RepID represses megakaryocytic differentiation by recruiting CRL4A-JARID1A at DAB2 promoter.

BACKGROUND: Megakaryocytes (MKs) are platelet precursors, which arise from hematopoietic stem cells (HSCs). While MK lineage commitment and differentiation are accompanied by changes in gene expression, many factors that modulate megakaryopoiesis remain to be uncovered. Replication initiation determinant protein (RepID) which has multiple histone-code reader including bromodomain, cryptic Tudor domain and WD40 domains and Cullin 4-RING E3 ubiquitin ligase complex (CRL4) recruited to chromatin mediated by RepID have potential roles in gene expression changes via epigenetic regulations. We aimed to investigate whether RepID-CRL4 participates in transcriptional changes required for MK differentiation. METHODS: The PCR array was performed using cDNAs derived from RepID-proficient or RepID-deficient K562 erythroleukemia cell lines. Correlation between RepID and DAB2 expression was examined in the Cancer Cell Line Encyclopedia (CCLE) through the CellMinerCDB portal. The acceleration of MK differentiation in RepID-deficient K562 cells was determined by estimating cell sizes as well as counting multinucleated cells known as MK phenotypes, and by qRT-PCR analysis to validate transcripts of MK markers using phorbol 12-myristate 13-acetate (PMA)-mediated MK differentiation condition. Interaction between CRL4 and histone methylation modifying enzymes were investigated using BioGRID database, immunoprecipitation and proximity ligation assay. Alterations of expression and chromatin binding affinities of RepID, CRL4 and histone methylation modifying enzymes were investigated using subcellular fractionation followed by immunoblotting. RepID-CRL4-JARID1A-based epigenetic changes on DAB2 promoter were analyzed by chromatin-immunoprecipitation and qPCR analysis. RESULTS: RepID-deficient K562 cells highly expressing MK markers showed accelerated MKs differentiation exhibiting increases in cell size, lobulated nuclei together with reaching maximum levels of MK marker expression earlier than RepID-proficient K562 cells. Recovery of WD40 domain-containing RepID constructs in RepID-deficient background repressed DAB2 expression. CRL4A formed complex with histone H3K4 demethylase JARID1A in soluble nucleus and loaded to the DAB2 promoter in a RepID-dependent manner during proliferation condition. RepID, CRL4A, and JARID1A were dissociated from the chromatin during MK differentiation, leading to euchromatinization of the DAB2 promoter. CONCLUSION: This study uncovered a role for the RepID-CRL4A-JARID1A pathway in the regulation of gene expression for MK differentiation, which can form the basis for the new therapeutic approaches to induce platelet production. Video Abstract.

RepID represses megakaryocytic differentiation by recruiting CRL4A-JARID1A at DAB2 promoter.

Background Megakaryocytes (MKs) are platelet precursors, which arise from hematopoietic stem cells (HSCs). While MK lineage commitment and differentiation are accompanied by changes in gene expression, many factors that modulate megakaryopoiesis remain to be uncovered. Replication origin binding protein (RepID) which has multiple histone-code reader including bromodomain, cryptic Tudor domain and WD40 domains and Cullin 4-RING ubiquitin ligase complex (CRL4) recruited to chromatin mediated by RepID have potential roles in gene expression changes via epigenetic regulations. We aimed to investigate whether RepID-CRL4 participates in transcriptional changes required for MK differentiation. Methods The PCR array was performed using cDNAs derived from RepID-proficient or RepID-deficient K562 erythroleukemia cell lines. Correlation between RepID and DAB2 expression was examined in the Cancer Cell Line Encyclopedia (CCLE) through the CellMinerCDB portal. The acceleration of MK differentiation in RepID-deficient K562 cells was determined by estimating cell sizes as well as counting multinucleated cells known as MK phenotypes, and by qRT-PCR analysis to validate transcripts of MK markers using phorbol 12-myristate 13-acetate (PMA)-mediated MK differentiation condition. Interaction between CRL4 and histone methylation modifying enzymes were investigated using BioGRID database, immunoprecipitation and proximity ligation assay. Alterations of expression and chromatin binding affinities of RepID, CRL4 and histone methylation modifying enzymes were investigated using subcellular fractionation followed by immunoblotting. RepID-CRL4-JARID1A-based epigenetic changes on DAB2 promoter were analyzed by chromatin-immunoprecipitation and qPCR analysis. Results RepID-deficient K562 cells highly expressing MK markers showed accelerated MKs differentiation exhibiting increases in cell size, lobulated nuclei together with reaching maximum levels of MK marker expression earlier than RepID-proficient K562 cells. Recovery of WD40 domain-containing RepID constructs in RepID-deficient background repressed DAB2 expression. CRL4A formed complex with histone H3K4 demethylase JARID1A in soluble nucleus and loaded to the DAB2 promoter in a RepID-dependent manner during proliferation condition. RepID, CRL4A, and JARID1A were dissociated from the chromatin during MK differentiation, leading to euchromatinization of the DAB2 promoter. Conclusion This study uncovered a role for the RepID-CRL4A-JARID1A pathway in the regulation of gene expression for MK differentiation, which can form the basis for the new therapeutic approaches to induce platelet production.

