scholarly journals Disease Classification and Biomarker Discovery Using ECG Data

2015 ◽  
Vol 2015 ◽  
pp. 1-7 ◽  
Author(s):  
Rong Huang ◽  
Yingchun Zhou
Keyword(s):  
Biomarker Discovery ◽  
Heart Diseases ◽  
Low Cost ◽  
Recent Decade ◽  
Extraction Methods ◽  
Disease Classification ◽  
Stepwise Discriminant Analysis ◽  
Heavy Tailed ◽  
Sensitivity Specificity ◽  
Sample Quantiles

In the recent decade, disease classification and biomarker discovery have become increasingly important in modern biological and medical research. ECGs are comparatively low-cost and noninvasive in screening and diagnosing heart diseases. With the development of personal ECG monitors, large amounts of ECGs are recorded and stored; therefore, fast and efficient algorithms are called for to analyze the data and make diagnosis. In this paper, an efficient and easy-to-interpret procedure of cardiac disease classification is developed through novel feature extraction methods and comparison of classifiers. Motivated by the observation that the distributions of various measures on ECGs of the diseased group are often skewed, heavy-tailed, or multimodal, we characterize the distributions by sample quantiles which outperform sample means. Three classifiers are compared in application both to all features and to dimension-reduced features by PCA: stepwise discriminant analysis (SDA), SVM, and LASSO logistic regression. It is found that SDA applied to dimension-reduced features by PCA is the most stable and effective procedure, with sensitivity, specificity, and accuracy being 89.68%, 84.62%, and 88.52%, respectively.

Overall evaluation of artichoke leftovers: Agricultural measurement and bioactive properties assessed after green and low-cost extraction methods

2021 ◽  
Vol 41 ◽  
pp. 100963
Author(s):  
Ignacio Zazzali ◽  
Julieta Gabilondo ◽  
Luana Peixoto Mallmann ◽  
Eliseu Rodrigues ◽  
Mercedes Perullini ◽  
...  
Keyword(s):  
Low Cost ◽  
Extraction Methods ◽  
Bioactive Properties ◽  
Overall Evaluation

Phytochemical content and potential health applications of pecan [Carya illinoinensis (Wangenh) K. Koch] nutshell

2022 ◽  
Vol 22 ◽  
Author(s):  
Nohemí del C. Reyes-Vázquez ◽  
Laura A. de la Rosa ◽  
Juan Luis Morales-Landa ◽  
Jorge Alberto García-Fajardo ◽  
Miguel Ángel García-Cruz
Keyword(s):  
Therapeutic Potential ◽  
Biological Activities ◽  
Heart Diseases ◽  
Extraction Methods ◽  
New Drugs ◽  
Potential Health ◽  
In Vivo Models ◽  
Phytochemical Content ◽  
Pecan Nutshell

Background: The pecan nutshell contains phytochemicals with various biological activities that are potentially useful in the prevention or treatment of diseases such as cancer, diabetes, and metabolic imbalances associated with heart diseases. Objective: To update this topic by means of a literature review and include those that contribute to the knowledge of the chemical composition and biological activities of pecan nutshell, particularly of those related to the therapeutic potential against some chronic degenerative diseases associated with oxidative stress. Method: Exhaustive and detailed review of the existing literature using electronic databases. Conclusion: The pecan nutshell is a promising natural product with pharmaceutical uses in various diseases. However, additional research related to the assessment of efficient extraction methods and characterization, particularly the evaluation of the mechanisms of action in new in vivo models, is necessary to confirm these findings and development of new drugs with therapeutic use.

scholarly journals Method for auxiliary use of thermography in diagnosing inflammation in the coxofemoral joint in dogs

2018 ◽  
Vol 39 (4) ◽  
pp. 1565
Author(s):  
Fernanda Lúcia Passos Fukahori ◽  
Daniela Maria Bastos de Souza ◽  
Eduardo Alberto Tudury ◽  
George Chaves Jimenez ◽  
José Ferreira da Silva Neto ◽  
...  
Keyword(s):  
Thermal Emission ◽  
Low Cost ◽  
Joint Inflammation ◽  
Domestic Animals ◽  
The Body ◽  
Joint Diseases ◽  
Infrared Images ◽  
Infrared Thermometry ◽  
Non Invasive ◽  
Sensitivity Specificity

