Simultaneous determination of wave speed and arrival time of reflected waves using the pressure-velocity loop

This is the post print version of the article. The official published version can be found at the link below.In a previous paper we demonstrated that the linear portion of the pressure–velocity loop (PU-loop) corresponding to early systole could be used to calculate the local wave speed. In this paper we extend this work to show that determination of the time at which the PU-loop first deviates from linearity provides a convenient way to determine the arrival time of reflected waves (Tr). We also present a new technique using the PU-loop that allows for the determination of wave speed and Tr simultaneously. We measured pressure and flow in elastic tubes of different diameters, where a strong reflection site existed at known distances away form the measurement site. We also measured pressure and flow in the ascending aorta of 11 anaesthetised dogs where a strong reflection site was produced through total arterial occlusion at four different sites. Wave speed was determined from the initial slope of the PU-loop and Tr was determined using a new algorithm that detects the sampling point at which the initial linear part of the PU-loop deviates from linearity. The results of the new technique for detecting Tr were comparable to those determined using the foot-to-foot and wave intensity analysis methods. In elastic tubes Tr detected using the new algorithm was almost identical to that detected using wave intensity analysis and foot-to-foot methods with a maximum difference of 2%. Tr detected using the PU-loop in vivo highly correlated with that detected using wave intensity analysis (r 2 = 0.83, P < 0.001). We conclude that the new technique described in this paper offers a convenient and objective method for detecting Tr, and allows for the dynamic determination of wave speed and Tr, simultaneously

Reliable Multimodal Heartbeat Classification using Deep Neural Networks

Copyright © 2023 Authors. Arrhythmias are deviations from the normal heart rhythm with impact on the cardiovascular health. Their prompt detection plays an important role in mitigating potential negative outcomes, particularly in patients in the intensive care units (ICU). Heartbeat detection has mainly been focused on electrocardiogram (ECG) signals. However, ICU patient mobility frequently leads to disconnection of certain ECG leads, potentially compromising the accurate heartbeat classification. Arterial line blood pressure (ABP) and central venous pressure (CVP) signals are routinely monitored in ICU patients. Changes in the ABP and CVP suggest alterations in the haemodynamic status and cardiac function of the patients. Thus, leveraging these signals for heartbeat classification, either independently or in conjunction with ECG data, present a viable approach to ensure that even in scenarios where ECG signals are unavailable, alarm systems alerting healthcare providers of arrhythmias remain functional. Moreover, while many researchers have successfully created methodologies to accurately classify heartbeats including paced beats, none were able to distinguish various sub-classes of paced heartbeats. A more comprehensive distinction is crucial as it not only aids in the identification of pacing settings but also facilitates the detection of inadequate pacing settings, a critical aspect in patient care. In this paper, we employ a hybrid model using long-short term memory networks (LSTM) and convolutional neural network (CNN), along with different residual CNN (ResNet) models for multimodal arrhythmia classification and for comprehensive paced heartbeats classification. When using all three channels, ResNet50 achieved the best accuracy of 99.58% on 5 different arrhythmia classes, whereas ResNet34 achieved an accuracy of 93.82% on 12 paced classes. The significant efficiency of utilizing ABP and CVP signals independently for classification, was also highlighted. ResNet50 was trained with ABP and CVP signals independently and correctly identified arrhythmias with an accuracy of 98.79% and 96.67%, respectively. For classifying 12 different paced heartbeats, ResNet34 achieved 74.04% accuracy with ABP signals and 74.38% with CVP signals. Moreover, the same ResNet50 model was trained on the MIT-BIH arrhythmia database, achieving an accuracy, sensitivity, and precision of 98.78%, 98.77% and 98.80%, which demonstrates the scalability of the proposed model.British Heart Foundation for sponsoring this project (No.FS/19/73/34690

Measurements of wave speed and reflected waves in elastic tubes and bifurcations.

