Evaluation of Respiratory Sounds Using Image-Based Approaches for Health Measurement Applications
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PROVIDER: S-EPMC9788675 | biostudies-literature | 2022 Jan
REPOSITORIES: biostudies-literature
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Project description:We show that heart rate enabled wearable devices can be used to measure respiratory rate. Respiration modulates the heart rate creating excess power in the heart rate variability at a frequency equal to the respiratory rate, a phenomenon known as respiratory sinus arrhythmia. We isolate this component from the power spectral density of the heart beat interval time series, and show that the respiratory rate thus estimated is in good agreement with a validation dataset acquired from sleep studies (root mean squared error = 0.648 min-1, mean absolute error = 0.46 min-1, mean absolute percentage error = 3%). We use this respiratory rate algorithm to illuminate two potential applications (a) understanding the distribution of nocturnal respiratory rate as a function of age and sex, and (b) examining changes in longitudinal nocturnal respiratory rate due to a respiratory infection such as COVID-19. 90% of respiratory rate values for healthy adults fall within the range 11.8-19.2 min-1 with a mean value of 15.4 min-1. Respiratory rate is shown to increase with nocturnal heart rate. It also varies with BMI, reaching a minimum at 25 kg/m2, and increasing for lower and higher BMI. The respiratory rate decreases slightly with age and is higher in females compared to males for age <50 years, with no difference between females and males thereafter. The 90% range for the coefficient of variation in a 14 day period for females (males) varies from 2.3-9.2% (2.3-9.5%) for ages 20-24 yr, to 2.5-16.8% (2.7-21.7%) for ages 65-69 yr. We show that respiratory rate is often elevated in subjects diagnosed with COVID-19. In a 7 day window from D-1 to D+5 (where D0 is the date when symptoms first present, for symptomatic individuals, and the test date for asymptomatic cases), we find that 36.4% (23.7%) of symptomatic (asymptomatic) individuals had at least one measurement of respiratory rate 3 min-1 higher than the regular rate.
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Project description:A comparison of methylMiner and MethylCap, two methylation enrichment kits The main analysis include two individuals (mice) in technical duplicates from various lab/technical conditions. The results are compared with previous studies of two human samples from a number of lab/technical conditions (human data available at dbGaP: record number phs000608.v1.p1). In addition a DNA from a third mouse is used for investigations of specific alterations of the protocol.
2014-12-20 | E-GEOD-64144 | biostudies-arrayexpress
Project description:Landslides are major and constantly changing threats to urban landscapes and infrastructure. It is essential to detect and capture landslide changes regularly. Traditional methods for monitoring landslides are time-consuming, costly, dangerous, and the quality and quantity of the data is sometimes unable to meet the necessary requirements of geotechnical projects. This motivates the development of more automatic and efficient remote sensing approaches for landslide progression evaluation. Automatic change detection involving low-altitude unmanned aerial vehicle image-based point clouds, although proven, is relatively unexplored, and little research has been done in terms of accounting for volumetric changes. In this study, a methodology for automatically deriving change displacement rates, in a horizontal direction based on comparisons between extracted landslide scarps from multiple time periods, has been developed. Compared with the iterative closest projected point (ICPP) registration method, the developed method takes full advantage of automated geometric measuring, leading to fast processing. The proposed approach easily processes a large number of images from different epochs and enables the creation of registered image-based point clouds without the use of extensive ground control point information or further processing such as interpretation and image correlation. The produced results are promising for use in the field of landslide research.
