Journal of electrocardiology最新文献

The electrocardiogram (ECG) signal is susceptible to interference from unknown noises during the acquisition process due to their low frequency and amplitude, resulting in the loss of significant information in the signals. Recent advancements in deep learning models have shown promising results in signal processing. However, these models lack robustness against various types of noise and often overlook the gradient difference between denoised and original signals. In this study, we propose a novel deep learning denoising method based on an attention half instance normalization block (AHIN block) and a gradient difference max loss function (GDM Loss). Our approach consists of two stages: firstly, we input the noisy ECG signal to obtain a denoised version; secondly, we reconstruct the denoised signal by fusing preliminary results from the first stage while correcting waveform distortions caused by initial denoising to minimize information loss. Additionally, we introduce a new loss function that considers differences between slopes of the denoised ECG signal and clean ECG signal. To validate our proposed method's effectiveness, extensive experiments were conducted on both our model architecture and loss function compared with other state-of-the-art methods. Results demonstrate that our approach achieves excellent performance in terms of both signal-to-noise ratio (SNR) and root-mean-square error (RMSE). The proposed noise reduction method improves 8.86%, 12.05% and 10.50% respectively under BW, MA and EM noise.

Background

Identifying bicuspid aortic valve (BAV) patients at risk for cardiac events remains challenging and the role of the electrocardiogram (ECG) has not yet been described. Therefore, this study aims to describe ECG parameters in BAV patients, and investigate their prognostic value.

Methods

In this single-center prospective study patients with BAV without a prior aortic valve replacement (AVR) were included. Transthoracic echocardiogram and 12‑lead resting-ECG were obtained. Associations between ECG parameters and the composite endpoint of all-cause mortality and AVR were assessed using Cox-proportional hazard analysis.

Results

120 patients with BAV were included (median age 30 years, 61% male). Median aortic jet velocity was 2.4 m/s [IQR: 1.7–3.4] and 5 patients (4%) had severe aortic regurgitation. All patients were in sinus rhythm. Any ECG abnormality was present in 57 patients (48%). Median PR-interval was 156 [IQR: 138–170] msec. A deviating QRS axis was found in 17 patients (14%) and Cornell criteria for LVH were fulfilled in 20 patients (17%). Repolarization abnormalities were present in 12 patients (10%). Median follow-up duration was 7.0 [6.3–9.8] years, during which 23 patients underwent AVR and 2 patients died. After adjusting for age, a longer PR-interval was associated with worse intervention-free survival (HR 1.02, 95% CI: 1.01–1.04).

Conclusion

Almost half of the patients with BAV had abnormalities on their ECG. Moreover, the PR-interval may be an interesting prognostic marker for intervention-free survival in BAV patients.

Background

Atrial fibrillation (AF) is often asymptomatic and thus under-observed. Given the high risks of stroke and heart failure among patients with AF, early prediction and effective management are crucial. Given the prevalence of obstructive sleep apnea among AF patients, electrocardiogram (ECG) analysis from polysomnography (PSG) offers a unique opportunity for early AF prediction. Our aim is to identify individuals at high risk of AF development from single‑lead ECGs during standard PSG.

Methods

We analyzed 18,782 single‑lead ECG recordings from 13,609 subjects undergoing PSG at the Massachusetts General Hospital sleep laboratory. AF presence was identified using ICD-9/10 codes. The dataset included 15,913 recordings without AF history and 2054 recordings from patients diagnosed with AF between one month to fifteen years post-PSG. Data were partitioned into training, validation, and test cohorts ensuring that individual patients remained exclusive to each cohort. The test set was held out during the training process.

We employed two different methods for feature extraction to build a final model for AF prediction: Extraction of hand-crafted ECG features and a deep learning method. For extraction of ECG-hand-crafted features, recordings were split into 30-s windows, and those with a signal quality index (SQI) below 0.95 were discarded. From each remaining window, 150 features were extracted from the time, frequency, time-frequency domains, and phase-space reconstructions of the ECG. A compilation of 12 statistical features summarized these window-specific features per recording, resulting in 1800 features (12 × 150).

A pre-trained deep neural network from the PhysioNet Challenge 2021 was updated using transfer learning to discriminate recordings with and without AF. The model processed PSG ECGs in 16-s windows to generate AF probabilities, from which 13 statistical features were extracted. Combining 1800 features from feature extraction with 13 from the deep learning model, we performed a feature selection and subsequently trained a shallow neural network to predict future AF and evaluated its performance on the test cohort.

Results

On the test set, our model exhibited sensitivity, specificity, and precision of 0.67, 0.81, and 0.3, respectively, for AF prediction. Survival analysis revealed a hazard ratio of 8.36 (p-value: 1.93 × 10⁻⁵²) for AF outcomes using the log-rank test.

Conclusions

Our proposed ECG analysis method, utilizing overnight PSG data, shows promise in AF prediction despite modest precision, suggesting false positives. This approach could enable low-cost screening and proactive treatment for high-risk patients. Refinements, including additional physiological parameters, may reduce false positives, enhancing clinical utility and accuracy.

Background

Electrocardiograms (ECGs) are vital for diagnosing cardiac conditions but obtaining clean signals in Left Ventricular Assist Device (LVAD) patients is hindered by electromagnetic interference (EMI). Traditional filters have limited efficacy. There is a current need for an easy and effective method.

Methods

Raw ECG data obtained from 5 patients with LVADs. LVAD types included HeartMate II, III at multiple impeller speeds, and a case with HeartMate III and a ProtekDuo. ECG spectral profiles were examined ensuring the presence of diverse types of EMI in the study. ECGs were then processed with four denoising techniques: Moving Average Filter, Finite Impulse Response Filter, Fast Fourier Transform, and Discrete Wavelet Transform.

Results

Discrete Wavelet Transform proved as the most promising method. It offered a one solution fits all, enabling automatic processing with minimal user input while preserving crucial high-frequency components and reducing LVAD EMI artifacts.

Conclusion

Our study demonstrates the practicality and efficiency of Discrete Wavelet Transform in obtaining high-fidelity ECGs in LVAD patients. This method could enhance clinical diagnosis and monitoring.