ECG classification via integration of adaptive beat segmentation and relative heart rate with deep learning networks.

We propose a state-of-the-art deep learning approach for accurate electrocardiogram (ECG) signal analysis, addressing both waveform delineation and beat type classification tasks. For beat type classification, we integrated two novel schemes into the deep learning model, significantly enhancing its performance. The first scheme is an adaptive beat segmentation method that determines the optimal duration for each heartbeat based on RR-intervals, mitigating segmenting errors from conventional fixed-period segmentation. The second scheme incorporates relative heart rate information of the target beat compared to neighboring beats, improving the model's ability to accurately detect premature atrial contractions (PACs) that are easily confused with normal beats due to similar morphology. Extensive evaluations on the PhysioNet QT Database, MIT-BIH Arrhythmia Database, and real-world wearable device data demonstrated the proposed approach's superior capabilities over existing methods in both tasks. The proposed approach achieved sensitivities of 99.81% for normal beats, 99.08% for premature ventricular contractions, and 97.83% for PACs in beat type classification. For waveform delineation, we achieved F1-scores of 0.9842 for non-waveform, 0.9798 for P-waves, 0.9749 for QRS complexes, and 0.9848 for T-waves. It significantly outperforms existing methods in PAC detection while maintaining high performance across both tasks. The integration of aforementioned two schemes into the deep learning model improved the accuracy of normal sinus rhythms and arrhythmia detection.

Derivative Method to Detect Sleep and Awake States through Heart Rate Variability Analysis Using Machine Learning Algorithms.

Sleep disorders can have harmful consequences in both the short and long term. They can lead to attention deficits, as well as cardiac, neurological and behavioral repercussions. One of the most widely used methods for assessing sleep disorders is polysomnography (PSG). A major challenge associated with this method is all the cables needed to connect the recording devices, making the examination more intrusive and usually requiring a clinical environment. This can have potential consequences on the test results and their accuracy. One simple way to assess the state of the central nervous system (CNS), a well-known indicator of sleep disorder, could be the use of a portable medical device. With this in mind, we implemented a simple model using both the RR interval (RRI) and its second derivative to accurately predict the awake and napping states of a subject using a feature classification model. For training and validation, we used a database providing measurements from nine healthy young adults (six men and three women), in which heart rate variability (HRV) associated with light-on, light-off, sleep onset and sleep offset events. Results show that using a 30 min RRI time series window suffices for this lightweight model to accurately predict whether the patient was awake or napping.

Heart Rate Variability for Evaluating Psychological Stress Changes in Healthy Adults: A Scoping Review.

The utility of heart rate variability (HRV) for characterizing psychological stress is primarily impacted by methodological considerations such as study populations, experienced versus induced stress, and method of stress assessment. Here, we review studies on the associations between HRV and psychological stress, examining the nature of stress, ways stress was assessed, and HRV metrics used. The review was performed according to the PRISMA guidelines on select databases. Studies that examined the HRV-stress relationship via repeated measurements and validated psychometric instruments were included (n = 15). Participant numbers and ages ranged between 10 and 403 subjects and 18 and 60 years, respectively. Both experimental (n = 9) and real-life stress (n = 6) have been explored. While RMSSD was the most reported HRV metric (n = 10) significantly associated with stress, other metrics, including LF/HF (n = 7) and HF power (n = 6) were also reported. Various linear and nonlinear HRV metrics have been utilized, with nonlinear metrics used less often. The most frequently used psychometric instrument was the State-Trait Anxiety Inventory (n = 10), though various other instruments have been reported. In conclusion, HRV is a valid measure of the psychological stress response. Standard stress induction and assessment protocols combined with validated HRV measures in different domains will improve the validity of findings.

Assessing the utility of atrial fibrillation induction to risk stratify children with Wolff-Parkinson-White syndrome.

