BP-Net: Cuff-less, Calibration-free, and Non-invasive Blood Pressure Estimation via a Generic Deep Convolutional Architecture

Objective: The paper focuses on development of robust and accurate processing solutions for continuous and cuff-less blood pressure (BP) monitoring. In this regard, a robust deep learning-based framework is proposed for computation of low latency, continuous, and calibration-free upper and lower bounds on the systolic and diastolic BP. Method: Referred to as the BP-Net, the proposed framework is a novel convolutional architecture that provides longer effective memory while achieving superior performance due to incorporation of casual dialated convolutions and residual connections. To utilize the real potential of deep learning in extraction of intrinsic features (deep features) and enhance the long-term robustness, the BP-Net uses raw Electrocardiograph (ECG) and Photoplethysmograph (PPG) signals without extraction of any form of hand-crafted features as it is common in existing solutions. Results: By capitalizing on the fact that datasets used in recent literature are not unified and properly defined, a benchmark dataset is constructed from the MIMIC-I and MIMIC-III databases obtained from PhysioNet. The proposed BP-Net is evaluated based on this benchmark dataset demonstrating promising performance and shows superior generalizable capacity. Conclusion: The proposed BP-Net architecture is more accurate than canonical recurrent networks and enhances the long-term robustness of the BP estimation task. Significance: The proposed BP-Net architecture addresses key drawbacks of existing BP estimation solutions, i.e., relying heavily on extraction of hand-crafted features, such as pulse arrival time (PAT), and; Lack of robustness. Finally, the constructed BP-Net dataset provides a unified base for evaluation and comparison of deep learning-based BP estimation algorithms.

BrainZ-BP: A Non-invasive Cuff-less Blood Pressure Estimation Approach Leveraging Brain Bio-impedance and Electrocardiogram

Accurate and continuous blood pressure (BP) monitoring is essential to the early prevention of cardiovascular diseases. Non-invasive and cuff-less BP estimation algorithm has gained much attention in recent years. Previous studies have demonstrated that brain bio-impedance (BIOZ) is a promising technique for non-invasive intracranial pressure (ICP) monitoring. Clinically, treatment for patients with traumatic brain injuries (TBI) requires monitoring the ICP and BP of patients simultaneously. Estimating BP by brain BIOZ directly can reduce the number of sensors attached to the patients, thus improving their comfort. To address the issues, in this study, we explore the feasibility of leveraging brain BIOZ for BP estimation and propose a novel cuff-less BP estimation approach called BrainZ-BP. Two electrodes are placed on the forehead and occipital bone of the head in the anterior-posterior direction for brain BIOZ measurement. Various features including pulse transit time and morphological features of brain BIOZ are extracted and fed into four regression models for BP estimation. Results show that the mean absolute error, root mean square error, and correlation coefficient of random forest regression model are 2.17 mmHg, 3.91 mmHg, and 0.90 for systolic pressure estimation, and are 1.71 mmHg, 3.02 mmHg, and 0.89 for diastolic pressure estimation. The presented BrainZ-BP can be applied in the brain BIOZ-based ICP monitoring scenario to monitor BP simultaneously.

Regression or Classification? Reflection on BP prediction from PPG data using Deep Neural Networks in the scope of practical applications

Photoplethysmographic (PPG) signals offer diagnostic potential beyond heart rate analysis or blood oxygen level monitoring. In the recent past, research focused extensively on non-invasive PPG-based approaches to blood pressure (BP) estimation. These approaches can be subdivided into regression and classification methods. The latter assign PPG signals to predefined BP intervals that represent clinically relevant ranges. The former predict systolic (SBP) and diastolic (DBP) BP as continuous variables and are of particular interest to the research community. However, the reported accuracies of BP regression methods vary widely among publications with some authors even questioning the feasibility of PPG-based BP regression altogether. In our work, we compare BP regression and classification approaches. We argue that BP classification might provide diagnostic value that is equivalent to regression in many clinically relevant scenarios while being similar or even superior in terms of performance. We compare several established neural architectures using publicly available PPG data for SBP regression and classification with and without personalization using subject-specific data. We found that classification and regression models perform similar before personalization. However, after personalization, the accuracy of classification based methods outperformed regression approaches. We conclude that BP classification might be preferable over BP regression in certain scenarios where a coarser segmentation of the BP range is sufficient.

