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Hu D, Gao W, Ang KK, Hu M, Chuai G, Huang R. Smart Sleep Monitoring: Sparse Sensor-Based Spatiotemporal CNN for Sleep Posture Detection. SENSORS (BASEL, SWITZERLAND) 2024; 24:4833. [PMID: 39123879 PMCID: PMC11314976 DOI: 10.3390/s24154833] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/25/2024] [Revised: 07/20/2024] [Accepted: 07/24/2024] [Indexed: 08/12/2024]
Abstract
Sleep quality is heavily influenced by sleep posture, with research indicating that a supine posture can worsen obstructive sleep apnea (OSA) while lateral postures promote better sleep. For patients confined to beds, regular changes in posture are crucial to prevent the development of ulcers and bedsores. This study presents a novel sparse sensor-based spatiotemporal convolutional neural network (S3CNN) for detecting sleep posture. This S3CNN holistically incorporates a pair of spatial convolution neural networks to capture cardiorespiratory activity maps and a pair of temporal convolution neural networks to capture the heart rate and respiratory rate. Sleep data were collected in actual sleep conditions from 22 subjects using a sparse sensor array. The S3CNN was then trained to capture the spatial pressure distribution from the cardiorespiratory activity and temporal cardiopulmonary variability from the heart and respiratory data. Its performance was evaluated using three rounds of 10 fold cross-validation on the 8583 data samples collected from the subjects. The results yielded 91.96% recall, 92.65% precision, and 93.02% accuracy, which are comparable to the state-of-the-art methods that use significantly more sensors for marginally enhanced accuracy. Hence, the proposed S3CNN shows promise for sleep posture monitoring using sparse sensors, demonstrating potential for a more cost-effective approach.
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Affiliation(s)
- Dikun Hu
- School of Information and Communication Engineering, Beijing University of Posts and Telecommunications (BUPT), No. 10 Xitucheng Road, Haidian District, Beijing 100876, China; (D.H.); (W.G.); (G.C.)
| | - Weidong Gao
- School of Information and Communication Engineering, Beijing University of Posts and Telecommunications (BUPT), No. 10 Xitucheng Road, Haidian District, Beijing 100876, China; (D.H.); (W.G.); (G.C.)
| | - Kai Keng Ang
- Institute for Infocomm Research, Agency for Science, Technology and Research (A*STAR), 1 Fusionopolis Way, #21-01 Connexis (South Tower), Singapore 138632, Singapore;
- College of Computing and Data Science, Nanyang Technological University, 50 Nanyang Ave., Singapore 639798, Singapore
| | - Mengjiao Hu
- Institute for Infocomm Research, Agency for Science, Technology and Research (A*STAR), 1 Fusionopolis Way, #21-01 Connexis (South Tower), Singapore 138632, Singapore;
| | - Gang Chuai
- School of Information and Communication Engineering, Beijing University of Posts and Telecommunications (BUPT), No. 10 Xitucheng Road, Haidian District, Beijing 100876, China; (D.H.); (W.G.); (G.C.)
| | - Rong Huang
- Department of Respiratory and Critical Care Medicine, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 1 Shuaifuyuan Wangfujing, Beijing 100730, China;
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Chen X, Zhang H, Li Z, Liu S, Zhou Y. Continuous Monitoring of Heart Rate Variability and Respiration for the Remote Diagnosis of Chronic Obstructive Pulmonary Disease: Prospective Observational Study. JMIR Mhealth Uhealth 2024; 12:e56226. [PMID: 39024559 PMCID: PMC11294786 DOI: 10.2196/56226] [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: 01/12/2024] [Revised: 06/07/2024] [Accepted: 06/18/2024] [Indexed: 07/20/2024] Open
Abstract
BACKGROUND Conventional daytime monitoring in a single day may be influenced by factors such as motion artifacts and emotions, and continuous monitoring of nighttime heart rate variability (HRV) and respiration to assist in chronic obstructive pulmonary disease (COPD) diagnosis has not been reported yet. OBJECTIVE The aim of this study was to explore and compare the effects of continuously monitored HRV, heart rate (HR), and respiration during night sleep on the remote diagnosis of COPD. METHODS We recruited patients with different severities of COPD and healthy controls between January 2021 and November 2022. Vital signs such as HRV, HR, and respiration were recorded using noncontact bed sensors from 10 PM to 8 AM of the following day, and the recordings of each patient lasted for at least 30 days. We obtained statistical means of HRV, HR, and respiration over time periods of 7, 14, and 30 days by continuous monitoring. Additionally, the effects that the statistical means of HRV, HR, and respiration had on COPD diagnosis were evaluated at different times of recordings. RESULTS