BACKGROUND: Wolff-Parkinson-White syndrome is associated with sudden cardiac death from rapid conduction through the accessory pathway in atrial fibrillation. Adult patients are at higher risk for sudden cardiac death if the shortest-pre-excited-RR-interval in atrial fibrillation (SPERRI) is ≤250 milliseconds (msec) during electrophysiologic study. Exclusive conduction through the atrioventricular node in atrial fibrillation is presumed to convey lower risk. The shortest-pre-excited-paced-cycle-length with atrial pacing has also served as a marker for risk stratification. OBJECTIVE: To determine accessory pathway characteristic of patients undergoing induction of atrial fibrillation during electrophysiologic study. METHODS: We reviewed 321 pediatric patients that underwent electrophysiologic study between 2010 and 2019. Induction of atrial fibrillation was attempted on patients while on isoproterenol and SPERRI was measured if atrial fibrillation was induced. Shortest-pre-excited-paced-cycle-length (SPPCL) was determined while on isoproterenol. RESULTS: Atrial fibrillation was induced in 233 (73%) patients. Of those, 104 (45%) patients conducted exclusively through the atrioventricular node during atrial fibrillation (Group A). The remaining 129 (55%) patients had some conduction through the accessory pathway (Group B). In Group A, SPPCL was 260 msec with 48 (46%) conducting through the accessory pathway at ≤250 msec. In Group B, SPPCL was 240 msec with 92 patients (71%) conducting at ≤250 msec (p < 0.05). In Group B, SPERRI was 250 msec and had a positive correlation with SPPCL (p < 0.001, R2 = 0.28). Almost half (46%) of those with exclusive conduction through the atrioventricular node in atrial fibrillation had rapid accessory pathway conduction with atrial pacing. CONCLUSION: Conduction in atrial fibrillation during electrophysiologic study on isoproterenol via the atrioventricular node may not exclude high-risk accessory pathways in pediatric patients.

Analysis of ECG-based arrhythmia detection system using machine learning.

The 3D Discrete Wavelet Transform (DWT) and Support Vector Machine (SVM) are used in this study to analyze and characterize Electrocardiogram (ECG) signals. This technique consists of three stages: ECG signal preprocessing, feature extraction, and ECG signal order. The 3D wavelet transform is a signal preprocessing technique, de-noising, along with wavelet coefficient extraction.•SVM is used to categorize the ECG through each of the nine heartbeat types recognized by the various classifiers. For this work, around 6400 ECG beats were looked at over the China Physiological Signal Challenge (CPSC) 2018 arrhythmia dataset.•The best degree of exactness was acquired when level 4 rough constants with Symlet-8 (Sym8) channel were utilized for arrangement. Utilizing the ECG signals from CPSC 2018 data set, the SVM classifier has a normal precision of 99.02%, which is much better than complex support vector machine (CSVM) 98.5%, and weighted support vector machine (WSVM) 99%.•The suggested approach is far superior to others in terms of accuracy, and classification of several diseases of arrhythmia.

Using Heart Rate Variability Methods for Health-Related Outcomes in Outdoor Contexts: A Scoping Review of Empirical Studies.

Heart rate variability (HRV) is a psychophysiological variable that is often used in applied analysis techniques to indicate health status because it provides a window into the intrinsic regulation of the autonomic nervous system. However, HRV data analysis methods are varied and complex, which has led to different approaches to data collection, analysis, and interpretation of results. Our scoping review aimed to explore the diverse use of HRV methods in studies designed to assess health outcomes in outdoor free-living contexts. Four database indexes were searched, which resulted in the identification of 17,505 candidate studies. There were 34 studies and eight systematic reviews that met the inclusion criteria. Just over half of the papers referenced the 1996 task force paper that outlined the standards of measurement and physiological interpretation of HRV data, with even fewer adhering to recommended HRV recording and analysis procedures. Most authors reported an increase in parasympathetic (n = 23) and a decrease in systematic nervous system activity (n = 20). Few studies mentioned methods-related limitations and challenges, despite a wide diversity of recording devices and analysis software used. We conclude our review with five recommendations for future research using HRV methods in outdoor and health-related contexts.

Comparisons of heart rate variability responses to head-up tilt with and without abdominal and lower-extremity compression in healthy young individuals: a randomized crossover study.