The differentially expressed gene signatures of the Cullin 3-RING ubiquitin ligases in neuroendocrine cancer.

Cullin-RING ubiquitin E3 ligase (CRLs) composed of four components including cullin scaffolds, adaptors, substrate receptors, and RING proteins mediates the ubiquitination of approximately 20% of cellular proteins that are involved in numerous biological processes. While CRLs deregulation contributes to the pathogenesis of many diseases, including cancer, how CRLs deregulation occurs is yet to be fully investigated. Here, we demonstrate that components of CRL3 and its transcriptional regulators are possible prognosis marker of neuroendocrine (NE) cancer. Analysis of Cancer Cell Line Encyclopedia (CCLE) through the CellMinerCDB portal revealed that expression of CRL3 scaffold Cullin 3 (CUL3) highly correlates with NE signature, and CUL3 silencing inhibited NE cancer proliferation. Moreover, subset of 151 BTB (Bric-a-brac, Tramtrack, Broad complex) domain-containing proteins that have dual roles as substrate receptors and adaptor subunits in CRL3, as well as the expression of transcription factors (TFs) that control the transcription of BTB genes were upregulated in NE cancer. Analysis using published ChIP-sequencing data in small cell lung cancer (SCLC), including NE or non-NE SCLC verified that gene promoter of candidates which show high correlation with NE signature enriched H3K27Ac. These observations suggest that CRL3 is a master regulator of NE cancer and knowledge of specifically regulated CRL3 genes in NE cancer may accelerate new therapeutic approaches.

COVID-19 Depression and Infection Prevention Behavior among College Students: A Health Belief Perspective.

The COVID-19 pandemic has resulted in an increase in depression among college students due to anxiety and fear of infection. Nonetheless, COVID-19 infection prevention measures should be actively implemented. In this study, the mediating effect of health belief on the relationship between depression and infection prevention behavior was investigated. A survey of 220 South Korean college students was conducted. Depression was found to be the independent variable, health belief the mediating variable, and infection prevention behavior the dependent variable. The model fit index according to confirmatory factor analysis was found to be suitable. Depression among college students was not directly related to COVID-19 infection prevention behavior; however, depression was confirmed to be related to infection prevention behavior via the mediation of health belief. Arbitration measures, focusing on perceived severity and susceptibility during health belief, are required.

A Prediction Algorithm for Hypoglycemia Based on Support Vector Machine Using Glucose Level and Electrocardiogram.

A prediction algorithm for hypoglycemic events is proposed using glucose levels and electrocardiogram (ECG) with support vector machine (SVM). We extracted the corrected QT interval and five heart rate variability parameters from the ECG, along with glucose level from a continuous glucose monitoring system (CGMS). This feature set is used as input to the SVM, and hypoglycemic events are predicted every 5 min using the trained SVM model for up to 30 min in advance. The proposed algorithm was developed and evaluated for nine Type-1 diabetes patients in the D1NAMO dataset. The prediction sensitivity, specificity, and accuracy values for the test set were 91.1%, 87.0%, and 89.0% (10 min before); 88.0%, 84.3%, and 86.2% (20 min before); 80.1%, 83.3%, and 81.7% (30 min before), respectively. These results show higher performance of the proposed method compared to previous studies and suggest the possibility of predicting hypoglycemia in advance.

