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Jain R, Ganesan RA. Single EOG channel performs well in distinguishing sleep from wake state for both healthy individuals and patients. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2022; 2022:781-784. [PMID: 36085763 DOI: 10.1109/embc48229.2022.9871161] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Using a single EOG channel, sleep-wake states of patients with different sleep disorders are accurately classified. We used polysomnography data of 27 patients (mixed apnea, periodic limb movement syndrome, sleep apnea-hypopnea syndrome, and dyssomnia) from DRMS-PAT and 20 healthy subjects from DRMS-SUB databases. We extracted a 67-dimensional feature vector, involving statistical features derived from ensemble empirical mode decomposition, approximate entropy, and relative powers in different frequency bands. Of these, the most relevant features are selected by exploiting mutual information between the features and corresponding labels. RUSBoost classifier is deployed to take care of the unbalanced data distribution. We achieved a high sensitivity of 97.5% and 95.3% as well as high specificity of 96.4% and 93.3% for sleep state in healthy and patients' groups, respectively. Ten-fold crossvalidation accuracies of 91.6% and 95% are achieved for patients and healthy individuals, respectively, using a single EOG channel. Clinical relevance-Accurate detection of sleep-wake states is crucial for the diagnosis of various sleep disorders including apnea-hypopnea syndrome and insomnia. Automated sleep-wake classification using EOG facilitates easy and convenient long-term sleep monitoring of patients without disturbing their sleep, thereby assisting the clinicians to analyze their sleeping patterns.
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Affiliation(s)
- Ritika Jain
- Indian Institute of Science,Department of Electrical Engineering,Bangalore,India,560012
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Jain R, Ganesan RA. Assessment of submentalis muscle activity for sleep-wake classification of healthy individuals and patients with sleep disorders. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2022; 2022:4942-4945. [PMID: 36085976 DOI: 10.1109/embc48229.2022.9871693] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
This work proposes a method utilizing only the submentalis EMG channel for the classification of sleep and wake states among the healthy individuals and patients with various sleep disorders such as sleep apnea hypopnea syndrome, dyssomnia, etc. We extracted autoregressive model parameters, discrete wavelet transform coefficients, Hjorth's complexity and mobility, relative bandpowers, Poincaré plot descriptors and statistical features from the EMG signal. We also used the energy of each epoch as a feature to distinguish between the sleep and wake states. Mutual information based feature selection approach was considered to obtain the top 25 features which provided maximum accuracy. For classification, we employed an ensemble of decision trees with random undersampling and boosting technique to deal with the class-imbalance problem in the sleep data. We achieved an overall accuracy of about 85% for the healthy population and about 70% on an average across different pathological groups. This work shows the potential of EMG chin activity for sleep analysis. Clinical Relevance- Automatic and reliable sleep-wake classification can reduce the burden of sleep experts in analyzing overnight sleep data (~ 8 hours) and also assist them to diagnose various neurological disorders at an early stage. Utilizing EMG channel provides an easier and convenient long-term recording of data without causing much disturbance in sleepunlike EEG which is inconvenient and hampers the natural sleep.
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Ghayvat H, Awais M, Bashir AK, Pandya S, Zuhair M, Rashid M, Nebhen J. AI-enabled radiologist in the loop: novel AI-based framework to augment radiologist performance for COVID-19 chest CT medical image annotation and classification from pneumonia. Neural Comput Appl 2022; 35:14591-14609. [PMID: 35250181 PMCID: PMC8886865 DOI: 10.1007/s00521-022-07055-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2021] [Accepted: 01/31/2022] [Indexed: 12/16/2022]
Abstract
A SARS-CoV-2 virus-specific reverse transcriptase-polymerase chain reaction (RT-PCR) test is usually used to diagnose COVID-19. However, this test requires up to 2 days for completion. Moreover, to avoid false-negative outcomes, serial testing may be essential. The availability of RT-PCR test kits is currently limited, highlighting the need for alternative approaches for the precise and rapid diagnosis of COVID-19. Patients suspected to be infected with SARS-CoV-2 can be assessed using chest CT scan images. However, CT images alone cannot be used for ruling out SARS-CoV-2 infection because individual patients may exhibit normal radiological results in the primary phases of the disease. A machine learning (ML)-based recognition and segmentation system was developed to spontaneously discover and compute infection areas in CT scans of COVID-19 patients. The computable assessment exhibited suitable performance for automatic infection region allocation. The ML models developed were suitable for the direct detection of COVID-19 (+). ML was confirmed to be a complementary diagnostic technique for diagnosing COVID-19(+) by forefront medical specialists. The complete manual delineation of COVID-19 often requires up to 225.5 min; however, the proposed RILML method decreases the delineation time to 7 min after four iterations of model updating.
