Optimizing Changepoint Detection Algorithms for Cardiac Time Series Analysis and Disease Classification

Konsep Inti

Changepoint detection algorithms can reveal underlying structure in non-stationary physiological time series, enabling improved analysis and classification of health conditions. The choice of changepoint detection algorithm depends on the characteristics of the data and the downstream application.

Abstrak

This work presents a comprehensive evaluation of eight key changepoint detection algorithms on realistic artificial cardiac time series data, as well as their application to real-world data for classifying patients with REM Behavior Disorder (RBD) from healthy controls.
The performance of each algorithm was assessed as a function of temporal tolerance, noise, and abnormal heart rhythms (ectopy) on the artificial data. The Recursive Mean Difference Maximization (RMDM) algorithm achieved the highest true positive rate, while the Modified Bayesian Changepoint Detection (mBOCD) algorithm provided superior positive predictive value.
When applied to the real cardiac time series data, features derived from the RMDM algorithm provided the highest leave-one-out cross-validated accuracy of 0.89 and true positive rate of 0.87 for classifying RBD patients. This demonstrates the utility of changepoint detection techniques and the importance of optimizing the algorithm parameters for the specific application.
The authors also introduced a modified version of the Bayesian Online Changepoint Detection (BOCD) algorithm, which addressed a limitation in the original algorithm's ability to correctly identify changepoints. This modified version, mBOCD, showed improved performance compared to the original BOCD approach.
The results highlight that the choice of changepoint detection algorithm can have a significant impact on the downstream application performance, and that optimizing the algorithm parameters using realistic artificial data can lead to improved results on real-world data.

Statistik

The true positive rate (TPR) of the Recursive Mean Difference Maximization (RMDM) algorithm was 0.91 ± 0.02 on the artificial data.
The positive predictive value (PPV) of the modified Bayesian Changepoint Detection (mBOCD) algorithm was 0.84 ± 0.02 on the artificial data.
The accuracy of the classification of REM Behavior Disorder (RBD) patients from healthy controls using features derived from the RMDM algorithm was 0.89.
The true positive rate of the classification of RBD patients from healthy controls using features derived from the RMDM algorithm was 0.87.

Kutipan

"Automatically detected changepoints provide useful information about subject's physiological state which cannot be directly observed."
"The choice of change point detection algorithm depends on the nature of the underlying data and the downstream application, such as a classification task."

Wawasan Utama Disaring Dari

Benchmarking changepoint detection algorithms on cardiac time series

by Ayse Cakmak,... pada arxiv.org 04-22-2024

https://arxiv.org/pdf/2404.12408.pdf

Pertanyaan yang Lebih Dalam

How can the insights from this changepoint detection analysis be extended to other physiological time series beyond cardiac data, such as respiratory or neural signals?

The insights gained from the changepoint detection analysis on cardiac time series can be extended to other physiological time series, such as respiratory or neural signals, by adapting the algorithms and methodologies to suit the specific characteristics of these signals.

Algorithm Adaptation: The algorithms used for changepoint detection in cardiac data, such as Recursive Mean Difference Maximization (RMDM) and Bayesian Blocks, can be modified to account for the unique patterns and features present in respiratory or neural signals. For example, respiratory signals may exhibit different types of non-stationarities compared to cardiac signals, requiring adjustments in the algorithm parameters.

Feature Extraction: The features derived from changepoint analysis in cardiac data, such as the distribution of intervals between changepoints, can be translated to respiratory or neural signals. For respiratory signals, features related to breathing patterns or airflow changes can be extracted, while neural signals may involve features related to spike patterns or signal amplitude variations.

Classification Tasks: Similar classification tasks can be performed on respiratory or neural data using the derived features from changepoint analysis. By training machine learning models on these features, it is possible to classify different physiological states or detect abnormalities in these signals.

Optimization and Validation: Just as in the cardiac data analysis, it is essential to optimize the parameters of the changepoint detection algorithms for respiratory or neural signals. Validation on real-world data sets, similar to the Physionet Cyclic Alternating Pattern Database used in the cardiac study, can help assess the performance and generalizability of the algorithms.

In summary, the principles and methodologies applied in the cardiac time series analysis can serve as a foundation for exploring changepoint detection in other physiological signals, with necessary adaptations and optimizations to suit the specific characteristics of each signal type.