Introduction: Abdominal and lower-extremity compression techniques can help reduce orthostatic heart rate increases. However, the effects of body compression on the cardiac autonomic systems, which control heart rate, remain unclear. The primary objective of this study was to compare heart rate variability, a reflection of cardiac autonomic regulation, during a head-up tilt test with and without abdominal and lower-extremity compression in healthy young individuals. The secondary objective was to conduct a subgroup analysis, considering participant sex, and compare heart rate and heart rate variability responses to head-up tilt with and without compression therapy. Methods: In a randomized crossover design, 39 healthy volunteers (20 females, aged 20.9 ± 1.2 years) underwent two head-up tilt tests with and without abdominal and lower-extremity compression. Heart rate and heart rate variability parameters were measured during the head-up tilt tests, including the Stress Index, root mean square of successive differences between adjacent R-R intervals, low- and high-frequency components, and low-to-high frequency ratio. Results: Abdominal and lower-extremity compression reduced the orthostatic increase in heart rate (p < 0.001). The tilt-induced changes in heart rate variability parameters, except for the low-frequency component, were smaller in the compression condition than in the no-compression condition (p < 0.001). These results were consistent regardless of sex. Additionally, multiple regression analysis with potentially confounding variables revealed that the compression-induced reduction in Stress Index during the head-up tilt position was a significant independent variable for the compression-induced reduction in heart rate in the head-up tilt position (coefficient = 0.411, p = 0.025). Conclusion: Comparative analyses revealed that abdominal and lower-extremity compression has a notable impact on the compensatory sympathetic activation and vagal withdrawal typically observed during orthostasis, resulting in a reduction of the increase in heart rate. Furthermore, this decrease in heart rate was primarily attributed to the attenuation of cardiac sympathetic activity associated with compression. Our findings could contribute to the appropriate application of compression therapy for preventing orthostatic tachycardia. This study is registered with UMIN000045179.

Non-Standard Electrode Placement Strategies for ECG Signal Acquisition.

BACKGROUND: Wearable technologies for monitoring cardiovascular parameters, including electrocardiography (ECG) and impedance cardiography (ICG), propose a challenging research subject. The expectancy for wearable devices to be unobtrusive and miniaturized sets a goal to develop smarter devices and better methods for signal acquisition, processing, and decision-making. METHODS: In this work, non-standard electrode placement configurations (EPC) on the thoracic area and single arm were experimented for ECG signal acquisition. The locations were selected for joint acquisition of ECG and ICG, targeted to suitability for integrating into wearable devices. The methodology for comparing the detected signals of ECG was developed, presented, and applied to determine the R, S, and T waves and RR interval. An algorithm was proposed to distinguish the R waves in the case of large T waves. RESULTS: Results show the feasibility of using non-standard EPCs, manifesting in recognizable signal waveforms with reasonable quality for post-processing. A considerably lower median sensitivity of R wave was verified (27.3%) compared with T wave (49%) and S wave (44.9%) throughout the used data. The proposed algorithm for distinguishing R wave from large T wave shows satisfactory results. CONCLUSIONS: The most suitable non-standard locations for ECG monitoring in conjunction with ICG were determined and proposed.

Effects of capacitive and resistive electric transfer and hot pack interventions on the autonomic nervous system in young women.

The effects of thermotherapy on autonomic nervous system activity and subjective sensations of fatigue and arousal are unclear. This study compared the effects of capacitive and resistive electric transfer (Cret) interventions (deep thermotherapy) and hot packs (superficial thermotherapy) on autonomic nervous system activity in healthy young women (n = 16). Heart rate and RR interval were measured using electrocardiography, and the coefficient of variation (CV) of the RR interval was used to evaluate autonomic nervous system activity. The subjective relaxation effect was evaluated using the Roken Arousal Scale (RAS) - a fatigue arousal index. The intervention was performed on the lumbar region for 20 minutes for both Cret and hot pack. After each intervention, the CV values increased only in the Cret condition, whereas the heart rate decreased in both conditions. This suggests that parasympathetic activity was enhanced in the Cret condition. In contrast, the subjective relaxation evaluation observed a psychological relaxation effect under both conditions. Our results suggest that Cret application in the lumbar region has greater relaxation effects than hot pack application in the same region.