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Affiliation(s)
- Hemant Ghayvat
- Innovation Division, Technical University of Denmark, Lyngby, Denmark
- Department of Computer Science and Media Technology, E-health Unit (Improved Data to and from Patients), Linnaeus University, Vaxjo, Sweden
- Building Realization and Robotics, Technical University of Munich, Munich, Germany
| | - Muhammad Awais
- Center for Intelligent Medical Electronics, Department of Electronic Engineering, School of Information Science and Technology, Fudan University, Shanghai, 200433 China
| | - A. K. Bashir
- Department of Computing and Mathematics, Manchester Metropolitan University, Manchester, UK
- School of Information and Communication Engineering, University of Electronics Science and Technology of China (UESTC), Chengdu, China
| | - Sharnil Pandya
- Symbiosis Centre for Applied Artificial Intelligence, Symbiosis International (Deemed University), Pune, Maharashtra India
| | - Mohd Zuhair
- Department of Computer Science and Engineering, Nirma University, Ahmedabad, Gujarat India
| | - Mamoon Rashid
- Department of Computer Engineering, Faculty of Science and Technology, Vishwakarma University, Pune, India
| | - Jamel Nebhen
- College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al-kharj, Saudi Arabia
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Montazeri Ghahjaverestan N, Akbarian S, Hafezi M, Saha S, Zhu K, Gavrilovic B, Taati B, Yadollahi A. Sleep/Wakefulness Detection Using Tracheal Sounds and Movements. Nat Sci Sleep 2020; 12:1009-1021. [PMID: 33235534 PMCID: PMC7680175 DOI: 10.2147/nss.s276107] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/08/2020] [Accepted: 10/08/2020] [Indexed: 11/23/2022] Open
Abstract
PURPOSE The current gold standard to detect sleep/wakefulness is based on electroencephalogram, which is inconvenient if included in portable sleep screening devices. Therefore, a challenge in the portable devices is sleeping time estimation. Without sleeping time, sleep parameters such as apnea/hypopnea index (AHI), an index for quantifying sleep apnea severity, can be underestimated. Recent studies have used tracheal sounds and movements for sleep screening and calculating AHI without considering sleeping time. In this study, we investigated the detection of sleep/wakefulness states and estimation of sleep parameters using tracheal sounds and movements. MATERIALS AND METHODS Participants with suspected sleep apnea who were referred for sleep screening were included in this study. Simultaneously with polysomnography, tracheal sounds and movements were recorded with a small wearable device, called the Patch, attached over the trachea. Each 30-second epoch of tracheal data was scored as sleep or wakefulness using an automatic classification algorithm. The performance of the algorithm was compared to the sleep/wakefulness scored blindly based on the polysomnography. RESULTS Eighty-eight subjects were included in this study. The accuracy of sleep/wakefulness detection was 82.3±8.66% with a sensitivity of 87.8±10.8 % (sleep), specificity of 71.4±18.5% (awake), F1 of 88.1±9.3% and Cohen's kappa of 0.54. The correlations between the estimated and polysomnography-based measures for total sleep time and sleep efficiency were 0.78 (p<0.001) and 0.70 (p<0.001), respectively. CONCLUSION Sleep/wakefulness periods can be detected using tracheal sound and movements. The results of this study combined with our previous studies on screening sleep apnea with tracheal sounds provide strong evidence that respiratory sounds analysis can be used to develop robust, convenient and cost-effective portable devices for sleep apnea monitoring.
