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Gupta K. A robust deep learning system for screening of obstructive sleep apnea using T-F spectrum of ECG signals. Comput Methods Biomech Biomed Engin 2024:1-13. [PMID: 38829354 DOI: 10.1080/10255842.2024.2359635] [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: 12/19/2023] [Accepted: 05/20/2024] [Indexed: 06/05/2024]
Abstract
Obstructive sleep apnea (OSA) is a non-communicable sleep-related medical condition marked by repeated disruptions in breathing during sleep. It may induce various cardiovascular and neurocognitive complications. Electrocardiography (ECG) is a useful method for detecting numerous health-related disorders. ECG signals provide a less complex and non-invasive solution for the screening of OSA. Automated and accurate detection of OSA may enhance diagnostic performance and reduce the clinician's workload. Traditional machine learning methods typically involve several labor-intensive manual procedures, including signal decomposition, feature evaluation, selection, and categorization. This article presents the time-frequency (T-F) spectrum classification of de-noised ECG data for the automatic screening of OSA patients using deep convolutional neural networks (DCNNs). At first, a filter-fusion algorithm is used to eliminate the artifacts from the raw ECG data. Stock-well transform (S-T) is employed to change filtered time-domain ECG into T-F spectrums. To discriminate between apnea and normal ECG signals, the obtained T-F spectrums are categorized using benchmark Alex-Net and Squeeze-Net, along with a less complex DCNN. The superiority of the presented system is measured by computing the sensitivity, specificity, accuracy, negative predicted value, precision, F1-score, and Fowlkes-Mallows index. The results of comparing all three utilized DCNNs reveal that the proposed DCNN requires fewer learning parameters and provides higher accuracy. An average accuracy of 95.31% is yielded using the proposed system. The presented deep learning system is lightweight and faster than Alex-Net and Squeeze-Net as it utilizes fewer learnable parameters, making it simple and reliable.
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Affiliation(s)
- Kapil Gupta
- School of Computer Sciences, University of Petroleum and Energy Studies (UPES), Dehradun, India
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Samynathan R, Venkidasamy B, Shanmugam A, Ramalingam S, Thiruvengadam M. Functional role of microRNA in the regulation of biotic and abiotic stress in agronomic plants. Front Genet 2023; 14:1272446. [PMID: 37886688 PMCID: PMC10597799 DOI: 10.3389/fgene.2023.1272446] [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: 08/04/2023] [Accepted: 09/25/2023] [Indexed: 10/28/2023] Open
Abstract
The increasing demand for food is the result of an increasing population. It is crucial to enhance crop yield for sustainable production. Recently, microRNAs (miRNAs) have gained importance because of their involvement in crop productivity by regulating gene transcription in numerous biological processes, such as growth, development and abiotic and biotic stresses. miRNAs are small, non-coding RNA involved in numerous other biological functions in a plant that range from genomic integrity, metabolism, growth, and development to environmental stress response, which collectively influence the agronomic traits of the crop species. Additionally, miRNA families associated with various agronomic properties are conserved across diverse plant species. The miRNA adaptive responses enhance the plants to survive environmental stresses, such as drought, salinity, cold, and heat conditions, as well as biotic stresses, such as pathogens and insect pests. Thus, understanding the detailed mechanism of the potential response of miRNAs during stress response is necessary to promote the agronomic traits of crops. In this review, we updated the details of the functional aspects of miRNAs as potential regulators of various stress-related responses in agronomic plants.
