An automatic method for arterial pulse waveform recognition using KNN and SVM classifiers

Pereira, Tânia; Paiva, Joana S.; Correia, Carlos; Cardoso, J. M. R.

doi:10.1007/s11517-015-1393-5

Cited by 18 publications

(22 citation statements)

References 48 publications

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“…Features commonly extracted from PPG time series are morphological descriptors, time domain statistics, frequency domain statistics, nonlinear measures, wavelet based measures, and cross-correlation measures. [53][54][55][56][57][58][59][60] There were generally three main ML approaches used in the reviewed studies: k-nearest neighbors (KNN), support vector machine (SVM), and decision trees (DT). KNN classification is a relatively simple clustering technique where a sample is classified by a plurality vote of its neighbors and assigned to the class based on the most common class among its k closest neighbors.…”

Section: Ppg Representationsmentioning

confidence: 99%

Photoplethysmography based atrial fibrillation detection: a review

et al. 2020

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Atrial fibrillation (AF) is a cardiac rhythm disorder associated with increased morbidity and mortality. It is the leading risk factor for cardioembolic stroke and its early detection is crucial in both primary and secondary stroke prevention. Continuous monitoring of cardiac rhythm is today possible thanks to consumer-grade wearable devices, enabling transformative diagnostic and patient management tools. Such monitoring is possible using low-cost easy-to-implement optical sensors that today equip the majority of wearables. These sensors record blood volume variations-a technology known as photoplethysmography (PPG)-from which the heart rate and other physiological parameters can be extracted to inform about user activity, fitness, sleep, and health. Recently, new wearable devices were introduced as being capable of AF detection, evidenced by large prospective trials in some cases. Such devices would allow for early screening of AF and initiation of therapy to prevent stroke. This review is a summary of a body of work on AF detection using PPG. A thorough account of the signal processing, machine learning, and deep learning approaches used in these studies is presented, followed by a discussion of their limitations and challenges towards clinical applications.npj Digital Medicine (2020) 3:3 ; https://doi.org/10.1038/s41746-019-0207-9 PHOTOPLETHYSMOGRAPHY PPG signal PPG waveform is generated during a cardiac cycle and typically measured at a peripheral site. Therefore, it is essentially a pulse pressure waveform that originates from the heart contraction and propagates through the vascular tree. As blood flow is controlled

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Section: Ppg Representationsmentioning

confidence: 99%

Photoplethysmography based atrial fibrillation detection: a review

et al. 2020

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“…The current study is a follow-up of a preliminary work about the development of an automatic method that was able to distinguish valid data (part containing arterial pulse waveform) from non-relevant information (noisy waveforms) acquired by the referred optical system during a clinical examination, in order to reduce the variability between operators [17]. In this previous work a pool of 37 features split in different subsets was used based on the following types: amplitude features, time domain statistics, wavelet features, cross-correlation features and frequency domain statistics.…”

Section: Introductionmentioning

confidence: 99%

Supervised learning methods for pathological arterial pulse wave differentiation: A SVM and neural networks approach

Paiva

Cardoso

Pereira

2018

International Journal of Medical Informatics

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“…The tests with AUC performance are followed by SVM based classification and cross-validation, where the selection of methods is made using the feature elimination and the grid search technique [28][29][30]. In order to obtain robust results in sound characterization, the accuracies are calculated after five repetitions dividing the recordings in a random manner.…”

Section: Resultsmentioning

confidence: 99%

“…The classification method is based on support vector machine (SVM) classifier, which is considered as a suitable tool for discrimination tasks [28][29][30]. Namely, SVM is applied as a classifier which distinguishes the data by finding a separating hyperplane with a maximal margin between the classes.…”

Section: Classification and Evaluationmentioning

confidence: 99%

“…The separation of the candidates is made during the cross-validation to properly estimate the overall performance, where the classification is performed without any prior knowledge, meaning that the sequences used in the training phase are not a part of the dataset used for testing. The recursive feature elimination technique is used to improve the classification accuracy by eliminating the least significant descriptors [29,30].…”

Section: Classification and Evaluationmentioning

confidence: 99%

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Identification of S1 and S2 Heart Sound Patterns Based on Fractal Theory and Shape Context

et al. 2017

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There has been a sustained effort in the research community over the recent years to develop algorithms that automatically analyze heart sounds. One of the major challenges is identifying primary heart sounds, S1 and S2, as they represent reference events for the analysis. The study presented in this paper analyzes the possibility of improving the structure characterization based on shape context and structure assessment using a small number of descriptors. Particularly, for the primary sound characterization, an adaptive waveform filtering is applied based on blanket fractal dimension for each preprocessed sound candidate belonging to pediatric subjects. This is followed by applying the shape based methods selected for the structure assessment of primary heart sounds. Different methods, such as the fractal ones, are used for the comparison. The analysis of heart sound patterns is performed using support vector machine classifier showing promising results (above 95% accuracy). The obtained results suggest that it is possible to improve the identification process using the shape related methods which are rarely applied. This can be helpful for applications involving automatic heart sound analysis.

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An automatic method for arterial pulse waveform recognition using KNN and SVM classifiers

Cited by 18 publications

References 48 publications

Photoplethysmography based atrial fibrillation detection: a review

Photoplethysmography based atrial fibrillation detection: a review

Supervised learning methods for pathological arterial pulse wave differentiation: A SVM and neural networks approach

Identification of S1 and S2 Heart Sound Patterns Based on Fractal Theory and Shape Context

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