordings post-treatment, at least twice daily for 30 days at home, using an iPhone 7 smartphone running the CORAI Heart Monitor PPG application simultaneously with a single-lead ECG recording (KardiaMobile). Photoplethysmography and ECG recordings were read independently by two experienced readers. In total, 280 patients recorded 18 005 simultaneous PPG and ECG recordings. Sufficient quality for diagnosis was seen in 96.9% (PPG) vs. 95.1% (ECG) of the recordings (P < 0.001). Manual reading of the PPG recordings, compared with manually interpreted ECG recordings, had a sensitivity, specificity, and overall accuracy of 97.7%, 99.4%, and 98.9% with AFL recordings included and 99.0%, 99.7%, and 99.5%, respectively, with AFL recordings excluded. A novel smartphone-PPG method can be used by patients unsupervised at home to achieve accurate heart rhythm diagnostics of AF and AFL with very high sensitivity and specificity. This smartphone-PPG device can be used as an independent heart rhythm diagnostic device following cardioversion, without the requirement of confirmation with ECG.

Comparative study on the quality of electrocardiogram and arrhythmia detection using wireless ambulatory Vigo SmartHeart Holter and conventional Holter

Abstract Background: Wireless Holter monitors can be a great alternative to the traditional wired holter monitoring potentially giving better patient outcomes. However, the technology is new and less explored for the quality of ECG wave forms. Objective: This study was taken up to compare the quality of electrocardiogram (ECG) and arrhythmia detection using a traditional Holter and a wireless ambulatory smart ECG – Vigo SmartHeart (VSH) that functions with a Mobile application. Materials and Methods: The present study was cross-sectional in nature. We enrolled the patients indicated for continuous ECG monitoring. All the enrolled patients were connected to VSH wireless Holter solution and a conventional Holter monitor simultaneously. The ECGs generated were compared through descriptive statistics such as mean, standard deviation, and correlation by Pearson’s correlation method. Results: Seventy-one patients had simultaneous ECG recordings by both systems. The Pearson correlation for P wave, QRS, PR, QT, and ST was measured as 0.93, 0.93, 0.99, 0.36, and 0.98, respectively. The minimum heart rate (HR), maximum HR, RR interval, P wave, QRS complex, PR, and ST segments showed a statistically significant correlation between Smart Heart and Conventional Holter. 17 ECG events were reported, of which 14 were detected by both. The events missed by conventional Holter and detected by VSH were Atrioventricular block (2), Supraventricular tachycardia (1), and Intraventricular conduction delay (1). Conclusions: The quality of the report generated by both the methods was comparable to ECG wave quality, However, arrhythmia was picked up by the SmartHeart with better precision compared to conventional Holter. Thus, VSH can serve as a viable and potential alternative tool to screen and help in reducing the morbidity and mortality related to arrhythmias.

Electrocardiogram to Determine Mitral and Aortic Valve Opening and Closure.

Knowledge of the timing of cardiac valve opening and closing is important in cardiac physiology. The relationship between valve motion and electrocardiogram (ECG) is often assumed, however is not clearly defined. Here we investigate the accuracy of cardiac valve timing estimated using only the ECG, compared to Doppler echocardiography (DE) flow imaging as the gold standard. DE was obtained in 37 patients with simultaneous ECG recording. ECG was digitally processed and identifiable features (QRS, T, P waves) were examined as potential reference points to determine opening and closure of aortic and mitral valves, as compared to DE outflow and inflow measurement. Timing offset of the cardiac valves opening and closure between ECG features and DE was measured from derivation set (n = 19). The obtained mean offset in combination with the ECG features model was then evaluated on a validation set (n = 18). Using the same approach, additional measurement was also done for the right sided valves. From the derivation set, we found a fixed offset of 22 ± 9 ms, 2 ± 13 ms, 90 ± 26 ms, and -2 ± -27 ms when comparing S to aortic valve opening, Tend to aortic valve closure, Tend to mitral valve opening, and R to mitral valve closure respectively. Application of this model to the validation set showed good estimation of aortic and mitral valve opening and closure timing value, with low model absolute error (median of the mean absolute error of the four events = 19 ms compared to the gold standard DE measurement). For the right-sided (tricuspid and pulmonic) valves in our patient set, there was considerably higher median of the mean absolute error of 42 ms for the model. ECG features can be used to estimate aortic and mitral valve timings with good accuracy as compared to DE, allowing useful hemodynamic information to be derived from this easily available test.