Deep Learning for Automatic Detection of Periodic Limb Movement Disorder Based on Electrocardiogram Signals.

In this study, a deep learning model (deepPLM) is shown to automatically detect periodic limb movement syndrome (PLMS) based on electrocardiogram (ECG) signals. The designed deepPLM model consists of four 1D convolutional layers, two long short-term memory units, and a fully connected layer. The Osteoporotic Fractures in Men sleep (MrOS) study dataset was used to construct the model, including training, validating, and testing the model. A single-lead ECG signal of the polysomnographic recording was used for each of the 52 subjects (26 controls and 26 patients) in the MrOS dataset. The ECG signal was normalized and segmented (10 s duration), and it was divided into a training set (66,560 episodes), a validation set (16,640 episodes), and a test set (20,800 episodes). The performance evaluation of the deepPLM model resulted in an F1-score of 92.0%, a precision score of 90.0%, and a recall score of 93.0% for the control set, and 92.0%, 93.0%, and 90.0%, respectively, for the patient set. The results demonstrate the possibility of automatic PLMS detection in patients by using the deepPLM model based on a single-lead ECG. This could be an alternative method for PLMS screening and a helpful tool for home healthcare services for the elderly population.

Subacute Oral Toxicity Evaluation of Expanded-Polystyrene-Fed Tenebrio molitor Larvae (Yellow Mealworm) Powder in Sprague-Dawley Rats.

Tenebrio molitor larvae, as known as edible insects, has advantages of being rich in protein, and has been recognized as a suitable alternate protein source for broiler and pig feed. Moreover, given their ability to biodegrade polystyrene, a major pollutant, Tenebrio molitor larvae has been proposed as an innovative solution to environmental problems. In the present study, we investigated the toxicity of Tenebrio molitor larvae powder (TMlp) ingested with expanded-polystyrene (W/ eps) through in vitro and in vivo experiments. The objective of this study was to determine whether TMlp W/ eps can be applied as livestock alternative protein source. For in vitro experiments, cytotoxicity test was performed to investigate the effects of TMlp-extract on the viability of estrogen-dependent MCF-7 cells. The possibility of estrogen response was investigated in two groups: Expanded-polystyrene-fed (W/ eps) TMlp group and without expanded-polystyrene-fed (W/o eps) TMlp group. For in vivo experiments, The male Sprague-Dawley rats were divided based on the dosage of TMlp administered and oral administration was performed to every day for 5 weeks. A toxicological assessments were performed, which included clinical signs, food consumption, body and organ weights, hematology, serum chemistry, and hematoxylin and eosin staining of liver and kidney. There were no specific adverse effect of TMlp W/ eps-related findings under the experimental conditions of this study, but further studies on both sexes and animal species differences should be investigated. In conclusion, TMlp W/ eps was considered non-toxic and observed to be applicable as an alternative protein source for livestock feed.

Deep Convolutional Recurrent Model for Automatic Scoring Sleep Stages Based on Single-Lead ECG Signal.

BACKGROUND: Sleep stage scoring, which is an essential step in the quantitative analysis of sleep monitoring, relies on human experts and is therefore subjective and time-consuming; thus, an easy and accurate method is needed for the automatic scoring of sleep stages. METHODS: In this study, we constructed a deep convolutional recurrent (DCR) model for the automatic scoring of sleep stages based on a raw single-lead electrocardiogram (ECG). The DCR model uses deep convolutional and recurrent neural networks to apply the complex and cyclic rhythms of human sleep. It consists of three convolutional and two recurrent layers and is optimized by dropout and batch normalization. The constructed DCR model was evaluated using multiclass classification, including five-class sleep stages (wake, N1, N2, N3, and rapid eye movement (REM)) and three-class sleep stages (wake, non-REM (NREM), and REM), using a raw single-lead ECG signal. The single-lead ECG signal was collected from 112 subjects in two groups: control (52 subjects) and sleep apnea (60 subjects). The single-lead ECG signal was preprocessed, segmented at a duration of 30 s, and divided into a training set of 89 subjects and test set of 23 subjects. RESULTS: We achieved an overall accuracy of 74.2% for five classes and 86.4% for three classes. CONCLUSIONS: These results show the DCR model's superior performance over those in the previous studies, highlighting that the model can be an alternative tool for sleep monitoring and sleep screening.