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Project description:PurposeRespiratory rate can provide auxiliary information on the physiological changes within the human body, such as physical and emotional stress. In a clinical setup, the abnormal respiratory rate can be indicative of the deterioration of the patient's condition. Most of the existing algorithms for the estimation of respiratory rate using photoplethysmography (PPG) are sensitive to external noise and may require the selection of certain algorithm-specific parameters, through the trial-and-error method.MethodsThis paper proposes a new algorithm to estimate the respiratory rate using a photoplethysmography sensor signal for health monitoring. The algorithm is resistant to signal loss and can handle low-quality signals from the sensor. It combines selective windowing, preprocessing and signal conditioning, modified Welch filtering and postprocessing to achieve high accuracy and robustness to noise.ResultsThe Mean Absolute Error and the Root Mean Square Error of the proposed algorithm, with the optimal signal window size, are determined to be 2.05 breaths count per minute and 2.47 breaths count per minute, respectively, when tested on a publicly available dataset. These results present a significant improvement in accuracy over previously reported methods. The proposed algorithm achieved comparable results to the existing algorithms in the literature on the BIDMC dataset (containing data of 53 subjects, each recorded for 8 min) for other signal window sizes.ConclusionThe results endorse that integration of the proposed algorithm to a commercially available pulse oximetry device would expand its functionality from the measurement of oxygen saturation level and heart rate to the continuous measurement of the respiratory rate with good efficiency at home and in a clinical setting.Supplementary informationThe online version contains supplementary material available at 10.1007/s40846-022-00700-z.
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Project description:A comparison of methylMiner and MethylCap, two methylation enrichment kits
2014-12-20 | GSE64144 | GEO
Project description:BackgroundDespite the increasing number of mobile health applications, the validity of their content is understudied. The objective of this study was to rate the content of HIV/AIDS-related mobile applications and to determine the extent to which evidence-based medicine is being incorporated into their content using a new tool called the Evidence-based content rating tool of mobile health applications (EBCRT-mHealth).MethodsAll available HIV/AIDS-related applications in Iran from Cafe Bazaar and Google Play Store were evaluated. This study was first conducted in 2018, then after almost two years in 2021 was done again. In this study, researchers developed the EBCRT-mHealth tool to rate the content of applications based on the evidence-based medicine pyramid. Its reliability was calculated (α = 0.78), and five specialists confirmed its validity. Two reviewers independently reviewed all HIV/AIDS applications directly downloaded and installed from the Google Play Store and Cafe Bazaar.ResultsOut of 980 retrieved applications, in 2018, 85, and in 2021, 78 applications were included in the study. Only in 17 (28%) out of the 60 in 2018, and 25 (51%) in 2021 Google Play store applications the source of content information was mentioned. All Cafe Bazaar mobile applications mentioned the source of information. The mean rating of all application content in 2018 was 2.38 (SD = 0.74), and in 2021 was 2.90 (SD = 1.35) out of 5. The mean rating of the content of Cafe Bazaar applications in 2018 was 2.10 (SD = 0.49), and in 2021 was 1.94 (SD = 0.29). The mean content rating of Google Play store applications in 2018 was 2.50 (SD = 0.80) and in 2021 was 3.86 (SD = 1.18).ConclusionAfter two years, the rating of the content of HIV/AIDS-related applications available in Iran that existed in Cafe Bazaar decreased from "poor" to "inappropriate". Also, the content score of the Google Play Store applications increased from "poor" to "good". It is critical to ensure the credibility of the sources used in developing their content and removing applications with inappropriate and unreliable content from the App Stores. Also, mobile health application developers should use the highest quality information in their applications.
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Project description:Genetic and chemical perturbations impact diverse cellular phenotypes, including multiple indicators of cell health. These readouts reveal toxicity and antitumorigenic effects relevant to drug discovery and personalized medicine. We developed two customized microscopy assays, one using four targeted reagents and the other three targeted reagents, to collectively measure 70 specific cell health phenotypes including proliferation, apoptosis, reactive oxygen species, DNA damage, and cell cycle stage. We then tested an approach to predict multiple cell health phenotypes using Cell Painting, an inexpensive and scalable image-based morphology assay. In matched CRISPR perturbations of three cancer cell lines, we collected both Cell Painting and cell health data. We found that simple machine learning algorithms can predict many cell health readouts directly from Cell Painting images, at less than half the cost. We hypothesized that these models can be applied to accurately predict cell health assay outcomes for any future or existing Cell Painting dataset. For Cell Painting images from a set of 1500+ compound perturbations across multiple doses, we validated predictions by orthogonal assay readouts. We provide a web app to browse predictions: http://broad.io/cell-health-app. Our approach can be used to add cell health annotations to Cell Painting datasets.