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Affiliation(s)
- Nasim Montazeri Ghahjaverestan
- Kite - Toronto Rehabilitation Institute, University Health Network, Toronto, ON, Canada.,Institute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada
| | - Sina Akbarian
- Kite - Toronto Rehabilitation Institute, University Health Network, Toronto, ON, Canada.,Institute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada
| | - Maziar Hafezi
- Kite - Toronto Rehabilitation Institute, University Health Network, Toronto, ON, Canada.,Institute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada
| | - Shumit Saha
- Kite - Toronto Rehabilitation Institute, University Health Network, Toronto, ON, Canada.,Institute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada
| | - Kaiyin Zhu
- Kite - Toronto Rehabilitation Institute, University Health Network, Toronto, ON, Canada
| | - Bojan Gavrilovic
- Kite - Toronto Rehabilitation Institute, University Health Network, Toronto, ON, Canada
| | - Babak Taati
- Kite - Toronto Rehabilitation Institute, University Health Network, Toronto, ON, Canada.,Institute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada.,Computer Science, University of Toronto, Toronto, ON, Canada
| | - Azadeh Yadollahi
- Kite - Toronto Rehabilitation Institute, University Health Network, Toronto, ON, Canada.,Institute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada
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Smart technologies toward sleep monitoring at home. Biomed Eng Lett 2019; 9:73-85. [PMID: 30956881 DOI: 10.1007/s13534-018-0091-2] [Citation(s) in RCA: 35] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2018] [Revised: 12/05/2018] [Accepted: 12/07/2018] [Indexed: 01/19/2023] Open
Abstract
With progress in sensors and communication technologies, the range of sleep monitoring is extending from professional clinics into our usual home environments. Information from conventional overnight polysomnographic recordings can be derived from much simpler devices and methods. The gold standard of sleep monitoring is laboratory polysomnography, which classifies brain states based mainly on EEGs. Single-channel EEGs have been used for sleep stage scoring with accuracies of 84.9%. Actigraphy can estimate sleep efficiency with an accuracy of 86.0%. Sleep scoring based on respiratory dynamics provides accuracies of 89.2% and 70.9% for identifying sleep stages and sleep efficiency, respectively, and a correlation coefficient of 0.94 for apnea-hypopnea detection. Modulation of autonomic balance during the sleep stages are well recognized and widely used for simpler sleep scoring and sleep parameter estimation. This modulation can be recorded by several types of cardiovascular measurements, including ECG, PPG, BCG, and PAT, and the results showed accuracies up to 96.5% and 92.5% for sleep efficiency and OSA severity detection, respectively. Instead of using recordings for the entire night, less than 5 min ECG recordings have used for sleep efficiency and AHI estimation and resulted in high correlations of 0.94 and 0.99, respectively. These methods are based on their own models that relate sleep dynamics with a limited number of biological signals. Parameters representing sleep quality and disturbed breathing are estimated with high accuracies that are close to the results obtained by polysomnography. These unconstrained technologies, making sleep monitoring easier and simpler, will enhance qualities of life by expanding the range of ubiquitous healthcare.
