Physiological measurement最新文献

{"title":"Detection of sleep arousal from STFT-based instantaneous features of single channel EEG signal.","authors":"Md Hussain Ali, Md Bashir Uddin","doi":"10.1088/1361-6579/ad7fcb","DOIUrl":"https://doi.org/10.1088/1361-6579/ad7fcb","url":null,"abstract":"<p><strong>Objective: </strong>Sleep arousal, a frequent interruption in sleep with complete or partial wakefulness from sleep, may indicate a breathing disorder, neurological disorder, or sleep-related disorders. These phenomena necessitate the detection of sleep arousals. Uses of deep learning methods to detect features inhibits the scope to understand the specific distinctive nature of the signals and reduces the interpretability of the model. To evade these inconsistencies and to improve the classification performance of the sleep arousal detection model, a model has been proposed in this study on the prospect of understandable features that are useful in detecting sleep arousals. &#xD;Approach: Time-frequency analysis of the electroencephalogram (EEG) signals was performed using Short-Time Fourier Transform (STFT). From the STFT coefficients, the spectrogram and instantaneous properties (frequency, bandwidth, power spectrum, band energy, local maxima, and band energy ratios) were investigated. From these properties, instantaneous features were generated by statistical analysis. Additive feature sets and reduced feature sets, formed by adding features successively and reducing features using the analysis of variance test respectively, were subjected to a tri-layered neural network classifier to evaluate the capability of the features to detect sleep arousal and normal sleep segments. &#xD;Main results: The reduced feature set (Set 6) has proved to be efficacious in facilitating superior classification performance metrics (accuracy, sensitivity, specificity, and AUC of 89.14%, 83.52%, 89.49%, and 93.84% respectively). &#xD;Significance: This efficient model can be incorporated with an automatic sleep apnea detection system where the estimation of hypopnea requires the detection of sleep arousal.&#xD;&#xD.</p>","PeriodicalId":20047,"journal":{"name":"Physiological measurement","volume":" ","pages":""},"PeriodicalIF":2.3,"publicationDate":"2024-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142351968","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluation of five methods for the interpolation of bad leads in the solution of the inverse electrocardiography problem.","authors":"Y Serinagaoglu Dogrusoz, L R Bear, J A Bergquist, A S Rababah, W Good, J Stoks, J Svehlikova, E van Dam, D H Brooks, R S MacLeod","doi":"10.1088/1361-6579/ad74d6","DOIUrl":"10.1088/1361-6579/ad74d6","url":null,"abstract":"<p><p><i>Objective.</i>This study aims to assess the sensitivity of epicardial potential-based electrocardiographic imaging (ECGI) to the removal or interpolation of bad leads.<i>Approach.</i>We utilized experimental data from two distinct centers. Langendorff-perfused pig (<i>n</i>= 2) and dog (<i>n</i>= 2) hearts were suspended in a human torso-shaped tank and paced from the ventricles. Six different bad lead configurations were designed based on clinical experience. Five interpolation methods were applied to estimate the missing data. Zero-order Tikhonov regularization was used to solve the inverse problem for complete data, data with removed bad leads, and interpolated data. We assessed the quality of interpolated ECG signals and ECGI reconstructions using several metrics, comparing the performance of interpolation methods and the impact of bad lead removal versus interpolation on ECGI.<i>Main results.</i>The performance of ECG interpolation strongly correlated with ECGI reconstruction. The hybrid method exhibited the best performance among interpolation techniques, followed closely by the inverse-forward and Kriging methods. Bad leads located over high amplitude/high gradient areas on the torso significantly impacted ECGI reconstructions, even with minor interpolation errors. The choice between removing or interpolating bad leads depends on the location of missing leads and confidence in interpolation performance. If uncertainty exists, removing bad leads is the safer option, particularly when they are positioned in high amplitude/high gradient regions. In instances where interpolation is necessary, the inverse-forward and Kriging methods, which do not require training, are recommended.