A Novel Method for ECG Artifact Removal from EEG without Simultaneous ECG.

The electrocardiogram (ECG) is a common source of electrical artifact in electroencephalogram (EEG). Here, we present a novel method for removing ECG artifact that requires neither simultaneous ECG nor transformation of the EEG signals. The approach relies upon processing a subset of EEG channels that contain ECG artifact to identify the times of each R-wave of the ECG. Within selected brief epochs, data in each EEG channel is signal-averaged ± 60 ms around each R-wave to derive an ECG template specific to each channel. This template is subtracted from each EEG channel which are aligned with the R-waves. The methodology was developed using two cohorts of infants: one with 128-lead EEG including an ECG reference and another with 32-lead EEG without ECG reference. The results for the first cohort validated the methodology the ECG reference and the second demonstrated its feasibility when ECG was not recorded. This method does not require independent, simultaneous recording of ECG, nor does it involve creation of an artifact template based on a mixture of EEG channel data as required by other methods such as Independent Component Analysis (ICA). Spectral analysis confirms that the method compares favorably to results using simultaneous recordings of ECG. The method removes ECG artifact on an epoch by epoch level and does not require stationarity of the artifact. Clinical Relevance - This approach facilitates the removal of ECG noise in frequency bands known to play a central role in brain mechanisms underlying cognitive processes.

When Adenosine Does Not Work: Apparent and Real Adenosine-Resistant Tachycardia.

Supraventricular tachycardia (SVT) is the most common arrhythmia in the pediatric population. Adenosine is widely accepted as the first-line pharmacological treatment for hemodynamically stable SVT, constituting a class I recommendation in the 2020 American Heart Association guidelines for pediatric life support (2020 American Heart Association Guidelines for cardiopulmonary resuscitation and emergency cardiovascular care). As most pediatric SVTs are dependent on the atrioventricular node (AVN) for their propagation, and adenosine acts primarily on the AVN, adenosine will frequently terminate the arrhythmia. The term "adenosine failure" is often used to describe when its administration does not result in sustained termination of the tachycardia. Because of its very short half-life, there is confusion between improper delivery, failure to have any effect on the tachycardia, or transient termination. There are some pediatric SVTs, which are not AVN dependent, and which truly are refractory to adenosine. Simultaneous electrocardiogram recording during administration can provide important information to differentiate between adenosine resistance and transient adenosine effect, thus guiding further management.

Applicability of the load dynamometric test and speckle tracking echocardiography in the heart failure with preserved ejection fraction diagnosis

Aim To compare myocardial deformation properties in patients with heart failure (HF) with preserved left ventricular ejection fraction (HFpEF) and in healthy volunteers at rest and during a dynamometric test; to determine a possibility of using this method for diagnosing HSpEF.Material and methods STE with simultaneous electrocardiogram recording was performed for patients with HSpEF and healthy volunteers of the control group at rest and during a dynamometric test. The subjects were instructed to maintain a submaximal compression of a hand dynamometer for 3 min and to apply a maximum effort during the last 30 sec while continuing to breathe regularly. Concentrations of markers for myocardial injury and HF were measured. Statistical analytical methods are provided in Results. For other calculations, a MS Office (Microsoft Excel) application software package was used.Results Differences in myocardial deformation variables between healthy volunteers and HF patients were more obvious during exercise, including changes of the variables from the resting values. As compared with the control group, patients with HFpEF had considerable deviations of the heart rate and the left ventricular global longitudinal strain (GLSLV) (significance of differences calculated with ANOVA was higher than 95% and 80%; p&lt;0.05 and p&lt;0.2, respectively). Patients with a significant change in the strain, regardless of the direction of the change, showed a decrease in the left ventricular ejection fraction (EFLV) during the test with a probability above 95% according to the Fischer test. Also, patients with HFpEF had higher concentrations of N-terminal pro-brain natriuretic peptide and symptoms of HF. Compared to evaluation of overall GLSLV, calculation of deformation variables by segment detected more differences between patients with HFpEF and control subjects.Conclusion STE with a dynamometric test is an effective, noninvasive method for diagnosing HFpEF that is easy to perform in the outpatient conditions.

