Abstract Data scarcity is a major challenge when training deep learning (DL) models. DL demands a large amount of data to achieve exceptional performance. Unfortunately, many applications have small or inadequate data to train DL frameworks. Usually, manual labeling is needed to provide labeled data, which typically involves human annotators with a vast background of knowledge. This annotation process is costly, time-consuming, and error-prone. Usually, every DL framework is fed by a significant amount of labeled data to automatically learn representations. Ultimately, a larger amount of data would generate a better DL model and its performance is also application dependent. This issue is the main barrier for many applications dismissing the use of DL. Having sufficient data is the first step toward any successful and trustworthy DL application. This paper presents a holistic survey on state-of-the-art techniques to deal with training DL models to overcome three challenges including small, imbalanced datasets, and lack of generalization. This survey starts by listing the learning techniques. Next, the types of DL architectures are introduced. After that, state-of-the-art solutions to address the issue of lack of training data are listed, such as Transfer Learning (TL), Self-Supervised Learning (SSL), Generative Adversarial Networks (GANs), Model Architecture (MA), Physics-Informed Neural Network (PINN), and Deep Synthetic Minority Oversampling Technique (DeepSMOTE). Then, these solutions were followed by some related tips about data acquisition needed prior to training purposes, as well as recommendations for ensuring the trustworthiness of the training dataset. The survey ends with a list of applications that suffer from data scarcity, several alternatives are proposed in order to generate more data in each application including Electromagnetic Imaging (EMI), Civil Structural Health Monitoring, Medical imaging, Meteorology, Wireless Communications, Fluid Mechanics, Microelectromechanical system, and Cybersecurity. To the best of the authors’ knowledge, this is the first review that offers a comprehensive overview on strategies to tackle data scarcity in DL. 

/pdf/a-survey-on-deep-learning-tools-dealing-with-data-scarcity-3amzf9cp.pdf

A survey on deep learning tools dealing with data scarcity: definitions, challenges, solutions, tips, and applications

Deep learning (DL) has been introduced in automatic heart-abnormality classification using ECG signals, while its application in practical medical procedures is limited. A systematic review is performed from perspectives of the ECG database, preprocessing, DL methodology, evaluation paradigm, performance metric, and code availability to identify research trends, challenges, and opportunities for DL-based ECG arrhythmia classification. Specifically, 368 studies meeting the eligibility criteria are included. A total of 223 (61%) studies use MIT-BIH Arrhythmia Database to design DL models. A total of 138 (38%) studies considered removing noise or artifacts in ECG signals, and 102 (28%) studies performed data augmentation to extend the minority arrhythmia categories. Convolutional neural networks are the dominant models (58.7%, 216) used in the reviewed studies while growing studies have integrated multiple DL structures in recent years. A total of 319 (86.7%) and 38 (10.3%) studies explicitly mention their evaluation paradigms, i.e., intra- and inter-patient paradigms, respectively, where notable performance degradation is observed in the inter-patient paradigm. Compared to the overall accuracy, the average F1 score, sensitivity, and precision are significantly lower in the selected studies. To implement the DL-based ECG classification in real clinical scenarios, leveraging diverse ECG databases, designing advanced denoising and data augmentation techniques, integrating novel DL models, and deeper investigation in the inter-patient paradigm could be future research opportunities.

/pdf/deep-learning-based-ecg-arrhythmia-classification-a-2ys1u19a.pdf

Deep Learning-Based ECG Arrhythmia Classification: A Systematic Review

Cardiovascular disease (CVD) causes significant mortality and remains the leading cause of death globally. Thus, to reduce mortality, early diagnosis by measurement of cardiac biomarkers and heartbeat signals presents fundamental importance. Traditional CVD examination requires bulky hospital instruments to conduct electrocardiography recording and immunoassay analysis, which are both time-consuming and inconvenient. Recently, development of biosensing technologies for rapid CVD marker screening attracted great attention. Thanks to the advancement in nanotechnology and bioelectronics, novel biosensor platforms are developed to achieve rapid detection, accurate quantification, and continuous monitoring throughout disease progression. A variety of sensing methodologies using chemical, electrochemical, optical, and electromechanical means are explored. This review first discusses the prevalence and common categories of CVD. Then, heartbeat signals and cardiac blood-based biomarkers that are widely employed in clinic, as well as their utilizations for disease prognosis, are summarized. Emerging CVD wearable and implantable biosensors and monitoring bioelectronics, allowing these cardiac markers to be continuously measured are introduced. Finally, comparisons of the pros and cons of these biosensing devices along with perspectives on future CVD biosensor research are presented.

Recent Advances in Cardiovascular Disease Biosensors and Monitoring Technologies.

