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Lai K, Wang X, Cao C. A Continuous Non-Invasive Blood Pressure Prediction Method Based on Deep Sparse Residual U-Net Combined with Improved Squeeze and Excitation Skip Connections. SENSORS (BASEL, SWITZERLAND) 2024; 24:2721. [PMID: 38732827 PMCID: PMC11086107 DOI: 10.3390/s24092721] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/10/2024] [Revised: 04/09/2024] [Accepted: 04/19/2024] [Indexed: 05/13/2024]
Abstract
Arterial blood pressure (ABP) serves as a pivotal clinical metric in cardiovascular health assessments, with the precise forecasting of continuous blood pressure assuming a critical role in both preventing and treating cardiovascular diseases. This study proposes a novel continuous non-invasive blood pressure prediction model, DSRUnet, based on deep sparse residual U-net combined with improved SE skip connections, which aim to enhance the accuracy of using photoplethysmography (PPG) signals for continuous blood pressure prediction. The model first introduces a sparse residual connection approach for path contraction and expansion, facilitating richer information fusion and feature expansion to better capture subtle variations in the original PPG signals, thereby enhancing the network's representational capacity and predictive performance and mitigating potential degradation in the network performance. Furthermore, an enhanced SE-GRU module was embedded in the skip connections to model and weight global information using an attention mechanism, capturing the temporal features of the PPG pulse signals through GRU layers to improve the quality of the transferred feature information and reduce redundant feature learning. Finally, a deep supervision mechanism was incorporated into the decoder module to guide the lower-level network to learn effective feature representations, alleviating the problem of gradient vanishing and facilitating effective training of the network. The proposed DSRUnet model was trained and tested on the publicly available UCI-BP dataset, with the average absolute errors for predicting systolic blood pressure (SBP), diastolic blood pressure (DBP), and mean blood pressure (MBP) being 3.36 ± 6.61 mmHg, 2.35 ± 4.54 mmHg, and 2.21 ± 4.36 mmHg, respectively, meeting the standards set by the Association for the Advancement of Medical Instrumentation (AAMI), and achieving Grade A according to the British Hypertension Society (BHS) Standard for SBP and DBP predictions. Through ablation experiments and comparisons with other state-of-the-art methods, the effectiveness of DSRUnet in blood pressure prediction tasks, particularly for SBP, which generally yields poor prediction results, was significantly higher. The experimental results demonstrate that the DSRUnet model can accurately utilize PPG signals for real-time continuous blood pressure prediction and obtain high-quality and high-precision blood pressure prediction waveforms. Due to its non-invasiveness, continuity, and clinical relevance, the model may have significant implications for clinical applications in hospitals and research on wearable devices in daily life.
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Affiliation(s)
- Kaixuan Lai
- The Faculty of Printing, Packaging Engineering and Digital Media Technology, Xi’an University of Technology, Xi’an 710048, China; (K.L.); (X.W.)
- The Printing and Packaging Engineering Technology Research Center of Shaanxi Province, Xi’an 710048, China
| | - Xusheng Wang
- The Faculty of Printing, Packaging Engineering and Digital Media Technology, Xi’an University of Technology, Xi’an 710048, China; (K.L.); (X.W.)
- The Printing and Packaging Engineering Technology Research Center of Shaanxi Province, Xi’an 710048, China
| | - Congjun Cao
- The Faculty of Printing, Packaging Engineering and Digital Media Technology, Xi’an University of Technology, Xi’an 710048, China; (K.L.); (X.W.)