Detection of Episodes of Sleep Apnea and Hypopnea in ECG and EEG Signals by Machine Learning

The article is devoted to the application of machine learning methods for computerized detection of sleep apnea episodes based on the analysis of single-channel signals of the electrocardiogram (ECG) and electroencephalogram (EEG). To study the possibilities of machine learning to detect apnea based on ECG and EEG analysis, we used Apnea-ECG database and MIT-BIH polysomnographic database from PhysioNet, which contain annotations to each minute of records indicating the presence or absence of apnea/hypopnea at the current time. In order to apply machine learning methods to the problem of automated detection of sleep apnea/hypopnea episodes in ECG and EEG signals, long-term polysomnograms available in MIT-BIH polysomnographic database were segmented according to annotations into shorter sections lasting 30 seconds each. The study used 267 segments lasting 30 seconds for the class "norm", 258 segments for the class "apnea" and 273 segments for the class "hypopnea", a total of 798 simultaneous ECG and EEG recordings. The aim of this work is to identify and compare informative signs of sleep apnea episodes in terms of heart rate variability (HRV) and brain electrical activity, as well as the choice of classification methods that provide the highest accuracy for this task. Features of cardiorhythmograms in time and frequency domains, spectral-temporal and wavelet characteristics, as well as parameters of EEG signals based on energy ratio of EEG rhythms, Hearst index, Higuchi fractal dimension and sample entropy for EEG signals are considered. Using different sets of features, the accuracy of classifiers based on decision trees, discriminant analysis, support vector machines, k-nearest neighbor method, and ensemble training was determined. Based on this, combination of features and classifiers is proposed, which provides the highest accuracy of recognition of sleep apnea episodes according to single-channel ECG and EEG signals, taken separately and in the case of a combination of their features. The best results of classification of signals "norm", "apnea" and "hypopnea" were obtained for the model trained using weighted method k nearest neighbors with 25 features of HRV: the total percentage of correctly identified cases for three classes was 99.9% (797 correctly identified cases of 798). By reducing the number of HRV parameters to 9, the best machine learning result was achieved using the bagging ensemble algorithm with 30 decision trees: the total percentage of correctly identified cases for all three classes was 99.4% (793 correctly identified cases from 798: for "norm" - 265 cases from 267, for "apnea" - 257 cases from 258, for "hypopnea" - 271 cases from 273). The use of EEG parameters as features for apnea/hypopnea recognition showed worse results compared to HRV parameters. In this case, the best result of machine learning was achieved using support vector machines with quadratic kernel function: the total percentage of correctly identified cases for three classes was 91.9% and the signals corresponding to norm were most badly recognized (27 cases were classified as hypopnea, and in 9 cases - as sleep apnea). The combination of HRV and EEG parameters gave the best accuracy of 99.1%, but the results are comparable to using only HRV parameters. The obtained results indicate that HRV parameters allow recognizing sleep apnea and hypopnea with higher accuracy than EEG parameters, but EEG signal undoubtedly reflects signs of sleep apnea/hypopnea and also can be used for apnea recognition.

Microelectrode array membranes to simultaneously assess cardiac and neurological signals of xenopus laevis under chemical exposures and environmental changes

Simultaneous monitoring of electrocardiogram (ECG) and electroencephalogram (EEG) in studied animal models requires innovative engineering techniques that can capture minute physiological changes. However, this is often administered with a bulky and/or invasive system that may cause discomfort to animals and signal distortions. Here, we develop an integrated bioelectronic sensing system to provide simultaneous recordings of ECG and EEG in real-time for Xenopus laevis. The microelectrode array (MEA) membrane and the distinct anatomy of Xenopus offer noninvasive multi-modal electrophysiological monitoring with favorable spatial resolution. The system was validated under different environmental conditions, including drug exposure and temperature changes. Under the exposure of Pentylenetetrazol (PTZ), an epilepsy-inducing drug, clear ECG and EEG alterations, including frequent ictal and interictal EEG events, 30 dB average EEG amplitude elevations, abnormal ECG morphology, and heart rate changes, were observed. Furthermore, the ECG and EEG were monitored and analyzed under different temperatures. A decrease in relative power of delta band was observed when cold environment was brought about, in contrast to an increase in relative power of other higher frequency bands while the ECG remained stable. Overall, the real-time electrophysiology monitoring system using the Xenopus model holds potential for many applications in drug screening and remote environmental monitoring.