Joint diseases are relatively common in domestic animals, such as dogs. The involved inflammation produces thermal emission, which can be imaged using specific sensors that allow capturing of infrared images. Given that there have been few reports on the use of thermography in the diagnosis of inflammation associated with diseases of the hip joint in dogs, we here propose a method for identification of inflammatory foci in dogs by using infrared thermometry. The present study aimed to find non-invasive and low-cost resources that couldfacilitate a clinical diagnosis in cases withinflammation in the coxofemoral joint of dogs.To this end, we developed a system in whichthe Flir Systems TG165 thermograph is coupled to a black PVC cannula with a 30-cm focus-to-animal distance.External effects of the environment on the temperature of the animalswere compared with the body temperature as measured by a conventional thermometer.Thirty-one dogs with and without inflammation in the coxofemoral joint underwent clinical evaluation.We verified that the temperature registered by the thermograph inthe animals with joint inflammation was significantlydifferentfrom that incontrol animals without inflammation, in the lateral projection.The method showed a sensitivity of 80%, specificity of 87.5%, and accuracy of 83.87%. This standardized method of diagnosis of inflammatory foci in the coxofemoral articulation of dogs by way of thermography showed sensitivity, specificity, and satisfactory accuracy.

scholarly journals Comparative study of colour Doppler vs. FNNAC findings in WHO grade II thyroid enlargement in adult females

2019 ◽  
Vol 5 (4) ◽  
pp. 1025
Author(s):  
Sudhakar Rao M. S. ◽  
Navneeth T. P. ◽  
John C. J.
Keyword(s):  
Pulsatility Index ◽  
Low Cost ◽  
Resistive Index ◽  
Colour Doppler ◽  
Who Grade ◽  
Predictive Values ◽  
Non Invasive ◽  
Adult Females ◽  
The Mean ◽  
Sensitivity Specificity

<p class="abstract"><strong>Background:</strong> Thyroid gland disorders form one of the most common endocrinal and surgical problems encountered in clinical practice. FNNAC is widely accepted as the primary and better method than FNAC for investigation but has its disadvantages. Colour Doppler is a non-invasive, low cost, easily available and repeatable investigation with least patient discomfort and can be valuable in detection of benign and malignant thyroid enlargements.</p><p class="abstract"><strong>Methods:</strong> Forty cases of adult females with WHO grade 2 thyroid enlargement attending the department of otorhinolaryngology selected on simple random basis were included in this study. Following written consent, Colour Doppler scanning and FNNAC test were done on the thyroid swelling and the results were analysed.  </p><p class="abstract"><strong>Results:</strong> The mean age of patients was 32.44 years. The mean age of malignancy was 44.66 years and showed statistically significant association. The Resistive and Pulsatility index and combination of both were found to have statistically significant results in detecting malignant and benign lesions The sensitivity, specificity, positive and negative predictive values of RI and PI were 83.33%, 94.12%, 71.43%, 96.97% and 50%, 94.12%, 60% and 91.43% respectively. On combining both the indices, the sensitivity was 91.67% and the positive predictive value was 97.06%.</p><p class="abstract"><strong>Conclusions:</strong> Colour Doppler can differentiate between benign and malignant thyroid enlargements using Resistive index (of&gt;0.75) and Pulsatility Index (of&gt;1.5) and can be a complementary diagnostic tool in the thyroid enlargement lesions, considering its accuracy, cost-effectiveness, easy availability and non-invasive repeatable nature.</p>

Lung Disease Classification by Novel Shape-Based Feature Extraction and New Hybrid Genetic Approach

2016 ◽  
pp. 321-346
Author(s):  
Bhuvaneswari Chandran ◽  
P. Aruna ◽  
D. Loganathan
Keyword(s):  
Feature Extraction ◽  
Extraction Method ◽  
Lung Diseases ◽  
Hybrid Genetic Algorithm ◽  
Extraction Methods ◽  
Disease Classification ◽  
Filter Method ◽  
Svm Classifier ◽  
Genetic Approach ◽  
Feature Extraction Method

