Machine learning for analysis of active stand responses in older adults with vasovagal syncope

AbstractObjectivesTo assess 1) differences in the hemodynamic response to the active stand test in older adults with a clinical diagnosis of vasovagal syncope compared to age-matched controls 2) if the active stand test combined with machine learning approaches can be used to identify the presence of vasovagal syncope in older adults.ApproachAdults aged 50 and over (Vasovagal Syncope N=46 Age=66.9±10.3; Control N=86 Age=65.3±9.5) completed an active stand test. Multiple features were extracted to characterize the hemodynamic responses to the active stand test and were compared between groups. Classification was performed using machine learning algorithms including linear discriminant analysis, quadratic discriminant analysis, support vector machine and an ensemble majority vote classifier.Main ResultsSubjects with vasovagal syncope demonstrated a higher resting (supine) heart rate (69.8±13.1 bpm vs 63.3±12.1 bpm; P=0.007), a smaller initial systolic blood pressure drop (−20.2±20.1% vs −27.3±17.5%; P=0.005), larger drops in stroke volume (−14.7±24.0% vs −2.7±23.3%; P=0.010) and cardiac output (−6.4±18.5% vs 5.8±22.3%;P<0.001) and a larger increase in total peripheral resistance (8.1±30.4% vs −6.03±22.8%; P=0.002) compared to controls. A majority vote classifier identified the presence of vasovagal syncope with 82.6% sensitivity, 76.8% specificity, and average accuracy of 78.9%.SignificanceOlder adults with vasovagal syncope display a unique hemodynamic and autonomic response to active standing characterized by relative autonomic hypersensitivity and larger drops in cardiac output compared to age-matched controls. With suitable machine learning algorithms, the active stand test holds the potential to be used to screen older adults for reflex syncopes and hypotensive susceptibility potentially reducing test time, cost, and patient discomfort. More broadly this paper presents a machine learning framework to support use of the active stand test for classification of clinical outcomes of interest.

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Tremor Identification Using Machine Learning in Parkinson's Disease

Early Detection of Neurological Disorders Using Machine Learning Systems - Advances in Medical Technologies and Clinical Practice ◽

10.4018/978-1-5225-8567-1.ch008 ◽

2019 ◽

pp. 128-151

Author(s):

Angana Saikia ◽

Vinayak Majhi ◽

Masaraf Hussain ◽

Sudip Paul ◽

Amitava Datta

Keyword(s):

Machine Learning ◽

Parkinson’S Disease ◽

Support Vector Machine ◽

Parkinson's Disease ◽

Discriminant Analysis ◽

Learning Algorithms ◽

The Body ◽

Machine Learning Algorithms ◽

Support Vector

Tremor is an involuntary quivering movement or shake. Characteristically occurring at rest, the classic slow, rhythmic tremor of Parkinson's disease (PD) typically starts in one hand, foot, or leg and can eventually affect both sides of the body. The resting tremor of PD can also occur in the jaw, chin, mouth, or tongue. Loss of dopamine leads to the symptoms of Parkinson's disease and may include a tremor. For some people, a tremor might be the first symptom of PD. Various studies have proposed measurable technologies and the analysis of the characteristics of Parkinsonian tremors using different techniques. Various machine-learning algorithms such as a support vector machine (SVM) with three kernels, a discriminant analysis, a random forest, and a kNN algorithm are also used to classify and identify various kinds of tremors. This chapter focuses on an in-depth review on identification and classification of various Parkinsonian tremors using machine learning algorithms.

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Introduction and Implementation of Machine Learning Algorithms in R

Advances in Business Information Systems and Analytics - Sentiment Analysis and Knowledge Discovery in Contemporary Business ◽

10.4018/978-1-5225-4999-4.ch008 ◽

2019 ◽

pp. 126-147

Author(s):

S. R. Mani Sekhar ◽

G. M. Siddesh

Keyword(s):

Machine Learning ◽

Neural Networks ◽

Support Vector Machine ◽

Discriminant Analysis ◽

Computer Science ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Support Vector ◽

Linear Discriminant ◽

The Given

Machine learning is one of the important areas in the field of computer science. It helps to provide an optimized solution for the real-world problems by using past knowledge or previous experience data. There are different types of machine learning algorithms present in computer science. This chapter provides the overview of some selected machine learning algorithms such as linear regression, linear discriminant analysis, support vector machine, naive Bayes classifier, neural networks, and decision trees. Each of these methods is illustrated in detail with an example and R code, which in turn assists the reader to generate their own solutions for the given problems.

