scholarly journals Deep Learning for Prediction of Diaphragm Activity from the Surface Electromyogram

2019 ◽  
Vol 5 (1) ◽  
pp. 17-20
Author(s):  
Niclas Bockelmann ◽  
Jan Graßhoff ◽  
Lasse Hansen ◽  
Giacomo Bellani ◽  
Mattias P. Heinrich ◽  
...  
Keyword(s):  
Electrical Activity ◽  
Pearson Correlation ◽  
Muscular Activity ◽  
Absolute Error ◽  
Skin Surface ◽  
Learning Task ◽  
Assisted Ventilation ◽  
Superior Performance ◽  
Respiratory Drive ◽  
Semg Signal

AbstractThe electrical activity of the diaphragm (EAdi) is a novel monitoring parameter for patients under assisted ventilation and is used for assessing the patient’s neural respiratory drive. It is recorded by an array of electrodes placed inside the esophagus at the level of the diaphragm. A noninvasive alternative is the measurement of the electromyogram by means of skin surface electrodes (sEMG). The respiratory sEMG signal, however, is subject to electrocardiographic interference and crosstalk from other muscles and may also pick up a different part of the muscular activity. In this work, we propose to use a deep neural network to predict the electrical activity of the diaphragm as measured by a nasogastric catheter from sEMG measurements. We use a ResNet based architecture and train the network to directly regress the EAdi as a supervised learning task - we further investigate a heatmap based regression approach. The proposed methods are evaluated on a clinical dataset consisting of 77 recordings from mechanically ventilated patients. For the direct regression task, the network’s predictions reach a Pearson correlation coefficient (PCC) of 0.818 with EAdi on the hold-out set. The heatmap regression increases the PCC to 0.830 while at the same time achieving a lower mean absolute error, indicating a superior performance. From our results we conclude that sEMG measurements may be used to predict the internal activity of the diaphragm as measured invasively using a nasogastric catheter.

Electrical activity and relative length changes of dog limb muscles as a function of speed and gait

1981 ◽  
Vol 94 (1) ◽  
pp. 15-42 ◽  
Author(s):  
G. E. Goslow ◽  
H. J. Seeherman ◽  
C. R. Taylor ◽  
M. N. McCutchin ◽  
N. C. Heglund
Keyword(s):  
Electrical Activity ◽  
Stance Phase ◽  
Biceps Brachii ◽  
Vastus Lateralis ◽  
Activity Patterns ◽  
Relative Length ◽  
Muscular Activity ◽  
Triceps Brachii ◽  
Limb Muscles ◽  
Length Changes

Electrical activity and length changes of 11 muscles of the fore- and hind- limbs of dogs walking, running, and galloping on a treadmill, were measured as a function of forward speed and gait. Our purpose was to find out whether the activity patterns of the major limb muscles were consistent with the two mechanisms proposed for storage and recovery of energy within a stride: a ‘pendulum-like’ mechanism during a walk, and a ‘spring-like’ mechanism during a run. In the stance phase of the walking dog, we found that the supraspinatus, long head of the triceps brachii, biceps brachii, vastus lateralis, and gastrocnemius underwent only minor length changes during a relatively long portion of their activity, Thus, a major part of their activity during the walk seems consistent with a role in stabilization of the joints as the dog ‘pole-vaulted’ over its limbs (and thereby conserved energy). In the stance phase of trotting and/or galloping dogs, we found that the supraspinatus, lateral head of the triceps, vastus lateralis, and gastrocnemius were active while being stretched prior to shortening (as would be required for elastic storage of energy), and that this type of activity increased with increasing speed. We also found muscular activity in the select limb flexors that was consistent with storage of kinetic energy at the end of the swing phase and recovery during the propulsive stroke. This activity pattern was apparent in the latissimus dorsi during a walk and trot, and in the biceps femoris during a trot and gallop. We conclude that, during locomotion, a significant fraction of the electrical activity of a number of limbs muscles occurs while they undergo little or no length change or are being stretched prior to shortening and that these types of activities occur in a manner that would enable the operation of pendulum-like and spring-like mechanisms for conserving energy within a stride. Therefore these forms of muscular activity, in addition to the more familiar activity associated with muscle shortening, should be considered to be important during locomotion.

scholarly journals Mapping Seasonal High-Resolution PM2.5 Concentrations with Spatiotemporal Bagged-Tree Model across China

2021 ◽  
Vol 10 (10) ◽  
pp. 676
Author(s):  
Junchen He ◽  
Zhili Jin ◽  
Wei Wang ◽  
Yixiao Zhang
Keyword(s):  
Remote Sensing ◽  
Mean Squared Error ◽  
Fine Particulate Matter ◽  
Absolute Error ◽  
Superior Performance ◽  
Tree Model ◽  
Mixed Effect ◽  
Proposed Model ◽  
High Concentrations ◽  
Pm2.5 Concentration

