Application of LightGBM hybrid model based on TPE algorithm optimization in sleep apnea detection

IntroductionSleep apnoea syndrome (SAS) is a serious sleep disorder and early detection of sleep apnoea not only reduces treatment costs but also saves lives. Conventional polysomnography (PSG) is widely regarded as the gold standard diagnostic tool for sleep apnoea. However, this method is expensive, time-consuming and inherently disruptive to sleep. Recent studies have pointed out that ECG analysis is a simple and effective diagnostic method for sleep apnea, which can effectively provide physicians with an aid to diagnosis and reduce patients’ suffering.MethodsTo this end, in this paper proposes a LightGBM hybrid model based on ECG signals for efficient detection of sleep apnea. Firstly, the improved Isolated Forest algorithm is introduced to remove abnormal data and solve the data sample imbalance problem. Secondly, the parameters of LightGBM algorithm are optimised by the improved TPE (Tree-structured Parzen Estimator) algorithm to determine the best parameter configuration of the model. Finally, the fusion model TPE_OptGBM is used to detect sleep apnoea. In the experimental phase, we validated the model based on the sleep apnoea ECG database provided by Phillips-University of Marburg, Germany.ResultsThe experimental results show that the model proposed in this paper achieves an accuracy of 95.08%, a precision of 94.80%, a recall of 97.51%, and an F1 value of 96.14%.DiscussionAll of these evaluation indicators are better than the current mainstream models, which is expected to assist the doctor’s diagnostic process and provide a better medical experience for patients

Error-bounded and Number-bounded Approximate Spatial Query for Interactive Visualization

In the big data era, an enormous amount of spatial and spatiotemporal data are generated every day. However, spatial query result sets that satisfy a query condition are very large, sometimes over hundreds or thousands of terabytes. Interactive visualization of big geospatial data calls for continuous query requests, and large query results prevent visual efficiency. Furthermore, traditional methods based on random sampling or line simplification are not suitable for spatial data visualization with bounded errors and bound vertex numbers. In this paper, we propose a vertex sampling method—the Balanced Douglas Peucker (B-DP) algorithm—to build hierarchical structures, where the order and weights of vertices are preserved in binary trees. Then, we develop query processing algorithms with bounded errors and bounded numbers, where the vertices are retrieved by binary trees’ breadth-first-searching (BFS) with a maximum-error-first (MEF) queue. Finally, we conduct an experimental study with OpenStreetMap (OSM) data to determine the effectiveness of our query method in interactive visualization. The results show that the proposed approach can markedly reduce the query results’ size and maintain high accuracy, and its performance is robust against the data volume

Enhancing Cinema Evacuation Efficiency: Impact of Flashing Lights on Emergency Egress Performance and Fire Safety

Evacuation lighting is a crucial component of cinema safety, significantly impacting operational safety and evacuation efficiency. It plays a key role in enhancing evacuation measures and ensuring the safety of cinema patrons. An experiment utilizing virtual reality technology was conducted at Beijing Forestry University with 62 subjects randomly assigned to either a control group or an experimental group. The experimental group was guided by a green flashing light as an evacuation indicator, while the control group relied on static lighting. Although some subjects overlooked the green flashing light, its presence still reduced the number of subjects choosing misleading exits. The flashing light notably improved pathfinding efficiency and evacuation performance, with the experimental group achieving an average evacuation time approximately 30% shorter than the control group. Additionally, subjects rated the sensory, cognitive, and functional aspects of the flashing light lighting from moderate to high. The findings indicate that dynamic and flashing evacuation lighting can effectively enhance fire escape efficiency in cinemas. The design of such systems should consider individual psychological responses and actual behavior patterns to optimize emergency evacuation instructions

Cloning, Characterization and Expression of the Phenylalanine Ammonia-Lyase Gene (PaPAL) from Spruce Picea asperata

Phenylalanine ammonia-lyase (PAL) is the crucial enzyme of the phenylpropanoid pathway, which plays an important role in plant disease resistance. To understand the function of PAL in Picea asperata, in this study, the full-length cDNA sequence of the PAL gene from this species was isolated and named PaPAL. The gene contains a 2160-bp open reading frame (ORF) encoding 720 amino acids with a calculated molecular weight of 78.7 kDa and a theoretical isoelectric point of 5.88. The deduced PaPAL protein possesses the specific signature motif (GTITASGDLVPLSYIA) of phenylalanine ammonia-lyases. Multiple alignment analysis revealed that PaPAL has high identity with other plant PALs. The tertiary structure of PaPAL was predicted using PcPAL from Petroselinum crispum as a template, and the results suggested that PaPAL may have a similar function to that of PcPAL. Furthermore, phylogenetic analysis indicated that PaPAL has a close relationship with other PALs from the Pinaceae species. The optimal expression condition of recombinant PaPAL in Escherichia coli BL21 (DE3) was 0.2 mM IPTG (isopropyl β-D-thiogalactoside) at 16 °C for 4 h, and the molecular weight of recombinant PaPAL was found to be approximately 82 kDa. Recombinant PaPAL was purified and exhibited high PAL activity at optimal conditions of pH 8.6 and 60 °C. Quantitative real-time PCR (qRT-PCR) showed the PaPAL gene to be expressed in all tissues of P. asperata tested, with the highest expression level in the needles. The PaPAL gene was induced by the pathogen (Lophodermium piceae), which caused needle cast disease, indicating that it might be involved in defense against needle cast disease. These results provide a basis for understanding the molecular mechanisms of the PAL gene in the process of P. asperata disease resistance