- The Printing and Packaging Engineering Technology Research Center of Shaanxi Province, Xi’an 710048, China
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Montanari A, Ferlini A, Balaji AN, Mascolo C, Kawsar F. EarSet: A Multi-Modal Dataset for Studying the Impact of Head and Facial Movements on In-Ear PPG Signals. Sci Data 2023; 10:850. [PMID: 38040725 PMCID: PMC10692189 DOI: 10.1038/s41597-023-02762-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Accepted: 11/17/2023] [Indexed: 12/03/2023] Open
Abstract
Photoplethysmography (PPG) is a simple, yet powerful technique to study blood volume changes by measuring light intensity variations. However, PPG is severely affected by motion artifacts, which hinder its trustworthiness. This problem is pressing in earables since head movements and facial expressions cause skin and tissue displacements around and inside the ear. Understanding such artifacts is fundamental to the success of earables for accurate cardiovascular health monitoring. However, the lack of in-ear PPG datasets prevents the research community from tackling this challenge. In this work, we report on the design of an ear tip featuring a 3-channels PPG and a co-located 6-axis motion sensor. This, enables sensing PPG data at multiple wavelengths and the corresponding motion signature from both ears. Leveraging our device, we collected a multi-modal dataset from 30 participants while performing 16 natural motions, including both head/face and full body movements. This unique dataset will greatly support research towards making in-ear vital signs sensing more accurate and robust, thus unlocking the full potential of the next-generation PPG-equipped earables.
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Affiliation(s)
| | - Andrea Ferlini
- Nokia Bell Labs, Cambridge, UK.
- University of Cambridge, Cambridge, UK.
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Pankaj, Kumar A, Kumar M, Komaragiri R. Optimized deep neural network models for blood pressure classification using Fourier analysis-based time-frequency spectrogram of photoplethysmography signal. Biomed Eng Lett 2023; 13:739-750. [PMID: 37872982 PMCID: PMC10590347 DOI: 10.1007/s13534-023-00296-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Revised: 05/28/2023] [Accepted: 06/09/2023] [Indexed: 10/25/2023] Open
Abstract
Appropriate blood pressure (BP) management through continuous monitoring and rapid diagnosis helps to take preventive care against cardiovascular diseases (CVD). As hypertension is one of the leading causes of CVDs, keeping hypertension under control by a timely screening of subjects becomes lifesaving. This work proposes estimating BP from motion artifact-affected photoplethysmography signals (PPG) by applying signal processing techniques in realtime. This paper proposes a deep neural network-based methodology to accurately classify PPG signals using a Fourier theory-based time-frequency (TF) spectrogram. This work uses the Fourier decomposition method (FDM) to transform a PPG signal into a TF spectrogram. In the proposed work, the last three layers of the pre-trained deep neural network, namely, GoogleNet, DenseNet, and AlexNet, are modified and then used to classify the PPG signal into normotension, pre-hypertension, and hypertension. The proposed framework is trained and tested using the MIMIC-III and PPG-BP databases using five-fold training and testing. Out of the three deep neural networks, the proposed framework with the DenseNet-201 network performs best, with a test accuracy of 96.5%. The proposed work uses FDM to compute the TF spectrogram to accurately separate the motion artifacts and noise components from a noise-corrupted PPG signal. Capturing more frequency components that contain more information from PPG signals makes the deep neural networks extract better and more meaningful features. Thus, training a deep neural network model with clean PPG signal features improves the generalized capability of a BP classification model when tested in realtime.
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Affiliation(s)
- Pankaj
- Department of Electronics and Communication Engineering, Bennett University, Greater Noida, India
| | - Ashish Kumar
- School of Electronics Engineering, Vellore Institute of Technology, Chennai, Tamil Nadu India
| | - Manjeet Kumar
- Department of Electronics and Communication Engineering, Delhi Technological University, Delhi, India
| | - Rama Komaragiri
- Department of Electronics and Communication Engineering, Bennett University, Greater Noida, India
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Gagnon-Turcotte G, Cote-Allard U, Mascret Q, Torresen J, Gosselin B. Photoplethysmography-based derivation of physiological information using the BioPoint. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-5. [PMID: 38083646 DOI: 10.1109/embc40787.2023.10340642] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
The BioPoint is a new wireless and wearable device, targeting both the ambulatory and on-site monitoring of biosignals. It is described as being capable of streaming and recording the i) electromyography, ii) electrocardiography, iii) electrodermal activity, iv) photoplethysmography, v) skin temperature and vi) actigraphy simultaneously, while making the raw signals recorded by the sensors readily available. However, an in-depth assessment of the biophysical signals recorded by this device, as well as its ability to derive vital signs and other health metrics, remains to be carried out. Consequently, this work proposes a preliminary study to evaluate the quality of the signals that can be acquired by this wearable with a focus on the derivation of heart rate and peripheral blood oxygenation via photoplethysmography. The device is quantitatively compared to the medical-grade pulse oximeter NoninConnect 3245, by Nonin inc. This study was performed with participants wearing the BioPoint at different positions on the body (finger, wrist, forearm, biceps and plantar arch), while the NoninConnect was worn on the fingertip and used as the ground truth. The results show that the BioPoint can accurately determine both heart rate and oxygen saturation from various locations on the body. However, as the BioPoint's photoplethysmograph is not calibrated it cannot be used for medical purposes (non-medical-grade).