Abstract Wave intensity analysis is a time domain method for studying waves in elastic tubes. Testing the ability of the method to extract information from complex pressure and velocity waveforms such as those generated by a wave passing through a mismatched elastic bifurcation is the primary aim of this research. The analysis provides a means for separating forward and backward waves, but the separation requires knowledge of the wave speed. The PU-loop method is a technique for determining the wave speed from measurements of pressure and velocity, and investigating the relative accuracy of this method is another aim of this research. We generated a single semi-sinusoidal wave in long elastic tubes and measured pressure and velocity at the inlet, and pressure at the exit of the tubes. In our experiments, the results of the PU-loop and the traditional foot-to-foot methods for determining the wave speed are comparable and the difference is on the order of 2.970.8%. A single semi-sinusoidal wave running through a mismatched elastic bifurcation generated complicated pressure and velocity waveforms. By using wave intensity analysis we have decomposed the complex waveforms into simple information of the times and magnitudes of waves passing by the observation site. We conclude that wave intensity analysis and the PU-loop method combined, provide a convenient, time-based technique for analysing waves in elastic tubes.

Multimodal Arrhythmia Classification Using Deep Neural Networks

Arrhythmias are deviations from the normal heart rhythm with impact on the cardiovascular health. Their prompt detection plays an important role in mitigating potential negative outcomes, particularly in patients in the intensive care units (ICU). The detection of arrhythmias has mainly been focused on electrocardiogram (ECG) signals. However, ICU patient mobility frequently leads to disconnection of certain ECG leads, potentially compromising the accurate arrhythmia detection. Arterial line blood pressure (ABP) and central venous pressure (CVP) signals are routinely monitored in ICU patients. Changes in the ABP and CVP suggest alterations in the haemodynamic status and cardiac function of the patients. Thus, leveraging these signals for arrhythmia detection, either independently or in conjunction with ECG data, present a viable approach to ensure that even in scenarios where ECG signals are unavailable, alarm systems alerting healthcare providers of arrhythmias remain functional. In this paper, we employ a hybrid model using long-short term memory networks (LSTM) and convolutional neural network (CNN), along with different residual CNN (ResNet) models for multimodal arrhythmia classification. When using all three channels, ResNet50 achieved the best accuracy of 99.58% on five different arrhythmia classes. The significant efficiency of utilizing ABP and CVP signals independently for the classification of arrhythmias, was also highlighted. ResNet50 was trained with ABP and CVP signals independently and correctly identified arrhythmias with an accuracy of 98.79% and 96.67%, respectively. Moreover, the same ResNet50 model was trained on the MIT-BIH arrhythmia database, achieving an accuracy, sensitivity, and precision of 98.78%, 98.77% and 98.80%, which demonstrates the scalability of the proposed model.British Heart Foundation for sponsoring this project (No.FS/19/73/34690)

A comparison of different methods to maximise signal extraction when using central venous pressure to optimise atrioventricular delay after cardiac surgery

Objective: Our group has shown that central venous pressure (CVP) can optimise atrioventricular (AV) delay in temporary pacing (TP) after cardiac surgery. However, the signal-to-noise ratio (SNR) is influenced both by the methods used to mitigate the pressure effects of respiration and the number of heartbeats analysed. This paper systematically studies the effect of different analysis methods on SNR to maximise the accuracy of this technique. Methods: We optimised AV delay in 16 patients with TP after cardiac surgery. Transitioning rapidly and repeatedly from a reference AV delay to different tested AV delays, we measured pressure differences before and after each transition. We analysed the resultant signals in different ways with the aim of maximising the SNR: (1) adjusting averaging window location (around versus after transition), (2) modifying window length (heartbeats analysed), and (3) applying different signal filtering methods to correct respiratory artefact. Results: (1) The SNR was 27 % higher for averaging windows around the transition versus post-transition windows. (2) The optimal window length for CVP analysis was two respiratory cycle lengths versus one respiratory cycle length for optimising SNR for arterial blood pressure (ABP) signals. (3) Filtering with discrete wavelet transform improved SNR by 62 % for CVP measurements. When applying the optimal window length and filtering techniques, the correlation between ABP and CVP peak optima exceeded that of a single cycle length (R = 0.71 vs. R = 0.50, p < 0.001). Conclusion: We demonstrated that utilising a specific set of techniques maximises the signal-to-noise ratio and hence the utility of this technique.British Heart Foundation (No. FS/19/73/34690)