Reassessment of the Effect of Transcutaneous Auricular Vagus Nerve Stimulation Using a Novel Burst Paradigm on Cardiac Autonomic Function in Healthy Young Adults

BackgroundTranscutaneous auricular vagus nerve stimulation (taVNS) may modulate cardiac autonomic function. However, the response rate of the traditional tonic paradigm is low, and the results remain inconsistent. A recent pilot study presented a novel burst paradigm to activate the cardiac parasympathetic system, which might offer a new approach to treat cardiac autonomic function. The present study reassessed the effect of burst taVNS on modulating heart rate variability and explored the difference between burst and traditional tonic paradigms. Materials and MethodsForty-two young adults were recruited for this study. Each participant underwent three types of taVNS with sham (30 sec of stimulation), tonic (25 Hz, 500 μsec), and burst (five pulses at 500 Hz every 200 msec) paradigms, respectively, with simultaneous electrocardiogram recording. One-way analysis of variance, multivariate analysis of variance, and linear regression were used for analysis. Multiple testing was performed using Bonferroni correction. ResultsBoth burst and tonic paradigms induced a significant decrease in heart rate, which continued until poststimulation, and increased cardiac parasympathetic activity. Moreover, two parasympathetic system indicators showed significant increase only in burst taVNS. The response rates during burst (35.7%) and tonic (38.1%) stimulations were both higher than that during sham stimulation (11.9%). The response to taVNS showed parameter specificity with few nonresponders to the tonic paradigm responding to the burst paradigm. The overall response rate increased from 38.1% in tonic taVNS to 54.8% in taVNS using both burst and tonic paradigms. For both burst and tonic responders, baseline cardiac parasympathetic activity was found to be significantly negatively correlated with changes during stimulation. ConclusionThe burst parameter could be used as an alternative strategy for regulating cardiac parasympathetic function by taVNS, which has the potential to be used as a complementary paradigm to traditional tonic taVNS for promoting clinical treatment efficacy.

Abstract 14031: Effects of Custodiol-HTK and Del Nido Solutions in a Coronary-Perfused Rabbit Ventricular Wedge Model

Introduction: Distinct cardioplegic solutions are used in cardiac surgery for myocardial protection. Custodiol-HTK (CUS) solution consists of histidine, tryptophan, ketoglutarate and mannitol to reduce cellular edema. Del Nido (DN) is a nonglucose-based solution containing low calcium and high lidocaine concentrations, preventing intracellular Ca2+ accumulation during ischemia. Hypothesis: Using a coronary-perfused rabbit experimental model, this study compares the effects of CUS and DN on transmural electrocardiogram (ECG) and tension parameters. Methods: Twelve left ventricular (LV) wedge preparations from 12 rabbit hearts were arterially perfused with Tyrode's solution and stimulated at a cycle length of 1000 ms. Simultaneous recording of transmural ECG and tension were obtained. After exposure to normal Tyrode, the LV wedge preparation was perfused with 20 cc of CUS (n=6) or 20 cc of DN (n=6) leading to complete arrest maintained for 30 minutes. ECG and tension recordings were obtained under baseline conditions and at 5, 15, and 30 minutes after reperfusion with normal Tyrode. Differences in change from baseline ECG and tension parameters after CUS or DN were compared using a mean differences regression model. Results: Reperfusion effects of CUS and DN on ECG and tension are shown in Table 1 . After reperfusion, DN group had a significantly greater increase in QRS duration, significantly more QT shortening and more ST segment change compared to CUS group. At 5- and 15-minutes post-reperfusion, the CUS group had significantly higher tension recovery. Negative T wave were observed in 1 of 6 wedges of the CUS group and 5/6 (83.3%) wedges of the DN group. Conduction block was observed in 0/6 wedges in the CUS group and 5/6 (83.3%) wedges in the DN group. Conclusions: In a LV coronary-perfused rabbit wedge preparation, Custodiol-HTK cardioplegia led to less ischemic changes in ECG and faster tension recovery following reperfusion compared to del Nido cardioplegia.

Detection of Atrial Fibrillation Using a Home Blood Pressure Monitor.