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Affiliation(s)
- Ramkumar Samynathan
- Department of Oral and Maxillofacial Surgery, Saveetha Dental College and Hospitals, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, Tamil Nadu, India
| | - Baskar Venkidasamy
- Department of Oral and Maxillofacial Surgery, Saveetha Dental College and Hospitals, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, Tamil Nadu, India
| | - Ashokraj Shanmugam
- Plant Physiology and Biotechnology Division, UPASI Tea Research Foundation, Coimbatore, Tamil Nadu, India
| | - Sathishkumar Ramalingam
- Plant Genetic Engineering Lab, Department of Biotechnology, Bharathiar University, Coimbatore, Tamil Nadu, India
| | - Muthu Thiruvengadam
- Department of Crop Science, College of Sanghuh Life Science, Konkuk University, Seoul, Republic of Korea
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Wei K, Zou L, Liu G, Wang C. MS-Net: Sleep apnea detection in PPG using multi-scale block and shadow module one-dimensional convolutional neural network. Comput Biol Med 2023; 155:106469. [PMID: 36842220 DOI: 10.1016/j.compbiomed.2022.106469] [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: 07/25/2022] [Revised: 11/11/2022] [Accepted: 12/19/2022] [Indexed: 01/11/2023]
Abstract
Sleep Apnea (SA) is a respiratory disorder that affects sleep. However, the SA detection method based on polysomnography is complex and not suitable for home use. The detection approach using Photoplethysmography is low cost and convenient, which can be used to widely detect SA. This study proposed a method combining a multi-scale one-dimensional convolutional neural network and a shadow one-dimensional convolutional neural network based on dual-channel input. The time-series feature information of different segments were extracted from multi-scale temporal structure. Moreover, shadow module was adopted to make full use of the redundant information generated after multi-scale convolution operation, which improved the accuracy and ensured the portability of the model. At the same time, we introduced balanced bootstrapping and class weight, which effectively alleviated the problem of unbalanced classes. Our method achieved the result of 82.0% average accuracy, 74.4% average sensitivity and 85.1% average specificity for per-segment SA detection, and reached 93.6% average accuracy for per-recording SA detection after 5-fold cross validation. Experimental results show that this method has good robustness. It can be regarded as an effective aid in SA detection in household use.
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Affiliation(s)
- Keming Wei
- School of Biomedical Engineering, Sun Yat-sen University, Guangzhou, Guangdong, China; Key Laboratory of Sensing Technology and Biomedical Instrument of Guangdong Province, Guangzhou, Guangdong, China.
| | - Lang Zou
- School of Biomedical Engineering, Sun Yat-sen University, Guangzhou, Guangdong, China; Key Laboratory of Sensing Technology and Biomedical Instrument of Guangdong Province, Guangzhou, Guangdong, China.
| | - Guanzheng Liu
- School of Biomedical Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen, China; Laboratory of Wearable Technology and Artificial Intelligence for Healthcare of Guangdong Province, Shenzhen, Guangdong, China.
| | - Changhong Wang
- School of Biomedical Engineering, Sun Yat-sen University, Guangzhou, Guangdong, China; Laboratory of Wearable Technology and Artificial Intelligence for Healthcare of Guangdong Province, Shenzhen, Guangdong, China.
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Chen M, Wu S, Chen T, Wang C, Liu G. Information-Based Similarity of Ordinal Pattern Sequences as a Novel Descriptor in Obstructive Sleep Apnea Screening Based on Wearable Photoplethysmography Bracelets. BIOSENSORS 2022; 12:1089. [PMID: 36551056 PMCID: PMC9775447 DOI: 10.3390/bios12121089] [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: 10/12/2022] [Revised: 11/11/2022] [Accepted: 11/24/2022] [Indexed: 06/17/2023]
Abstract
Obstructive sleep apnea (OSA) is a common respiratory disorder associated with autonomic nervous system (ANS) dysfunction, resulting in abnormal heart rate variability (HRV). Capable of acquiring heart rate (HR) information with more convenience, wearable photoplethysmography (PPG) bracelets are proven to be a potential surrogate for electrocardiogram (ECG)-based devices. Meanwhile, bracelet-type PPG has been heavily marketed and widely accepted. This study aims to investigate the algorithm that can identify OSA with wearable devices. The information-based similarity of ordinal pattern sequences (OP_IBS), which is a modified version of the information-based similarity (IBS), has been proposed as a novel index to detect OSA based on wearable PPG signals. A total of 92 PPG recordings (29 normal subjects, 39 mild-moderate OSA subjects and 24 severe OSA subjects) were included in this study. OP_IBS along with classical indices were calculated. For severe OSA detection, the accuracy of OP_IBS was 85.9%, much higher than that of the low-frequency power to high-frequency power ratio (70.7%). The combination of OP_IBS, IBS, CV and LF/HF can achieve 91.3% accuracy, 91.0% sensitivity and 91.5% specificity. The performance of OP_IBS is significantly improved compared with our previous study based on the same database with the IBS method. In the Physionet database, OP_IBS also performed exceptionally well with an accuracy of 91.7%. This research shows that the OP_IBS method can access the HR dynamics of OSA subjects and help diagnose OSA in clinical environments.