QRS complex detection using stationary wavelet transform and adaptive thresholding.

Purpose. Electrocardiogram (ECG) signal is a record of the electrical activity of the heart and contains important clinical data about cardiovascular-related misfunctioning. The goal of the present work is to develop an improved QRS detection algorithm for the detection of heart abnormalities.Methods. In this present work stationary wavelet transforms (SWT) based method has been proposed for precise detection of QRS complex with 'sym2' mother wavelet. The stationary wavelet transform is a systematic mathematical tool to decompose the signal without downsampling using scale analysis and provides high detection of QRS complex and accurate localization of signal components. In the proposed method four level of decomposition is applied and the initial thresholding value is computed by the maximum amplitude of scale one at level four in SWT coefficients without the zero-crossing amplitude detection method. The multi-layered dynamic thresholding method has been applied to detect the true R-peak values and locate the QRS complex in the ECG signal.Results. For evaluation of results, the presented methodology is assessed on MIT-BIH, QTDB, and Noise stress test databases. In MIT-BIH, the sensitivity = 99.88%, positive predictivity = 99.93%, accuracy = 99.80% and detection error rate = 0.18% is achieved. In NSTD database, sensitivity = 97.46%, positive predictivity = 94.20%, accuracy = 91.95% and detection error rate = 8.47% and in QTDB, sensitivity = 99.95%, positive predictivity = 99.90%, accuracy = 99.71% and detection error rate = 0.16% is executed.Conclusion. In the presented proposed methodology, the computation complexity is low and exhibits a simple technique rather than an empirical approach. The proposed technique corroborates the performance for the detection of QRS complex with improved accuracy.

The First Electrophysiological Abnormality in New-onset DM: Autonomic Tests.

Introduction: Conventional nerve conduction studies (NCS) are used in the electrodiagnosis of diabetic neuropathy. The aim of our study was to investigate diabetic small fiber neuropathy in newly diagnosed type 2 diabetes mellitus (DM) patient group by using autonomic tests. Methods: Our study was conducted on 49 patients (24 female, 25 male) who were newly diagnosed with type 2 DM in the last 3 months and a control group of 25 volunteers. In addition to conventional NCS, sympathetic skin response (SSR) and RR interval variability (RRIV) tests were performed. Results: The mean upper limb SSR latency of the patient group was more prolonged than that of the control group, whereas the mean lower limb SSR amplitude of the patient group was lower than that of the control group (p=0.002, p<0.001; respectively). The mean resting (R) and deep inspiration (D) RRIV values of the patient group were lower than that of the control group (p=0.037, p<0.001; respectively). In the patient group, the mean R-RRIV and D-RRIV values were found to be positively correlated with the lower limb SSR amplitude (r=0.006, r=0.011; respectively). The mean R-RRIV and D-RRIV change rate of the patient group (D-R)/R was found to be lower than that of the control group (p=0.002). Conclusion: In our study, we showed that autonomic function tests were impaired in newly diagnosed type 2 DM patients who were found not to have diabetic polyneuropathy by standard electrophysiological study. These findings suggest that standard electrophysiological tests are not sufficient in the early stages of the disease.

Atrioventricular Synchronization for Detection of Atrial Fibrillation and Flutter in One to Twelve ECG Leads Using a Dense Neural Network Classifier.