PurposeAtrial fibrillation (AF) is the most common arrhythmia and is associated with an increased risk of complications. A screening test has the potential to prevent AF-related complications. This study investigated the diagnostic accuracy of an automated device for home blood pressure (BP) monitoring, which implements an algorithm for AF detection.Patients and MethodsA modified, automated oscillometric device for home BP monitoring (Omron BP785N (HEM-7321-Z), Omron Healthcare) with an AF detector was used to measure the BP in patients. During each BP measurements, the electrocardiogram (ECG) was recorded simultaneously. Simultaneous BP measurements and ECG recordings were obtained from 99 subjects.ResultsTwenty out of 20 patients with atrial fibrillation were correctly recognized by the device and the device correctly identified 67 patients with sinus rhythm as “Not-AF”. On the other hand, 12 patients with basic rhythm: sinus rhythm were incorrectly referred to as “atrial fibrillation”. In summary, the device has a diagnostic accuracy of 87.88% with a sensitivity of 100% and a specificity of 84.8%. On the other hand, in 23 patients, the raw data of the device showed that a body movement occurred during the measurement of the blood pressure. If these subjects were excluded of the analysis, then the diagnostic accuracy of the device would be even better, namely 90.79%. The sensitivity would be 100% and the specificity 89.5%.ConclusionThese data suggest that an automated device for home blood pressure has an excellent diagnostic accuracy for detecting an AF and could be used as a reliable screening test for early diagnosis of atrial fibrillation. Body movements have an impact of the accuracy and specificity of a blood pressure monitor.

Detection and localization of S1 and S2 heart sounds by 3rd order normalized average Shannon energy envelope algorithm.

Adults born after 1970s are more prone to cardiovascular diseases. Death rate percentage is quite high due to heart related diseases. Therefore, there is necessity to enquire the problem or detection of heart diseases earlier for their proper treatment. As, Valvular heart disease, that is, stenosis and regurgitation of heart valve, are also a major cause of heart failure; which can be diagnosed at early-stage by detection and analysis of heart sound signal, that is, HS signal. In this proposed work, an attempt has been made to detect and localize the major heart sounds, that is, S1 and S2. The work in this article consists of three parts. Firstly, self-acquisition of Phonocardiogram (PCG) and Electrocardiogram (ECG) signal through a self-assembled, data-acquisition set-up. The Phonocardiogram (PCG) signal is acquired from all the four auscultation areas, that is, Aortic, Pulmonic, Tricuspid and Mitral on human chest, using electronic stethoscope. Secondly, the major heart sounds, that is, S1 and S2are detected using 3rd Order Normalized Average Shannon energy Envelope (3rd Order NASE) Algorithm. Further, an auto-thresholding has been used to localize time gates of S1 and S2 and that of R-peaks of simultaneously recorded ECG signal. In third part; the successful detection rate of S1 and S2, from self-acquired PCG signals is computed and compared. A total of 280 samples from same subjects as well as from different subjects (of age group 15-30 years) have been taken in which 70 samples are taken from each auscultation area of human chest. Moreover, simultaneous recording of ECG has also been performed. It was analyzed and observed that detection and localization of S1 and S2 found 74% successful for the self-acquired heart sound signal, if the heart sound data is recorded from pulmonic position of Human chest. The success rate could be much higher, if standard data base of heart sound signal would be used for the same analysis method. The, remaining three auscultations areas, that is, Aortic, Tricuspid, and Mitral have smaller success rate of detection of S1 and S2 from self-acquired PCG signals. So, this work justifies that the Pulmonic position of heart is most suitable auscultation area for acquiring PCG signal for detection and localization of S1 and S2 much accurately and for analysis purpose.

Simultaneous electrocardiogram during routine electroencephalogram: arrhythmia rates through the eyes of the cardiologist.