In this study, 146 individuals were enrolled: 37 patients with COPD in the case group and 109 participants in the control group. The median number of continuous night-sleep monitoring days per person was 56.5 (IQR 32.0-113.0) days. Using the features regarding the statistical means of HRV, HR, and respiration over 1, 7, 14, and 30 days, binary logistic regression classification of COPD yielded an accuracy, Youden index, and area under the receiver operating characteristic curve of 0.958, 0.904, and 0.989, respectively. The classification performance for COPD diagnosis was directionally proportional to the monitoring duration of vital signs at night. The importance of the features for diagnosis was determined by the statistical means of respiration, HRV, and HR, which followed the order of respiration > HRV > HR. Specifically, the statistical means of the duration of respiration rate faster than 21 times/min (RRF), high frequency band power of 0.15-0.40 Hz (HF), and respiration rate (RR) were identified as the top 3 most significant features for classification, corresponding to cutoff values of 0.1 minute, 1316.3 nU, and 16.3 times/min, respectively. CONCLUSIONS Continuous monitoring of nocturnal vital signs has significant potential for the remote diagnosis of COPD. As the duration of night-sleep monitoring increased from 1 to 30 days, the statistical means of HRV, HR, and respiration showed a better reflection of an individual's health condition compared to monitoring the vital signs in a single day or night, and better was the classification performance for COPD diagnosis. Further, the statistical means of RRF, HF, and RR are crucial features for diagnosing COPD, demonstrating the importance of monitoring HRV and respiration during night sleep.
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Affiliation(s)
- Xiaolan Chen
- Guangdong Basic Research Center of Excellence for Structure and Fundamental Interactions of Matter, Guangdong Provincial Key Laboratory of Quantum Engineering and Quantum Materials, School of Physics, South China Normal University, Guangzhou, China
- Guangdong Provincial Engineering Technology Research Center of Cardiovascular Individual Medicine and Big Data, School of Electronic and Information Engineering, South China Normal University, Foshan, China
| | - Han Zhang
- Guangdong Provincial Engineering Technology Research Center of Cardiovascular Individual Medicine and Big Data, School of Electronic and Information Engineering, South China Normal University, Foshan, China
| | - Zhiwen Li
- Key Laboratory of Reproductive Health National Health Commission of the People's Republic of China, Institute of Reproductive and Child Health, Peking University, Beijing, China
| | - Shuang Liu
- Department of Pulmonary and Critical Care Medicine, Guangdong Provincial Technology Research Center of Chronic Obstructive Pulmonary Disease Rehabilitation, Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Yuqi Zhou
- Department of Pulmonary and Critical Care Medicine, Guangdong Provincial Technology Research Center of Chronic Obstructive Pulmonary Disease Rehabilitation, Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
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Ma F, Wang S, Guo Y, Dai C, Meng J. Image segmentation of mouse eye in vivo with optical coherence tomography based on Bayesian classification. BIOMED ENG-BIOMED TE 2024; 69:307-315. [PMID: 38178615 DOI: 10.1515/bmt-2023-0266] [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: 06/16/2023] [Accepted: 12/22/2023] [Indexed: 01/06/2024]
Abstract
OBJECTIVES Optical coherence tomography (OCT) is a new imaging technology that uses an optical analog of ultrasound imaging for biological tissues. Image segmentation plays an important role in dealing with quantitative analysis of medical images. METHODS We have proposed a novel framework to deal with the low intensity problem, based on the labeled patches and Bayesian classification (LPBC) model. The proposed method includes training and testing phases. During the training phase, firstly, we manually select the sub-images of background and Region of Interest (ROI) from the training image, and then extract features by patches. Finally, we train the Bayesian model with the features. The segmentation threshold of each patch is computed by the learned Bayesian model. RESULTS In addition, we have collected a new dataset of mouse eyes in vivo with OCT, named MEVOCT, which can be found at URL https://17861318579.github.io/LPBC. MEVOCT consists of 20 high-resolution images. The resolution of every image is 2048 × 2048 pixels. CONCLUSIONS The experimental results demonstrate the effectiveness of the LPBC method on the new MEVOCT dataset. The ROI segmentation is of great importance for the distortion correction.