Pulse Broadening Effects on Ranging Performance of a Laser Altimeter with Return-to-Zero Pseudorandom Noise Code Modulation.

A laser altimeter using code modulation techniques receives a backscattered pulse wider than the transmitted rectangular pulse when scanning a rough or sloped target surface. This leads to degrading the ranging performance in terms of signal-to-noise ratio (SNR) and detection probability. Unlike the pulsed techniques, little work has focused on the pulse broadening effect of the code modulation techniques. In this study, mathematical models were derived to investigate the pulse broadening effect on the ranging performance of a return-to-zero pseudorandom noise (RZPN) laser altimeter. Considering that the impulse response can be approximated by a Gaussian function, the analytical waveform was derived using a new flat-topped multi-Gaussian beam (FMGB) model. The closed-form expressions were also analytically derived for a peak cross-correlation, SNR, and detection probability in terms of the pulse broadening effect. With the use of a three-dimensional model of asteroid Itokawa for practical surface profiles, the analytical expressions were validated by comparing to the results obtained from numerical simulations. It was also demonstrated that the pulse broadening effect dropped down the peak cross-correlation and then deteriorated the ranging performance. These analytical expressions will play an important role in not only designing a laser altimeter using the RZPN code modulation technique but also analyzing its ranging performance.

A Prediction Model of Incident Cardiovascular Disease in Patients with Sleep-Disordered Breathing.

(1) Purpose: this study proposes a method of prediction of cardiovascular diseases (CVDs) that can develop within ten years in patients with sleep-disordered breathing (SDB). (2) Methods: For the design and evaluation of the algorithm, the Sleep Heart Health Study (SHHS) data from the 3367 participants were divided into a training set, validation set, and test set in the ratio of 5:3:2. From the data during a baseline period when patients did not have any CVD, we extracted 18 features from electrography (ECG) based on signal processing methods, 30 ECG features based on artificial intelligence (AI), ten clinical risk factors for CVD. We trained the model and evaluated it by using CVD outcomes result, monitored in follow-ups. The optimal feature vectors were selected through statistical analysis and support vector machine recursive feature elimination (SVM-RFE) of the extracted feature vectors. Features based on AI, a novel proposal from this study, showed excellent performance out of all selected feature vectors. In addition, new parameters based on AI were possibly meaningful predictors for CVD, when used in addition to the predictors for CVD that are already known. The selected features were used as inputs to the prediction model based on SVM for CVD, determining the development of CVD-free, coronary heart disease (CHD), heart failure (HF), or stroke within ten years. (3) Results: As a result, the respective recall and precision values were 82.9% and 87.5% for CVD-free; 71.9% and 63.8% for CVD; 57.2% and 55.4% for CHD; 52.6% and 40.8% for HF; 52.4% and 44.6% for stroke. The F1-score between CVD and CVD-free was 76.5%, and it was 59.1% in class four. (4) Conclusion: In conclusion, our results confirm the excellence of the prediction model for CVD in patients with SDB and verify the possibility of prediction within ten years of the CVDs that may occur in patients with SDB.

Light-absorption enhancement of black carbon in the Asian outflow inferred from airborne SP2 and in-situ measurements during KORUS-AQ.

