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Wu YC, Lin CH, Chiu LW, Wu BF, Chung ML, Tang SC, Sun Y. Contact-Free Atrial Fibrillation Screening With Attention Network. IEEE J Biomed Health Inform 2024; 28:5124-5135. [PMID: 38412073 DOI: 10.1109/jbhi.2024.3368049] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/29/2024]
Abstract
Atrial Fibrillation (AF) screening from face videos has become popular with the trend of telemedicine and telehealth in recent years. In this study, the largest facial image database for camera-based AF detection is proposed. There are 657 participants from two clinical sites and each of them is recorded for about 10 minutes of video data, which can be further processed as over 10 000 segments around 30 seconds, where the duration setting is referred to the guideline of AF diagnosis. It is also worth noting that, 2 979 segments are segment-wise labeled, that is, every rhythm is independently labeled with AF or not. Besides, all labels are confirmed by the cardiologist manually. Various environments, talking, facial expressions, and head movements are involved in data collection, which meets the situations in practical usage. Specific to camera-based AF screening, a novel CNN-based architecture equipped with an attention mechanism is proposed. It is capable of fusing heartbeat consistency, heart rate variability derived from remote photoplethysmography, and motion features simultaneously to reliable outputs. With the proposed model, the performance of intra-database evaluation comes up to 96.62% of sensitivity, 90.61% of specificity, and 0.96 of AUC. Furthermore, to check the capability of adaptation of the proposed method thoroughly, the cross-database evaluation is also conducted, and the performance also reaches about 90% on average with the AUCs being over 0.94 in both clinical sites.
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Wang Z, Fan J, Dai Y, Zheng H, Wang P, Chen H, Wu Z. Intelligent Detection Method of Atrial Fibrillation by CEPNCC-BiLSTM Based on Long-Term Photoplethysmography Data. SENSORS (BASEL, SWITZERLAND) 2024; 24:5243. [PMID: 39204938 PMCID: PMC11359430 DOI: 10.3390/s24165243] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/18/2024] [Revised: 07/14/2024] [Accepted: 07/25/2024] [Indexed: 09/04/2024]
Abstract
Atrial fibrillation (AF) is the most prevalent arrhythmia characterized by intermittent and asymptomatic episodes. However, traditional detection methods often fail to capture the sporadic and intricate nature of AF, resulting in an increased risk of false-positive diagnoses. To address these challenges, this study proposes an intelligent AF detection and diagnosis method that integrates Complementary Ensemble Empirical Mode Decomposition, Power-Normalized Cepstral Coefficients, Bi-directional Long Short-term Memory (CEPNCC-BiLSTM), and photoelectric volumetric pulse wave technology to enhance accuracy in detecting AF. Compared to other approaches, the proposed method demonstrates faster preprocessing efficiency and higher sensitivity in detecting AF while effectively filtering out false alarms from photoplethysmography (PPG) recordings of non-AF patients. Considering the limitations of conventional AF detection evaluation systems that lack a comprehensive assessment of efficiency and accuracy, this study proposes the ET-score evaluation system based on F-measurement, which incorporates both computational speed and accuracy to provide a holistic assessment of overall performance. Evaluated with the ET-score, the CEPNCC-BiLSTM method outperforms EEMD-based improved Power-Normalized Cepstral Coefficients and Bi-directional Long Short-term Memory (EPNCC-BiLSTM), Support Vector Machine (SVM), EPNCC-SVM, and CEPNCC-SVM methods. Notably, this approach achieves an outstanding accuracy rate of up to 99.2% while processing PPG recordings within 5 s, highlighting its potential for long-term AF monitoring.
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Affiliation(s)
- Zhifeng Wang
- School of Mechatronics Engineering and Automation, Foshan University, Foshan 528000, China; (Z.W.); (J.F.); (H.Z.); (Z.W.)
- Guangdong Provincial Key Laboratory of Industrial Intelligent Inspection Technology, Foshan University, Foshan 528000, China
| | - Jinwei Fan
- School of Mechatronics Engineering and Automation, Foshan University, Foshan 528000, China; (Z.W.); (J.F.); (H.Z.); (Z.W.)
