Evaluation of the signal quality of wrist-based photoplethysmography
TL;DR: Wearable devices with embedded photoplethysmography (PPG) sensors enable continuous monitoring of cardiovascular activity, allowing for the detection cardiovascular problems, such as arrhythmias, unless methods can be identified to improve low quality signal segments.
Abstract: Objective Wearable devices with embedded photoplethysmography (PPG) sensors enable continuous monitoring of cardiovascular activity, allowing for the detection cardiovascular problems, such as arrhythmias. However, the quality of wrist-based PPG is highly variable, and is subject to artifacts from motion and other interferences. The goal of this paper is to evaluate the signal quality obtained from wrist-based PPG when used in an ambulatory setting. Approach Ambulatory data were collected over a 24 h period for 10 elderly, and 16 non-elderly participants. Visual assessment is used as the gold standard for PPG signal quality, with inter-rater agreement evaluated using Fleiss' Kappa. With this gold standard, 5 classifiers were evaluated using a modified 13-fold cross-validation approach. Main results A Random Forest quality classification algorithm showed the best performance, with an accuracy of 74.5%, and was then used to evaluate 24 h long ambulatory wrist-based PPG measurements. Significance In general, data quality was high at night, and low during the day. Our results suggest wrist-based PPG may be best for continuous cardiovascular monitoring applications during the night, but less useful during the day unless methods can be identified to improve low quality signal segments.
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05 Feb 2006
TL;DR: In this article, Naive Bayes Classifiers are used for Naïve Bayes classifiers to train a classifier for classifier training, and the classifier is evaluated.
Abstract: 在Naive Bayes Classifiers模型中，要求父节点下的子节点（特征变量）之间相对独立，然而在现实世界中，特征与特征之间是非独立的、相关的。提出一种预处理方法，实验结果表明，该方法明显地提高了分类精度。
213 citations
TL;DR: Although there is no standardized pre-processing pipeline for PPG signal processing, as PPG data are acquired and accumulated in various ways, the recently proposed machine learning-based method is expected to offer a promising solution.
Abstract: Beyond its use in a clinical environment, photoplethysmogram (PPG) is increasingly used for measuring the physiological state of an individual in daily life. This review aims to examine existing research on photoplethysmogram concerning its generation mechanisms, measurement principles, clinical applications, noise definition, pre-processing techniques, feature detection techniques, and post-processing techniques for photoplethysmogram processing, especially from an engineering point of view. We performed an extensive search with the PubMed, Google Scholar, Institute of Electrical and Electronics Engineers (IEEE), ScienceDirect, and Web of Science databases. Exclusion conditions did not include the year of publication, but articles not published in English were excluded. Based on 118 articles, we identified four main topics of enabling PPG: (A) PPG waveform, (B) PPG features and clinical applications including basic features based on the original PPG waveform, combined features of PPG, and derivative features of PPG, (C) PPG noise including motion artifact baseline wandering and hypoperfusion, and (D) PPG signal processing including PPG preprocessing, PPG peak detection, and signal quality index. The application field of photoplethysmogram has been extending from the clinical to the mobile environment. Although there is no standardized pre-processing pipeline for PPG signal processing, as PPG data are acquired and accumulated in various ways, the recently proposed machine learning-based method is expected to offer a promising solution.
43 citations
07 Oct 2020
TL;DR: An unsupervised learning approach is described for identification of ‘clean’, ‘partly clean’ and ‘corrupted’ segments in the MA contaminated PPG data and achieves better result than recently published work utilizing non-segmenting approach based PPG SQA.