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Qin K, Huang W, Zhang T, Tang S. Machine learning and deep learning for blood pressure prediction: a methodological review from multiple perspectives. Artif Intell Rev 2022. [DOI: 10.1007/s10462-022-10353-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
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Mehrabadi MA, Aqajari SAH, Zargari AHA, Dutt N, Rahmani AM. Novel Blood Pressure Waveform Reconstruction from Photoplethysmography using Cycle Generative Adversarial Networks. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2022; 2022:1906-1909. [PMID: 36086575 DOI: 10.1109/embc48229.2022.9871962] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Continuous monitoring of blood pressure (BP) can help individuals manage their chronic diseases such as hypertension, requiring non-invasive measurement methods in free-living conditions. Recent approaches fuse Photoplethys-mograph (PPG) and electrocardiographic (ECG) signals using different machine and deep learning approaches to non-invasively estimate BP; however, they fail to reconstruct the complete signal, leading to less accurate models. In this paper, we propose a cycle generative adversarial network (CycleGAN) based approach to extract a BP signal known as ambulatory blood pressure (ABP) from a clean PPG signal. Our approach uses a cycle generative adversarial network that extends the GAN architecture for domain translation, and outperforms state-of-the-art approaches by up to 2× in BP estimation.
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Langlais ÉL, Thériault-Lauzier P, Marquis-Gravel G, Kulbay M, So DY, Tanguay JF, Ly HQ, Gallo R, Lesage F, Avram R. Novel Artificial Intelligence Applications in Cardiology: Current Landscape, Limitations, and the Road to Real-World Applications. J Cardiovasc Transl Res 2022:10.1007/s12265-022-10260-x. [PMID: 35460017 DOI: 10.1007/s12265-022-10260-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/21/2022] [Accepted: 04/11/2022] [Indexed: 11/25/2022]
Abstract
Cardiovascular diseases are the leading cause of death globally and contribute significantly to the cost of healthcare. Artificial intelligence (AI) is poised to reshape cardiology. Using supervised and unsupervised learning, the two main branches of AI, several applications have been developed in recent years to improve risk prediction, allow large-scale analysis of medical data, and phenotype patients for personalized medicine. In this review, we examine the key advances in AI in cardiology and its limitations regarding bias in the data, standardization in reporting, data access, and model trust and accountability in cases of error. Finally, we discuss implementation methods to unleash AI's potential in making healthcare more accurate and efficient. Several steps need to be followed and challenges overcome in order to successfully integrate AI in clinical practice and ensure its longevity.