<i>Significance.</i>This study represents the first comprehensive evaluation of the advantages and drawbacks of interpolating versus removing bad leads in the context of ECGI, providing valuable insights into ECGI performance.</p>","PeriodicalId":20047,"journal":{"name":"Physiological measurement","volume":" ","pages":""},"PeriodicalIF":2.3,"publicationDate":"2024-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142093679","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predicting stroke volume variation using central venous pressure waveform: a deep learning approach.","authors":"Insun Park, Jae Hyon Park, Bon-Wook Koo, Jin-Hee Kim, Young-Tae Jeon, Hyo-Seok Na, Ah-Young Oh","doi":"10.1088/1361-6579/ad75e4","DOIUrl":"10.1088/1361-6579/ad75e4","url":null,"abstract":"<p><p><i>Objective</i>. This study evaluated the predictive performance of a deep learning approach to predict stroke volume variation (SVV) from central venous pressure (CVP) waveforms.<i>Approach</i>. Long short-term memory (LSTM) and the feed-forward neural network were sequenced to predict SVV using CVP waveforms obtained from the VitalDB database, an open-source registry. The input for the LSTM consisted of 10 s CVP waveforms sampled at 2 s intervals throughout the anesthesia duration. Inputs of the feed-forward network were the outputs of LSTM and demographic data such as age, sex, weight, and height. The final output of the feed-forward network was the SVV. The performance of SVV predicted by the deep learning model was compared to SVV estimated derived from arterial pulse waveform analysis using a commercialized model, EV1000.<i>Main results</i>. The model hyperparameters consisted of 12 memory cells in the LSTM layer and 32 nodes in the hidden layer of the feed-forward network. A total of 224 cases comprising 1717 978 CVP waveforms and EV1000/SVV data were used to construct and test the deep learning models. The concordance correlation coefficient between estimated SVV from the deep learning model were 0.993 (95% confidence interval, 0.992-0.993) for SVV measured by EV1000.<i>Significance</i>. Using a deep learning approach, CVP waveforms can accurately approximate SVV values close to those estimated using commercial arterial pulse waveform analysis.</p>","PeriodicalId":20047,"journal":{"name":"Physiological measurement","volume":" ","pages":""},"PeriodicalIF":2.3,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142110751","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Recurrence quantification analysis of uterine vectormyometriogram reveals differences between normal-weight and overweight parturient women.","authors":"José Javier Reyes-Lagos, Eric Alonso Abarca-Castro, Claudia Ivette Ledesma-Ramírez, Adriana Cristina Pliego-Carrillo, Guadalupe Dorantes-Méndez, Araceli Espinosa-Guerrero","doi":"10.1088/1361-6579/ad7777","DOIUrl":"10.1088/1361-6579/ad7777","url":null,"abstract":"<p><p><i>Objective.</i>This study aims to use recurrence quantification analysis (RQA) of uterine vectormyometriogram (VMG) created from the slow wave (SW) and high wave (HW) bands of electrohysterogram (EHG) signals and assess the directionality of the EHG activity (horizontal or<i>X</i>, vertical or<i>Y</i>) in normal-weight (NW) and overweight (OW) women during the first stage of labor.<i>Approach</i>. The study involved 41 parturient women (NW = 21 and OW = 20) during the first stage of labor, all of whom were attended at the Gynecology and Obstetrics Hospital of the Maternal and Child Institute of the State of Mexico in Toluca, Mexico. Twenty-minute EHG signals were analyzed in horizontal and vertical directions. Linear and nonlinear indices such as dominant frequency (Dom), Sample Entropy (SampEn), and RQA measures of VMG were computed for SW and HW bands.<i>Main results</i>. Significant differences in SampEn and Dom were observed in the SW band between NW and OW in both<i>X</i>and<i>Y</i>directions, indicating more regular dynamics of electrical uterine activity and a higher Dom in NW parturient women compared to OW women. Additionally, the RQA indices calculated from the VMG of SW were consistent and revealed that NW women exhibit more regular dynamics compared to OW women.