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Tremor Identification Using Machine Learning in Parkinson's Disease

Research Anthology on Diagnosing and Treating Neurocognitive Disorders ◽

10.4018/978-1-7998-3441-0.ch018 ◽

2021 ◽

pp. 341-365

Author(s):

Angana Saikia ◽

Vinayak Majhi ◽

Masaraf Hussain ◽

Sudip Paul ◽

Amitava Datta

Keyword(s):

Machine Learning ◽

Parkinson’S Disease ◽

Support Vector Machine ◽

Parkinson's Disease ◽

Discriminant Analysis ◽

Learning Algorithms ◽

The Body ◽

Machine Learning Algorithms ◽

Support Vector

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MULTIPLE CLASSIFICATION TECHNIQUES TOWARD A ROBUST AND RELIABLE P300 BCI SYSTEM

Biomedical Engineering Applications Basis and Communications ◽

10.4015/s1016237220500106 ◽

2020 ◽

Vol 32 (02) ◽

pp. 2050010

Author(s):

Fatma EL-Zahraa M. Labib ◽

Islam A. Fouad ◽

Mai S. Mabrouk ◽

Amr A. Sharawy

Keyword(s):

Machine Learning ◽

Support Vector Machine ◽

Discriminant Analysis ◽

Linear Discriminant Analysis ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Support Vector ◽

Linear Discriminant ◽

The Brain ◽

Bci System

A brain–computer interface (BCI) can be used for people with severe physical disabilities such as ALS or amyotrophic lateral sclerosis. BCI can allow these individuals to communicate again by creating a new communication channel directly from the brain to an output device. BCI technology can allow paralyzed people to share their intent with others, and thereby demonstrate that direct communication from the brain to the external world is possible and that it might serve useful functions. BCI systems include machine learning algorithms (MLAs). Their performance depends on the feature extraction and classification techniques employed. In this paper, we propose a system to exploit the P300 signal in the brain, a positive deflection in event-related potentials. The P300 signal can be incorporated into a spelling device. There are two benefits behind this kind of research. First of all, this work presents the research status and the advantages of communication via a BCI system, especially the P300 BCI system for disordered people, and the related literature review is presented. Secondly, the paper discusses the performance of different machine learning algorithms. Two different datasets are presented: the first dataset 2004 and the second dataset 2019. A preprocessing step is introduced to the subjects in both datasets first to extract the important features before applying the proposed machine learning methods: linear discriminant analysis (LDA I and LDA II), support vector machine (SVM I, SVM II, SVM III, and SVM IV), linear regression (LREG), Bayesian linear discriminant analysis (BLDA), and twin support vector machine (TSVM). By comparing the performance of the different machine learning systems, in the first dataset it is found that BLDA and SVMIV classifiers yield the highest performance for both subjects “A” and “B”. BLDA yields 98% and 66% for 15th and 5th sequences, respectively, whereas SVMIV yields 98% and 54.4% for 15th and 5th sequences, respectively. While in the second dataset, it is obvious that BLDA classifier yields the highest performance for both subjects “1” and “2”, it achieves 90.115%. The paper summarizes the P300 BCI system for the two introduced datasets. It discusses the proposed system, compares the classification methods performances, and considers some aspects for the future work to be handled. The results show high accuracy and less computational time which makes the system more applicable for online applications.