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Yoon H, Hwang SH, Choi SH, Choi JW, Lee YJ, Jeong DU, Park KS. Wakefulness evaluation during sleep for healthy subjects and OSA patients using a patch-type device. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2018; 155:127-138. [PMID: 29512493 DOI: 10.1016/j.cmpb.2017.12.010] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/19/2017] [Revised: 10/30/2017] [Accepted: 12/11/2017] [Indexed: 06/08/2023]
Abstract
OBJECTIVES Obstructive sleep apnea (OSA) is a major sleep disorder that causes insufficient sleep, which is linked with daytime fatigue and accidents. Long-term sleep monitoring can provide meaningful information for patients with OSA to prevent and manage their symptoms. Even though various methods have been proposed to objectively measure sleep in ambulatory environments, less reliable information was provided in comparison with standard polysomnography (PSG). Therefore, this paper proposes an algorithm for distinguishing wakefulness from sleep using a patch-type device, which is applicable for both healthy individuals and patients with OSA. METHODS Electrocardiogram (ECG) and 3-axis accelerometer signals were gathered from the single device. Wakefulness was determined with six parallel methods based on information about movement and autonomic nervous activity. The performance evaluation was conducted with five-fold cross validation using the data from 15 subjects with a low respiratory disturbance index (RDI) and 10 subjects with high RDI. In addition, wakefulness information, including total sleep time (TST), sleep efficiency (SE), sleep onset latency (SOL), and wake after sleep onset (WASO), were extracted from the proposed algorithm and compared with those from PSG. RESULTS According to epoch-by-epoch (30 s) analysis, the performance results of detecting wakefulness were an average Cohen's kappa of 0.60, accuracy of 91.24%, sensitivity of 64.12%, and specificity of 95.73%. Moreover, significant correlations were observed in TST, SE, SOL, and WASO between the proposed algorithm and PSG (p < 0.001). CONCLUSIONS Wakefulness-related information was successfully provided using data from the patch-type device. In addition, the performance results of the proposed algorithm for wakefulness detection were competitive with those from previous studies. Therefore, the proposed system could be an appropriate solution for long-term objective sleep monitoring in both healthy individuals and patients with OSA.
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Affiliation(s)
- Heenam Yoon
- Interdisciplinary Program in Bioengineering, Seoul National University, Seoul, South Korea
| | - Su Hwan Hwang
- Interdisciplinary Program in Bioengineering, Seoul National University, Seoul, South Korea
| | - Sang Ho Choi
- Interdisciplinary Program in Bioengineering, Seoul National University, Seoul, South Korea
| | - Jae-Won Choi
- Department of Neuropsychiatry, Eulji University School of Medicine, Eulji General Hospital, Seoul, South Korea
| | - Yu Jin Lee
- Department of Neuropsychiatry and Center for Sleep and Chronobiology, Seoul National University Hospital, Seoul, South Korea
| | - Do-Un Jeong
- Department of Neuropsychiatry and Center for Sleep and Chronobiology, Seoul National University Hospital, Seoul, South Korea
| | - Kwang Suk Park
- Department of Biomedical Engineering, College of Medicine, Seoul National University, Seoul, South Korea.
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Dafna E, Tarasiuk A, Zigel Y. Sleep-wake evaluation from whole-night non-contact audio recordings of breathing sounds. PLoS One 2015; 10:e0117382. [PMID: 25710495 PMCID: PMC4339734 DOI: 10.1371/journal.pone.0117382] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2014] [Accepted: 12/22/2014] [Indexed: 11/18/2022] Open
Abstract
STUDY OBJECTIVES To develop and validate a novel non-contact system for whole-night sleep evaluation using breathing sounds analysis (BSA). DESIGN Whole-night breathing sounds (using ambient microphone) and polysomnography (PSG) were simultaneously collected at a sleep laboratory (mean recording time 7.1 hours). A set of acoustic features quantifying breathing pattern were developed to distinguish between sleep and wake epochs (30 sec segments). Epochs (n = 59,108 design study and n = 68,560 validation study) were classified using AdaBoost classifier and validated epoch-by-epoch for sensitivity, specificity, positive and negative predictive values, accuracy, and Cohen's kappa. Sleep quality parameters were calculated based on the sleep/wake classifications and compared with PSG for validity. SETTING University affiliated sleep-wake disorder center and biomedical signal processing laboratory. PATIENTS One hundred and fifty patients (age 54.0±14.8 years, BMI 31.6±5.5 kg/m2, m/f 97/53) referred for PSG were prospectively and consecutively recruited. The system was trained (design study) on 80 subjects; validation study was blindly performed on the additional 70 subjects. MEASUREMENTS AND RESULTS Epoch-by-epoch accuracy rate for the validation study was 83.3% with sensitivity of 92.2% (sleep as sleep), specificity of 56.6% (awake as awake), and Cohen's kappa of 0.508. Comparing sleep quality parameters of BSA and PSG demonstrate average error of sleep latency, total sleep time, wake after sleep onset, and sleep efficiency of 16.6 min, 35.8 min, and 29.6 min, and 8%, respectively. CONCLUSIONS This study provides evidence that sleep-wake activity and sleep quality parameters can be reliably estimated solely using breathing sound analysis. This study highlights the potential of this innovative approach to measure sleep in research and clinical circumstances.