<i>Significance</i>. The study demonstrates that RQA of VMG signals and EHG directionality differentiate uterine activity between NW and OW women during the first stage of labor. These findings suggest that the uterine vector may become more periodic, predictable, and stable in NW women compared to OW women. This highlights the importance of tailored clinical strategies for managing labor in OW women to improve maternal and infant outcomes.</p>","PeriodicalId":20047,"journal":{"name":"Physiological measurement","volume":" ","pages":""},"PeriodicalIF":2.3,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142133455","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Adaptive threshold algorithm for detecting EEG-interburst intervals in extremely preterm neonates.","authors":"Johannes Caspar Mader,Manfred Hartmann,Anastasia Dressler,Lisa Oberdorfer,Zsofia Rona,Sarah Glatter,Christine Czaba-Hnizdo,Johannes Herta,Tilmann Kluge,Tobias Werther,Angelika Berger,Johannes Koren,Katrin Klebermass-Schrehof,Vito Giordano","doi":"10.1088/1361-6579/ad7c05","DOIUrl":"https://doi.org/10.1088/1361-6579/ad7c05","url":null,"abstract":"This study provides an adaptive threshold algorithm for burst detection in electroencephalograms (EEG) of preterm infantes and evaluates its performance using clinical real-world EEG data.&#xD;&#xD;Approach: We developed an adaptive threshold algorithm for burst detection in EEG recordings from preterm infants. To assess its applicability in the real-world, we tested the algorithm on a dataset of 30 clinical EEG recordings which were not preselected for good quality, to ensure a real-world scenario.&#xD;&#xD;Main results: Interrater agreement was substantial at a kappa of 0.73 (0.68 - 0.79 inter-quantile range). The performance of the algorithm showed a similar agreement with one clinical expert of 0.73 (0.67 - 0.76) and a sensitivity and specificity of 0.90 (0.82 - 0.94) and 0.95 (0.93 - 0.97), respectively.&#xD;&#xD;Significance: The adaptive threshold algorithm demonstrated robust performance in detecting burst patterns in clinical EEG data from preterm infants, highlighting its practical utility. The fine-tuned algorithm achieved similar performance to human raters. The algorithm proves to be a valuable tool for automated burst detection in the EEG of preterm infants.","PeriodicalId":20047,"journal":{"name":"Physiological measurement","volume":"30 1","pages":""},"PeriodicalIF":3.2,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142252824","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Variability of morphology in photoplethysmographic waveform quantified with unsupervised wave-shape manifold learning for clinical assessment.","authors":"Yu-Chieh Ho, Te-Sheng Lin, Shen-Chih Wang, Cheng-Hsi Chang, Yu-Ting Lin","doi":"10.1088/1361-6579/ad7779","DOIUrl":"10.1088/1361-6579/ad7779","url":null,"abstract":"<p><p><i>Objective.</i>We investigated fluctuations of the photoplethysmography (PPG) waveform in patients undergoing surgery. There is an association between the morphologic variation extracted from arterial blood pressure (ABP) signals and short-term surgical outcomes. The underlying physiology could be the numerous regulatory mechanisms on the cardiovascular system. We hypothesized that similar information might exist in PPG waveform. However, due to the principles of light absorption, the noninvasive PPG signals are more susceptible to artifacts and necessitate meticulous signal processing.<i>Approach.</i>Employing the unsupervised manifold learning algorithm, dynamic diffusion map, we quantified multivariate waveform morphological variations from the PPG continuous waveform signal. Additionally, we developed several data analysis techniques to mitigate PPG signal artifacts to enhance performance and subsequently validated them using real-life clinical database.<i>Main results.</i>Our findings show similar associations between PPG waveform during surgery and short-term surgical outcomes, consistent with the observations from ABP waveform analysis.<i>Significance.</i>The variation of morphology information in the PPG waveform signal in major surgery provides clinical meanings, which may offer new opportunity of PPG waveform in a wider range of biomedical applications, due to its non-invasive nature.