Abstract: Photoplethysmography (PPG) is gradually becoming popular tool for cardiovascular and respiratory function monitoring under ambulatory condition. However, these measurements are prone to motion artifact (MA) corruption, and hence, signal quality assessment (SQA) is essential before computerized analysis. The published research on PPG SQA, mostly utilizing supervised learning approaches, suffer from the universality of feature selection against PPG morphology variability. Secondly, beat detection from the MA corrupted is a challenging task and partly limits the success of SQA utilizing beat segmenting approaches. The present research describes an unsupervised learning approach for identification of ‘clean’, ‘partly clean’ and ‘corrupted’ segments in the MA contaminated PPG data. Few entropy features and some signal complexity related features calculated by statistical methods in a 5 s window were fed to a self-organizing map (SOM) for direct quality assessment of PPG data. The number of input node to the SOM was 7 and the output was connected to a square matrix consisting of 25 nodes. The multiclass classification model achieved 94.10%, 89.27%, 92.67% accuracy score for the three classes respectively on 200 min of PPG data collected from 30 healthy and CVD human volunteers under mild to high level of hand movement. The model achieved better result than recently published work utilizing non-segmenting approach based PPG SQA.
17 citations
Cites methods from "Evaluation of the signal quality of..."
...Pradhan [14] Binary Supervised learning - 58....
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...The non-segmenting approaches were illustrated in [11], [12], [13], [14], [15] and [16]....
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...In [13] and [14], machine learning based approach like k-nearest neighbor (kNN), decision tree (DT), SVM etc....
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01 Jan 2022
TL;DR: A comprehensive overview of wearable photoplethysmography devices can be found in this paper , where the authors present key considerations in the design of wearable PPG devices, the physiological parameters that can be estimated from wearable pPG signals, commercially available devices, and potential applications in health and fitness monitoring.
Abstract: The wearables market has expanded greatly in recent years, with wrist-worn devices now widely used. Smart wearables provide opportunity to monitor health and fitness in daily life. Wearables such as fitness bands and smartwatches routinely monitor the photoplethysmogram (PPG) signal, an optical measure of the arterial pulse wave which is strongly influenced by the heart and blood vessels. This chapter presents a comprehensive overview of the state-of-the-art of wearable photoplethysmography devices. It summarizes: (i) key considerations in the design of wearable PPG devices; (ii) the physiological parameters that can be estimated from wearable PPG signals; (iii) commercially available devices; and (iv) potential applications in health and fitness monitoring.
14 citations
TL;DR: In this article, a model for simulating motion-induced artifacts in the wrist photoplethysmogram (PPG) is proposed for the purpose to improve realism of PPG models.
9 citations
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TL;DR: Internal estimates monitor error, strength, and correlation and these are used to show the response to increasing the number of features used in the forest, and are also applicable to regression.
Abstract: Random forests are a combination of tree predictors such that each tree depends on the values of a random vector sampled independently and with the same distribution for all trees in the forest. The generalization error for forests converges a.s. to a limit as the number of trees in the forest becomes large. The generalization error of a forest of tree classifiers depends on the strength of the individual trees in the forest and the correlation between them. Using a random selection of features to split each node yields error rates that compare favorably to Adaboost (Y. Freund & R. Schapire, Machine Learning: Proceedings of the Thirteenth International conference, aaa, 148–156), but are more robust with respect to noise. Internal estimates monitor error, strength, and correlation and these are used to show the response to increasing the number of features used in the splitting. Internal estimates are also used to measure variable importance. These ideas are also applicable to regression.
79,257 citations
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TL;DR: A general statistical methodology for the analysis of multivariate categorical data arising from observer reliability studies is presented and tests for interobserver bias are presented in terms of first-order marginal homogeneity and measures of interob server agreement are developed as generalized kappa-type statistics.
Abstract: This paper presents a general statistical methodology for the analysis of multivariate categorical data arising from observer reliability studies. The procedure essentially involves the construction of functions of the observed proportions which are directed at the extent to which the observers agree among themselves and the construction of test statistics for hypotheses involving these functions. Tests for interobserver bias are presented in terms of first-order marginal homogeneity and measures of interobserver agreement are developed as generalized kappa-type statistics. These procedures are illustrated with a clinical diagnosis example from the epidemiological literature.
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TL;DR: The contributions of this special issue cover a wide range of aspects of variable selection: providing a better definition of the objective function, feature construction, feature ranking, multivariate feature selection, efficient search methods, and feature validity assessment methods.
