Assessing Atrial Fibrillation Substrates by P Wave Analysis: A Comprehensive Review

Design and Implementation of an Atrial Fibrillation Detection Algorithm on the ARM Cortex-M4 Microcontroller

At present, a medium-level microcontroller is capable of performing edge computing and can handle the computation of neural network kernel functions. This makes it possible to implement a complete end-to-end solution incorporating signal acquisition, digital signal processing, and machine learning for the classification of cardiac arrhythmias on a small wearable device. In this work, we describe the design and implementation of several classifiers for atrial fibrillation detection on a general-purpose ARM Cortex-M4 microcontroller. We used the CMSIS-DSP library, which supports Naïve Bayes and Support Vector Machine classifiers, with different kernel functions. We also developed Python scripts to automatically transfer the Python model (trained in Scikit-learn) to the C environment. To train and evaluate the models, we used part of the data from the PhysioNet/Computing in Cardiology Challenge 2020 and performed simple classification of atrial fibrillation based on heart-rate irregularity. The performance of the classifiers was tested on a general-purpose ARM Cortex-M4 microcontroller (STM32WB55RG). Our study reveals that among the tested classifiers, the SVM classifier with RBF kernel function achieves the highest accuracy of 96.9%, sensitivity of 98.4%, and specificity of 95.8%. The execution time of this classifier was 720 μs per recording. We also discuss the advantages of moving computing tasks to edge devices, including increased power efficiency of the system, improved patient data privacy and security, and reduced overall system operation costs. In addition, we highlight a problem with false-positive detection and unclear significance of device-detected atrial fibrillation.

CircCAMTA1 facilitates atrial fibrosis by regulating the miR-214-3p/TGFBR1 axis in atrial fibrillation

Circular RNAs (circRNAs) have been shown to be associated with cardiac fibrosis. Atrial fibrosis is an important pathophysiological event in the progression of atrial fibrillation (AF). Although a novel circRNA calmodulin binding transcription activator 1 (circCAMTA1) has been reported to be related with the development of AF, the detailed molecular mechanisms remain largely unknown. In this study, we found that circCAMTA1 was upregulated in atrial muscle tissues of AF patients and angiotensin-II (Ang-II)-treated human atrial fibroblasts (HAFs). Moreover, circCAMTA1 expression was positively correlated with the expression of collagen (I and III) and α-SMA in atrial muscle tissues of AF patients. In vitro experiments, knockdown of circCAMTA1 significantly suppressed Ang-II-induced HAFs proliferation and reduced the expression of atrial fibrosis-associated genes, but overexpression of circCAMTA1 exhibited opposite results. In vivo experiments, circCAMTA1 knockdown ameliorated Ang-II-induced atrial fibrosis by reducing AF incidence, AF duration, and collagen synthesis. Functionally, circCAMTA1 facilitated Ang-II-induced atrial fibrosis in vitro and in vivo via downregulating the inhibitory effect of miR-214-3p on transforming growth factor β receptor 1 (TGFBR1) expression. In conclusions, circCAMTA1 knockdown alleviated atrial fibrosis through downregulating TGFBR1 expression intermediated by miR-214-3p in AF, suggesting circCAMTA1/miR-214-3p/TGFBR1 axis may be a novel therapeutic target for AF treatment in clinic.

Impact of surgical weight loss on novel P-wave-related variables which are nominated as predictors of atrial arrhythmias

BACKGROUND

Bariatric surgery has been associated with reduced cardiovascular mortality and morbidity in obese patients. In this study, we aimed to evaluate the alterations of novel P-wave related atrial arrhythmia predictors in patients who achieved effective weight loss with bariatric surgery.

METHODS

The study included 58 patients who underwent bariatric surgery. We measured heart rate, PR, P wave (PW) max, PW min, Average P axis, P wave peak time (PWPT) in lead D2 and lead V1, terminal force in lead V1 (V1TF), and we estimated P wave dispersion (PWdis) interval both pre-operation and 6 months after operation.