</p>","PeriodicalId":20047,"journal":{"name":"Physiological measurement","volume":" ","pages":""},"PeriodicalIF":2.3,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142133434","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The impact of controlled breathing on autonomic nervous system modulation: analysis using phase-rectified signal averaging, entropy and heart rate variability.","authors":"Agnieszka Uryga, Mikołaj Najda, Ignacy Berent, Cyprian Mataczyński, Piotr Urbański, Magdalena Kasprowicz, Teodor Buchner","doi":"10.1088/1361-6579/ad7778","DOIUrl":"10.1088/1361-6579/ad7778","url":null,"abstract":"<p><p><i>Objective.</i>The present study investigated how breathing stimuli affect both non-linear and linear metrics of the autonomic nervous system (ANS).<i>Approach.</i>The analysed dataset consisted of 70 young, healthy volunteers, in whom arterial blood pressure (ABP) was measured noninvasively during 5 min sessions of controlled breathing at three different frequencies: 6, 10 and 15 breaths min^-1. CO₂concentration and respiratory rate were continuously monitored throughout the controlled breathing sessions. The ANS was characterized using non-linear methods, including phase-rectified signal averaging (PRSA) for estimating heart acceleration and deceleration capacity (AC, DC), multiscale entropy, approximate entropy, sample entropy, and fuzzy entropy, as well as time and frequency-domain measures (low frequency, LF; high-frequency, HF; total power, TP) of heart rate variability (HRV).<i>Main results.</i>Higher breathing rates resulted in a significant decrease in end-tidal CO₂concentration (<i>p</i>< 0.001), accompanied by increases in both ABP (<i>p <</i>0.001) and heart rate (HR,<i>p <</i>0.001). A strong, linear decline in AC and DC (<i>p <</i>0.001 for both) was observed with increasing breathing rate. All entropy metrics increased with breathing frequency (<i>p <</i>0.001). In the time-domain, HRV metrics significantly decreased with breathing frequency (<i>p <</i>0.01 for all). In the frequency-domain, HRV LF and HRV HF decreased (<i>p</i>= 0.038 and<i>p</i>= 0.040, respectively), although these changes were modest. There was no significant change in HRV TP with breathing frequencies.<i>Significance.</i>Alterations in CO₂levels, a potent chemoreceptor trigger, and changes in HR most likely modulate ANS metrics. Non-linear PRSA and entropy appear to be more sensitive to breathing stimuli compared to frequency-dependent HRV metrics. Further research involving a larger cohort of healthy subjects is needed to validate our observations.</p>","PeriodicalId":20047,"journal":{"name":"Physiological measurement","volume":" ","pages":""},"PeriodicalIF":2.3,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142133433","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cycle-frequency content EEG analysis improves the assessment of respiratory-related cortical activity.","authors":"Xavier Navarro-Sune, Mathieu Raux, Anna L Hudson, Thomas Similowski, Mario Chavez","doi":"10.1088/1361-6579/ad74d7","DOIUrl":"10.1088/1361-6579/ad74d7","url":null,"abstract":"<p><p><i>Objective</i>. Time-frequency (T-F) analysis of electroencephalographic (EEG) is a common technique to characterise spectral changes in neural activity. This study explores the limitations of utilizing conventional spectral techniques in examining cyclic event-related cortical activities due to challenges, including high inter-trial variability.<i>Approach</i>. Introducing the cycle-frequency (C-F) analysis, we aim to enhance the evaluation of cycle-locked respiratory events. For synthetic EEG that mimicked cycle-locked pre-motor activity, C-F had more accurate frequency and time localization compared to conventional T-F analysis, even for a significantly reduced number of trials and a variability of breathing rhythm.<i>Main results</i>. Preliminary validations using real EEG data during both unloaded breathing and loaded breathing (that evokes pre-motor activity) suggest potential benefits of using the C-F method, particularly in normalizing time units to cyclic activity phases and refining baseline placement and duration.<i>Significance</i>. The proposed approach could provide new insights for the study of rhythmic neural activities, complementing T-F analysis.