Cloning, Characterization and Expression of the Phenylalanine Ammonia-Lyase Gene (PaPAL) from Spruce Picea asperata

Phenylalanine ammonia-lyase (PAL) is the crucial enzyme of the phenylpropanoid pathway, which plays an important role in plant disease resistance. To understand the function of PAL in Picea asperata, in this study, the full-length cDNA sequence of the PAL gene from this species was isolated and named PaPAL. The gene contains a 2160-bp open reading frame (ORF) encoding 720 amino acids with a calculated molecular weight of 78.7 kDa and a theoretical isoelectric point of 5.88. The deduced PaPAL protein possesses the specific signature motif (GTITASGDLVPLSYIA) of phenylalanine ammonia-lyases. Multiple alignment analysis revealed that PaPAL has high identity with other plant PALs. The tertiary structure of PaPAL was predicted using PcPAL from Petroselinum crispum as a template, and the results suggested that PaPAL may have a similar function to that of PcPAL. Furthermore, phylogenetic analysis indicated that PaPAL has a close relationship with other PALs from the Pinaceae species. The optimal expression condition of recombinant PaPAL in Escherichia coli BL21 (DE3) was 0.2 mM IPTG (isopropyl β-D-thiogalactoside) at 16 °C for 4 h, and the molecular weight of recombinant PaPAL was found to be approximately 82 kDa. Recombinant PaPAL was purified and exhibited high PAL activity at optimal conditions of pH 8.6 and 60 °C. Quantitative real-time PCR (qRT-PCR) showed the PaPAL gene to be expressed in all tissues of P. asperata tested, with the highest expression level in the needles. The PaPAL gene was induced by the pathogen (Lophodermium piceae), which caused needle cast disease, indicating that it might be involved in defense against needle cast disease. These results provide a basis for understanding the molecular mechanisms of the PAL gene in the process of P. asperata disease resistance.</jats:p

Evaluating the Effectiveness of the Earthquake Early Warning Message in China: An Affordance Perspective Using Immersive Virtual Reality

The early earthquake warning (EEW) system is essential for mitigating the effects of seismic incidents. However, in China, the design of EEW messages has not received much attention. This study employs affordance theory to examine the effectiveness of the EEW message generated by the Institute of Care-Life (ICL) in China, specifically by investigating four aspects of affordances: functional, cognitive, sensory, and emotional affordance. With 68 participants, we conducted an immersive virtual reality experiment. The results revealed that the ICL EEW message has a strong emotional affordance but inadequate functional, cognitive, and sensory affordance. These data provide recommendations for enhancing EEW messages, which could result in better interaction during earthquakes in China. This study investigated the viability of immersive virtual reality as a research tool for EEW. It increases understanding of the elements that determine the effectiveness of EEW communications, leading to better preparedness and response measures, reducing the impact of earthquakes and saving lives and property

Research on Ocular Artifacts Removal from Single-Channel Electroencephalogram Signals in Obstructive Sleep Apnea Patients Based on Support Vector Machine, Improved Variational Mode Decomposition, and Second-Order Blind Identification

The electroencephalogram (EEG) has recently emerged as a pivotal tool in brain imaging analysis, playing a crucial role in accurately interpreting brain functions and states. To address the problem that the presence of ocular artifacts in the EEG signals of patients with obstructive sleep apnea syndrome (OSAS) severely affects the accuracy of sleep staging recognition, we propose a method that integrates a support vector machine (SVM) with genetic algorithm (GA)-optimized variational mode decomposition (VMD) and second-order blind identification (SOBI) for the removal of ocular artifacts from single-channel EEG signals. The SVM is utilized to identify artifact-contaminated segments within preprocessed single-channel EEG signals. Subsequently, these signals are decomposed into variational modal components across different frequency bands using the GA-optimized VMD algorithm. These components undergo further decomposition via the SOBI algorithm, followed by the computation of their approximate entropy. An approximate entropy threshold is set to identify and remove components laden with ocular artifacts. Finally, the signal is reconstructed using the inverse SOBI and VMD algorithms. To validate the efficacy of our proposed method, we conducted experiments utilizing both simulated data and real OSAS sleep EEG data. The experimental results demonstrate that our algorithm not only effectively mitigates the presence of ocular artifacts but also minimizes EEG signal distortion, thereby enhancing the precision of sleep staging recognition based on the EEG signals of OSAS patients