RESULTS

Heart rate, PR, PW max, PW min, PWdis, Average P axis, PWPTD2, PWPTV1 and V1TF values, which were close to the upper limit in the pre-op period, showed statistically significant decreases at 6 months after the operation. The most prominent changes were observed in PW dis (51.15 ± 9.70 ms vs. 48.79 ± 9.50 ms, p = .010), PWPTD2 (55.75 ± 6.91 ms vs. 50.59 ± 7.67 ms, p < .001), PWPTV1 (54.10 ± 7.06 ms vs. 48.05 ± 7.64 ms, p < .001) and V1TF (25 [43.1%] vs. 12 [20.7%], p < .001).

CONCLUSIONS

The results of our study indicated that bariatric surgery has positive effects on the regression of ECG parameters which are predictors of atrial arrhythmias, particularly atrial fibrillation (AF).

P-wave signal-averaged electrocardiography: Reference values, clinical correlates, and heritability in the Framingham Heart Study

BACKGROUND

P-wave signal-averaged electrocardiography (P-SAECG) quantifies atrial electrical activity. P-SAECG measures and their clinical correlates and heritability have had limited characterization in community-based cohorts.

OBJECTIVE

The purpose of this study was to (1) establish reference values; (2) identify clinical risk factors associated with P-SAECG; and (3) estimate genetic heritability for P-SAECG traits.

METHODS

We performed P-SAECG in 2 generations of Framingham Heart Study participants. We performed backward elimination regression models to assess associations of clinical factors with each SAECG trait (P-wave [PW] duration, root mean square voltage in terminal 40 ms [RMS40], terminal 30 ms RMS30, terminal 20 ms RMS20, RMS PW, and PW integral). We estimated the adjusted genetic heritability of P-SAECG measures using the Sequential Oligogenic Linkage Analysis Routines (SOLAR) program.

RESULTS

We included 4307 participants (age 55 ± 14 years; 56% female). The reference values were derived from 1752 participants without cardiovascular risk factors. Median (2.5th percentile; 97.5th percentile) total PW duration was 118 ms (93; 146) in women and 128 ms (104; 158) in men in the reference sample, and 121 ms (94; 151) in women and 129 ms (103; 159) in the entire study cohort (broad sample). In the broad sample, after adjusting for age and sex, total PW duration was positively associated with height, weight, prevalent heart failure, history of atrial fibrillation (AF), and atrioventricular node blockers, and negatively associated with smoking, waist circumference, heart rate, and diabetes. The estimated heritability of P-SAECG traits was moderate, ranging from 11.9% for RMS30 to 24.9% for PW integral.

CONCLUSION

P-SAECG traits are associated with multiple AF-related risk factors and are moderately heritable.

Artificial intelligence-enhanced electrocardiography in cardiovascular disease management

The application of artificial intelligence (AI) to the electrocardiogram (ECG), a ubiquitous and standardized test, is an example of the ongoing transformative effect of AI on cardiovascular medicine. Although the ECG has long offered valuable insights into cardiac and non-cardiac health and disease, its interpretation requires considerable human expertise. Advanced AI methods, such as deep-learning convolutional neural networks, have enabled rapid, human-like interpretation of the ECG, while signals and patterns largely unrecognizable to human interpreters can be detected by multilayer AI networks with precision, making the ECG a powerful, non-invasive biomarker. Large sets of digital ECGs linked to rich clinical data have been used to develop AI models for the detection of left ventricular dysfunction, silent (previously undocumented and asymptomatic) atrial fibrillation and hypertrophic cardiomyopathy, as well as the determination of a person's age, sex and race, among other phenotypes. The clinical and population-level implications of AI-based ECG phenotyping continue to emerge, particularly with the rapid rise in the availability of mobile and wearable ECG technologies. In this Review, we summarize the current and future state of the AI-enhanced ECG in the detection of cardiovascular disease in at-risk populations, discuss its implications for clinical decision-making in patients with cardiovascular disease and critically appraise potential limitations and unknowns.