How can the insights from this changepoint detection analysis be extended to other physiological time series beyond cardiac data, such as respiratory or neural signals?

The insights gained from the changepoint detection analysis on cardiac time series can be extended to other physiological time series, such as respiratory or neural signals, by adapting the algorithms and methodologies to suit the specific characteristics of these signals.

Algorithm Adaptation: The algorithms used for changepoint detection in cardiac data, such as Recursive Mean Difference Maximization (RMDM) and Bayesian Blocks, can be modified to account for the unique patterns and features present in respiratory or neural signals. For example, respiratory signals may exhibit different types of non-stationarities compared to cardiac signals, requiring adjustments in the algorithm parameters.

Feature Extraction: The features derived from changepoint analysis in cardiac data, such as the distribution of intervals between changepoints, can be translated to respiratory or neural signals. For respiratory signals, features related to breathing patterns or airflow changes can be extracted, while neural signals may involve features related to spike patterns or signal amplitude variations.

Classification Tasks: Similar classification tasks can be performed on respiratory or neural data using the derived features from changepoint analysis. By training machine learning models on these features, it is possible to classify different physiological states or detect abnormalities in these signals.

Optimization and Validation: Just as in the cardiac data analysis, it is essential to optimize the parameters of the changepoint detection algorithms for respiratory or neural signals. Validation on real-world data sets, similar to the Physionet Cyclic Alternating Pattern Database used in the cardiac study, can help assess the performance and generalizability of the algorithms.

In summary, the principles and methodologies applied in the cardiac time series analysis can serve as a foundation for exploring changepoint detection in other physiological signals, with necessary adaptations and optimizations to suit the specific characteristics of each signal type.

How can the insights from this changepoint detection analysis be extended to other physiological time series beyond cardiac data, such as respiratory or neural signals?

The insights gained from the changepoint detection analysis on cardiac time series can be extended to other physiological time series, such as respiratory or neural signals, by adapting the algorithms and methodologies to suit the specific characteristics of these signals.

Algorithm Adaptation: The algorithms used for changepoint detection in cardiac data, such as Recursive Mean Difference Maximization (RMDM) and Bayesian Blocks, can be modified to account for the unique patterns and features present in respiratory or neural signals. For example, respiratory signals may exhibit different types of non-stationarities compared to cardiac signals, requiring adjustments in the algorithm parameters.

Feature Extraction: The features derived from changepoint analysis in cardiac data, such as the distribution of intervals between changepoints, can be translated to respiratory or neural signals. For respiratory signals, features related to breathing patterns or airflow changes can be extracted, while neural signals may involve features related to spike patterns or signal amplitude variations.

Classification Tasks: Similar classification tasks can be performed on respiratory or neural data using the derived features from changepoint analysis. By training machine learning models on these features, it is possible to classify different physiological states or detect abnormalities in these signals.

Optimization and Validation: Just as in the cardiac data analysis, it is essential to optimize the parameters of the changepoint detection algorithms for respiratory or neural signals. Validation on real-world data sets, similar to the Physionet Cyclic Alternating Pattern Database used in the cardiac study, can help assess the performance and generalizability of the algorithms.

In summary, the principles and methodologies applied in the cardiac time series analysis can serve as a foundation for exploring changepoint detection in other physiological signals, with necessary adaptations and optimizations to suit the specific characteristics of each signal type.

How can the insights from this changepoint detection analysis be extended to other physiological time series beyond cardiac data, such as respiratory or neural signals?

The insights gained from the changepoint detection analysis on cardiac time series can be extended to other physiological time series, such as respiratory or neural signals, by adapting the algorithms and methodologies to suit the specific characteristics of these signals.

Algorithm Adaptation: The algorithms used for changepoint detection in cardiac data, such as Recursive Mean Difference Maximization (RMDM) and Bayesian Blocks, can be modified to account for the unique patterns and features present in respiratory or neural signals. For example, respiratory signals may exhibit different types of non-stationarities compared to cardiac signals, requiring adjustments in the algorithm parameters.

Feature Extraction: The features derived from changepoint analysis in cardiac data, such as the distribution of intervals between changepoints, can be translated to respiratory or neural signals. For respiratory signals, features related to breathing patterns or airflow changes can be extracted, while neural signals may involve features related to spike patterns or signal amplitude variations.

Classification Tasks: Similar classification tasks can be performed on respiratory or neural data using the derived features from changepoint analysis. By training machine learning models on these features, it is possible to classify different physiological states or detect abnormalities in these signals.

