Atrial fibrillation (AF) is one of the most common sustained chronic cardiac arrhythmia in elderly population, associated with a high mortality and morbidity in stroke, heart failure, coronary artery disease, systemic thromboembolism, etc. The early detection of AF is necessary for averting the possibility of disability or mortality. However, AF detection remains problematic due to its episodic pattern. In this paper, a multiscaled fusion of deep convolutional neural network (MS-CNN) is proposed to screen out AF recordings from single lead short electrocardiogram (ECG) recordings. The MS-CNN employs the architecture of two-stream convolutional networks with different filter sizes to capture features of different scales. The experimental results show that the proposed MS-CNN achieves 96.99% of classification accuracy on ECG recordings cropped/padded to 5 s. Especially, the best classification accuracy, 98.13%, is obtained on ECG recordings of 20 s. Compared with artificial neural network, shallow single-stream CNN, and VisualGeometry group network, the MS-CNN can achieve the better classification performance. Meanwhile, visualization of the learned features from the MS-CNN demonstrates its superiority in extracting linear separable ECG features without hand-craft feature engineering. The excellent AF screening performance of the MS-CNN can satisfy the most elders for daily monitoring with wearable devices.

With the rapid development of wearable IoT devices, a significant amount of sensitive/private information collected by them poses a considerable challenge to the security of the wearable IoT devices. The accelerometer-based gait recognition is considered as an emerging and fast-evolving technology in security and access control fields and has achieved outstanding performance at certain fixed walking speeds. However, the gait recognition performance of the above technology deteriorates dramatically when the walking speed varies. To address this issue, both the speed-adaptive gait cycle segmentation method and individualized matching threshold generation method were proposed in this paper. Furthermore, the contrast experiments were conducted on the ZJU-GaitAcc public dataset sampled from five different body locations and the self-collected dataset sampled at various walking speeds. The experimental results indicated the average gait recognition and user authentication rates of 96.9% and 91.75%, respectively. As compared to the available state-of-the-art methods based on the fixed walking speeds and constant thresholds, the proposed method improved the gait recognition by 25.8% and user authentication by 21.5%.

Sleep stage classification is of great importance in human health monitoring and disease diagnosing. Clinically, visual-inspected classifying sleep into different stages is quite time consuming and highly relies on the expertise of sleep specialists. Many automated models for sleep stage classification have been proposed in previous studies but their performances still exist a gap to the real clinical application. In this work, we propose a novel multi-view fusion network named MVF-SleepNet based on multi-modal physiological signals of electroencephalography (EEG), electrocardiography (ECG), electrooculography (EOG), and electromyography (EMG). To capture the relationship representation among multi-modal physiological signals, we construct two views of Time-frequency images (TF images) and Graph-learned graphs (GL graphs). To learn the spectral-temporal representation from sequentially timed TF images, the combination of VGG-16 and GRU networks is utilized. To learn the spatial-temporal representation from sequentially timed GL graphs, the combination of Chebyshev graph convolution and temporal convolution networks is employed. Fusing the spectral-temporal representation and spatial-temporal representation can further boost the performance of sleep stage classification. A large number of experiment results on the publicly available datasets of ISRUC-S1 and ISRUC-S3 show that the MVF-SleepNet achieves overall accuracy of 0.821, $F_{1}$ score of 0.802 and Kappa of 0.768 on ISRUC-S1 dataset, and accuracy of 0.841, $F_{1}$ score of 0.828 and Kappa of 0.795 on ISRUC-S3 dataset. The MVF-SleepNet achieves competitive results on both datasets of ISRUC-S1 and ISRUC-S3 for sleep stage classification compared to the state-of-the-art baselines. The source code of MVF-SleepNet is available on Github ( https://github.com/YJPai65/MVF-SleepNet ).

Background: Major depressive disorder (MDD) is a common mental illness, characterized by persistent depression, sadness, despair, etc., troubling people’s daily life and work seriously. Methods: In this work, we present a novel automatic MDD detection framework based on EEG signals. First of all, we derive highly MDD-correlated features, calculating the ratio of extracted features from EEG signals at frequency bands between $$\beta$$β and $$\alpha$$α . Then, a two-stage feature selection method named PAR is presented with the sequential combination of Pearson correlation coefficient (PCC) and recursive feature elimination (RFE), where the advantages lie in minimizing the feature searching space. Finally, we employ widely used machine learning methods of support vector machine (SVM), logistic regression (LR), and linear regression (LNR) for MDD detection with the merit of feature interpretability. Results: Experiment results show that our proposed MDD detection framework achieves competitive results. The accuracy and $$F_{1}$$F 1 score are up to 0.9895 and 0.9846, respectively. Meanwhile, the regression determination coefficient $$R^2$$R 2 for MDD severity assessment is up to 0.9479. Compared with existing MDD detection methods with the best accuracy of 0.9840 and $$F_1$$F 1 score of 0.97, our proposed framework achieves the state-of-the-art MDD detection performance. Conclusions: Development of this MDD detection framework can be potentially deployed into a medical system to aid physicians to screen out MDD patients.

Sleep apnea (SA) is a common sleep-related breathing disorder that tends to induce a series of complications, such as pediatric intracranial hypertension, psoriasis, and even sudden death. Therefore, early diagnosis and treatment can effectively prevent malignant complications SA incurs. Portable monitoring (PM) is a widely used tool for people to monitor their sleep conditions outside of hospitals. In this study, we focus on SA detection based on single-lead electrocardiogram (ECG) signals which are easily collected by PM. We propose a bottleneck attention based fusion network named BAFNet, which mainly includes five parts of RRI (R-R intervals) stream network, RPA (R-peak amplitudes) stream network, global query generation, feature fusion, and classifier. To learn the feature representation of RRI/RPA segments, fully convolutional networks (FCN) with cross-learning are proposed. Meanwhile, to control the information flow between RRI and RPA networks, a global query generation with bottleneck attention is proposed. To further improve the SA detection performance, a hard sample scheme with k-means clustering is employed. Experiment results show that BAFNet can achieve competitive results, which are superior to the state-of-the-art SA detection methods. It means that BAFNet has great potential to be applied in the home sleep apnea test (HSAT) for sleep condition monitoring. The source code is released at https://github.com/Bettycxh/Bottleneck-Attention-Based-Fusion-Network-for-Sleep-Apnea-Detection.