High concentrations of fine particulate matter (PM2.5) are well known to reduce environmental quality, visibility, atmospheric radiation, and damage the human respiratory system. Satellite-based aerosol retrievals are widely used to estimate surface PM2.5 levels because satellite remote sensing can break through the spatial limitations caused by sparse observation stations. In this work, a spatiotemporal weighted bagged-tree remote sensing (STBT) model that simultaneously considers the effects of aerosol optical depth, meteorological parameters, and topographic factors was proposed to map PM2.5 concentrations across China that occurred in 2018. The proposed model shows superior performance with the determination coefficient (R2) of 0.84, mean-absolute error (MAE) of 8.77 μg/m3 and root-mean-squared error (RMSE) of 15.14 μg/m3 when compared with the traditional multiple linear regression (R2 = 0.38, MAE = 18.15 μg/m3, RMSE = 29.06 μg/m3) and linear mixed-effect (R2 = 0.52, MAE = 15.43 μg/m3, RMSE = 25.41 μg/m3) models by the 10-fold cross-validation method. The results collectively demonstrate the superiority of the STBT model to other models for PM2.5 concentration monitoring. Thus, this method may provide important data support for atmospheric environmental monitoring and epidemiological research.

scholarly journals Deep Learning to Estimate Cardiac Magnetic Resonance-Derived Left Ventricular Mass

2020 ◽  
Author(s):  
Shaan Khurshid ◽  
Samuel Friedman ◽  
James P. Pirruccello ◽  
Paolo Di Achille ◽  
Nathaniel Diamant ◽  
...  
Keyword(s):  
Deep Learning ◽  
Cardiac Magnetic Resonance ◽  
Magnetic Resonance ◽  
Open Source ◽  
Pearson Correlation ◽  
Absolute Error ◽  
Learning Model ◽  
Left Ventricular ◽  
Lv Mass ◽  
Deep Learning Model

ABSTRACTBackgroundCardiac magnetic resonance (CMR) is the gold standard for left ventricular hypertrophy (LVH) diagnosis. CMR-derived LV mass can be estimated using proprietary algorithms (e.g., inlineVF), but their accuracy and availability may be limited.ObjectiveTo develop an open-source deep learning model to estimate CMR-derived LV mass.MethodsWithin participants of the UK Biobank prospective cohort undergoing CMR, we trained two convolutional neural networks to estimate LV mass. The first (ML4Hreg) performed regression informed by manually labeled LV mass (available in 5,065 individuals), while the second (ML4Hseg) performed LV segmentation informed by inlineVF contours. We compared ML4Hreg, ML4Hseg, and inlineVF against manually labeled LV mass within an independent holdout set using Pearson correlation and mean absolute error (MAE). We assessed associations between CMR-derived LVH and prevalent cardiovascular disease using logistic regression adjusted for age and sex.ResultsWe generated CMR-derived LV mass estimates within 38,574 individuals. Among 891 individuals in the holdout set, ML4Hseg reproduced manually labeled LV mass more accurately (r=0.864, 95% CI 0.847-0.880; MAE 10.41g, 95% CI 9.82-10.99) than ML4Hreg (r=0.843, 95% CI 0.823-0.861; MAE 10.51, 95% CI 9.86-11.15, p=0.01) and inlineVF (r=0.795, 95% CI 0.770-0.818; MAE 14.30, 95% CI 13.46-11.01, p<0.01). LVH defined using ML4Hseg demonstrated the strongest associations with hypertension (odds ratio 2.76, 95% CI 2.51-3.04), atrial fibrillation (1.75, 95% CI 1.37-2.20), and heart failure (4.53, 95% CI 3.16-6.33).ConclusionsML4Hseg is an open-source deep learning model providing automated quantification of CMR-derived LV mass. Deep learning models characterizing cardiac structure may facilitate broad cardiovascular discovery.