The importance of simultaneous 2-lead electrocardiogram (ECG) recording during routine electroencephalogram (EEG) has been reported several times on clinical grounds. To investigate arrhythmia rates detected by simultaneous 2-lead ECG in our patient sample undergoing routine EEG. Remarkably, we sought to assess the possible expansion of results with a more experienced interpretation of simultaneous ECG. Simultaneous 2-lead ECG recordings during routine EEG, performed between January and March, 2016, have been retrospectively analyzed by a cardiology specialist. In addition, EEG reports were screened with the keywords 'arrhythmia, tachycardia, bradycardia, atrial fibrillation, extrasystole' to evaluate the neurologist interpretation. Overall, 478 routine EEG recordings were scanned. The mean age of the patients was 42.8±19.8 (16-95), with a sex ratio of 264/214 (F/M). In 80 (17%) patients, findings compatible with arrhythmia were identified on simultaneous ECG after a cardiologist's evaluation. The detected arrhythmia subtypes were: ventricular extrasystole (n=27; 5.6%), supraventricular extrasystole (n=23; 4.8%), tachycardia (n=9; 1.8%), prolonged QRS duration (n=7; 8.7%), atrial fibrillation (n=6; 1.2%), and block (n=6; 1.2%). On the other hand, keywords related to arrhythmia were present in 45 (9.4%) of EEG reports. The reported statements were tachycardia (3.3%), arrhythmia (2.5%), bradycardia (2.1%), and extrasystole (1.5%). A considerably high rate of arrhythmia cases was determined on simultaneous ECG during routine EEG after being interpreted by a cardiologist. However, the screening results of EEG reports revealed relatively low arrhythmia rates. These results suggest that the detection rates of ECG abnormalities during routine EEG may be potentially improved.

Evaluation of blood pressure using Korotkoff’s sounds in case of auscultatory gap

Objective. To assess the clinical signifcance of auscultatory gap (AG) found by the 24-hour ambulatory blood pressure monitoring (ABPM) using the Korotkoff’s sounds (KS) and its potential causes.Design and methods. 24-hour ABPM was performed in 60 patients (mean age 51,3 years, range 22–85 years), including 24 women and 36 men. In 24 patients, hypertension was diagnosed. In total, 2333 blood pressure (BP) measurements without “noise contamination” were selected for subsequent analysis (38,9 measurements per patient; from 28 to 73 measurements). The simultaneous recording of electrocardiogram (ECG), brachial cuff pressure, and KS phonograms on the right hand, as well as continuous recording of non-invasive BP curve in the left fnger using the volume clamp technique were performed in 10 patients with AG.Results. Based on the 24-hour ABPM, AG was shown in 43 of 60 subjects (71,7%). The AG was found to be associated with a decreased amplitude of fluctuations of the cuff pressure; upon its termination, the amplitude recovered. The above synchrony of changes in KS and amplitude of pressure fluctuations was found in all 124 cases which demonstrated AG during BP measurement. We assume that AG is a true variation of BP rather than a feature of KS, as the fluctuation amplitude depends on the ratio of BP and cuff pressure but does not depend on the KS phase. To test the hypothesis, the cuff pressure and KS were compared to the continuously monitored arterial pressure. When the systolic arterial pressure exceeded the cuff pressure, the KS appeared. Later on, the BP fluctuations led to a decrease in systolic BP; the arterial pressure did not reach the cuff pressure and, therefore, KS were not recorded. Subsequent decrease in the cuff pressure led to re-appearance of KS.Conclusions. The auscultative gap phenomenon is associated with true (mostly respiratory) variations of systolic BP rather than with features of KS in individual patients. The initial and repetitive KS occur at certain systolic BP levels depensing on the BP respiratory fluctuations and may indirectly indicate the amplitude of fluctuations.

A new heart sounds segmentation approach based on the correlation between ECG and PCG signals

During the cardiac cycle, both electrical and mechanical events are present. In fact, the electrocardiogram (ECG) allows the exploration of the electrical activity of the heart, while the phonocardiogram (PCG) constitutes a complementary tool to record the mechanical activity of the heart. The aim of our work is to develop a new approach for heart sounds segmentation based on the correlation between ECG and PCG signals for measurement of systolic time interval. Two different groups of simultaneous recordings of ECG and PCG signals were tested in this study. The performance of automatic heart sounds localisation process is compared with that of manual measurements made by two experts. The results obtained are interesting especially for pathological cases heart sound.

Diurnal Differences in Risk of Cardiac Arrhythmias During Spontaneous Hypoglycemia in Young People With Type 1 Diabetes.

