Analysis of the microcirculatory pulse wave: age-related alterations

Detection of Atrial Fibrillation based on Feature Fusion using Attention-based BiLSTM

Atrial fibrillation (AF) is a common cardiac arrhythmia, and its early detection is crucial for timely treatment. Conventional methods, such as Electrocardiogram (ECG), can be intrusive and require specialized equipment, whereas Photoplethysmography (PPG) offers a non-invasive alternative. In this study, we present a feature fusion approach for AF detection using attention-based Bidirectional Long Short-Term Memory (BiLSTM) and PPG signals. We extract frequency domain (FD) and time domain (TD) features from PPG signals, combine them with deep learning features generated from an attention-based BiLSTM network, and pass the fusion features through a softmax function. Our approach achieves high accuracy (96.5%) and favorable performance metrics (recall 93.20%, precision 94.50%, and F-score 93.09%), improving AF prediction and diagnosis, and providing support for clinicians in their diagnostic processes.

Can Blood Flow be Used to Monitor Changes in Peripheral Vascular Function That Occur in Response to Segmental Vibration Exposure?

OBJECTIVES

Laser Doppler blood flow measurements have been used for diagnosis or detection of peripheral vascular dysfunction. This study used a rat tail model of vibration-induced vascular injury to determine how laser Doppler measurements were affected by acute and repeated exposures to vibration, and to identify changes in the Doppler signal that were associated with the exposure.

METHODS

Blood flow was measured immediately after a single exposure to vibration, or before vibration exposure on days 1, 5, 10, 15, and 20 of a 20 days exposure.

RESULTS

After a single exposure to vibration, average tail blood flow was reduced. With 20 days of exposure, there was a reduction in the amplitude of the arterial pulse on days 10 to 20 in vibrated rats and days 15 to 20 in control rats.

CONCLUSIONS

More detailed statistical analyses of laser Doppler data may be needed to identify early changes in peripheral circulation after exposure to vibration.

Detecting Vascular Age Using the Analysis of Peripheral Pulse

Vascular ageing is known to be accompanied by arterial stiffening and vascular endothelial dysfunction, and represents an independent factor contributing to the development of cardiovascular disease. The microvascular pulse is affected by the biomechanical alterations of the circulatory system, and has been the focus of studies aiming at the development of non-invasive methods able to extract physiologically relevant features.

OBJECTIVE

proposing an approach for the assessment of vascular ageing based on a support vector machine (SVM) learning from features of the pulse contour.

METHODS

the supervised classifier was trained and validated over 20935 models of pulse wave, obtained with a multi-Gaussian decomposition algorithm, applied to laser Doppler flowmetry signals of 54 healthy, non-smoker subjects.

RESULTS

the multi-Gaussian model showed a mean R² of 0.98 and an average normalized root mean square error of 0.90, demonstrating the ability to reconstruct the pulse shape. Over 30 training and validation experiments, the SVM showed a mean Pearson's r of 0.808 between the rate of waves classified as old and the age of the subjects, along with an average area under the ROC curve of 0.953.

CONCLUSION

the SVM showed the capability to discriminate differently aged individuals.

SIGNIFICANCE

the proposed method might detect the ageing-related modifications of the vascular tree; furthermore, since diabetes promotes vascular alterations comparable to ageing, this approach may be also suitable for the screening of diabetic angiopathy.