This study investigates the use of atrioventricular (AV) synchronization as an important diagnostic criterion for atrial fibrillation and flutter (AF) using one to twelve ECG leads. Heart rate, lead-specific AV conduction time, and P-/f-wave amplitude were evaluated by three representative ECG metrics (mean value, standard deviation), namely RR-interval (RRi-mean, RRi-std), PQ-interval (PQi-mean, PQI-std), and PQ-amplitude (PQa-mean, PQa-std), in 71,545 standard 12-lead ECG records from the six largest PhysioNet CinC Challenge 2021 databases. Two rhythm classes were considered (AF, non-AF), randomly assigning records into training (70%), validation (20%), and test (10%) datasets. In a grid search of 19, 55, and 83 dense neural network (DenseNet) architectures and five independent training runs, we optimized models for one-lead, six-lead (chest or limb), and twelve-lead input features. Lead-set performance and SHapley Additive exPlanations (SHAP) input feature importance were evaluated on the test set. Optimal DenseNet architectures with the number of neurons in sequential [1st, 2nd, 3rd] hidden layers were assessed for sensitivity and specificity: DenseNet [16,16,0] with primary leads (I or II) had 87.9-88.3 and 90.5-91.5%; DenseNet [32,32,32] with six limb leads had 90.7 and 94.2%; DenseNet [32,32,4] with six chest leads had 92.1 and 93.2%; and DenseNet [128,8,8] with all 12 leads had 91.8 and 95.8%, indicating sensitivity and specificity values, respectively. Mean SHAP values on the entire test set highlighted the importance of RRi-mean (100%), RR-std (84%), and atrial synchronization (40-60%) for the PQa-mean (aVR, I), PQi-std (V2, aVF, II), and PQi-mean (aVL, aVR). Our focus on finding the strongest AV synchronization predictors of AF in 12-lead ECGs would lead to a comprehensive understanding of the decision-making process in advanced neural network classifiers. DenseNet self-learned to rely on a few ECG behavioral characteristics: first, characteristics usually associated with AF conduction such as rapid heart rate, enhanced heart rate variability, and large PQ-interval deviation in V2 and inferior leads (aVF, II); second, characteristics related to a typical P-wave pattern in sinus rhythm, which is best distinguished from AF by the earliest negative P-peak deflection of the right atrium in the lead (aVR) and late positive left atrial deflection in lateral leads (I, aVL). Our results on lead-selection and feature-selection practices for AF detection should be considered for one- to twelve-lead ECG signal processing settings, particularly those measuring heart rate, AV conduction times, and P-/f-wave amplitudes. Performances are limited to the AF diagnostic potential of these three metrics. SHAP value importance can be used in combination with a human expert's ECG interpretation to change the focus from a broad observation of 12-lead ECG morphology to focusing on the few AV synchronization findings strongly predictive of AF or non-AF arrhythmias. Our results are representative of AV synchronization findings across a broad taxonomy of cardiac arrhythmias in large 12-lead ECG databases.

A method to detect sleep apnea using residual attention mechanism network from single-lead ECG signal.

Sleep apnea is a sleep disorder caused by weakened or suspended breathing during sleep, which seriously affects the work and health of patients. The traditional polysomnography (PSG) detection process is complicated and expensive, which has attracted researchers to explore a rapid detection method based on single-lead ECG signals. However, existing ECG-based sleep apnea detection methods have certain limitations and complexities, mainly relying on human-crafted features. To solve the problem, the paper develops a sleep apnea detection method based on a residual attention mechanism network. The method uses the RR interval signal and the R-peak signal derived from the ECG signal as input, realizes feature extraction through the residual network (ResNet), and adds the SENet attention mechanism to deepen the mining of channel features. Experimental results show that the per-segment accuracy of the proposed method can reach 86.2%. Compared with existing works, its accuracy has increased by 1.1-8.1%. These results show that the proposed residual attention network can effectively use ECG signals to quickly detect sleep apnea. Meanwhile, compared with existing works, the proposed method overcomes the limitations and complexity of human-crafted features in sleep apnea detection research.

MGNN: A multiscale grouped convolutional neural network for efficient atrial fibrillation detection.