Time-Weighted Collaborative Filtering Algorithm Based on Improved Mini Batch K-Means Clustering

2021 ◽  
Vol 105 ◽  
pp. 309-317
Author(s):  
Xue Han ◽  
Zhong Wang ◽  
Hui Jun Xu
Keyword(s):  
Collaborative Filtering ◽  
Sparse Matrix ◽  
Pearson Correlation ◽  
Absolute Error ◽  
User Interest ◽  
User Similarity ◽  
Filtering Algorithm ◽  
Collaborative Filtering Algorithm ◽  
Low Efficiency ◽  
Score Matrix

The traditional collaborative filtering recommendation algorithm has the defects of sparse score matrix, weak scalability and user interest deviation, which lead to the low efficiency of algorithm and low accuracy of score prediction. Aiming at the above problems, this paper proposed a time-weighted collaborative filtering algorithm based on improved Mini Batch K-Means clustering. Firstly, the algorithm selected the Pearson correlation coefficient to improve the Mini Batch K-Means clustering, and used the improved Mini Batch K-Means algorithm to cluster the sparse scoring matrix, calculated the user interest score to complete the filling of the sparse matrix. Then, considering the influence of user interest drift with time, the algorithm introduced the Newton cooling time-weighted to improve user similarity. And then calculated user similarity based on the filled score matrix, which helped to get the last predicted score of unrated items The experimental results show that, compared with the traditional collaborative filtering algorithms, the mean absolute error of Proposed improved algorithm is d, and the Precision, Recall and F1 value of MBKT-CF also get a large improvement, which has a higher rating prediction accuracy.

scholarly journals Noninvasive muscle activity imaging using magnetography

2020 ◽  
Vol 117 (9) ◽  
pp. 4942-4947 ◽  
Author(s):  
Rodolfo R. Llinás ◽  
Mikhail Ustinin ◽  
Stanislav Rykunov ◽  
Kerry D. Walton ◽  
Guilherme M. Rabello ◽  
...  
Keyword(s):  
Magnetic Field ◽  
Contrast Media ◽  
Electrical Activity ◽  
Real Time ◽  
Human Body ◽  
Muscle Activity ◽  
Muscular Activity ◽  
Exponential Increase ◽  
Somatic Musculature ◽  
The Magnetic Field

A spectroscopic paradigm has been developed that allows the magnetic field emissions generated by the electrical activity in the human body to be imaged in real time. The growing significance of imaging modalities in biology is evident by the almost exponential increase of their use in research, from the molecular to the ecological level. The method of analysis described here allows totally noninvasive imaging of muscular activity (heart, somatic musculature). Such imaging can be obtained without additional methodological steps such as the use of contrast media.

scholarly journals Predicting and Analyzing Language Specificity in Social Media Posts

2019 ◽  
Vol 33 ◽  
pp. 6415-6422 ◽  
Author(s):  
Yifan Gao ◽  
Yang Zhong ◽  
Daniel Preoţiuc-Pietro ◽  
Junyi Jessy Li
Keyword(s):  
Social Media ◽  
Computational Linguistics ◽  
Large Scale ◽  
Pearson Correlation ◽  
Absolute Error ◽  
Fine Grained ◽  
Argumentation Mining ◽  
Sentence Level ◽  
Prediction Systems ◽  
Mental Health Factors

In computational linguistics, specificity quantifies how much detail is engaged in text. It is an important characteristic of speaker intention and language style, and is useful in NLP applications such as summarization and argumentation mining. Yet to date, expert-annotated data for sentence-level specificity are scarce and confined to the news genre. In addition, systems that predict sentence specificity are classifiers trained to produce binary labels (general or specific).We collect a dataset of over 7,000 tweets annotated with specificity on a fine-grained scale. Using this dataset, we train a supervised regression model that accurately estimates specificity in social media posts, reaching a mean absolute error of 0.3578 (for ratings on a scale of 1-5) and 0.73 Pearson correlation, significantly improving over baselines and previous sentence specificity prediction systems. We also present the first large-scale study revealing the social, temporal and mental health factors underlying language specificity on social media.

scholarly journals Validation of rainfall data estimated by GPM satellite on Southern Amazon region

2019 ◽  
Vol 14 (1) ◽  
pp. 1
Author(s):  
Luiz Octavio Fabricio dos Santos ◽  
Carlos Alexandre Santos Querino ◽  
Juliane Kayse Albuquerque da Silva Querino ◽  
Altemar Lopes Pedreira Junior ◽  
Aryanne Resende de Melo Moura ◽  
...  
Keyword(s):  
Pearson Correlation ◽  
Absolute Error ◽  
High Rate ◽  
Satellite Precipitation ◽  
Precipitation Measurement ◽  
Surface Data ◽  
Precipitation Estimates ◽  
Amazonas State ◽  
Global Precipitation Measurement ◽  
Global Precipitation