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Project description:Detection of defects including cracks and spalls on wall surface in high-rise buildings is a crucial task of buildings' maintenance. If left undetected and untreated, these defects can significantly affect the structural integrity and the aesthetic aspect of buildings. Timely and cost-effective methods of building condition survey are of practicing need for the building owners and maintenance agencies to replace the time- and labor-consuming approach of manual survey. This study constructs an image processing approach for periodically evaluating the condition of wall structures. Image processing algorithms of steerable filters and projection integrals are employed to extract useful features from digital images. The newly developed model relies on the Support vector machine and least squares support vector machine to generalize the classification boundaries that categorize conditions of wall into five labels: longitudinal crack, transverse crack, diagonal crack, spall damage, and intact wall. A data set consisting of 500 image samples has been collected to train and test the machine learning based classifiers. Experimental results point out that the proposed model that combines the image processing and machine learning algorithms can achieve a good classification performance with a classification accuracy rate = 85.33%. Therefore, the newly developed method can be a promising alternative to assist maintenance agencies in periodic building surveys.
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Project description:IntroductionAdvancing industrial-scale manufacture of cells as therapeutic products is an example of the wide applications of regenerative medicine. However, one bottleneck in establishing stable and efficient cell manufacture is quality control. Owing to the lack of effective in-process measurement technology, analyzing the time-consuming and complex cell culture process that essentially determines cellular quality is difficult and only performed by manual microscopic observation. Our group has been applying advanced image-processing and machine-learning modeling techniques to construct prediction models that support quality evaluations during cell culture. In this study, as a model of errors during the cell culture process, intentional errors were compared to the standard culture and analyzed based only on the time-course morphological information of the cells.MethodsTwenty-one lots of human mesenchymal stem cells (MSCs), including both bone-marrow-derived MSCs and adipose-derived MSCs, were cultured under 5 conditions (one standard and 4 types of intentional errors, such as clear failure of handlings and machinery malfunctions). Using time-course microscopic images, cell morphological profiles were quantitatively measured and utilized for visualization and prediction modeling. For visualization, modified principal component analysis (PCA) was used. For prediction modeling, linear regression analysis and the MT method were applied.ResultsBy modified PCA visualization, the differences in cellular lots and culture conditions were illustrated as traits on a morphological transition line plot and found to be effective descriptors for discriminating the deviated samples in a real-time manner. In prediction modeling, both the cell growth rate and error condition discrimination showed high accuracy (>80%), which required only 2 days of culture. Moreover, we demonstrated the applicability of different concepts of machine learning using the MT method, which is effective for manufacture processes that mostly collect standard data but not a large amount of failure data.ConclusionsMorphological information that can be quantitatively acquired during cell culture has great potential as an in-process measurement tool for quality control in cell manufacturing processes.
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Project description:Dolphins and whales use tonal whistles for communication, and it is known that frequency modulation encodes contextual information. An automated mathematical algorithm could characterize the frequency modulation of tonal calls for use with clustering and classification. Most automatic cetacean whistle processing techniques are based on peak or edge detection or require analyst assistance in verifying detections. An alternative paradigm is introduced using techniques of image processing. Frequency information is extracted as ridges in whistle spectrograms. Spectral ridges are the fundamental structure of tonal vocalizations, and ridge detection is a well-established image processing technique, easily applied to vocalization spectrograms. This paradigm is implemented as freely available matlab scripts, coined IPRiT (image processing ridge tracker). Its fidelity in the reconstruction of synthesized whistles is compared to another published whistle detection software package, silbido. Both algorithms are also applied to real-world recordings of bottlenose dolphin (Tursiops trunactus) signature whistles and tested for the ability to identify whistles belonging to different individuals. IPRiT gave higher fidelity and lower false detection than silbido with synthesized whistles, and reconstructed dolphin identity groups from signature whistles, whereas silbido could not. IPRiT appears to be superior to silbido for the extraction of the precise frequency variation of the whistle.
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