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Affiliation(s)
- Mingjing Chen
- School of Biomedical Engineering, Sun Yat-Sen University, Shenzhen 518107, China
- Alfred E. Mann Department of Biomedical Engineering, University of Southern California, Los Angeles, CA 90089-1112, USA
| | - Shan Wu
- School of Biomedical Engineering, Sun Yat-Sen University, Shenzhen 518107, China
| | - Tian Chen
- School of Biomedical Engineering, Sun Yat-Sen University, Shenzhen 518107, China
| | - Changhong Wang
- School of Biomedical Engineering, Sun Yat-Sen University, Shenzhen 518107, China
| | - Guanzheng Liu
- School of Biomedical Engineering, Sun Yat-Sen University, Shenzhen 518107, China
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Qin H, Liu G. A dual-model deep learning method for sleep apnea detection based on representation learning and temporal dependence. Neurocomputing 2022. [DOI: 10.1016/j.neucom.2021.12.001] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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Weng P, Wei K, Chen T, Chen M, Liu G. Fuzzy Approximate Entropy of Extrema Based on Multiple Moving Averages as a Novel Approach in Obstructive Sleep Apnea Screening. IEEE JOURNAL OF TRANSLATIONAL ENGINEERING IN HEALTH AND MEDICINE 2022; 10:4901211. [PMID: 36247084 PMCID: PMC9564195 DOI: 10.1109/jtehm.2022.3197084] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Revised: 06/17/2022] [Accepted: 07/25/2022] [Indexed: 11/29/2022]
Abstract
Objective: Obstructive sleep apnea (OSA) is a respiratory disease associated with autonomic nervous system dysfunction. As a novel method for analyzing OSA depending on heart rate variability, fuzzy approximate entropy of extrema based on multiple moving averages (Emma-fApEn) can effectively assess the sympathetic tension limits, thereby realizing a good performance in the disease severity screening. Method: Sixty 6-h electrocardiogram recordings (20 healthy, 16 mild/moderate OSA and 34 severe OSA) from the PhysioNet database were used in this study. The performances of minima of Emma-fApEn (fApEn-minima), maxima of Emma-fApEn (fApEn-maxima) and classic time-frequency domain indices for each recording were assessed by significance analysis, correlation analysis, parameter optimization and OSA screening. Results: fApEn-minima and fApEn-maxima had significant differences between the severe OSA group and the other two groups, while the mean value (Mean) and the ratio of low-frequency power and high-frequency power (LH) could significantly differentiate OSA recordings from healthy recordings. The correlation coefficient between fApEn-minima and apnea-hypopnea index was the highest (|R| = 0.705). Machine learning methods were used to evaluate the performances of the above four indices. Random forest (RF) achieved the highest accuracy of 96.67% in OSA screening and 91.67% in severe OSA screening, with a good balance in both. Conclusion: Emma-fApEn may be used as a simple preliminary detection tool to assess the severity of OSA prior to polysomnography analysis.