The reliable detection of atrial fibrillation (AF) is of great significance for monitoring disease progression and developing tailored care paths. In this work, we proposed a novel and robust method based on deep learning for the accurate detection of AF. Using RR interval sequences, a multiscale grouped convolutional neural network (MGNN) combined with self-attention was designed for automatic feature extraction, and AF and non-AF classification. An average accuracy of 97.07% was obtained in the 5-fold cross-validation. The generalization ability of the proposed MGNN was further independently tested on four other unseen datasets, and the accuracy was 92.23%, 96.86%, 94.23% and 95.91%. Moreover, comparison of the network structures indicated that the MGNN had not only better detection performance but also lower computational complexity. In conclusion, the proposed model is shown to be an efficient AF detector that has great potential for use in clinical auxiliary diagnosis and long-term home monitoring based on wearable devices.

Short-Term Beat-to-Beat QT Variability Appears Influenced More Strongly by Recording Quality Than by Beat-to-Beat RR Variability.

Increases in beat-to-beat variability of electrocardiographic QT interval duration have repeatedly been associated with increased risk of cardiovascular events and complications. The measurements of QT variability are frequently normalized for the underlying RR interval variability. Such normalization supports the concept of the so-called immediate RR effect which relates each QT interval to the preceding RR interval. The validity of this concept was investigated in the present study together with the analysis of the influence of electrocardiographic morphological stability on QT variability measurements. The analyses involved QT and RR measurements in 6,114,562 individual beats of 642,708 separate 10-s ECG samples recorded in 523 healthy volunteers (259 females). Only beats with high morphology correlation (r > 0.99) with representative waveforms of the 10-s ECG samples were analyzed, assuring that only good quality recordings were included. In addition to these high correlations, SDs of the ECG signal difference between representative waveforms and individual beats expressed morphological instability and ECG noise. In the intra-subject analyses of both individual beats and of 10-s averages, QT interval variability was substantially more strongly related to the ECG noise than to the underlying RR variability. In approximately one-third of the analyzed ECG beats, the prolongation or shortening of the preceding RR interval was followed by the opposite change of the QT interval. In linear regression analyses, underlying RR variability within each 10-s ECG sample explained only 5.7 and 11.1% of QT interval variability in females and males, respectively. On the contrary, the underlying ECG noise contents of the 10-s samples explained 56.5 and 60.1% of the QT interval variability in females and males, respectively. The study concludes that the concept of stable and uniform immediate RR interval effect on the duration of subsequent QT interval duration is highly questionable. Even if only stable beat-to-beat measurements of QT interval are used, the QT interval variability is still substantially influenced by morphological variability and noise pollution of the source ECG recordings. Even when good quality recordings are used, noise contents of the electrocardiograms should be objectively examined in future studies of QT interval variability.

Characterisation of neonatal cardiac dynamics using ordinal partition network.

The maturation of the autonomic nervous system (ANS) starts in the gestation period and it is completed after birth in a variable time, reaching its peak in adulthood. However, the development of ANS maturation is not entirely understood in newborns. Clinically, the ANS condition is evaluated with monitoring of gestational age, Apgar score, heart rate, and by quantification of heart rate variability using linear methods. Few researchers have addressed this problem from the perspective nonlinear data analysis. This paper proposes a new data-driven methodology using nonlinear time series analysis, based on complex networks, to classify ANS conditions in newborns. We map 74 time series given by RR intervals from premature and full-term newborns to ordinal partition networks and use complexity quantifiers to discriminate the dynamical process present in both conditions. We obtain three complexity quantifiers (permutation, conditional, and global node entropies) using network mappings from forward and reverse directions, and considering different time lags and embedding dimensions. The results indicate that time asymmetry is present in the data of both groups and the complexity quantifiers can differentiate the groups analysed. We show that the conditional and global node entropies are sensitive for detecting subtle differences between the neonates, particularly for small embedding dimensions (m < 7). This study reinforces the assessment of nonlinear techniques for RR interval time series analysis. Graphical Abstract.

Effect of Age on Heart Rate Variability in Patients with Mitral Valve Prolapse: An Observational Study.