We investigated the changes in the size distribution, coating thickness, and mass absorption cross-section (MAC) of black carbon (BC) with aging and estimated the light absorption enhancement (Eabs) in the Asian outflow from airborne in-situ measurements during 2016 KORUS-AQ campaign. The BC number concentration decreased, but mass mean diameter increased with increasing altitude in the West Coast (WC) and Seoul Metropolitan Area (SMA), reflecting the contrast between freshly emitted BC-containing particles at the surface and more aged aerosol associated with aggregation during vertical mixing and transport. Contradistinctively, BC number and mass size distributions were relatively invariant with altitude over the Yellow Sea (YS) because sufficiently aged BC from eastern China were horizontally transported to all altitudes over the YS, and there are no significant sources at the surface. The averaged inferred MAC of refractory BC in three regions reflecting differences in their size distributions increased to 9.8 ± 1.0 m2 g-1 (YS), 9.3 ± 0.9 m2 g-1 (WC), and 8.2 ± 0.9 m2 g-1 (SMA) as BC coating thickness increased from 20 nm to 120 nm. The absorption coefficient of BC calculated from the coating thickness and MAC were highly correlated with the filter-based absorption measurements with the slope of 1.16 and R2 of 0.96 at 550 nm, revealing that the thickly coated BC had a large MAC and absorption coefficient. The Eabs due to the inferred coatings was estimated as 1.0-1.6, which was about 30% lower than those from climate models and laboratory experiments, suggesting that the increase in the BC absorption by the coatings in the Asian outflow is not as large as calculated in the previous studies. Organics contributed to the largest Eabs accounting for 69% (YS), 61% (WC), and 64% (SMA). This implies that organics are largely responsible for the lensing effect of BC rather than sulfates in the Asian outflow.

Identification of Sleep Apnea Severity Based on Deep Learning from a Short-term Normal ECG.

BACKGROUND: This paper proposes a novel method for automatically identifying sleep apnea (SA) severity based on deep learning from a short-term normal electrocardiography (ECG) signal. METHODS: A convolutional neural network (CNN) was used as an identification model and implemented using a one-dimensional convolutional, pooling, and fully connected layer. An optimal architecture is incorporated into the CNN model for the precise identification of SA severity. A total of 144 subjects were studied. The nocturnal single-lead ECG signal was collected, and the short-term normal ECG was extracted from them. The short-term normal ECG was segmented for a duration of 30 seconds and divided into two datasets for training and evaluation. The training set consists of 82,952 segments (66,360 training set, 16,592 validation set) from 117 subjects, while the test set has 20,738 segments from 27 subjects. RESULTS: F1-score of 98.0% was obtained from the test set. Mild and moderate SA can be identified with an accuracy of 99.0%. CONCLUSION: The results showed the possibility of automatically identifying SA severity based on a short-term normal ECG signal.

Estimation of Arterial Blood Pressure Based on Artificial Intelligence Using Single Earlobe Photoplethysmography during Cardiopulmonary Resuscitation.

This study investigates the feasibility of estimation of blood pressure (BP) using a single earlobe photoplethysmography (Ear PPG) during cardiopulmonary resuscitation (CPR). We have designed a system that carries out Ear PPG for estimation of BP. In particular, the BP signals are estimated according to a long short-term memory (LSTM) model using an Ear PPG. To investigate the proposed method, two statistical analyses were conducted for comparison between BP measured by the micromanometer-based gold standard method (BPMEAS) and the Ear PPG-based proposed method (BPEST) for swine cardiac model. First, Pearson's correlation analysis showed high positive correlations (r = 0.92, p < 0.01) between BPMEAS and BPEST. Second, the paired-samples t-test on the BP parameters (systolic and diastolic blood pressure) of the two methods indicated no significant differences (p > 0.05). Therefore, the proposed method has the potential for estimation of BP for CPR biofeedback based on LSTM using a single Ear PPG.

Multi-Class Classification of Sleep Apnea/Hypopnea Events Based on Long Short-Term Memory Using a Photoplethysmography Signal.