The purpose of the chapter is to present a novel method to classify lung diseases from the computed tomography images which assist physicians in the diagnosis of lung diseases. The method is based on a new approach which combines a proposed M2 feature extraction method and a novel hybrid genetic approach with different types of classifiers. The feature extraction methods performed in this work are moment invariants, proposed multiscale filter method and proposed M2 feature extraction method. The essential features which are the results of the feature extraction technique are selected by the novel hybrid genetic algorithm feature selection algorithms. Classification is performed by the support vector machine, multilayer perceptron neural network and Bayes Net classifiers. The result obtained proves that the proposed technique is an efficient and robust method. The performance of the proposed M2 feature extraction with proposed hybrid GA and SVM classifier combination achieves maximum classification accuracy.

scholarly journals Using Deep Learning for Image-Based Potato Tuber Disease Detection

2019 ◽  
Vol 109 (6) ◽  
pp. 1083-1087 ◽  
Author(s):  
Dor Oppenheim ◽  
Guy Shani ◽  
Orly Erlich ◽  
Leah Tsror
Keyword(s):  
Deep Learning ◽  
Low Cost ◽  
Image Data ◽  
Object Identification ◽  
Plant Diseases ◽  
Disease Classification ◽  
Successful Implementation ◽  
Potato Tubers ◽  
Data Set ◽  
Convolutional Networks

Many plant diseases have distinct visual symptoms, which can be used to identify and classify them correctly. This article presents a potato disease classification algorithm that leverages these distinct appearances and advances in computer vision made possible by deep learning. The algorithm uses a deep convolutional neural network, training it to classify the tubers into five classes: namely, four disease classes and a healthy potato class. The database of images used in this study, containing potato tubers of different cultivars, sizes, and diseases, was acquired, classified, and labeled manually by experts. The models were trained over different train-test splits to better understand the amount of image data needed to apply deep learning for such classification tasks. The models were tested over a data set of images taken using standard low-cost RGB (red, green, and blue) sensors and were tagged by experts, demonstrating high classification accuracy. This is the first article to report the successful implementation of deep convolutional networks, popular in object identification, to the task of disease identification in potato tubers, showing the potential of deep learning techniques in agricultural tasks.

scholarly journals Non-contact smartphone-based fundus imaging compared to conventional fundus imaging: a low-cost alternative for retinopathy of prematurity screening and documentation

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Maximilian W. M. Wintergerst ◽  
Michael Petrak ◽  
Jeany Q. Li ◽  
Petra P. Larsen ◽  
Moritz Berger ◽  
...  
Keyword(s):  
Retinopathy Of Prematurity ◽  
Low Cost ◽  
Cost Effective ◽  
Field Of View ◽  
Fundus Examination ◽  
Fundus Imaging ◽  
Examination Time ◽  
Sensitivity Specificity ◽  
Rop Screening ◽  
Good Agreement

AbstractRetinopathy of prematurity (ROP) is a frequent cause of treatable childhood blindness. The current dependency of telemedicine-based ROP screening on cost-intensive equipment does not meet the needs in economically disadvantaged regions. Smartphone-based fundus imaging (SBFI) allows for affordable and mobile fundus examination and, therefore, could facilitate cost-effective telemedicine-based ROP screening in low-resources settings. We compared non-contact SBFI and conventional contact fundus imaging (CFI) in terms of feasibility for ROP screening and documentation. Twenty-six eyes were imaged with both SBFI and CFI. Field-of-view was smaller (ratio of diameters, 1:2.5), level of detail was equal, and examination time was longer for SBFI as compared to CFI (109.0 ± 57.8 vs. 75.9 ± 36.3 seconds, p < 0.01). Good agreement with clinical evaluation by indirect funduscopy was achieved for assessment of plus disease and ROP stage for both SBFI (squared Cohen’s kappa, 0.88 and 0.81, respectively) and CFI (0.86 and 0.93). Likewise, sensitivity/specificity for detection of plus disease and ROP was high for both SBFI (90%/100% and 88%/93%, respectively) and CFI (80%/100% and 100%/96%). SBFI is a non-contact and low-cost alternative to CFI for ROP screening and documentation that has the potential to considerably improve ROP care in middle- and low-resources settings.