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Introduction and Implementation of Machine Learning Algorithms in R

Research Anthology on Artificial Intelligence Applications in Security ◽

10.4018/978-1-7998-7705-9.ch004 ◽

2021 ◽

pp. 67-88

Author(s):

S. R. Mani Sekhar ◽

G. M. Siddesh

Keyword(s):

Machine Learning ◽

Neural Networks ◽

Support Vector Machine ◽

Discriminant Analysis ◽

Computer Science ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Support Vector ◽

Linear Discriminant ◽

The Given

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Hierarchical Tactile Sensation Integration from Prosthetic Fingertips Enables Multi-Texture Surface Recognition

Sensors ◽

10.3390/s21134324 ◽

2021 ◽

Vol 21 (13) ◽

pp. 4324

Author(s):

Moaed A. Abd ◽

Rudy Paul ◽

Aparna Aravelli ◽

Ou Bai ◽

Leonel Lagos ◽

...

Keyword(s):

Machine Learning ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Sliding Contact ◽

Tactile Sensation ◽

Support Vector ◽

Textured Surfaces ◽

K Nearest Neighbor ◽

Tactile Sensors ◽

Time Frequency

Multifunctional flexible tactile sensors could be useful to improve the control of prosthetic hands. To that end, highly stretchable liquid metal tactile sensors (LMS) were designed, manufactured via photolithography, and incorporated into the fingertips of a prosthetic hand. Three novel contributions were made with the LMS. First, individual fingertips were used to distinguish between different speeds of sliding contact with different surfaces. Second, differences in surface textures were reliably detected during sliding contact. Third, the capacity for hierarchical tactile sensor integration was demonstrated by using four LMS signals simultaneously to distinguish between ten complex multi-textured surfaces. Four different machine learning algorithms were compared for their successful classification capabilities: K-nearest neighbor (KNN), support vector machine (SVM), random forest (RF), and neural network (NN). The time-frequency features of the LMSs were extracted to train and test the machine learning algorithms. The NN generally performed the best at the speed and texture detection with a single finger and had a 99.2 ± 0.8% accuracy to distinguish between ten different multi-textured surfaces using four LMSs from four fingers simultaneously. The capability for hierarchical multi-finger tactile sensation integration could be useful to provide a higher level of intelligence for artificial hands.

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Machine Learning Models for Finger Bend Evaluation using Implemented Low cost Flex Sensor

International Journal for Research in Applied Science and Engineering Technology ◽

10.22214/ijraset.2021.35742 ◽

2021 ◽

Vol 9 (VI) ◽

pp. 3605-3611

Author(s):

Pratyush Kaware

Keyword(s):

Machine Learning ◽

Support Vector Machine ◽

Low Cost ◽

Learning Algorithms ◽

Cost Effective ◽

Machine Learning Algorithms ◽

Support Vector ◽

Learning Models ◽

Machine Learning Models

In this paper a cost-effective sensor has been implemented to read finger bend signals, by attaching the sensor to a finger, so as to classify them based on the degree of bent as well as the joint about which the finger was being bent. This was done by testing with various machine learning algorithms to get the most accurate and consistent classifier. Finally, we found that Support Vector Machine was the best algorithm suited to classify our data, using we were able predict live state of a finger, i.e., the degree of bent and the joints involved. The live voltage values from the sensor were transmitted using a NodeMCU micro-controller which were converted to digital and uploaded on a database for analysis.

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Heart disease prediction using machine learning techniques : a survey

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i2.8.10557 ◽

2018 ◽

Vol 7 (2.8) ◽

pp. 684 ◽

Cited By ~ 12

Author(s):

V V. Ramalingam ◽

Ayantan Dandapath ◽

M Karthik Raja

Keyword(s):

Machine Learning ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Machine Learning Techniques ◽

Support Vector ◽

Complex Data ◽

Learning Techniques ◽

Vector Machines ◽

Supervised Learning Algorithms ◽

Life Threatening

Heart related diseases or Cardiovascular Diseases (CVDs) are the main reason for a huge number of death in the world over the last few decades and has emerged as the most life-threatening disease, not only in India but in the whole world. So, there is a need of reliable, accurate and feasible system to diagnose such diseases in time for proper treatment. Machine Learning algorithms and techniques have been applied to various medical datasets to automate the analysis of large and complex data. Many researchers, in recent times, have been using several machine learning techniques to help the health care industry and the professionals in the diagnosis of heart related diseases. This paper presents a survey of various models based on such algorithms and techniques andanalyze their performance. Models based on supervised learning algorithms such as Support Vector Machines (SVM), K-Nearest Neighbour (KNN), NaïveBayes, Decision Trees (DT), Random Forest (RF) and ensemble models are found very popular among the researchers.

