Nonlinear dynamic approaches to identify atrial fibrillation progression based on topological methods

Safarbali, Bahareh; Golpayegani, Seyed Mohammad Reza Hashemi

doi:10.1016/j.bspc.2019.101563

Cited by 22 publications

(16 citation statements)

References 41 publications

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“…Some applications directly consider the barcode as features [26], i.e., directly use the barcode intervals or statistical parameter of barcodes as input of the classifier in the machine learning task. Some other applications of barcode and persistence diagram are using Bottleneck and Wasserstein distance for comparison the topological similarity between persistence diagrams, for example in protein binding analysis [20].…”

Section: Persistence Landscapementioning

confidence: 99%

“…The red bars are the lifetime for the 1-dimensional homologies, and then the barcodes can be used as features to distinguish the abnormal groups from the healthy control group. In [26], using the length of the bars in the Barcodes from the point cloud as the time-of-life features was proposed in classification tasks. Likewise, in [23], one typical bar in the Barcodes (lifetime of one typical homology) is one marker for the variation for detection tasks.…”

Section: Barcodesmentioning

confidence: 99%

“…Recently in signal analysis tasks, TDA showed great potential for pattern classification and outlier detection tasks [22][23][24][25][26], than the traditional statistical-based methods. Motivated by these works, the main contribution of this work includes:…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Gait Rhythm Dynamics for Neuro-Degenerative Disease Classification via Persistence Landscape- Based Topological Representation

Yan

Ivanov

Omisore

et al. 2020

Sensors

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Neuro-degenerative disease is a common progressive nervous system disorder that leads to serious clinical consequences. Gait rhythm dynamics analysis is essential for evaluating clinical states and improving quality of life for neuro-degenerative patients. The magnitude of stride-to-stride fluctuations and corresponding changes over time—gait dynamics—reflects the physiology of gait, in quantifying the pathologic alterations in the locomotor control system of health subjects and patients with neuro-degenerative diseases. Motivated by algebra topology theory, a topological data analysis-inspired nonlinear framework was adopted in the study of the gait dynamics. Meanwhile, the topological representation–persistence landscapes were used as input of classifiers in order to distinguish different neuro-degenerative disease type from healthy. In this work, stride-to-stride time series from healthy control (HC) subjects are compared with the gait dynamics from patients with amyotrophic lateral sclerosis (ALS), Huntington’s disease (HD), and Parkinson’s disease (PD). The obtained results show that the proposed methodology discriminates healthy subjects from subjects with other neuro-degenerative diseases with relatively high accuracy. In summary, our study is the first attempt to provide a topological representation-based method into the disease classification with gait rhythms measured from the stride intervals to visualize gait dynamics and classify neuro-degenerative diseases. The proposed method could be potentially used in earlier interventions and state monitoring.

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Section: Persistence Landscapementioning

confidence: 99%

Section: Barcodesmentioning

confidence: 99%

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Gait Rhythm Dynamics for Neuro-Degenerative Disease Classification via Persistence Landscape- Based Topological Representation

Yan

Ivanov

Omisore

et al. 2020

Sensors

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“…From the perspective of machine learning applications, barcode generated with the topological method can be considered as an alternative feature sets. Some applications directly consider the barcode as an estimator in statistics or features [29], i.e. directly use the barcode intervals as representation.…”

Section: Topological Signature: From Barcode To Persistence Landscapementioning

confidence: 99%

“…In [28] a term topological signal representation was proposed using time-delay embedding and TDA. Besides, Safarbali [29] proposed a statistical analysis using the time-of-life representation in persistent homology of TDA, toward the atrial fibrillation nonlinear dynamic analysis, and Dindin [30] used the Betti curves as an alternative features of the deep learning representations, using in a recognition system with a cascaded modular neural network.…”

Section: Introductionmentioning

confidence: 99%

Persistence Landscape based Topological Data Analysis for Personalized Arrhythmia Classification

Yan¹,

Ivanov²,

Cen³

et al. 2019

Preprint

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Data can be illustrated in shapes, and the shapes could provide insight for data modeling and information extraction. Topological data analysis provides an alternative insight in biomedical data analysis and knowledge discovery with the algebra topology tools. In present work, we study the application of topological data analysis for personalized electrocardiographic signal classification toward arrhythmia analysis. Using phase space reconstruction technique, the signal samples are converted into point clouds for topological analysis facility. With topological techniques the persistence landscapes from the point clouds are extracted as features to perform the arrhythmia classification task. We find that the proposed method is robust to the training set size, with only a training set size of 20% percents, the normal heartbeat class are 100% recognized, ventricular beats for 97.13%, supra-ventricular beats for 94.27% and fusion beats for 94.27% within the corresponding experiments. The property of keeping high performance when using smaller training sample proves that the proposed method is especially applicable to personalized analysis. With the present study, we show that the topological data analysis technique could be a useful tool in biomedical signal analysis, and provide powerful ability in personalized analysis.

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Emotion recognition based on phase-locking value brain functional network and topological data analysis

Wang,

Li,

Zhang

et al. 2024

Neural Comput & Applic

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Nonlinear dynamic approaches to identify atrial fibrillation progression based on topological methods

Cited by 22 publications

References 41 publications

Gait Rhythm Dynamics for Neuro-Degenerative Disease Classification via Persistence Landscape- Based Topological Representation

Gait Rhythm Dynamics for Neuro-Degenerative Disease Classification via Persistence Landscape- Based Topological Representation

Persistence Landscape based Topological Data Analysis for Personalized Arrhythmia Classification

Emotion recognition based on phase-locking value brain functional network and topological data analysis

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