scholarly journals Biomarker Discovery, Disease Classification, and Similarity Query Processing on High-Throughput MS/MS Data of Inborn Errors of Metabolism

2005 ◽  
Vol 11 (1) ◽  
pp. 90-99 ◽  
Author(s):  
Christian Baumgartner ◽  
Daniela Baumgartner
Keyword(s):  
Inborn Errors Of Metabolism ◽  
Biomarker Discovery ◽  
Expert Knowledge ◽  
Propionic Acidemia ◽  
Disease Classification ◽  
Therapeutic Interventions ◽  
Data Sets ◽  
Type I ◽  
Inborn Errors ◽  
Discriminatory Performance

In newborn errors of metabolism, biomarkers are urgently needed for disease screening, diagnosis, and monitoring of therapeutic interventions. This article describes a 2-step approach to discovermetabolic markers, which involves (1) the identification ofmarker candidates and (2) the prioritization of thembased on expert knowledge of diseasemetabolism. For step 1, the authors developed a new algorithm, the biomarker identifier (BMI), to identifymarkers fromquantified diseased versus normal tandemmass spectrometry data sets. BMI produces a ranked list ofmarker candidates and discards irrelevant metabolites based on a quality measure, taking into account the discriminatory performance, discriminatory space, and variance ofmetabolites’ concentrations at the state of disease. To determine the ability of identified markers to classify subjects, the authors compared the discriminatory performance of several machine-learning paradigms and described a retrieval technique that searches and classifies abnormal metabolic profiles from a screening database. Seven inborn errors of metabolism— phenylketonuria (PKU), glutaric acidemia type I (GA-I), 3-methylcrotonylglycinemia deficiency (3-MCCD), methylmalonic acidemia (MMA), propionic acidemia (PA), medium-chain acylCoAdehydrogenase deficiency (MCADD), and 3-OH longchain acyl CoA dehydrogenase deficiency (LCHADD)—were investigated. All primarily prioritized marker candidates could be confirmed by literature. Somenovel secondary candidateswere identified (i.e., C16:1 andC4DCfor PKU, C4DCfor GA-I, and C18:1 forMCADD), which require further validation to confirmtheir biochemical role during health and disease.

scholarly journals A Non-Invasive Medical Device for Parkinson’s Patients with Episodes of Freezing of Gait

Sensors ◽  
2019 ◽  
Vol 19 (3) ◽  
pp. 737 ◽  
Author(s):  
Catalina Punin ◽  
Boris Barzallo ◽  
Roger Clotet ◽  
Alexander Bermeo ◽  
Marco Bravo ◽  
...  
Keyword(s):  
Wavelet Transform ◽  
Mobile Application ◽  
Low Cost ◽  
Freezing Of Gait ◽  
Discrete Wavelet ◽  
Vibratory Stimulation ◽  
Non Invasive ◽  
Sensitivity Specificity ◽  
Gait Monitoring ◽  
Stimulation Of

A critical symptom of Parkinson’s disease (PD) is the occurrence of Freezing of Gait (FOG), an episodic disorder that causes frequent falls and consequential injuries in PD patients. There are various auditory, visual, tactile, and other types of stimulation interventions that can be used to induce PD patients to escape FOG episodes. In this article, we describe a low cost wearable system for non-invasive gait monitoring and external delivery of superficial vibratory stimulation to the lower extremities triggered by FOG episodes. The intended purpose is to reduce the duration of the FOG episode, thus allowing prompt resumption of gait to prevent major injuries. The system, based on an Android mobile application, uses a tri-axial accelerometer device for gait data acquisition. Gathered data is processed via a discrete wavelet transform-based algorithm that precisely detects FOG episodes in real time. Detection activates external vibratory stimulation of the legs to reduce FOG time. The integration of detection and stimulation in one low cost device is the chief novel contribution of this work. We present analyses of sensitivity, specificity and effectiveness of the proposed system to validate its usefulness.

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