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Affiliation(s)
- Eliran Dafna
- Department of Biomedical Engineering, Faculty of Engineering, Ben-Gurion University of the Negev, Beer–Sheva, Israel
| | - Ariel Tarasiuk
- Sleep-Wake Disorders Unit, Soroka University Medical Center, and Department of Physiology, Faculty of Health Sciences, Ben-Gurion University of the Negev, Israel
| | - Yaniv Zigel
- Department of Biomedical Engineering, Faculty of Engineering, Ben-Gurion University of the Negev, Beer–Sheva, Israel
- * E-mail:
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Li H, Zhao G, Zhou Y, Chen X, Ji Z, Wang L. Relationship of EMG/SMG features and muscle strength level: an exploratory study on tibialis anterior muscles during plantar-flexion among hemiplegia patients. Biomed Eng Online 2014; 13:5. [PMID: 24461052 PMCID: PMC3923562 DOI: 10.1186/1475-925x-13-5] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2013] [Accepted: 01/22/2014] [Indexed: 11/23/2022] Open
Abstract
BACKGROUND Improvement in muscle strength is an important aim for the rehabilitation of hemiplegia patients. Presently, the rehabilitation prescription depends on the evaluation results of muscle strength, which are routinely estimated by experienced physicians and therefore not finely quantitative. Widely-used quantification methods for disability, such as Barthel Index (BI) and motor component of Functional Independent Measure (M-FIM), yet have limitations in their application, since both of them differentiated disability better in lower than higher disability, and they are subjective and recorded in wide scales. In this paper, to explore finely quantitative measures for evaluation of muscle strength level (MSL), we start with the study on quantified electromyography (EMG) and sonomyography (SMG) features of tibialis anterior (TA) muscles among hemiplegia patients. METHODS 12 hemiplegia subjects volunteered to perform several sets of plantar-flexion movements in the study, and their EMG signals and SMG signals were recorded on TA independently to avoid interference. EMG data were filtered and then the root-mean-square (RMS) was computed. SMG signals, specifically speaking, the muscle thickness of TA, were manually measured by two experienced operators using ultrasonography. Reproducibility of the SMG assessment on TA between operators was evaluated by non-parametric test (independent sample T test). Possible relationship between muscle thickness changes (TC) of TA and muscle strength level of hemiplegia patients was estimated. RESULTS Mean of EMG RMS between subjects is found linearly correlated with MSL (R2 = 0.903). And mean of TA muscle TC amplitudes is also linearly correlated with MSL among dysfunctional legs (R2 = 0.949). Moreover, rectified TC amplitudes (dysfunctional leg/ healthy leg, DLHL) and rectified EMG signals (DLHL) are found in linear correlation with MSL, with R2 = 0.756 and R2 = 0.676 respectively. Meanwhile, the preliminary results demonstrate that patients' peak values of TC are generally proportional to their personal EMG peak values in 12 dysfunctional legs and 12 healthy legs (R2 = 0.521). CONCLUSIONS It's concluded that SMG could be a promising option to quantitatively estimate MSL for hemiplegia patients during rehabilitation besides EMG. However, after this exploratory study, they should be further investigated on a larger number of subjects.
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Affiliation(s)
- Huihui Li
- Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
- Shenzhen Key Lab for Low-cost Healthcare, Shenzhen, China
| | - Guoru Zhao
- Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
- Shenzhen Key Lab for Low-cost Healthcare, Shenzhen, China
| | - Yongjin Zhou
- Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
- Shenzhen Key Lab for Low-cost Healthcare, Shenzhen, China
| | - Xin Chen
- Shenzhen University, Shenzhen, China
| | - Zhen Ji
- Shenzhen University, Shenzhen, China
| | - Lei Wang
- Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
- Shenzhen Key Lab for Low-cost Healthcare, Shenzhen, China
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Patil SP. Preoperative Evaluation of Obstructive Sleep Apnea. Sleep Med Clin 2013. [DOI: 10.1016/j.jsmc.2012.12.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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