In this study, we proposed a new method for multi-class classification of sleep apnea/hypopnea events based on a long short-term memory (LSTM) using photoplethysmography (PPG) signals. The three-layer LSTM model was used with batch-normalization and dropout to classify the multi-class events including normal, apnea, and hypopnea. The PPG signals, which were measured by the nocturnal polysomnography with 7 h from 82 patients suffered from sleep apnea, were used to model training and evaluation. The performance of the proposed method was evaluated on the training set from 63 patients and test set from 13 patients. The results of the LSTM model showed the following high performances: the positive predictive value of 94.16% for normal, 81.38% for apnea, and 97.92% for hypopnea; sensitivity of 86.03% for normal, 91.24% for apnea, and 99.38% for hypopnea events. The proposed method had especially higher performance of hypopnea classification which had been a drawback of previous studies. Furthermore, it can be applied to a system that can classify sleep apnea/hypopnea and normal events automatically without expert's intervention at home.

Deep learning approaches for automatic detection of sleep apnea events from an electrocardiogram.

BACKGROUND AND OBJECTIVE: This study demonstrates deep learning approaches with an aim to find the optimal method to automatically detect sleep apnea (SA) events from an electrocardiogram (ECG) signal. METHODS: Six deep learning approaches were designed and implemented for automatic detection of SA events including deep neural network (DNN), one-dimensional (1D) convolutional neural networks (CNN), two-dimensional (2D) CNN, recurrent neural networks (RNN), long short-term memory, and gated-recurrent unit (GRU). Designed deep learning models were analyzed and compared in the performances. The ECG signal was pre-processed, normalized, and segmented into 10 s intervals. Subsequently, the signal was converted into a 2D form for analysis in the 2D CNN model. A dataset collected from 86 patients with SA was used. The training set comprised data from 69 of the patients, while the test set contained data from the remaining 17 patients. RESULTS: The accuracy of the best-performing model was 99.0%, and the 1D CNN and GRU models had 99.0% recall rates. CONCLUSIONS: The designed deep learning approaches performed better than those developed and tested in previous studies in terms of detecting SA events, and they could distinguish between apnea and hypopnea events using an ECG signal. The deep learning approaches such as 1D CNN and GRU can be helpful tools to automatically detect SA in sleep apnea screening and related studies.

Automatic Prediction of Atrial Fibrillation Based on Convolutional Neural Network Using a Short-term Normal Electrocardiogram Signal.

BACKGROUND: In this study, we propose a method for automatically predicting atrial fibrillation (AF) based on convolutional neural network (CNN) using a short-term normal electrocardiogram (ECG) signal. METHODS: We designed a CNN model and optimized it by dropout and normalization. One-dimensional convolution, max-pooling, and fully-connected multiple perceptron were used to analyze the short-term normal ECG. The ECG signal was preprocessed and segmented to train and evaluate the proposed CNN model. The training and test sets consisted of the two AF and one normal dataset from the MIT-BIH database. RESULTS: The proposed CNN model for the automatic prediction of AF achieved a high performance with a sensitivity of 98.6%, a specificity of 98.7%, and an accuracy of 98.7%. CONCLUSION: The results show the possibility of automatically predicting AF based on the CNN model using a short-term normal ECG signal. The proposed CNN model for the automatic prediction of AF can be a helpful tool for the early diagnosis of AF in healthcare fields.

Multiclass classification of obstructive sleep apnea/hypopnea based on a convolutional neural network from a single-lead electrocardiogram.

OBJECTIVE: In this paper, we propose a convolutional neural network (CNN)-based deep learning architecture for multiclass classification of obstructive sleep apnea and hypopnea (OSAH) using single-lead electrocardiogram (ECG) recordings. OSAH is the most common sleep-related breathing disorder. Many subjects who suffer from OSAH remain undiagnosed; thus, early detection of OSAH is important. APPROACH: In this study, automatic classification of three classes-normal, hypopnea, and apnea-based on a CNN is performed. An optimal six-layer CNN model is trained on a training dataset (45 096 events) and evaluated on a test dataset (11 274 events). The training set (69 subjects) and test set (17 subjects) were collected from 86 subjects with length of approximately 6 h and segmented into 10 s durations. MAIN RESULTS: The proposed CNN model reaches a mean [Formula: see text]-score of 93.0 for the training dataset and 87.0 for the test dataset. SIGNIFICANCE: Thus, proposed deep learning architecture achieved a high performance for multiclass classification of OSAH using single-lead ECG recordings. The proposed method can be employed in screening of patients suspected of having OSAH.