Worldwide, population aging and unhealthy lifestyles have increased the incidence of high-risk health conditions such as cardiovascular diseases, sleep apnea, and other conditions. Recently, to facilitate early identification and diagnosis, efforts have been made in the research and development of new wearable devices to make them smaller, more comfortable, more accurate, and increasingly compatible with artificial intelligence technologies. These efforts can pave the way to the longer and continuous health monitoring of different biosignals, including the real-time detection of diseases, thus providing more timely and accurate predictions of health events that can drastically improve the healthcare management of patients. Most recent reviews focus on a specific category of disease, the use of artificial intelligence in 12-lead electrocardiograms, or on wearable technology. However, we present recent advances in the use of electrocardiogram signals acquired with wearable devices or from publicly available databases and the analysis of such signals with artificial intelligence methods to detect and predict diseases. As expected, most of the available research focuses on heart diseases, sleep apnea, and other emerging areas, such as mental stress. From a methodological point of view, although traditional statistical methods and machine learning are still widely used, we observe an increasing use of more advanced deep learning methods, specifically architectures that can handle the complexity of biosignal data. These deep learning methods typically include convolutional and recurrent neural networks. Moreover, when proposing new artificial intelligence methods, we observe that the prevalent choice is to use publicly available databases rather than collecting new data.

/pdf/electrocardiogram-monitoring-wearable-devices-and-artificial-10r2v4pf.pdf

Electrocardiogram Monitoring Wearable Devices and Artificial-Intelligence-Enabled Diagnostic Capabilities: A Review

Accurate diagnosis is the key to providing prompt and explicit treatment and disease management. The recognized biological method for the molecular diagnosis of infectious pathogens is polymerase chain reaction (PCR). Recently, deep learning approaches are playing a vital role in accurately identifying disease-related genes for diagnosis, prognosis, and treatment. The models reduce the time and cost used by wet-lab experimental procedures. Consequently, sophisticated computational approaches have been developed to facilitate the detection of cancer, a leading cause of death globally, and other complex diseases. In this review, we systematically evaluate the recent trends in multi-omics data analysis based on deep learning techniques and their application in disease prediction. We highlight the current challenges in the field and discuss how advances in deep learning methods and their optimization for application is vital in overcoming them. Ultimately, this review promotes the development of novel deep-learning methodologies for data integration, which is essential for disease detection and treatment.

A review of multi-omics data integration through deep learning approaches for disease diagnosis, prognosis, and treatment

The accurate electrocardiogram (ECG) interpretation is important for several potentially life-threatening cardiac diseases. Recently developed deep learning methods show their ability to distinguish some severe heart diseases. However, since deep neural network requires a high cost on memory consumption and computation, implementation scenarios of these interpretation methods are constrained to nonportable devices. Few commercial portable devices only have heartbeat detection ability, and therefore, only a few simple cardiac diseases can be diagnosed. In this article, to achieve diagnosing a wide range of cardiac diseases and continuous monitoring, a homecare-oriented ECG diagnosis platform is designed based on a large-scale multilabel deep conventional neural network. The accuracy of the proposed neural network model is guaranteed by our constructed large-scale ECG dataset, which is comprised of 206 468 standard 12-lead ECG recordings from 89 488 patients, with respect to 26 types of most common heart rhythms and conduction abnormalities. Meanwhile, targeting lightweight homecare or wearable applications, algorithm-hardware co-optimization is conducted to accelerate the model computation on an embedded platform with field-programmable gate array (FPGA) for continuous monitoring. Channel-level pruning and parameters quantization strategy are employed to optimize the network, and a reconfigurable accelerator hardware architecture is designed to accelerate the convolution computation on FPGA. The final quantified model achieved a promising $F_{1}$ score of 0.913% and 86.7% exact match ratio, in which parameters and floating-point operations per second (FLOPs) are significantly penalized compared to the original large-scale model. Real-time analysis is performed. Specifically, the average processing time for each ECG record is 2.895 s, and it can be applied to homecare or portable ECG diagnosis devices for continuous monitoring.

Homecare-Oriented ECG Diagnosis With Large-Scale Deep Neural Network for Continuous Monitoring on Embedded Devices

Accurate prediction of gas concentration is of great importance in many safe-based systems and applications. However, prediction accuracy of gas concentration is limited by not only the temporal evolution of gas concentration but also the spatial characteristics of gas dispersion. To capture the spatial and temporal dependences simultaneously, an evolutionary attention-based temporal graph convolutional network (EAT-GCN) is proposed, which has three outstanding features: (1) graph convolutional network (GCN) is used to capture spatial dependence by learning topological structures of a gas sensor network; (2) gated recurrent unit (GRU) is adopted to retain temporal dependence by learning dynamic changes of gas concentration, and (3) evolutionary attention is introduced to improve the ability of GRU to pay different degrees of attention to the sub-window features within multiple time steps. Finally, kernel extrapolation distribution mapping algorithm is employed to visualize the predicted results of gas concentration and update the gas distribution map. Compared with CNN, GCN, GRU, T-GCN, A3T-GCN and EA-GRU models, the proposed EAT-GCN model improves the prediction accuracy by 13.46%, 124.21%, 33.92%, 23.39%, 46.63%, and 23.97%, respectively. Experiments demonstrate that the designed model captures spatiotemporal correlation from gas concentration data and achieves better prediction accuracy than state-of-the-art baseline methods. 

