Persistence Landscape based Topological Data Analysis for Personalized Arrhythmia Classification

Yan, Yan; Ivanov, Kamen; Cen, Jian; Liu, Qiu-Hua; Wang, Lei

doi:10.20944/preprints201908.0320.v1

Cited by 6 publications

(5 citation statements)

References 54 publications

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“…In another recent study, Yan and coworkers [16] explored the use of topological data analysis to classify electrocardiographic signals and detect arrhythmias. Cardiac arrhythmias are abnormal heart rhythms or irregularities in the heartbeat that may be too fast (tachycardia), too slow (bradycardia), or irregular.…”

Section: Ecg Data and Heart Rate Signalsmentioning

confidence: 99%

“…In the particular case of reference [16], phase space reconstruction was used to convert the signals into point clouds, which were then analyzed using topological techniques to extract persistence landscapes as features for the classification task. The authors found that the proposed method was effective, with a normal heartbeat class recognition rate of 100% when using just 20% of the training set, and recognition rates of 97.13% for ventricular beats, 94.27% for supraventricular beats, and 94.27% for fusion beats.…”

Section: Ecg Data and Heart Rate Signalsmentioning

confidence: 99%

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Topological Data Analysis in Cardiovascular Signals: An Overview

Hernández-Lemus,

Miramontes,

Martínez-García

2024

Entropy

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Topological data analysis (TDA) is a recent approach for analyzing and interpreting complex data sets based on ideas a branch of mathematics called algebraic topology. TDA has proven useful to disentangle non-trivial data structures in a broad range of data analytics problems including the study of cardiovascular signals. Here, we aim to provide an overview of the application of TDA to cardiovascular signals and its potential to enhance the understanding of cardiovascular diseases and their treatment in the form of a literature or narrative review. We first introduce the concept of TDA and its key techniques, including persistent homology, Mapper, and multidimensional scaling. We then discuss the use of TDA in analyzing various cardiovascular signals, including electrocardiography, photoplethysmography, and arterial stiffness. We also discuss the potential of TDA to improve the diagnosis and prognosis of cardiovascular diseases, as well as its limitations and challenges. Finally, we outline future directions for the use of TDA in cardiovascular signal analysis and its potential impact on clinical practice. Overall, TDA shows great promise as a powerful tool for the analysis of complex cardiovascular signals and may offer significant insights into the understanding and management of cardiovascular diseases.

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Section: Ecg Data and Heart Rate Signalsmentioning

confidence: 99%

Section: Ecg Data and Heart Rate Signalsmentioning

confidence: 99%

Topological Data Analysis in Cardiovascular Signals: An Overview

Hernández-Lemus,

Miramontes,

Martínez-García

2024

Entropy

View full text Add to dashboard Cite

show abstract

“…Study ( Graff et al, 2021 ) examined when persistence diagram was obtained by SLS filtering, and a set of indicators was extracted to distinguish the RR interval of healthy subjects and stroke patients. In addition ( Yan et al, 2019 ) applied TDA to reconstruct a signal point cloud to extract persistent landscape features to classify heart rate variability. The accuracy of a normal heartbeat was 100%, of ventricular beating was 97.13%, of supraventricular beating was 94.27%, and of fusion beating was 94.27%.…”

Section: Introductionmentioning

confidence: 99%

Dynamic ECG signal quality evaluation based on persistent homology and GoogLeNet method

et al. 2023

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Cardiovascular disease is a serious health problem. Continuous Electrocardiograph (ECG) monitoring plays a vital role in the early detection of cardiovascular disease. As the Internet of Things technology continues to mature, wearable ECG signal monitors have been widely used. However, dynamic ECG signals are extremely susceptible to contamination. Therefore, it is necessary to evaluate the quality of wearable dynamic ECG signals. The topological data analysis method (TDA) with persistent homology, which can effectively capture the topological information of high-dimensional data space, has been widely studied. In this study, a brand-new quality assessment method of wearable dynamic ECG signals was proposed based on the TDA with persistent homology method. The point cloud of an ECG signal was constructed, and then the complex sequence was generated and displayed as a persistent barcode. Finally, GoogLeNet based on the transfer learning model with a 10-fold cross-validation method was used to train the classification model. A total of 12-leads ECGs Dataset and single-lead ECGs Dataset, established based on the 2011 PhysioNet/CinC challenge dataset, were both used to verify the performance of this method. In the study, 773 “acceptable” and 225 “unacceptable” signals were used as 12-leads ECGs Dataset. We relabeled 12,000 ECG signals in the challenge dataset, and treated them as single-lead ECGs Dataset after empty lead detection and balance datasets. Compared with the traditional ECG signal quality assessment method mainly based on waveform characteristics and time-frequency characteristics, the performance of the quality assessment method proposed. In this study, the classification performance of the proposed method are fairly great, mAcc = 98.04%, F1 = 98.40%, Se = 97.15%, Sp = 98.93% for 12-leads ECGs Dataset and mAcc = 98.55%, F1 = 98.62%, Se = 98.37%, Sp = 98.85% for single-lead ECGs Dataset.

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“…Consistent with this, some studies have proposed the use of TDA metrics as a preliminary stage to extract features for machine learning classifiers, e.g., for medical image analysis [16,17,18], chemical components [19,20], or computer science problems [21]. Thus, these classification problems do not operate in the multidimensional dataset or in a subsequent reduced dimensionality space [22,23], but rather in the domain of meaningful topological feature vectors [24,25,26], with a consequent interpretability gain.…”

Section: Introductionmentioning

confidence: 99%

A topological classifier to characterize brain states: When shape matters more than variance

Ferrà¹,

Cecchini²,

Fisas³

et al. 2023

Preprint

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Despite the remarkable accuracies attained by machine learning classifiers to separate complex datasets in a supervised fashion, most of their operation falls short to provide an informed intuition about the structure of data, and, what is more important, about the phenomena being characterized by the given datasets. By contrast, topological data analysis (TDA) is devoted to study the shape of data clouds by means of persistence descriptors and provides a quantitative characterization of specific topological features of the dataset under scrutiny.In this article we introduce a novel TDA-based classifier that works on the principle of assessing quantifiable changes on topological metrics caused by the addition of new input to a subset of data. We used this classifier with a high-dimensional electro-encephalographic (EEG) dataset recorded from eleven participants during a decision-making experiment in which three motivational states were induced through a manipulation of social pressure. After processing a band-pass filtered version of EEG signals, we calculated silhouettes from persistence diagrams associated with each motivated state, and classified unlabeled signals according to their impact on each reference silhouette. Our results show that in addition to providing accuracies within the range of those of a nearest neighbour classifier, the TDA classifier provides formal intuition of the structure of the dataset as well as an estimate of its intrinsic dimension. Towards this end, we incorporated dimensionality reduction methods to our procedure and found that the accuracy of our TDA classifier is generally not sensitive to explained variance but rather to shape, contrary to what happens with most machine learning classifiers.

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Persistence Landscape based Topological Data Analysis for Personalized Arrhythmia Classification

Cited by 6 publications

References 54 publications

Topological Data Analysis in Cardiovascular Signals: An Overview

Topological Data Analysis in Cardiovascular Signals: An Overview

Dynamic ECG signal quality evaluation based on persistent homology and GoogLeNet method

A topological classifier to characterize brain states: When shape matters more than variance

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