Hypoglycemia may exert proarrhythmogenic effects on the heart via sympathoadrenal stimulation and hypokalemia. Hypoglycemia-induced cardiac dysrhythmias are linked to the "dead-in-bed syndrome," a rare but devastating condition. We examined the effect of nocturnal and daytime clinical hypoglycemia on electrocardiogram (ECG) in young people with type 1 diabetes. Thirty-seven individuals with type 1 diabetes underwent 96 h of simultaneous ambulatory ECG and blinded continuous interstitial glucose monitoring (CGM) while symptomatic hypoglycemia was recorded. Frequency of arrhythmias, heart rate variability, and cardiac repolarization were measured during hypoglycemia and compared with time-matched euglycemia during night and day. A total of 2,395 h of simultaneous ECG and CGM recordings were obtained; 159 h were designated hypoglycemia and 1,355 h euglycemia. A median duration of nocturnal hypoglycemia of 60 min (interquartile range 40-135) was longer than daytime hypoglycemia of 44 min (30-70) (P = 0.020). Only 24.1% of nocturnal and 51.0% of daytime episodes were symptomatic. Bradycardia was more frequent during nocturnal hypoglycemia compared with matched euglycemia (incident rate ratio [IRR] 6.44 [95% CI 6.26, 6.63], P < 0.001). During daytime hypoglycemia, bradycardia was less frequent (IRR 0.023 [95% CI 0.002, 0.26], P = 0.002) and atrial ectopics more frequent (IRR 2.29 [95% CI 1.19, 4.39], P = 0.013). Prolonged QTc, T-peak to T-end interval duration, and decreased T-wave symmetry were detected during nocturnal and daytime hypoglycemia. Asymptomatic hypoglycemia was common. We identified differences in arrhythmic risk and cardiac repolarization during nocturnal versus daytime hypoglycemia in young adults with type 1 diabetes. Our data provide further evidence that hypoglycemia is proarrhythmogenic.

Correlation between RR, inter-systolic and inter-diastolic intervals and their differences for the analysis of spontaneous heart rate variability

Analysis of heart rate variability (HRV) is performed through interbeat interval time series derived from either electrocardiographic or arterial pressure (AP) recordings. However, little attention has been given to the reliability of calculating the time series from different sources, i.e. electrocardiogram (ECG) or pulse intervals (PI). Therefore, the present study aimed to evaluate the correlation between interbeat interval time series obtained from RR, inter-systolic (SS) and inter-diastolic (DD) intervals, as well as their impact on indices of HRV calculated from series of RR or PI. Conscious rats previously instrumented with subcutaneous electrodes and a catheter into the femoral artery were subjected to simultaneous ECG and AP recording for 5 min. Correlation and Bland–Altman plots between RR and PI were evaluated. Moreover, HRV was analyzed in time (mean cardiac interval, SDNN and RMSSD) and frequency domain (power in LF and HF spectral bands) as well as by nonlinear approaches (symbolic dynamics and sample entropy). First, RR showed a stronger correlation with PI calculated by DD than SS. Second, most HRV indices showed similar results when calculated with RR or DD series, but not with SS series. Considering RR interval as the gold standard for the calculation of cardiac cycle, when using PI inter diastolic intervals are the better choice to study HRV. These findings are quite relevant, especially when AP recording is used for HRV analysis.

Comparison of baroreflex sensitivity estimated from ECG R-R and inter-systolic intervals obtained by finger plethysmography and radial tonometry.

Spontaneous BRS estimates may considerable vary according to the technique of blood pressure and heart rate assessment. To optimise and standardise BRS estimation for clinical use we evaluated possible differences between spontaneous BRS indices estimated from either finger plethysmography or radial tonometry. Forty-five healthy volunteers underwent simultaneous recordings of electrocardiogram, finger plethysmography and radial tonometry in supine position and during 60° head-up tilt. BRS was computed by spectral analysis from either R-R time series and/or arterial pressure pulse. Radial tonometry generated higher mean BRS estimates than finger plethysmography. The difference decreased upon postural change from supine to upright. In the upright position, BRS estimates based on R-R interval proved to be generally lower compared to BRS indices estimated from arterial pressure pulse. The ratio of low-to-high-frequency power of inter-systolic interval and systolic blood pressure from tonometry was lower than that from plethysmography in supine and approximated in upright position. Spectral parameters of inter-systolic interval and R-R interval did not differ in supine but diverged in upright position. Changes of spectral parameters were most pronounced in R-R interval. Arterial pressure pulse is adequate for estimation of BRS under resting conditions but it may distort BRS estimates under physical load. We, therefore, recommend using an ECG signal for BRS estimation especially in non-stationary conditions.