Prediction of gas concentration evolution with evolutionary attention-based temporal graph convolutional network

In clinical practice, one patient may suffer from more than one arrhythmia simultaneously, that is, one ECG record may be associated with multiple types of arrhythmias. In fact, there are inherent dependencies between arrhythmias. However, previous studies have mainly focused on multi-class (single-label) ECG classification, which addresses each type of arrhythmia independently and ignores the multi-label correlation between different ECG abnormalities. To address the lack of ECG multi-label classification methods, we proposed a label correlation embedding guided network (LCEGNet) model to effectively recognize multi-label ECG arrhythmias and explore the correlation between ECG abnormalities. First, label correlation embedding was obtained based on the correlation matrix between different arrhythmias to guide feature extraction. Subsequently, the category-specific attention coefficient was obtained by calculating the cosine similarity coefficient between the label embedding and feature spaces. Experiments on public and self-collected ECG datasets were conducted. The LCEGNet achieved F1 scores of 0.777 and 0.872 and subset accuracy of 0.750 and 0.828 on the two datasets, respectively. A classification speed of 7.796 ms was achieved. The experimental results demonstrate that the proposed LCEGNet achieved approximately a 11% and 9.1% improvement in the F1 score and subset accuracy, respectively, compared with traditional ResNet architecture and a 4.3% and 5.54% improvement in the F1 score and subset accuracy, respectively, compared with the state-of-the-art approaches. 

Label correlation embedding guided network for multi-label ECG arrhythmia diagnosis

With the increasing penetration of wind power in power grid, accurate and reliable wind power forecasting is of great significance for the economic operation and safe dispatching of electrical power system. In practice, there exists complex spatial correlation between wind power variables, which brings great challenges to the accurate forecasting of wind power. However, traditional deep learning-based methods mostly focus on temporal feature while ignoring the spatial correlation between wind power variables, leading to low forecasting accuracy. To explore the spatial correlation among wind power variables and extract temporal features simultaneously, we propose a double attention-based spatial–temporal neural network (DA-STNet). First, graph attention network is employed to explore the spatial correlation between wind power variables based on a graph constructed by maximal information coefficient, which can consider the combined influences of multivariate on the wind power output. Then, by incorporating causal reasoning and data-driven element-wise attention measures, a novel temporal attention layer is proposed to extract the temporal feature of wind power sequences. Comprehensive experiments were conducted on one self-collected and one public dataset with three different multi-steps ahead forecasting tasks, and the experimental results demonstrated that the performance of proposed DA-STNet is superior to the existing methods on both real-world datasets. In the 24 h ahead experiment on the NWWPF dataset, the MSE of our model can reach as low as 0.136 and MAE can be decreased to 0.275. 

Spatial correlation learning based on graph neural network for medium-term wind power forecasting

3D semantic segmentation is significant for scene understanding in various domains, such as autonomous driving, mapping, and robotics. Existing research often enhances prediction accuracy by integrating data from camera and LiDAR (light detection and ranging). However, current fusion methods face two primary challenges. Firstly, they frequently project point clouds onto 2D coordinates and employ 2D segmentation networks, resulting in a significant loss of spatial depth information. Secondly, prevailing approaches struggle to establish long-range contextual understanding, limiting comprehensive predictions across multiple objects. To address these challenges, our work proposes the Adaptive Point-Pixel Fusion Network (APPFNet). In the initial stage of modality fusion, this method correlates points with pixels to provide spatial depth information to Red, Green, and Blue (RGB) images. Subsequently, we leverage our adaptive Multi-scale Modal Fusion module (MSMFM) and Dispersed Self-attention (DFSA) mechanism for further feature extraction and fusion. Additionally, we propose a flexible Neighbor Feature Aggregation module (NFAM) that enhances the model's ability to establish long-range contextual understanding by fusing information from neighboring points. Importantly, this module can be easily transplanted into other networks with simple modifications. Extensive experiments on SemanticKITTI and nuScenes demonstrate the superiority of our proposed approach, where APPFNet achieves a higher mean accuracy (mAcc) in predicting small objects in distant areas compared to state-of-the-art (SOTA) fusion methods. 

APPFNet: Adaptive point-pixel fusion network for 3D semantic segmentation with neighbor feature aggregation

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