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Feature Selection and Comparison of Machine Learning Algorithms in Classification of Grazing and Rumination Behaviour in Sheep

Sensors ◽

10.3390/s18103532 ◽

2018 ◽

Vol 18 (10) ◽

pp. 3532 ◽

Cited By ~ 16

Author(s):

Nicola Mansbridge ◽

Jurgen Mitsch ◽

Nicola Bollard ◽

Keith Ellis ◽

Giuliana Miguel-Pacheco ◽

...

Keyword(s):

Machine Learning ◽

Random Forest ◽

Time Budget ◽

Learning Algorithms ◽

Eating Behaviour ◽

Machine Learning Algorithms ◽

Support Vector ◽

Optimum Number ◽

Eating Behaviours ◽

Adaptive Boosting

Grazing and ruminating are the most important behaviours for ruminants, as they spend most of their daily time budget performing these. Continuous surveillance of eating behaviour is an important means for monitoring ruminant health, productivity and welfare. However, surveillance performed by human operators is prone to human variance, time-consuming and costly, especially on animals kept at pasture or free-ranging. The use of sensors to automatically acquire data, and software to classify and identify behaviours, offers significant potential in addressing such issues. In this work, data collected from sheep by means of an accelerometer/gyroscope sensor attached to the ear and collar, sampled at 16 Hz, were used to develop classifiers for grazing and ruminating behaviour using various machine learning algorithms: random forest (RF), support vector machine (SVM), k nearest neighbour (kNN) and adaptive boosting (Adaboost). Multiple features extracted from the signals were ranked on their importance for classification. Several performance indicators were considered when comparing classifiers as a function of algorithm used, sensor localisation and number of used features. Random forest yielded the highest overall accuracies: 92% for collar and 91% for ear. Gyroscope-based features were shown to have the greatest relative importance for eating behaviours. The optimum number of feature characteristics to be incorporated into the model was 39, from both ear and collar data. The findings suggest that one can successfully classify eating behaviours in sheep with very high accuracy; this could be used to develop a device for automatic monitoring of feed intake in the sheep sector to monitor health and welfare.

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A Comparative Analysis of Machine Learning Algorithms Modeled from Machine Vision-Based Lettuce Growth Stage Classification in Smart Aquaponics

International Journal of Environmental Science and Development ◽

10.18178/ijesd.2020.11.9.1288 ◽

2020 ◽

Vol 11 (9) ◽

pp. 442-449 ◽

Cited By ~ 1

Author(s):

Sandy C. Lauguico ◽

◽

Ronnie S. Concepcion II ◽

Jonnel D. Alejandrino ◽

Rogelio Ruzcko Tobias ◽

...

Keyword(s):

Machine Learning ◽

Comparative Analysis ◽

Machine Vision ◽

Nearest Neighbor ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Support Vector ◽

Urban Farming ◽

K Nearest Neighbor ◽

Lettuce Growth

The arising problem on food scarcity drives the innovation of urban farming. One of the methods in urban farming is the smart aquaponics. However, for a smart aquaponics to yield crops successfully, it needs intensive monitoring, control, and automation. An efficient way of implementing this is the utilization of vision systems and machine learning algorithms to optimize the capabilities of the farming technique. To realize this, a comparative analysis of three machine learning estimators: Logistic Regression (LR), K-Nearest Neighbor (KNN), and Linear Support Vector Machine (L-SVM) was conducted. This was done by modeling each algorithm from the machine vision-feature extracted images of lettuce which were raised in a smart aquaponics setup. Each of the model was optimized to increase cross and hold-out validations. The results showed that KNN having the tuned hyperparameters of n_neighbors=24, weights='distance', algorithm='auto', leaf_size = 10 was the most effective model for the given dataset, yielding a cross-validation mean accuracy of 87.06% and a classification accuracy of 91.67%.

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