Analysis of Relevant Features from Photoplethysmographic Signals for Atrial Fibrillation Classification

Atrial Fibrillation (AF) is the most common cardiac arrhythmia found in clinical practice. It affects an estimated 33.5 million people, representing approximately 0.5% of the world’s population. Electrocardiogram (ECG) is the main diagnostic criterion for AF. Recently, photoplethysmography (PPG) has emerged as a simple and portable alternative for AF detection. However, it is not completely clear which are the most important features of the PPG signal to perform this process. The objective of this paper is to determine which are the most relevant features for PPG signal analysis in the detection of AF. This study is divided into two stages: (a) a systematic review carried out following the Preferred Reporting Items for a Systematic Review and Meta-analysis of Diagnostic Test Accuracy Studies (PRISMA-DTA) statement in six databases, in order to identify the features of the PPG signal reported in the literature for the detection of AF, and (b) an experimental evaluation of them, using machine learning, in order to determine which have the greatest influence on the process of detecting AF. Forty-four features were found when analyzing the signal in the time, frequency, or time–frequency domains. From those 44 features, 27 were implemented, and through machine learning, it was found that only 11 are relevant in the detection process. An algorithm was developed for the detection of AF based on these 11 features, which obtained an optimal performance in terms of sensitivity (98.43%), specificity (99.52%), and accuracy (98.97%).

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Diagnostic test accuracy for use of machine learning in diagnosis of autism spectrum disorder: A Systematic Review and Meta-Analysis (Preprint)

10.2196/preprints.14108 ◽

2019 ◽

Author(s):

Sun Jae Moon ◽

Jin Seub Hwang ◽

Rajesh Kana ◽

John Torous ◽

Jung Won Kim

Keyword(s):

Machine Learning ◽

Systematic Review ◽

Autism Spectrum Disorder ◽

Meta Analysis ◽

Learning Algorithms ◽

Structural Mri ◽

Autism Spectrum ◽

Machine Learning Algorithms ◽

Spectrum Disorder ◽

Test Accuracy

BACKGROUND Over the recent years, machine learning algorithms have been more widely and increasingly applied in biomedical fields. In particular, its application has been drawing more attention in the field of psychiatry, for instance, as diagnostic tests/tools for autism spectrum disorder. However, given its complexity and potential clinical implications, there is ongoing need for further research on its accuracy. OBJECTIVE The current study aims to summarize the evidence for the accuracy of use of machine learning algorithms in diagnosing autism spectrum disorder (ASD) through systematic review and meta-analysis. METHODS MEDLINE, Embase, CINAHL Complete (with OpenDissertations), PsyINFO and IEEE Xplore Digital Library databases were searched on November 28th, 2018. Studies, which used a machine learning algorithm partially or fully in classifying ASD from controls and provided accuracy measures, were included in our analysis. Bivariate random effects model was applied to the pooled data in meta-analysis. Subgroup analysis was used to investigate and resolve the source of heterogeneity between studies. True-positive, false-positive, false negative and true-negative values from individual studies were used to calculate the pooled sensitivity and specificity values, draw SROC curves, and obtain area under the curve (AUC) and partial AUC. RESULTS A total of 43 studies were included for the final analysis, of which meta-analysis was performed on 40 studies (53 samples with 12,128 participants). A structural MRI subgroup meta-analysis (12 samples with 1,776 participants) showed the sensitivity at 0.83 (95% CI-0.76 to 0.89), specificity at 0.84 (95% CI -0.74 to 0.91), and AUC/pAUC at 0.90/0.83. An fMRI/deep neural network (DNN) subgroup meta-analysis (five samples with 1,345 participants) showed the sensitivity at 0.69 (95% CI- 0.62 to 0.75), the specificity at 0.66 (95% CI -0.61 to 0.70), and AUC/pAUC at 0.71/0.67. CONCLUSIONS Machine learning algorithms that used structural MRI features in diagnosis of ASD were shown to have accuracy that is similar to currently used diagnostic tools.

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Machine Learning Algorithms for Prediabetes Risk Calculation: a Protocol of Systematic Review and Meta-Analysis

10.21203/rs.3.rs-578915/v1 ◽

2021 ◽

Author(s):

Yaltafit Abror Jeem ◽

Refa Nabila ◽

Dwi Ditha Emelia ◽

Lutfan Lazuardi ◽

Hari Kusnanto Josef

Keyword(s):

Machine Learning ◽

Systematic Review ◽

Diagnostic Test ◽

Meta Analysis ◽

Characteristic Curve ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Assessment Tools ◽

Forest Plot ◽

Test Accuracy

Abstract Background One strategy to resolve the increasing prevalence of T2DM is to identify and administer interventions to prediabetes patients. Risk assessment tools help detect diseases, by allowing screening to the high risk group. Machine learning is also used to help diagnosis and identification of prediabetes. This review aims to determine the diagnostic test accuracy of various machine learning algorithms for calculating prediabetes risk.Methods This protocol was written in compliance with the Preferred Reporting Items for Systematic Review and Meta-Analysis for Protocols (PRISMA-P) statement. The databases that will be used include PubMed, ProQuest and EBSCO restricted to January 1999 and May 2019 in English language only. Identification of articles will be done independently by two reviewers through the titles, the abstracts, and then the full-text-articles. Any disagreement will be resolved by consensus. The Newcastle-Ottawa Quality Assessment Scale will be used to measure the quality and potential of bias. Data extraction and content analysis will be performed systematically. Quantitative data will be visualized using a forest plot with the 95% Confidence Intervals. The diagnostic test outcome will be described by the summary receiver operating characteristic curve. Data will be analyzed using Review Manager 5.3 (RevMan 5.3) software package.Discussion We will obtain diagnostic accuracy of various machine learning algorithms for prediabetes risk estimation using this proposed systematic review and meta-analysis. Systematic review registration: This protocol has been registered in the Prospective Registry of Systematic Review (PROSPERO) database. The registration number is CRD42021251242.

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