Exploring Complexity Changes in Diseased ECG Signals for Enhanced Classification
Fuente:
arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866918164516831232 |
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| author | Quintero, Camilo Quiceno George, Sandip Varkey |
| author_facet | Quintero, Camilo Quiceno George, Sandip Varkey |
| contents | The complex dynamics of the heart are reflected in its electrical activity, captured through electrocardiograms (ECGs). In this study we use nonlinear time series analysis to understand how ECG complexity varies with cardiac pathology. Using the large PTB-XL dataset, we extracted nonlinear measures from lead II ECGs, and cross-channel metrics (leads II, V2, AVL) using Spearman correlations and mutual information. Significant differences between diseased and healthy individuals were found in almost all measures between healthy and diseased classes, and between 5 diagnostic superclasses ($p<.001$). Moreover, incorporating these complexity quantifiers into machine learning models substantially improved classification accuracy measured using area under the ROC curve (AUC) from 0.86 (baseline) to 0.87 (nonlinear measures) and 0.90 (including cross-time series metrics). |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_17810 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Exploring Complexity Changes in Diseased ECG Signals for Enhanced Classification Quintero, Camilo Quiceno George, Sandip Varkey Signal Processing Machine Learning Chaotic Dynamics Data Analysis, Statistics and Probability The complex dynamics of the heart are reflected in its electrical activity, captured through electrocardiograms (ECGs). In this study we use nonlinear time series analysis to understand how ECG complexity varies with cardiac pathology. Using the large PTB-XL dataset, we extracted nonlinear measures from lead II ECGs, and cross-channel metrics (leads II, V2, AVL) using Spearman correlations and mutual information. Significant differences between diseased and healthy individuals were found in almost all measures between healthy and diseased classes, and between 5 diagnostic superclasses ($p<.001$). Moreover, incorporating these complexity quantifiers into machine learning models substantially improved classification accuracy measured using area under the ROC curve (AUC) from 0.86 (baseline) to 0.87 (nonlinear measures) and 0.90 (including cross-time series metrics). |
| title | Exploring Complexity Changes in Diseased ECG Signals for Enhanced Classification |
| topic | Signal Processing Machine Learning Chaotic Dynamics Data Analysis, Statistics and Probability |
| url | https://arxiv.org/abs/2510.17810 |