Automated Detection of Obstructive Sleep Apnea Events from a Single-Lead Electrocardiogram Using a Convolutional Neural Network.

In this study, we propose a method for the automated detection of obstructive sleep apnea (OSA) from a single-lead electrocardiogram (ECG) using a convolutional neural network (CNN). A CNN model was designed with six optimized convolution layers including activation, pooling, and dropout layers. One-dimensional (1D) convolution, rectified linear units (ReLU), and max pooling were applied to the convolution, activation, and pooling layers, respectively. For training and evaluation of the CNN model, a single-lead ECG dataset was collected from 82 subjects with OSA and was divided into training (including data from 63 patients with 34,281 events) and testing (including data from 19 patients with 8571 events) datasets. Using this CNN model, a precision of 0.99%, a recall of 0.99%, and an F1-score of 0.99% were attained with the training dataset; these values were all 0.96% when the CNN was applied to the testing dataset. These results show that the proposed CNN model can be used to detect OSA accurately on the basis of a single-lead ECG. Ultimately, this CNN model may be used as a screening tool for those suspected to suffer from OSA.

Cardiopulmonary Coupling Analysis Using Home Sleep Monitoring System Based on Air Mattress.

This study investigates the feasibility of cardiopulmonary coupling (CPC) using home sleep monitoring system. We have designed a system to measure respiratory signals and normal-to-normal (NN) interval series in a non-contact based on air mattress. Then, CPC analysis was conducted using extracted respiratory signals and NN interval series, and six CPC parameters were extracted (VLFC, LFC, HFC, e-LFC, e-LFCNB and e-LFCBB). To evaluate the proposed method, two statistical analyses were conducted between the CPC parameters extracted by the electrocardiogram-based conventional method and the air mattress-based proposed method for five patients with obstructive sleep apnea and hypopnea (OSAH). Wilcoxon's signed rank test on the CPC parameters of the two methods indicated no significant differences (p > 0.05) and Spearman's rank correlation analysis showed high positive correlations (r ≥ 0.7, p < 0.05) between the two methods. Therefore, the proposed method has the potential for performing CPC analysis using air mattress-based system.

Estimating sleep parameters using nasal pressure signals applicable to continuous positive airway pressure devices.

OBJECTIVE: This paper proposes a method for classifying sleep-wakefulness and estimating sleep parameters using nasal pressure signals applicable to a continuous positive airway pressure (CPAP) device. APPROACH: In order to classify the sleep-wakefulness states of patients with sleep-disordered breathing (SDB), apnea-hypopnea and snoring events are first detected. Epochs detected as SDB are classified as sleep, and time-domain- and frequency-domain-based features are extracted from the epochs that are detected as normal breathing. Subsequently, sleep-wakefulness is classified using a support vector machine (SVM) classifier in the normal breathing epoch. Finally, four sleep parameters-sleep onset, wake after sleep onset, total sleep time and sleep efficiency-are estimated based on the classified sleep-wakefulness. In order to develop and test the algorithm, 110 patients diagnosed with SDB participated in this study. Ninety of the subjects underwent full-night polysomnography (PSG) and twenty underwent split-night PSG. The subjects were divided into 50 patients of a training set (full/split: 42/8), 30 of a validation set (full/split: 24/6) and 30 of a test set (full/split: 24/6). MAIN RESULTS: In the experiments conducted, sleep-wakefulness classification accuracy was found to be 83.2% in the test set, compared with the PSG scoring results of clinical experts. Furthermore, all four sleep parameters showed higher correlations than the results obtained via PSG (r ⩾ 0.84, p < 0.05). In order to determine whether the proposed method is applicable to CPAP, sleep-wakefulness classification performances were evaluated for each CPAP in the split-night PSG data. The results indicate that the accuracy and sensitivity of sleep-wakefulness classification by CPAP variation shows no statistically significant difference (p < 0.05). SIGNIFICANCE: The contributions made in this study are applicable to the automatic classification of sleep-wakefulness states in CPAP devices and evaluation of the quality of sleep.