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Affiliation(s)
- Fei Ma
- School of Computer Science, Qufu Normal University, Rizhao, Shandong, China
| | - Shengbo Wang
- School of Computer Science, Qufu Normal University, Rizhao, Shandong, China
| | - Yanfei Guo
- School of Computer Science, Qufu Normal University, Rizhao, Shandong, China
| | - Cuixia Dai
- Department of College Science, Shanghai Institute of Technology, Shanghai, Shanghai, China
| | - Jing Meng
- School of Computer Science, Qufu Normal University, Rizhao, Shandong, China
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He L, Zhang L, Lin X, Qin Y. A novel deep-learning model based on τ-shaped convolutional network (τNet) with long short-term memory (LSTM) for physiological fatigue detection from EEG and EOG signals. Med Biol Eng Comput 2024; 62:1781-1793. [PMID: 38374416 DOI: 10.1007/s11517-024-03033-y] [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: 09/06/2023] [Accepted: 01/21/2024] [Indexed: 02/21/2024]
Abstract
In recent years, fatigue driving has become the main cause of traffic accidents, leading to increased attention towards fatigue detection systems. However, the pooling and strided convolutional operations in fatigue detection algorithm based on traditional deep learning methods may led to the loss of some useful information. This paper proposed a novel τ -shaped convolutional network ( τ Net ) aiming to address this issue. Unlike traditional network structures, τ Net incorporates the operations of upsampling features and concatenating high- and low-level features, enabling full utilization of useful information. Moreover, considering that the fatigue state is a mental state involving temporal evolution, we proposed the novel long short-term memory (LSTM)- τ -shaped convolutional network (LSTM- τ Net ), a parallel structure composed of LSTM and τ Net for fatigue detection, where τ Net extracts time-invariant features with location information, and LSTM extracts long temporal dependencies. We compared LSTM- τ Net with six competing methods based on two datasets. Results showed that the proposed algorithm achieved higher classification accuracy than the other methods, with 94.25% on EEG data (binary classification) and 82.19% on EOG data (triple classification). Additionally, the proposed algorithm exhibits low computational cost, good training stability, and robustness against insufficient training. Therefore, it is promising for further implementation of fatigue online detection systems.
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Affiliation(s)
- Le He
- State Key Laboratory of Power Transmission Equipment Technology, School of Electrical Engineering, Chongqing University, Chongqing, 400044, People's Republic of China
| | - Li Zhang
- State Key Laboratory of Power Transmission Equipment Technology, School of Electrical Engineering, Chongqing University, Chongqing, 400044, People's Republic of China.
| | - Xiangtian Lin
- State Key Laboratory of Power Transmission Equipment Technology, School of Electrical Engineering, Chongqing University, Chongqing, 400044, People's Republic of China
| | - Yunfeng Qin
- State Key Laboratory of Power Transmission Equipment Technology, School of Electrical Engineering, Chongqing University, Chongqing, 400044, People's Republic of China
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Guo Y, Tang Q, Li S, Chen Z. Reconstruction of Missing Electrocardiography Signals from Photoplethysmography Data Using Deep Neural Network. Bioengineering (Basel) 2024; 11:365. [PMID: 38671786 PMCID: PMC11048151 DOI: 10.3390/bioengineering11040365] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2024] [Revised: 04/04/2024] [Accepted: 04/08/2024] [Indexed: 04/28/2024] Open
Abstract
ECG helps in diagnosing heart disease by recording heart activity. During long-term measurements, data loss occurs due to sensor detachment. Therefore, research into the reconstruction of missing ECG data is essential. However, ECG requires user participation and cannot be used for continuous heart monitoring. Continuous monitoring of PPG signals is conversely low-cost and easy to carry out. In this study, a deep neural network model is proposed for the reconstruction of missing ECG signals using PPG data. This model is an end-to-end deep learning neural network utilizing WNet architecture as a basis, on which a bidirectional long short-term memory network is added in establishing a second model. The performance of both models is verified using 146 records from the MIMIC III matched subset. Compared with the reference, the ECG reconstructed using the proposed model has a Pearson's correlation coefficient of 0.851, root mean square error (RMSE) of 0.075, percentage root mean square difference (PRD) of 5.452, and a Fréchet distance (FD) of 0.302. The experimental results demonstrate that it is feasible to reconstruct missing ECG signals from PPG.