- Guangdong Provincial Key Laboratory of Industrial Intelligent Inspection Technology, Foshan University, Foshan 528000, China
| | - Yi Dai
- School of Education, City University of Macau, Macau 999078, China
| | - Huannan Zheng
- School of Mechatronics Engineering and Automation, Foshan University, Foshan 528000, China; (Z.W.); (J.F.); (H.Z.); (Z.W.)
- Guangdong Provincial Key Laboratory of Industrial Intelligent Inspection Technology, Foshan University, Foshan 528000, China
| | - Peizhou Wang
- Cosmetic Dermatology Department, Dermatology Hospital of Southern Medical University, Guangzhou 510091, China;
| | - Haichu Chen
- School of Mechatronics Engineering and Automation, Foshan University, Foshan 528000, China; (Z.W.); (J.F.); (H.Z.); (Z.W.)
- Guangdong Provincial Key Laboratory of Industrial Intelligent Inspection Technology, Foshan University, Foshan 528000, China
| | - Zetao Wu
- School of Mechatronics Engineering and Automation, Foshan University, Foshan 528000, China; (Z.W.); (J.F.); (H.Z.); (Z.W.)
- Guangdong Provincial Key Laboratory of Industrial Intelligent Inspection Technology, Foshan University, Foshan 528000, China
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Huang B, Hu S, Liu Z, Lin CL, Su J, Zhao C, Wang L, Wang W. Challenges and prospects of visual contactless physiological monitoring in clinical study. NPJ Digit Med 2023; 6:231. [PMID: 38097771 PMCID: PMC10721846 DOI: 10.1038/s41746-023-00973-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2023] [Accepted: 11/21/2023] [Indexed: 12/17/2023] Open
Abstract
The monitoring of physiological parameters is a crucial topic in promoting human health and an indispensable approach for assessing physiological status and diagnosing diseases. Particularly, it holds significant value for patients who require long-term monitoring or with underlying cardiovascular disease. To this end, Visual Contactless Physiological Monitoring (VCPM) is capable of using videos recorded by a consumer camera to monitor blood volume pulse (BVP) signal, heart rate (HR), respiratory rate (RR), oxygen saturation (SpO2) and blood pressure (BP). Recently, deep learning-based pipelines have attracted numerous scholars and achieved unprecedented development. Although VCPM is still an emerging digital medical technology and presents many challenges and opportunities, it has the potential to revolutionize clinical medicine, digital health, telemedicine as well as other areas. The VCPM technology presents a viable solution that can be integrated into these systems for measuring vital parameters during video consultation, owing to its merits of contactless measurement, cost-effectiveness, user-friendly passive monitoring and the sole requirement of an off-the-shelf camera. In fact, the studies of VCPM technologies have been rocketing recently, particularly AI-based approaches, but few are employed in clinical settings. Here we provide a comprehensive overview of the applications, challenges, and prospects of VCPM from the perspective of clinical settings and AI technologies for the first time. The thorough exploration and analysis of clinical scenarios will provide profound guidance for the research and development of VCPM technologies in clinical settings.
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Affiliation(s)
- Bin Huang
- AI Research Center, Hangzhou Innovation Institute, Beihang University, 99 Juhang Rd., Binjiang Dist., Hangzhou, Zhejiang, China.
- School of Automation Science and Electrical Engineering, Beihang University, Beijing, China.
| | - Shen Hu
- Department of Obstetrics, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
- Department of Epidemiology, The Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Zimeng Liu
- School of Automation Science and Electrical Engineering, Beihang University, Beijing, China
| | - Chun-Liang Lin
- College of Electrical Engineering and Computer Science, National Chung Hsing University, 145 Xingda Rd., South Dist., Taichung, Taiwan.
| | - Junfeng Su
- Department of General Intensive Care Unit, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
- Key Laboratory of Early Warning and Intervention of Multiple Organ Failure, China National Ministry of Education, Hangzhou, Zhejiang, China
| | - Changchen Zhao
- AI Research Center, Hangzhou Innovation Institute, Beihang University, 99 Juhang Rd., Binjiang Dist., Hangzhou, Zhejiang, China
| | - Li Wang
- Department of Rehabilitation Medicine, The First Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Wenjin Wang
- Department of Biomedical Engineering, Southern University of Science and Technology, 1088 Xueyuan Ave, Nanshan Dist., Shenzhen, Guangdong, China.