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Affiliation(s)
- Élodie Labrecque Langlais
- Division of Cardiology, Department of Medicine, Montreal Heart Institute, University of Montreal, 5000 Belanger Street, Montreal, QC, H1T 1C8, Canada
- Biomedical Engineering, École Polytechnique de Montréal, 2500 Chemin de Polytechnique, Montreal, QC, H3T 1J4, Canada
| | - Pascal Thériault-Lauzier
- Division of Cardiology, Department of Medicine, University of Ottawa Heart Institute, University of Ottawa, 40 Ruskin Street, Ottawa, ON, K1Y 4W7, Canada
| | - Guillaume Marquis-Gravel
- Division of Cardiology, Department of Medicine, Montreal Heart Institute, University of Montreal, 5000 Belanger Street, Montreal, QC, H1T 1C8, Canada
- Department of Medicine, University of Montreal, 2900 Edouard Montpetit Blvd, Montreal, QC, H3T 1J4, Canada
| | - Merve Kulbay
- Department of Medicine, University of Montreal, 2900 Edouard Montpetit Blvd, Montreal, QC, H3T 1J4, Canada
| | - Derek Y So
- Division of Cardiology, Department of Medicine, University of Ottawa Heart Institute, University of Ottawa, 40 Ruskin Street, Ottawa, ON, K1Y 4W7, Canada
| | - Jean-François Tanguay
- Division of Cardiology, Department of Medicine, Montreal Heart Institute, University of Montreal, 5000 Belanger Street, Montreal, QC, H1T 1C8, Canada
| | - Hung Q Ly
- Division of Cardiology, Department of Medicine, Montreal Heart Institute, University of Montreal, 5000 Belanger Street, Montreal, QC, H1T 1C8, Canada
| | - Richard Gallo
- Division of Cardiology, Department of Medicine, Montreal Heart Institute, University of Montreal, 5000 Belanger Street, Montreal, QC, H1T 1C8, Canada
- Department of Medicine, University of Montreal, 2900 Edouard Montpetit Blvd, Montreal, QC, H3T 1J4, Canada
| | - Frédéric Lesage
- Division of Cardiology, Department of Medicine, Montreal Heart Institute, University of Montreal, 5000 Belanger Street, Montreal, QC, H1T 1C8, Canada
- Biomedical Engineering, École Polytechnique de Montréal, 2500 Chemin de Polytechnique, Montreal, QC, H3T 1J4, Canada
| | - Robert Avram
- Division of Cardiology, Department of Medicine, Montreal Heart Institute, University of Montreal, 5000 Belanger Street, Montreal, QC, H1T 1C8, Canada.
- Department of Medicine, University of Montreal, 2900 Edouard Montpetit Blvd, Montreal, QC, H3T 1J4, Canada.
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Park J, Seok HS, Kim SS, Shin H. Photoplethysmogram Analysis and Applications: An Integrative Review. Front Physiol 2022; 12:808451. [PMID: 35300400 PMCID: PMC8920970 DOI: 10.3389/fphys.2021.808451] [Citation(s) in RCA: 49] [Impact Index Per Article: 24.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2021] [Accepted: 12/21/2021] [Indexed: 12/03/2022] Open
Abstract
Beyond its use in a clinical environment, photoplethysmogram (PPG) is increasingly used for measuring the physiological state of an individual in daily life. This review aims to examine existing research on photoplethysmogram concerning its generation mechanisms, measurement principles, clinical applications, noise definition, pre-processing techniques, feature detection techniques, and post-processing techniques for photoplethysmogram processing, especially from an engineering point of view. We performed an extensive search with the PubMed, Google Scholar, Institute of Electrical and Electronics Engineers (IEEE), ScienceDirect, and Web of Science databases. Exclusion conditions did not include the year of publication, but articles not published in English were excluded. Based on 118 articles, we identified four main topics of enabling PPG: (A) PPG waveform, (B) PPG features and clinical applications including basic features based on the original PPG waveform, combined features of PPG, and derivative features of PPG, (C) PPG noise including motion artifact baseline wandering and hypoperfusion, and (D) PPG signal processing including PPG preprocessing, PPG peak detection, and signal quality index. The application field of photoplethysmogram has been extending from the clinical to the mobile environment. Although there is no standardized pre-processing pipeline for PPG signal processing, as PPG data are acquired and accumulated in various ways, the recently proposed machine learning-based method is expected to offer a promising solution.
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Affiliation(s)
- Junyung Park
- Department of Biomedical Engineering, Chonnam National University, Yeosu, South Korea
| | - Hyeon Seok Seok
- Department of Biomedical Engineering, Chonnam National University, Yeosu, South Korea
| | - Sang-Su Kim
- Department of Biomedical Engineering, Chonnam National University, Yeosu, South Korea
| | - Hangsik Shin
- Department of Convergence Medicine, University of Ulsan College of Medicine, Asan Medical Center, Seoul, South Korea
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