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Affiliation(s)
- Yanke Guo
- School of Electronic Engineering and Automation, Guilin University of Electronic Technology, Guilin 541004, China; (Y.G.); (S.L.)
| | - Qunfeng Tang
- School of Life and Environmental Sciences, Guilin University of Electronic Technology, Guilin 541004, China;
| | - Shiyong Li
- School of Electronic Engineering and Automation, Guilin University of Electronic Technology, Guilin 541004, China; (Y.G.); (S.L.)
| | - Zhencheng Chen
- School of Life and Environmental Sciences, Guilin University of Electronic Technology, Guilin 541004, China;
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Geng D, Yin Y, Fu Z, Pang G, Xu G, Geng Y, Wang A. Heart rate detection method based on Ballistocardiogram signal of wearable device:Algorithm development and validation. Heliyon 2024; 10:e27369. [PMID: 38486774 PMCID: PMC10937685 DOI: 10.1016/j.heliyon.2024.e27369] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Revised: 02/27/2024] [Accepted: 02/28/2024] [Indexed: 03/17/2024] Open
Abstract
Background Heart rate, as the four vital signs of human body, is a basic indicator to measure a person's health status. Traditional electrocardiography (ECG) measurement, which is routinely monitored, requires subjects to wear lead electrodes frequently, which undoubtedly places great restrictions on participants' activities during the normal test. At present, the boom of wearable devices has created hope for non-invasive, simple operation and low-cost daily heart rate monitoring, among them, Ballistocardiogram signal (BCG) is an effective heart rate measurement method, but in the actual acquisition process, the robustness of non-invasive vital sign collection is limited. Therefore, it is necessary to develop a method to improve the robustness of heart rate monitoring. Objective Therefore, in view of the problem that the accuracy of untethered monitoring heart rate is not high, we propose a method aimed at detecting the heartbeat cycle based on BCG to accurately obtain the beat-to-beat heart rate in the sleep state. Methods In this study, we implement an innovative J-wave detection algorithm based on BCG signals. By collecting BCG signals recorded by 28 healthy subjects in different sleeping positions, after preprocessing, the data feature set is formed according to the clustering of morphological features in the heartbeat interval. Finally, a J-wave recognition model is constructed based on bi-directional long short-term memory (BiLSTM), and then the number of J-waves in the input sequence is counted to realize real-time detection of heartbeat. The performance of the proposed heartbeat detection scheme is cross-verified, and the proposed method is compared with the previous wearable device algorithm. Results The accuracy of J wave recognition in BCG signal is 99.67%, and the deviation rate of heart rate detection is only 0.27%, which has higher accuracy than previous wearable device algorithms. To assess consistency between method results and heart rates obtained by the ECG, seven subjects are compared using Bland-Altman plots, which show no significant difference between BCG and ECG results for heartbeat cycles. Conclusions Compared with other studies, the proposed method is more accurate in J-wave recognition, which improves the accuracy and generalization ability of BCG-based continuous heartbeat cycle extraction, and provides preliminary support for wearable-based untethered daily monitoring.