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Li B, Zhang W, Fu H, Liu H, Xu F. Multi-Level Constrained Intra and Inter Subject Feature Representation for Facial Video Based BVP Signal Measurement. IEEE J Biomed Health Inform 2023; 27:3948-3957. [PMID: 37224379 DOI: 10.1109/jbhi.2023.3273557] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/26/2023]
Abstract
Facial video-based blood volume pulse (BVP) signal measurement holds great potential for remote health monitoring, while existing methods have issues with convolutional kernel perceptual field constraints. This article proposes an end-to-end multi-level constrained spatiotemporal representation structure for facial video-based BVP signal measurement. First, an intra- and inter-subject feature representation is proposed to strengthen the BVP-related features generation at high, semantic, and shallow levels, respectively. Second, the global-local association is presented to enhance BVP signal period pattern learning, and the global temporal features are introduced into the local spatial convolution of each frame by adaptive kernel weights. Finally, the multi-dimensional fused features are mapped to one-dimensional BVP signals by the task-oriented signal estimator. The experimental results on the publicly available MMSE-HR dataset demonstrate that the proposed structure overperforms state-of-the-art methods (e.g., AutoHR) in BVP signal measurement, with a 20% and 40% reduction in mean absolute error and root mean squared error, respectively. The proposed structure would be a powerful tool for telemedical and non-contact heart health monitoring.
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Aldughayfiq B, Ashfaq F, Jhanjhi NZ, Humayun M. A Deep Learning Approach for Atrial Fibrillation Classification Using Multi-Feature Time Series Data from ECG and PPG. Diagnostics (Basel) 2023; 13:2442. [PMID: 37510187 PMCID: PMC10377944 DOI: 10.3390/diagnostics13142442] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Revised: 07/08/2023] [Accepted: 07/13/2023] [Indexed: 07/30/2023] Open
Abstract
Atrial fibrillation is a prevalent cardiac arrhythmia that poses significant health risks to patients. The use of non-invasive methods for AF detection, such as Electrocardiogram and Photoplethysmogram, has gained attention due to their accessibility and ease of use. However, there are challenges associated with ECG-based AF detection, and the significance of PPG signals in this context has been increasingly recognized. The limitations of ECG and the untapped potential of PPG are taken into account as this work attempts to classify AF and non-AF using PPG time series data and deep learning. In this work, we emploted a hybrid deep neural network comprising of 1D CNN and BiLSTM for the task of AF classification. We addressed the under-researched area of applying deep learning methods to transmissive PPG signals by proposing a novel approach. Our approach involved integrating ECG and PPG signals as multi-featured time series data and training deep learning models for AF classification. Our hybrid 1D CNN and BiLSTM model achieved an accuracy of 95% on test data in identifying atrial fibrillation, showcasing its strong performance and reliable predictive capabilities. Furthermore, we evaluated the performance of our model using additional metrics. The precision of our classification model was measured at 0.88, indicating its ability to accurately identify true positive cases of AF. The recall, or sensitivity, was measured at 0.85, illustrating the model's capacity to detect a high proportion of actual AF cases. Additionally, the F1 score, which combines both precision and recall, was calculated at 0.84, highlighting the overall effectiveness of our model in classifying AF and non-AF cases.
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Affiliation(s)
- Bader Aldughayfiq
- Department of Information Systems, College of Computer and Information Sciences, Jouf University, Sakaka 72388, Saudi Arabia
| | - Farzeen Ashfaq
- School of Computer Science, SCS, Taylor's University, Subang Jaya 47500, Malaysia
| | - N Z Jhanjhi
- School of Computer Science, SCS, Taylor's University, Subang Jaya 47500, Malaysia
| | - Mamoona Humayun
- Department of Information Systems, College of Computer and Information Sciences, Jouf University, Sakaka 72388, Saudi Arabia
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