Age is an important determinant of heart rate variability (HRV) in healthy individuals. The incidence of arrhythmia is high in patients with mitral valve prolapse (MVP). However, the correlation of HRV in patients with MVP in different age groups is not well established. We presumed that increasing age would be prospectively associated with declining HRV measurement in MVP. Sixty patients with MVP and 120 control individuals were included and underwent 24 h HRV analysis. No significant difference was found in all parameters calculated in the time domain or in the frequency domain between the two groups. However, as patients' age increased, a significant time domain (SDNN, RMSSD, NN50, and pNN50) decline was found in the MVP group, but not in the control group. This suggests that patients with MVP may have autonomic nervous system involvement that increases the risk of arrhythmia and heart disease with increasing age.

Using noninvasive adjusted pulse transit time for tracking beat-to-beat systolic blood pressure during ventricular arrhythmia.

Tracking beat-to-beat blood pressure noninvasively during ventricular arrhythmia (VA) is of great importance but rarely reported. The goal of our study was to investigate the potential utility of the adjusted pulse transit time (APTT) to track beat-to-beat femoral systolic blood pressure (SBP) during VA. Patients who underwent radiofrequency ablation for arrhythmias at Fuwai Hospital were enrolled. Electrocardiograms (ECGs), finger photoplethysmograms, and femoral arterial blood pressure were recorded simultaneously during VA. The APTT was calculated as the ratio between the square of the conventional pulse transit time (cPTT) and the RR interval of the ECG waveform. Forty-five patients were enrolled in our study, and 22,849 beats were collected during their VA. The inverse of the APTT showed a good correlation with femoral SBP during VA (r = 0.70 ± 0.18). The APTT-derived SBP demonstrated acceptable accuracy in terms of the mean difference ± standard deviation (-0.01 ± 10.54 mmHg) from the invasive femoral SBP. The area under the receiver operating characteristic (ROC) curve for the ability of the APTT to detect ≥30% decreases in femoral SBP was 0.903 (95% confidential interval, 0.895-0.911). In addition, the APTT performed better than the cPTT and RR interval in the above analysis (all P < 0.05). Therefore, the APTT has acceptable accuracy in tracking beat-to-beat femoral SBP and could detect substantially decreased femoral SBP. These findings indicate that the APTT may be a promising noninvasive surrogate for invasive femoral SBP during VA. A multiparameter model combining APTT and other parameters is needed to further improve the accuracy.

Old stuff still trending: use of propafenone as a safety net until catheter ablation in a patient with documented pre-excited atrial fibrillation and Wolff-Parkinson-White syndrome - a classic case report.

BACKGROUND: Atrial fibrillation in Wolff-Parkinson-White syndrome may result in life-threateningly rapid antegrade conduction over a bypass tract, manifested by an irregular broad-complex (pre-excited) tachycardia that can degenerate to ventricular fibrillation. The shortest pre-excited RR interval below 250 ms during atrial fibrillation (AF) predicts increased risk of sudden cardiac death. CASE SUMMARY: We report a case of a 43-year-old man with unremarkable cardiac history who presented due to sudden-onset feeling of palpitations and pre-syncope after strenuous lifting. Electrocardiography depicted fast pre-excited AF. The shortest pre-excited RR interval was estimated at 160 ms, indicating an accessory pathway (AP) with short antegrade refractory period at risk for mediating sudden cardiac death. Direct current cardioversion restored sinus rhythm unravelling delta waves. The patient was put on propafenone 450 mg/day having an uneventful clinical course. On Day 10 post-admission, electrophysiological study induced rapid AF but the shortest pre-excited RR interval was substantially increased to 264 ms. A left anterolateral AP was ablated. The patient remained symptom free until his latest follow-up in the 3rd-month post-ablation without manifest pre-excitation on the surface electrocardiogram. DISCUSSION: Treatment options of pre-excited AF include anti-arrhythmic agents but mainly electrical cardioversion. Cardioversion can safely restore sinus rhythm, while use of anti-arrhythmics often requires intensive care unit monitoring due to the risk of QT prolongation. Catheter ablation is the mainstay of therapy for symptomatic patients. Our rare report highlights the direct impact of propafenone on prolonging the refractoriness of the AP, effectively and safely, and reappraises propafenone's worthiness as a protective measure following pre-excited AF episode until ablation.