Optimization and Validation: Just as in the cardiac data analysis, it is essential to optimize the parameters of the changepoint detection algorithms for respiratory or neural signals. Validation on real-world data sets, similar to the Physionet Cyclic Alternating Pattern Database used in the cardiac study, can help assess the performance and generalizability of the algorithms.

In summary, the principles and methodologies applied in the cardiac time series analysis can serve as a foundation for exploring changepoint detection in other physiological signals, with necessary adaptations and optimizations to suit the specific characteristics of each signal type.

How can the insights from this changepoint detection analysis be extended to other physiological time series beyond cardiac data, such as respiratory or neural signals?

The insights gained from the changepoint detection analysis on cardiac time series can be extended to other physiological time series, such as respiratory or neural signals, by adapting the algorithms and methodologies to suit the specific characteristics of these signals.

Algorithm Adaptation: The algorithms used for changepoint detection in cardiac data, such as Recursive Mean Difference Maximization (RMDM) and Bayesian Blocks, can be modified to account for the unique patterns and features present in respiratory or neural signals. For example, respiratory signals may exhibit different types of non-stationarities compared to cardiac signals, requiring adjustments in the algorithm parameters.

Feature Extraction: The features derived from changepoint analysis in cardiac data, such as the distribution of intervals between changepoints, can be translated to respiratory or neural signals. For respiratory signals, features related to breathing patterns or airflow changes can be extracted, while neural signals may involve features related to spike patterns or signal amplitude variations.

Classification Tasks: Similar classification tasks can be performed on respiratory or neural data using the derived features from changepoint analysis. By training machine learning models on these features, it is possible to classify different physiological states or detect abnormalities in these signals.

Optimization and Validation: Just as in the cardiac data analysis, it is essential to optimize the parameters of the changepoint detection algorithms for respiratory or neural signals. Validation on real-world data sets, similar to the Physionet Cyclic Alternating Pattern Database used in the cardiac study, can help assess the performance and generalizability of the algorithms.

In summary, the principles and methodologies applied in the cardiac time series analysis can serve as a foundation for exploring changepoint detection in other physiological signals, with necessary adaptations and optimizations to suit the specific characteristics of each signal type.

How can the insights from this changepoint detection analysis be extended to other physiological time series beyond cardiac data, such as respiratory or neural signals?

The insights gained from the changepoint detection analysis on cardiac time series can be extended to other physiological time series, such as respiratory or neural signals, by adapting the algorithms and methodologies to suit the specific characteristics of these signals.

Algorithm Adaptation: The algorithms used for changepoint detection in cardiac data, such as Recursive Mean Difference Maximization (RMDM) and Bayesian Blocks, can be modified to account for the unique patterns and features present in respiratory or neural signals. For example, respiratory signals may exhibit different types of non-stationarities compared to cardiac signals, requiring adjustments in the algorithm parameters.

Feature Extraction: The features derived from changepoint analysis in cardiac data, such as the distribution of intervals between changepoints, can be translated to respiratory or neural signals. For respiratory signals, features related to breathing patterns or airflow changes can be extracted, while neural signals may involve features related to spike patterns or signal amplitude variations.

Classification Tasks: Similar classification tasks can be performed on respiratory or neural data using the derived features from changepoint analysis. By training machine learning models on these features, it is possible to classify different physiological states or detect abnormalities in these signals.

Optimization and Validation: Just as in the cardiac data analysis, it is essential to optimize the parameters of the changepoint detection algorithms for respiratory or neural signals. Validation on real-world data sets, similar to the Physionet Cyclic Alternating Pattern Database used in the cardiac study, can help assess the performance and generalizability of the algorithms.

In summary, the principles and methodologies applied in the cardiac time series analysis can serve as a foundation for exploring changepoint detection in other physiological signals, with necessary adaptations and optimizations to suit the specific characteristics of each signal type.

What other machine learning techniques beyond K-Nearest Neighbors could be explored to leverage the changepoint-derived features for improved disease classification?

Beyond K-Nearest Neighbors (KNN), several other machine learning techniques can be explored to leverage the changepoint-derived features for improved disease classification. These techniques offer different approaches to handling data and may provide enhanced performance in certain scenarios:

Support Vector Machines (SVM): SVMs are effective for binary classification tasks and