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Affiliation(s)
- Peiyu Weng
- Key Laboratory of Sensing Technology and Biomedical Instrument of Guangdong Province, School of Biomedical Engineering, Sun Yat-sen University, Guangzhou, China
| | - Keming Wei
- Key Laboratory of Sensing Technology and Biomedical Instrument of Guangdong Province, School of Biomedical Engineering, Sun Yat-sen University, Guangzhou, China
| | - Tian Chen
- Key Laboratory of Sensing Technology and Biomedical Instrument of Guangdong Province, School of Biomedical Engineering, Sun Yat-sen University, Guangzhou, China
| | - Mingjing Chen
- Key Laboratory of Sensing Technology and Biomedical Instrument of Guangdong Province, School of Biomedical Engineering, Sun Yat-sen University, Guangzhou, China
| | - Guanzheng Liu
- Key Laboratory of Sensing Technology and Biomedical Instrument of Guangdong Province, School of Biomedical Engineering, Sun Yat-sen University, Guangzhou, China
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Liu Z, Chen T, Wei K, Liu G, Liu B. Similarity Changes Analysis for Heart Rate Fluctuation Regularity as a New Screening Method for Congestive Heart Failure. ENTROPY (BASEL, SWITZERLAND) 2021; 23:1669. [PMID: 34945975 PMCID: PMC8700114 DOI: 10.3390/e23121669] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/10/2021] [Revised: 11/26/2021] [Accepted: 11/30/2021] [Indexed: 11/21/2022]
Abstract
Congestive heart failure (CHF) is a chronic cardiovascular condition associated with dysfunction of the autonomic nervous system (ANS). Heart rate variability (HRV) has been widely used to assess ANS. This paper proposes a new HRV analysis method, which uses information-based similarity (IBS) transformation and fuzzy approximate entropy (fApEn) algorithm to obtain the fApEn_IBS index, which is used to observe the complexity of autonomic fluctuations in CHF within 24 h. We used 98 ECG records (54 health records and 44 CHF records) from the PhysioNet database. The fApEn_IBS index was statistically significant between the control and CHF groups (p < 0.001). Compared with the classical indices low-to-high frequency power ratio (LF/HF) and IBS, the fApEn_IBS index further utilizes the changes in the rhythm of heart rate (HR) fluctuations between RR intervals to fully extract relevant information between adjacent time intervals and significantly improves the performance of CHF screening. The CHF classification accuracy of fApEn_IBS was 84.69%, higher than LF/HF (77.55%) and IBS (83.67%). Moreover, the combination of IBS, fApEn_IBS, and LF/HF reached the highest CHF screening accuracy (98.98%) with the random forest (RF) classifier, indicating that the IBS and LF/HF had good complementarity. Therefore, fApEn_IBS effusively reflects the complexity of autonomic nerves in CHF and is a valuable CHF assessment tool.
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Affiliation(s)
- Zeming Liu
- School of Biomedical Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, China; (Z.L.); (T.C.); (K.W.)
- School of Science, Hua Zhong Agricultural University, Wuhan 430070, China
| | - Tian Chen
- School of Biomedical Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, China; (Z.L.); (T.C.); (K.W.)
| | - Keming Wei
- School of Biomedical Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, China; (Z.L.); (T.C.); (K.W.)
| | - Guanzheng Liu
- School of Biomedical Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, China; (Z.L.); (T.C.); (K.W.)
| | - Bin Liu
- School of Biomedical Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, China; (Z.L.); (T.C.); (K.W.)
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Wu S, Chen M, Wei K, Liu G. Sleep apnea screening based on Photoplethysmography data from wearable bracelets using an information-based similarity approach. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021; 211:106442. [PMID: 34624633 DOI: 10.1016/j.cmpb.2021.106442] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/08/2021] [Accepted: 09/22/2021] [Indexed: 06/13/2023]
Abstract
BACKGROUND AND OBJECTIVE Sleep apnea (SA) is a common sleep disorder in daily life and is also an aggravating factor for various diseases. Having the potential to replace traditional but complicated diagnostic equipment, portable medical devices are receiving increasing attention, and thus, the demand for supporting algorithms is growing. This study aims to identify SA with wearable devices. METHODS Static information-based similarity (sIBS) and dynamic information-based similarity (dIBS) were proposed to analyze short-term fluctuations in heart rate (HR) with wearable devices. This study included overnight photoplethysmography (PPG) signals from 92 subjects obtained from wearable bracelets. RESULTS The results showed that sIBS achieved the highest correlation coefficient with the apnea-hypopnea index (R=-0.653, p=0). dIBS showed a good balance in sensitivity and specificity (75.0% and 72.1%, respectively). Combining sIBS and dIBS with other classical time-frequency domain indices could simultaneously achieve good accuracy and balance (84.7% accuracy, 76.7% sensitivity and 89.6% specificity). CONCLUSIONS This research showed that both classic time-frequency domain indices and IBS indices changed significantly only in the severe SA group. This novel method could serve as an effective way to assess SA and provide new insight into its pathophysiology.