Classification of arrhythmias using an LSTM- and GAN-based approach to ECG signal augmentation

EHRA 2023 Abstract Supplement, 9.3.1 - Electrocardiography (ECG).Copyright . Introduction: Automated classification of arrhythmias in ECGs is becoming increasingly important. Publicly available ECG datasets have been widely used by the research community to create novel artificial intelligence models that improve these detection rates. The development of these models requires access to large volume of labelled data. However, access to such databases is becoming increasingly limited. In addition, the datasets are often unbalanced because abnormal rhythms are far outweighed by normal samples. The unbalanced nature of the datasets can lead to less accurate models. Therefore, generating realistic synthetic signals can augment the real signals found in such databases and provide data that allows sophisticated model development. Purpose: In this study, we propose a deep learning-based approach for synthetic ECG signal generation that uses long short-term memory (LSTM) autoencoder and generative adversarial networks (GAN) to generate signals that mimic the distribution of arrhythmia signals (Figure 1). Methods: The LSTM autoencoder is composed of two parts: an encoder and a decoder (Figure 1b). The encoder takes original ECG signal as its input and uses LSTM layers to compress the signal into a set of features. The decoder is formed by reversing the encoding process, which uses the encoded features as its input and converts them back into the original signal. To generate synthetic signals, we inserted GANs between the LSTM encoder and the decoder. GANs are composed of a generator and a discriminator (Figure 1c). The generator produces synthetic ECG features based on noise, whereas the discriminator tries to distinguish between real features and results received from the generator. The pathological beats studies were: left bundle branch block (LBBB), right bundle branch block (RBBB), aberrated atrial premature (AA), and normal beats (N) from the MIT-BIH arrhythmia database, using lead II only. To evaluate the quality of our synthetic signals, we trained an LSTM classifier on a combination of our real and synthetic data and compared the testing results with a model trained on real data alone. Results: The LSTM encoder, decoder and GAN were trained individually for each beat type, and examples of generated signals are illustrated in Figure 2. The average accuracy of the classification for the original dataset was 90%, with a recall of 98% for N, 36% for AA, 39% for LBBB and 97% for RBBB. Once synthetic signals were added to the training set, the average testing classification accuracy increased to 98%, with a recall of 99% for N, 83% for AA, 99% for LBBB and 99% for RBBB. For fair comparison, the testing set contained only real data and remained unchanged for both groups. Conclusion: In this work, we proved the capability of GANs to generate realistic synthetic signals that helped to improve the detection rates of arrhythmias as measured by both increased overall accuracy and recall.British Heart Foundation grant no: FS/19/73/34690

On the Mechanics Underlying the Reservoir-Excess Separation in Systemic Arteries and their Implications for Pulse Wave Analysis

Several works have separated the pressure waveform p in systemic arteries into reservoir pr and excess pexc components, p = pr + pexc, to improve pulse wave analysis, using windkessel models to calculate the reservoir pressure. However, the mechanics underlying this separation and the physical meaning of pr and pexc have not yet been established. They are studied here using the time-domain, inviscid and linear one-dimensional (1-D) equations of blood flow in elastic vessels. Solution of these equations in a distributed model of the 55 larger human arteries shows that pr calculated using a two-element windkessel model is space-independent and well approximated by the compliance-weighted space-average pressure of the arterial network. When arterial junctions are well-matched for the propagation of forward-travelling waves, pr calculated using a three-element windkessel model is space-dependent in systole and early diastole and is made of all the reflected waves originated at the terminal (peripheral) reflection sites, whereas pexc is the sum of the rest of the waves, which are obtained by propagating the left ventricular flow ejection without any peripheral reflection. In addition, new definitions of the reservoir and excess pressures from simultaneous pressure and flow measurements at an arbitrary location are proposed here. They provide valuable information for pulse wave analysis and overcome the limitations of the current two- and three-element windkessel models to calculate pr

Vascular device interaction with the endothelium

The interaction between cerebral stents and endothelium, as well as the interaction between intra aortic balloon pumps, temporary cardiac assist devices, with aortic tissues are described in view of the numerical modelling of the above vascular devices