Abstract: Variable and feature selection have become the focus of much research in areas of application for which datasets with tens or hundreds of thousands of variables are available. These areas include text processing of internet documents, gene expression array analysis, and combinatorial chemistry. The objective of variable selection is three-fold: improving the prediction performance of the predictors, providing faster and more cost-effective predictors, and providing a better understanding of the underlying process that generated the data. The contributions of this special issue cover a wide range of aspects of such problems: providing a better definition of the objective function, feature construction, feature ranking, multivariate feature selection, efficient search methods, and feature validity assessment methods.
14,509 citations
Book•
01 Jan 2013
TL;DR: This book discusses data mining through the lens of cluster analysis, which examines the relationships between data, clusters, and algorithms, and some of the techniques used to solve these problems.
Abstract: 1 Introduction 1.1 What is Data Mining? 1.2 Motivating Challenges 1.3 The Origins of Data Mining 1.4 Data Mining Tasks 1.5 Scope and Organization of the Book 1.6 Bibliographic Notes 1.7 Exercises 2 Data 2.1 Types of Data 2.2 Data Quality 2.3 Data Preprocessing 2.4 Measures of Similarity and Dissimilarity 2.5 Bibliographic Notes 2.6 Exercises 3 Exploring Data 3.1 The Iris Data Set 3.2 Summary Statistics 3.3 Visualization 3.4 OLAP and Multidimensional Data Analysis 3.5 Bibliographic Notes 3.6 Exercises 4 Classification: Basic Concepts, Decision Trees, and Model Evaluation 4.1 Preliminaries 4.2 General Approach to Solving a Classification Problem 4.3 Decision Tree Induction 4.4 Model Overfitting 4.5 Evaluating the Performance of a Classifier 4.6 Methods for Comparing Classifiers 4.7 Bibliographic Notes 4.8 Exercises 5 Classification: Alternative Techniques 5.1 Rule-Based Classifier 5.2 Nearest-Neighbor Classifiers 5.3 Bayesian Classifiers 5.4 Artificial Neural Network (ANN) 5.5 Support Vector Machine (SVM) 5.6 Ensemble Methods 5.7 Class Imbalance Problem 5.8 Multiclass Problem 5.9 Bibliographic Notes 5.10 Exercises 6 Association Analysis: Basic Concepts and Algorithms 6.1 Problem Definition 6.2 Frequent Itemset Generation 6.3 Rule Generation 6.4 Compact Representation of Frequent Itemsets 6.5 Alternative Methods for Generating Frequent Itemsets 6.6 FP-Growth Algorithm 6.7 Evaluation of Association Patterns 6.8 Effect of Skewed Support Distribution 6.9 Bibliographic Notes 6.10 Exercises 7 Association Analysis: Advanced Concepts 7.1 Handling Categorical Attributes 7.2 Handling Continuous Attributes 7.3 Handling a Concept Hierarchy 7.4 Sequential Patterns 7.5 Subgraph Patterns 7.6 Infrequent Patterns 7.7 Bibliographic Notes 7.8 Exercises 8 Cluster Analysis: Basic Concepts and Algorithms 8.1 Overview 8.2 K-means 8.3 Agglomerative Hierarchical Clustering 8.4 DBSCAN 8.5 Cluster Evaluation 8.6 Bibliographic Notes 8.7 Exercises 9 Cluster Analysis: Additional Issues and Algorithms 9.1 Characteristics of Data, Clusters, and Clustering Algorithms 9.2 Prototype-Based Clustering 9.3 Density-Based Clustering 9.4 Graph-Based Clustering 9.5 Scalable Clustering Algorithms 9.6 Which Clustering Algorithm? 9.7 Bibliographic Notes 9.8 Exercises 10 Anomaly Detection 10.1 Preliminaries 10.2 Statistical Approaches 10.3 Proximity-Based Outlier Detection 10.4 Density-Based Outlier Detection 10.5 Clustering-Based Techniques 10.6 Bibliographic Notes 10.7 Exercises Appendix A Linear Algebra Appendix B Dimensionality Reduction Appendix C Probability and Statistics Appendix D Regression Appendix E Optimization Author Index Subject Index
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7,318 citations
"Evaluation of the signal quality of..." refers methods in this paper
...Agreement between raters, Pi, was computed for each PPG segment using (1), the Fleiss (1971) κ....
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