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Affiliation(s)
- Shan Wu
- School of Biomedical Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen, China.
| | - Mingjing Chen
- School of Biomedical Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen, China.
| | - Keming Wei
- School of Biomedical Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen, China.
| | - Guanzheng Liu
- School of Biomedical Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen, China.
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Afrakhteh S, Ayatollahi A, Soltani F. Classification of sleep apnea using EMD-based features and PSO-trained neural networks. BIOMED ENG-BIOMED TE 2021; 66:459-472. [PMID: 33930264 DOI: 10.1515/bmt-2021-0025] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2021] [Accepted: 04/12/2021] [Indexed: 11/15/2022]
Abstract
In this study, we propose a method for detecting obstructive sleep apnea (OSA) based on the features extracted from empirical mode decomposition (EMD) and the neural networks trained by particle swarm optimization (PSO) in the classification phase. After extracting the features from the intrinsic mode functions (IMF) of each heart rate variability (HRV) signal of each segment, these features were applied to the input of popular classifiers such as multi-layer perceptron neural networks (MLPNN), Naïve Bayes, linear discriminant analysis (LDA), k-nearest neighborhood (KNN), and support vector machines (SVM) were applied. The results show that the MLPNN learned with back propagation (BP) algorithm has a diagnostic accuracy of less than 90%, and this may be due to being derivative based property of the BP algorithm, which causes trapping in the local minima. For Improving MLPNN's performance, we used the PSO algorithm instead of the BP method in training part. Therefore, the MLPNN's accuracy improved from 89.36 to 97.66% after the application of the PSO algorithm. The proposed method has also reached to 97.78 and 97.96% in sensitivity and specificity, respectively. So, it can be concluded that the proposed method achieves better or comparable results when compared with the previous works in this field.
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Affiliation(s)
- Sajjad Afrakhteh
- Department of Electrical Engineering, Iran University of Science and Technology, Narmak, Tehran, 16846-13114, Iran
| | - Ahmad Ayatollahi
- Department of Electrical Engineering, Iran University of Science and Technology, Narmak, Tehran, 16846-13114, Iran
| | - Fatemeh Soltani
- Department of Electrical Engineering, Iran University of Science and Technology, Narmak, Tehran, 16846-13114, Iran
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Tang L, Liu G. The novel approach of temporal dependency complexity analysis of heart rate variability in obstructive sleep apnea. Comput Biol Med 2021; 135:104632. [PMID: 34265554 DOI: 10.1016/j.compbiomed.2021.104632] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2021] [Revised: 07/02/2021] [Accepted: 07/02/2021] [Indexed: 12/21/2022]
Abstract
Obstructive sleep apnea (OSA) is a serious sleep disorder, which leads to changes in autonomic nerve function and increases the risk of cardiovascular disease. Heart rate variability (HRV) has been widely used as a non-invasive method for assessing the autonomic nervous system (ANS). We proposed the two-dimensional sample entropy of the coarse-grained Gramian angular summation field image (CgSampEn2D) index. It is a new index for HRV analysis based on the temporal dependency complexity. In this study, we used 60 electrocardiogram (ECG) records from the Apnea-ECG database of PhysioNet (20 healthy records and 40 OSA records). These records were divided into 5-min segments. Compared with the classical indices low-to-high frequency power ratio (LF/HF) and sample entropy (SampEn), CgSampEn2D utilizes the correlation information between different time intervals in the RR sequences and preserves the temporal dependency of the RR sequences, which improves the OSA detection performance significantly. The OSA screening accuracy of CgSampEn2D (93.3%) is higher than that of LF/HF (80.0%) and SampEn (73.3%). Additionally, CgSampEn2D has a significant association with the apnea-hypopnea index (AHI) (R = -0.740, p = 0). CgSampEn2D reflects the complexity of the OSA autonomic nerve more comprehensively and provides a novel idea for the screening of OSA disease.
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Affiliation(s)
- Lan Tang
- The School of Biomedical Engineering, Sun Yat-sen University, Guangzhou, 510275, China.
| | - Guanzheng Liu
- The School of Biomedical Engineering, Sun Yat-sen University, Guangzhou, 510275, China.