Rainfall is a meteorological variable of great importance for hydric balance and for weather studies. Rainfall estimation, carried out by satellites, has increased the climatological dataset related to precipitation. However, the accuracy of these data is questionable. This paper aimed to validate the estimates done by the Global Precipitation Measurement (GPM) satellite for the mesoregion of Southern Amazonas State, Brazil. The surface data were collected by the National Water Agency – ANA and National Institute of Meteorology – INMET, and is available at both institutions’ websites. The satellite precipitation data were accessed directly from the NASA webpage. Statistical analysis of Pearson correlation was used, as well as the Willmott’s “d” index and errors from the MAE (Mean Absolute Error) and RMSE (Root Mean Square Error). The GPM satellite satisfactorily estimated the precipitation, once it had correlations above 73% and high Willmott coefficients (between 0.86 and 0.97). The MAE and RMSE showed values that varied from 36.50 mm to 72.49 mm and 13.81 mm to 71.76 mm, respectively. Seasonal rain variations are represented accordingly. In some cases, either an underestimation or an overestimation of the rain data was observed. In the yearly totals, a high rate of similarity between the estimated and measured values was observed. We concluded that the GPM-based multi-satellite precipitation estimates can be used, even though they are not 100% reliable. However, adjustments in calibration for the region are necessary and recommended.

scholarly journals Comparison of Predictability Using Barrett Universal II and SRK/T Formulas according to Keratometry

2020 ◽  
Vol 2020 ◽  
pp. 1-5
Author(s):  
Kei Iijima ◽  
Kazutaka Kamiya ◽  
Yoshihiko Iida ◽  
Nobuyuki Shoji
Keyword(s):  
Prediction Error ◽  
Pearson Correlation ◽  
Absolute Error ◽  
Power Calculation ◽  
Universal Formula ◽  
Iol Power Calculation ◽  
Iol Power ◽  
The Absolute ◽  
Relationship Of ◽  
The Relationship

Purpose. To compare the predictability of intraocular lens (IOL) power calculation using the Barrett Universal II and the SRK/T formulas, according to the keratometry. Methods. We retrospectively reviewed the clinical charts of 335 consecutive eyes undergoing standard cataract surgery. IOL power calculations were performed using the Barrett Universal II and the SRK/T formulas. We compared the prediction error, the absolute error, and the percentages within ±0.25, ±0.5, and ±1.0 D of the targeted refraction, 1 month postoperatively, and also investigated the relationship of these outcomes with the keratometric readings, using the two formulas. Results. The prediction error using the SRK/T formula was significantly more myopic than that using the Barrett Universal II formula (the paired t-test, p<0.001). The absolute error using the SRK/T formula was significantly larger than that using the Barrett Universal II formula (p=0.006). We found a significant correlation between the prediction error and the keratometric readings using the SRK/T formula (Pearson correlation coefficient, r = −0.522, p<0.001), but there was no significant correlation between them using the Barrett Universal II formula (r = −0.031, p=0.576). Conclusions. The Barrett Universal II formula provides a better predictability of IOL power calculation and is less susceptible to the effect of the corneal shape, than the SRK/T formula. The Barrett Universal formula, instead of the SRK/T formula, may be clinically helpful for improving the refractive accuracy, especially in eyes with steep or flat corneas.

Respiratory Drive and Timing during Assisted Ventilation in Dogs

Respiration ◽  
1986 ◽  
Vol 49 (2) ◽  
pp. 130-139 ◽  
Author(s):  
Alan C. Jasper ◽  
Catherine S.H.T. Sassoon ◽  
D.H. Simmons
Keyword(s):  
Assisted Ventilation ◽  
Respiratory Drive

Neural Respiratory Drive during CO2 Rebreathing Assessed by a Combined Esophageal Electrode Catheter

2011 ◽  
Vol 140 ◽  
pp. 191-194
Author(s):  
Kang Xin Tan ◽  
Dong Xiao Lu ◽  
Yuan Ming Luo
Keyword(s):  
Electrical Activity ◽  
Linear Relationship ◽  
Healthy Subjects ◽  
Respiratory Drive ◽  
Transdiaphragmatic Pressure ◽  
Good Linear ◽  
Diagnosis And Management ◽  
Breathing Difficulty ◽  
End Tidal ◽  
Spontaneously Breathing

Assessment of neural respiratory drive is useful for diagnosis and management of breathing difficulty. The diaphragm is the most important muscle of respiration, therefore its electrical activity during spontaneously breathing could be used to reflect neural respiratory drive. In this study a catheter which was composed of 10 electrodes and two balloons was developed and was used to assess neural respiratory drive during CO2rebreathing in six healthy subjects. There was a good linear relationship between diaphragm EMG and end-tidal CO2(r = 0.98±0.01) during CO2rebreathing. Transdiaphragmatic pressure was also well correlated with end-tidal CO2during CO2rebreathing. We concluded that the combined catheter developed in this study can be used to assess neural respiratory drive.

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