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Affiliation(s)
- Duyan Geng
- Hebei University of Technology, State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Tianjin, 300130, PR China
- Hebei University of Technology, School of Electrical Engineering, Tianjin, 300130, PR China
| | - Yue Yin
- Hebei University of Technology, State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Tianjin, 300130, PR China
- Hebei University of Technology, School of Electrical Engineering, Tianjin, 300130, PR China
| | - Zhigang Fu
- Physical Examination Center of the Fourth Joint Logistics Support Unit of the 983rd Hospital of the Tianjin Chinese People's Liberation Army, Tianjin, 300142, PR China
| | - Geng Pang
- Hebei University of Technology, State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Tianjin, 300130, PR China
- Hebei University of Technology, School of Electrical Engineering, Tianjin, 300130, PR China
| | - Guizhi Xu
- Hebei University of Technology, State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Tianjin, 300130, PR China
- Hebei University of Technology, School of Electrical Engineering, Tianjin, 300130, PR China
| | - Yan Geng
- Hebei Institute for Drug and Medical Device Control, Shijiazhuang, 050200, PR China
| | - Alan Wang
- Centre for Brain Research, Faculty of Medical and Health Sciences, University of Auckland, Auckland, New Zealand
- Centre for Medical Imaging, Faculty of Medical and Health Sciences, University of Auckland, Auckland, New Zealand
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Yang T, Yuan H, Yang J, Zhou Z, Abe M, Nakayama Y, Huang SY, Yu W. Solving variability: Accurately extracting feature components from ballistocardiograms. Digit Health 2024; 10:20552076241277746. [PMID: 39247094 PMCID: PMC11378244 DOI: 10.1177/20552076241277746] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2024] [Accepted: 08/08/2024] [Indexed: 09/10/2024] Open
Abstract
Objective A ballistocardiogram (BCG) is a vibration signal generated by the ejection of the blood in each cardiac cycle. The BCG has significant variability in amplitude, temporal aspects, and the deficiency of waveform components, attributed to individual differences, instantaneous heart rate, and the posture of the person being measured. This variability may make methods of extracting J-waves, the most distinct components of BCG less generalizable so that the J-waves could not be precisely localized, and further analysis is difficult. This study is dedicated to solving the variability of BCG to achieve accurate feature extraction. Methods Inspired by the generation mechanism of the BCG, we proposed an original method based on a profile of second-order derivative of BCG waveform (2ndD-P) to capture the nature of vibration and solve the variability, thereby accurately localizing the components especially when the J-wave is not prominent. Results In this study, 51 recordings of resting state and 11 recordings of high-heart-rate from 24 participants were used to validate the algorithm. Each recording lasts about 3 min. For resting state data, the sensitivity and positive predictivity of proposed method are: 98.29% and 98.64%, respectively. For high-heart-rate data, the proposed method achieved a performance comparable to those of low-heart-rate: 97.14% and 99.01% for sensitivity and positive predictivity, respectively. Conclusion Our proposed method can detect the peaks of the J-wave more accurately than conventional extraction methods, under the presence of different types of variability. Higher performance was achieved for BCG with non-prominent J-waves, in both low- and high-heart-rate cases.
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Affiliation(s)
- Tianyi Yang
- Department of Medical Engineering, Chiba University, Chiba City, Chiba Prefecture, Japan
| | - Haihang Yuan
- Department of Medical Engineering, Chiba University, Chiba City, Chiba Prefecture, Japan
| | - Junqi Yang
- Department of Medical Engineering, Chiba University, Chiba City, Chiba Prefecture, Japan
| | - Zhongchao Zhou
- Department of Medical Engineering, Chiba University, Chiba City, Chiba Prefecture, Japan
| | - Masayuki Abe
- Nanayume Co. Ltd, Chiba City, Chiba Prefecture, Japan
| | - Yoshitake Nakayama
- Center for Preventive Medical Sciences, Chiba University, Chiba City, Chiba Prefecture, Japan
| | - Shao Ying Huang
- Engineering Product Development, Singapore University of Technology and Design, Singapore, Singapore
| | - Wenwei Yu
- Department of Medical Engineering, Chiba University, Chiba City, Chiba Prefecture, Japan
- Center for Frontier Medical Engineering, Chiba University, Chiba City, Chiba Prefecture, Japan
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Wang Q, Lyu W, Zhou J, Yu C. Sleep condition detection and assessment with optical fiber interferometer based on machine learning. iScience 2023; 26:107244. [PMID: 37496677 PMCID: PMC10366502 DOI: 10.1016/j.isci.2023.107244] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2023] [Revised: 05/21/2023] [Accepted: 06/26/2023] [Indexed: 07/28/2023] Open
Abstract
The prevalence of sleep disorders has increased because of the fast-paced and stressful modern lifestyle, negatively impacting the quality of human life and work efficiency. It is crucial to address sleep problems. However, the current practice of diagnosing sleep disorders using polysomnography (PSG) has limitations such as complexity, large equipment, and low portability, hindering its practicality for daily use. To overcome these challenges, in this article an optical fiber sensor is proposed as a viable solution for sleep monitoring. This device offers benefits like low power consumption, non-invasiveness, absence of interference, and real-time health monitoring. We introduce the sensor with an optical fiber interferometer to capture ballistocardiography (BCG) and electrocardiogram (ECG) signals from the human body. Furthermore, a new machine learning method is proposed for sleep condition detection. Experimental results demonstrate the superior performance of this architecture and the proposed model in monitoring and assessing sleep quality.