BP-Net: Efficient Deep Learning for Continuous Arterial Blood Pressure Estimation using Photoplethysmogram

Blood pressure (BP) is one of the most influential bio-markers for cardiovascular diseases and stroke; therefore, it needs to be regularly monitored to diagnose and prevent any advent of medical complications. Current cuffless approaches to continuous BP monitoring, though non-invasive and unobtrusive, involve explicit feature engineering surrounding fingertip Photoplethysmogram (PPG) signals. To circumvent this, we present an end-to-end deep learning solution, BP-Net, that uses PPG waveform to estimate Systolic BP (SBP), Mean Average Pressure (MAP), and Diastolic BP (DBP) through intermediate continuous Arterial BP (ABP) waveform. Under the terms of the British Hypertension Society (BHS) standard, BP-Net achieves Grade A for DBP and MAP estimation and Grade B for SBP estimation. BP-Net also satisfies Advancement of Medical Instrumentation (AAMI) criteria for DBP and MAP estimation and achieves Mean Absolute Error (MAE) of 5.16 mmHg and 2.89 mmHg for SBP and DBP, respectively. Further, we establish the ubiquitous potential of our approach by deploying BP-Net on a Raspberry Pi 4 device and achieve 4.25 ms inference time for our model to translate the PPG waveform to ABP waveform.

Probabilistic Receiver Architecture Combining BP, MF, and EP for Multi-Signal Detection

Receiver algorithms which combine belief propagation (BP) with the mean field (MF) approximation are well-suited for inference of both continuous and discrete random variables. In wireless scenarios involving detection of multiple signals, the standard construction of the combined BP-MF framework includes the equalization or multi-user detection functions within the MF subgraph. In this paper, we show that the MF approximation is not particularly effective for multi-signal detection. We develop a new factor graph construction for application of the BP-MF framework to problems involving the detection of multiple signals. We then develop a low-complexity variant to the proposed construction in which Gaussian BP is applied to the equalization factors. In this case, the factor graph of the joint probability distribution is divided into three subgraphs: (i) a MF subgraph comprised of the observation factors and channel estimation, (ii) a Gaussian BP subgraph which is applied to multi-signal detection, and (iii) a discrete BP subgraph which is applied to demodulation and decoding. Expectation propagation is used to approximate discrete distributions with a Gaussian distribution and links the discrete BP and Gaussian BP subgraphs. The result is a probabilistic receiver architecture with strong theoretical justification which can be applied to multi-signal detection.

Ensemble Learning Model on Artificial Neural Network-Backpropagation (ANN-BP) Architecture for Coal Pillar Stability Classification

Pillars are important structural units used to ensure mining safety in underground hard rock mines. Therefore, precise predictions regarding the stability of underground pillars are required. One common index that is often used to assess pillar stability is the Safety Factor (SF). Unfortunately, such crisp boundaries in pillar stability assessment using SF are unreliable. This paper presents a novel application of Artificial Neural Network-Backpropagation (ANN-BP) and Deep Ensemble Learning for pillar stability classification. There are three types of ANN-BP used for the classification of pillar stability distinguished by their activation functions: ANN-BP ReLU, ANN-BP ELU, and ANN-BP GELU. This research also presents a new labeling alternative for pillar stability by considering its suitability with the SF. Thus, pillar stability is expanded into four categories: failed with a suitable safety factor, intact with a suitable safety factor, failed without a suitable safety factor, and intact without a suitable safety factor. There are five inputs used for each model: pillar width, mining height, bord width, depth to floor, and ratio. The results showed that the ANN-BP model with Ensemble Learning could improve ANN-BP performance with an average accuracy of 86.48% and an F_2-score of 96.35% for the category of failed with a suitable safety factor.