</p>","PeriodicalId":20047,"journal":{"name":"Physiological measurement","volume":" ","pages":""},"PeriodicalIF":2.3,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142093678","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Energy expenditure prediction in preschool children: a machine learning approach using accelerometry and external validation.","authors":"Hannah J Coyle-Asbil,Lukas Burk,Mirko Brandes,Berit Brandes,Christoph Buck,Marvin N Wright,Lori Ann Vallis","doi":"10.1088/1361-6579/ad7ad2","DOIUrl":"https://doi.org/10.1088/1361-6579/ad7ad2","url":null,"abstract":"&#xD;This study aimed to develop convolutional neural networks (CNN) models to predict the energy expenditure (EE) of children from raw accelerometer data. Additionally, this study sought to external validation of the CNN models in addition to the linear regression (LM), random forest (RF), and full connected neural network (FcNN) models published inet al (2019).&#xD;Approach:&#xD;Included in this study were 41 German children (3.0 to 6.99 years) for the training and internal validation who were equipped with GENEActiv, GT3X+, and activPAL accelerometers. The external validation dataset consisted of 39 Canadian children (3.0 to 5.99 years) that were equipped with OPAL, GT9X, GENEActiv, and GT3X+ accelerometers. EE was recorded simultaneously in both datasets using a portable metabolic unit. The protocols consisted of a semi-structured activities ranging from low to high intensities. The root mean square error (RMSE) values were calculated and used to evaluate model performances.&#xD;Main results:&#xD;1) The CNNs outperformed the LM (13.17% to 23.81% lower mean RMSE values), FcNN (8.13% to 27.27% lower RMSE values) and the RF models (3.59% to 18.84% lower RMSE values) in the internal dataset. 2) In contrast, it was found that when applied to the external Canadian dataset, the CNN models had consistently higher RMSE values compared to the LM, FcNN, and RF.&#xD;Significance:&#xD;Although CNNs can enhance EE prediction accuracy, their ability to generalize to new datasets and accelerometer brands/models, is more limited compared to LM, RF, and FcNN models.&#xD.","PeriodicalId":20047,"journal":{"name":"Physiological measurement","volume":"42 1","pages":""},"PeriodicalIF":3.2,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142252826","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessment of alternative metrics in the application of infrared thermography to detect muscle damage in sports.","authors":"Lukas Verderber,Willian da Silva,Inmaculada Aparicio,Andresa M C Germano,Felipe Carpes,Jose Ignacio Priego Quesada","doi":"10.1088/1361-6579/ad7ad3","DOIUrl":"https://doi.org/10.1088/1361-6579/ad7ad3","url":null,"abstract":"&#xD;The association between muscle damage and skin temperature is controversial. We hypothesize that including metrics that are more sensitive to individual responses by considering variability and regions representative of higher temperature could influence skin temperature outcomes. Here, the objective of the study was to determine whether using alternative metrics (TMAX, entropy, and pixelgraphy) leads to different results than mean, maximum, minimum, and standard deviation skin temperature when addressing muscle damage using infrared thermography. &#xD;Approach: Thermal images from four previous investigations measuring skin temperature before and after muscle damage in the anterior thigh and the posterior lower leg were used. The TMAX, entropy, and pixelgraphy (percentage of pixels above 33ºC) metrics were applied. &#xD;Main results: On 48h after running a marathon or half-marathon, no differences were found in skin temperature when applying any metric. Mean, minimum, maximum, TMAX, and pixelgraphy were lower 48h after than at basal condition following quadriceps muscle damage (p<0.05). Maximum skin temperature and pixelgraphy were lower 48h after than the basal condition following muscle damage to the triceps sural (p<0.05). Overall, TMAX strongly correlated with mean (r=0.85) and maximum temperatures (r=0.99) and moderately with minimum (r=0.66) and pixelgraphy parameter (r=0.64). Entropy strongly correlates with standard deviation (r=0.94) and inversely moderately with minimum temperature (r=-0.53). The pixelgraphy moderately correlated with mean (r=0.68), maximum (r=0.62), minimum (r=0.58), and TMAX (r=0.64). &#xD;Significance: Using alternative metrics does not change skin temperature outcomes following muscle damage of lower extremity muscle groups.","PeriodicalId":20047,"journal":{"name":"Physiological measurement","volume":"9 1","pages":""},"PeriodicalIF":3.2,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142252825","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}