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Affiliation(s)
- Qing Wang
- Department of Electronic and Information Engineering, The Hong Kong Polytechnic University, Kowloon, Hong Kong
| | - Weimin Lyu
- Department of Electronic and Information Engineering, The Hong Kong Polytechnic University, Kowloon, Hong Kong
| | - Jing Zhou
- Department of Electronic and Information Engineering, The Hong Kong Polytechnic University, Kowloon, Hong Kong
| | - Changyuan Yu
- Department of Electronic and Information Engineering, The Hong Kong Polytechnic University, Kowloon, Hong Kong
- Shenzhen Research Institute, The Hong Kong Polytechnic University, Shenzhen 518057, China
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Feng S, Wu X, Bao A, Lin G, Sun P, Cen H, Chen S, Liu Y, He W, Pang Z, Zhang H. Machine learning-aided detection of heart failure (LVEF ≤ 49%) by using ballistocardiography and respiratory effort signals. Front Physiol 2023; 13:1068824. [PMID: 36741807 PMCID: PMC9892650 DOI: 10.3389/fphys.2022.1068824] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Accepted: 12/28/2022] [Indexed: 01/20/2023] Open
Abstract
Purpose: Under the influence of COVID-19 and the in-hospital cost, the in-home detection of cardiovascular disease with smart sensing devices is becoming more popular recently. In the presence of the qualified signals, ballistocardiography (BCG) can not only reflect the cardiac mechanical movements, but also detect the HF in a non-contact manner. However, for the potential HF patients, the additional quality assessment with ECG-aided requires more procedures and brings the inconvenience to their in-home HF diagnosis. To enable the HF detection in many real applications, we proposed a machine learning-aided scheme for the HF detection in this paper, where the BCG signals recorded from the force sensor were employed without the heartbeat location, and the respiratory effort signals separated from force sensors provided more HF features due to the connection between the heart and the lung systems. Finally, the effectiveness of the proposed HF detection scheme was verified in comparative experiments. Methods: First, a piezoelectric sensor was used to record a signal sequences of the two-dimensional vital sign, which includes the BCG and the respiratory effort. Then, the linear and the non-linear features w.r.t. BCG and respiratory effort signals were extracted to serve the HF detection. Finally, the improved HF detection performance was verified through the LOO and the LOSO cross-validation settings with different machine learning classifiers. Results: The proposed machine learning-aided scheme achieved the robust performance in the HF detection by using 4 different classifiers, and yielded an accuracy of 94.97% and 87.00% in the LOO and the LOSO experiments, respectively. In addition, experimental results demonstrated that the designed respiratory and cardiopulmonary features are beneficial to the HF detection (LVEF ≤ 49 % ). Conclusion: This study proposed a machine learning-aided HF diagnostic scheme. Experimental results demonstrated that the proposed scheme can fully exploit the relationship between the heart and the lung systems to potentially improve the in-home HF detection performance by using both the BCG, the respiratory and the cardiopulmonary-related features.
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Affiliation(s)
- Shen Feng
- Department of Electronics and Information Engineering, South China Normal University (SCNU), Foshan, China
- School of Physics and Telecommunication Engineering, South China Normal University (SCNU), Guangzhou, China
| | - Xianda Wu
- Department of Electronics and Information Engineering, South China Normal University (SCNU), Foshan, China
- School of Physics and Telecommunication Engineering, South China Normal University (SCNU), Guangzhou, China
| | - Andong Bao
- Department of Electronics and Information Engineering, South China Normal University (SCNU), Foshan, China
- School of Physics and Telecommunication Engineering, South China Normal University (SCNU), Guangzhou, China
| | - Guanyang Lin
- Department of Electronics and Information Engineering, South China Normal University (SCNU), Foshan, China
- School of Physics and Telecommunication Engineering, South China Normal University (SCNU), Guangzhou, China
| | - Pengtao Sun
- Department of Ultrasonography, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Huan Cen
- Department of Ultrasonography, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Sinan Chen
- Department of Ultrasonography, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Yuexia Liu
- Department of Ultrasonography, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Wenning He
- Guangzhou SENVIV Technology Co., Ltd., Guangzhou, China
| | - Zhiqiang Pang
- Guangzhou SENVIV Technology Co., Ltd., Guangzhou, China
| | - Han Zhang
- Department of Electronics and Information Engineering, South China Normal University (SCNU), Foshan, China
- School of Physics and Telecommunication Engineering, South China Normal University (SCNU), Guangzhou, China
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