An Iterative BP-CNN Architecture for Channel Decoding

Inspired by recent advances in deep learning, we propose a novel iterative BP-CNN architecture for channel decoding under correlated noise. This architecture concatenates a trained convolutional neural network (CNN) with a standard belief-propagation (BP) decoder. The standard BP decoder is used to estimate the coded bits, followed by a CNN to remove the estimation errors of the BP decoder and obtain a more accurate estimation of the channel noise. Iterating between BP and CNN will gradually improve the decoding SNR and hence result in better decoding performance. To train a well-behaved CNN model, we define a new loss function which involves not only the accuracy of the noise estimation but also the normality test for the estimation errors, i.e., to measure how likely the estimation errors follow a Gaussian distribution. The introduction of the normality test to the CNN training shapes the residual noise distribution and further reduces the BER of the iterative decoding, compared to using the standard quadratic loss function. We carry out extensive experiments to analyze and verify the proposed framework. The iterative BP-CNN decoder has better BER performance with lower complexity, is suitable for parallel implementation, does not rely on any specific channel model or encoding method, and is robust against training mismatches. All of these features make it a good candidate for decoding modern channel codes.

When Explanations Lie: Why Modified BP Attribution Fails

Modified backpropagation methods are a popular group of attribution methods. We analyse the most prominent methods: Deep Taylor Decomposition, Layer-wise Relevance Propagation, Excitation BP, PatternAttribution, Deconv, and Guided BP. We found empirically that the explanations of the mentioned modified BP methods are independent of the parameters of later layers and show that the $z^+$ rule used by multiple methods converges to a rank-1 matrix. This can explain well why the actual network's decision is ignored. We also develop a new metric cosine similarity convergence (CSC) to directly quantify the convergence of the modified BP methods to a rank-1 matrix. Our conclusion is that many modified BP methods do not explain the predictions of deep neural networks faithfully.

BP-MVSNet: Belief-Propagation-Layers for Multi-View-Stereo

In this work, we propose BP-MVSNet, a convolutional neural network (CNN)-based Multi-View-Stereo (MVS) method that uses a differentiable Conditional Random Field (CRF) layer for regularization. To this end, we propose to extend the BP layer and add what is necessary to successfully use it in the MVS setting. We therefore show how we can calculate a normalization based on the expected 3D error, which we can then use to normalize the label jumps in the CRF. This is required to make the BP layer invariant to different scales in the MVS setting. In order to also enable fractional label jumps, we propose a differentiable interpolation step, which we embed into the computation of the pairwise term. These extensions allow us to integrate the BP layer into a multi-scale MVS network, where we continuously improve a rough initial estimate until we get high quality depth maps as a result. We evaluate the proposed BP-MVSNet in an ablation study and conduct extensive experiments on the DTU, Tanks and Temples and ETH3D data sets. The experiments show that we can significantly outperform the baseline and achieve state-of-the-art results.

Unlocking Deep Learning: A BP-Free Approach for Parallel Block-Wise Training of Neural Networks

Backpropagation (BP) has been a successful optimization technique for deep learning models. However, its limitations, such as backward- and update-locking, and its biological implausibility, hinder the concurrent updating of layers and do not mimic the local learning processes observed in the human brain. To address these issues, recent research has suggested using local error signals to asynchronously train network blocks. However, this approach often involves extensive trial-and-error iterations to determine the best configuration for local training. This includes decisions on how to decouple network blocks and which auxiliary networks to use for each block. In our work, we introduce a novel BP-free approach: a block-wise BP-free (BWBPF) neural network that leverages local error signals to optimize distinct sub-neural networks separately, where the global loss is only responsible for updating the output layer. The local error signals used in the BP-free model can be computed in parallel, enabling a potential speed-up in the weight update process through parallel implementation. Our experimental results consistently show that this approach can identify transferable decoupled architectures for VGG and ResNet variations, outperforming models trained with end-to-end backpropagation and other state-of-the-art block-wise learning techniques on datasets such as CIFAR-10 and Tiny-ImageNet. The code is released at https://github.com/Belis0811/BWBPF.