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Wu S, Liang D, Yang Q, Liu G. Regularity of heart rate fluctuations analysis in obstructive sleep apnea patients using information-based similarity. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2020.102370] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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12
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Liang D, Wu S, Tang L, Feng K, Liu G. Short-Term HRV Analysis Using Nonparametric Sample Entropy for Obstructive Sleep Apnea. ENTROPY 2021; 23:e23030267. [PMID: 33668394 PMCID: PMC7996273 DOI: 10.3390/e23030267] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/08/2020] [Revised: 01/12/2021] [Accepted: 01/13/2021] [Indexed: 12/14/2022]
Abstract
Obstructive sleep apnea (OSA) is associated with reduced heart rate variability (HRV) and autonomic nervous system dysfunction. Sample entropy (SampEn) is commonly used for regularity analysis. However, it has limitations in processing short-term segments of HRV signals due to the extreme dependence of its functional parameters. We used the nonparametric sample entropy (NPSampEn) as a novel index for short-term HRV analysis in the case of OSA. The manuscript included 60 6-h electrocardiogram recordings (20 healthy, 14 mild-moderate OSA, and 26 severe OSA) from the PhysioNet database. The NPSampEn value was compared with the SampEn value and frequency domain indices. The empirical results showed that NPSampEn could better differentiate the three groups (p < 0.01) than the ratio of low frequency power to high frequency power (LF/HF) and SampEn. Moreover, NPSampEn (83.3%) approached a higher OSA screening accuracy than the LF/HF (73.3%) and SampEn (68.3%). Compared with SampEn (|r| = 0.602, p < 0.05), NPSampEn (|r| = 0.756, p < 0.05) had a significantly stronger association with the apnea-hypopnea index (AHI). Hence, NPSampEn can fully overcome the influence of individual differences that are prevalent in biomedical signal processing, and might be useful in processing short-term segments of HRV signal.
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Affiliation(s)
- Duan Liang
- School of Biomedical Engineering, Sun Yat-Sen University, Guangzhou 510275, China; (D.L.); (S.W.); (L.T.); (K.F.)
- Key Laboratory of Sensing Technology and Biomedical Instruments of Guangdong Province, School of Engineering, Sun Yat-Sen University, Guangzhou 510275, China
- Guangdong Provincial Engineering and Technology Centre of Advanced and Portable Medical Device, Guangzhou 510006, China
| | - Shan Wu
- School of Biomedical Engineering, Sun Yat-Sen University, Guangzhou 510275, China; (D.L.); (S.W.); (L.T.); (K.F.)
- Key Laboratory of Sensing Technology and Biomedical Instruments of Guangdong Province, School of Engineering, Sun Yat-Sen University, Guangzhou 510275, China
- Guangdong Provincial Engineering and Technology Centre of Advanced and Portable Medical Device, Guangzhou 510006, China
| | - Lan Tang
- School of Biomedical Engineering, Sun Yat-Sen University, Guangzhou 510275, China; (D.L.); (S.W.); (L.T.); (K.F.)
- Key Laboratory of Sensing Technology and Biomedical Instruments of Guangdong Province, School of Engineering, Sun Yat-Sen University, Guangzhou 510275, China
- Guangdong Provincial Engineering and Technology Centre of Advanced and Portable Medical Device, Guangzhou 510006, China
| | - Kaicheng Feng
- School of Biomedical Engineering, Sun Yat-Sen University, Guangzhou 510275, China; (D.L.); (S.W.); (L.T.); (K.F.)
- Key Laboratory of Sensing Technology and Biomedical Instruments of Guangdong Province, School of Engineering, Sun Yat-Sen University, Guangzhou 510275, China
- Guangdong Provincial Engineering and Technology Centre of Advanced and Portable Medical Device, Guangzhou 510006, China
| | - Guanzheng Liu
- School of Biomedical Engineering, Sun Yat-Sen University, Guangzhou 510275, China; (D.L.); (S.W.); (L.T.); (K.F.)
- Key Laboratory of Sensing Technology and Biomedical Instruments of Guangdong Province, School of Engineering, Sun Yat-Sen University, Guangzhou 510275, China
- Guangdong Provincial Engineering and Technology Centre of Advanced and Portable Medical Device, Guangzhou 510006, China
- Correspondence:
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