Aging and cardiovascular complexity: effect of the length of RR tachograms

Balasubramanian, Karthi; Nagaraj, Nithin

doi:10.7717/peerj.2755

Cited by 22 publications

(16 citation statements)

References 52 publications

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“…It has been demonstrated that both LZ and ETC outperform Shannon entropy in accurately characterizing the dynamical complexity of both stochastic (Markov) and deterministic chaotic systems in the presence of noise (Nagaraj & Balasubramanian, 2017a;Nagaraj & Balasubramanian, 2017b). Further, ETC has shown to reliably capture complexity of very short time series where even LZ fails (Nagaraj & Balasubramanian, 2017a), and for analyzing short RR tachograms from healthy young and old subjects (Balasubramanian & Nagaraj, 2016). Recently, ETC has been used to propose a compression-complexity measure for networks (Virmani & Nagaraj, 2019).…”

Section: Dynamical Complexity (Dc) and Dynamical Compression-compleximentioning

confidence: 99%

“…• Infotheoretic causality measures such as TE and others need to estimate joint probability densities which are very difficult to reliably estimate with short and noisy time series. On the other hand, CCC uses Effort-To-Compress (ETC) complexity measure over short windows to capture time-varying causality and it is well established in literature that ETC outperforms infotheoretic measures for short and noisy data (Nagaraj & Balasubramanian, 2017a;Balasubramanian & Nagaraj, 2016).…”

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confidence: 99%

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Data-based intervention approach for Complexity-Causality measure

Kathpalia

Nagaraj

2019

PeerJ Computer Science

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Causality testing methods are being widely used in various disciplines of science. Model-free methods for causality estimation are very useful, as the underlying model generating the data is often unknown. However, existing model-free/data-driven measures assume separability of cause and effect at the level of individual samples of measurements and unlike model-based methods do not perform any intervention to learn causal relationships. These measures can thus only capture causality which is by the associational occurrence of ‘cause’ and ‘effect’ between well separated samples. In real-world processes, often ‘cause’ and ‘effect’ are inherently inseparable or become inseparable in the acquired measurements. We propose a novel measure that uses an adaptive interventional scheme to capture causality which is not merely associational. The scheme is based on characterizing complexities associated with the dynamical evolution of processes on short windows of measurements. The formulated measure, Compression-Complexity Causality is rigorously tested on simulated and real datasets and its performance is compared with that of existing measures such as Granger Causality and Transfer Entropy. The proposed measure is robust to the presence of noise, long-term memory, filtering and decimation, low temporal resolution (including aliasing), non-uniform sampling, finite length signals and presence of common driving variables. Our measure outperforms existing state-of-the-art measures, establishing itself as an effective tool for causality testing in real world applications.

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Section: Dynamical Complexity (Dc) and Dynamical Compression-compleximentioning

confidence: 99%

mentioning

confidence: 99%

Data-based intervention approach for Complexity-Causality measure

Kathpalia

Nagaraj

2019

PeerJ Computer Science

Self Cite

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show abstract

“…It has been demonstrated that both LZ and ETC outperform Shannon entropy in accurately characterizing the dynamical complexity of both stochastic (Markov) and deterministic chaotic systems in the presence of noise (Nagaraj and Balasubramanian, 2017a,b). Further, ETC has shown to reliably capture complexity of very short time series where even LZ fails (Nagaraj and Balasubramanian, 2017a), and for analyzing short RR tachograms from healthy young and old subjects (Balasubramanian and Nagaraj, 2016). Recently, ETC has been used to propose a compression-complexity measure for networks (Virmani and Nagaraj, 2019).…”

Section: /17mentioning

confidence: 99%

Peer Review #2 of "Data-based intervention approach for Complexity-Causality measure (v0.2)"

2019

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Causality testing methods are being widely used in various disciplines of science. Modelfree methods for causality estimation are very useful as the underlying model generating the data is often unknown. However, existing model-free/ data-driven measures assume separability of cause and effect at the level of individual samples of measurements and unlike model-based methods do not perform any intervention to learn causal relationships. These measures can thus only capture causality which is by the associational occurrence of 'cause' and 'effect' between well separated samples. In real-world processes, often 'cause' and 'effect' are inherently inseparable or become inseparable in the acquired measurements. We propose a novel measure that uses an adaptive interventional scheme to capture causality which is not merely associational. The scheme is based on characterizing complexities associated with the dynamical evolution of processes on short windows of measurements. The formulated measure, Compression-Complexity Causality is rigorously tested on simulated and real datasets and its performance is compared with that of existing measures such as Granger Causality and Transfer Entropy. The proposed measure is robust to presence of noise, long-term memory, filtering and decimation, low temporal resolution (including aliasing), non-uniform sampling, finite length signals and presence of common driving variables. Our measure outperforms existing state-of-the-art measures, establishing itself as an effective tool for causality testing in real world applications.

show abstract

“…It has been demonstrated that both LZ and ETC outperform Shannon entropy in accurately characterizing the dynamical complexity of both stochastic (Markov) and deterministic chaotic systems in the presence of noise (Nagaraj and Balasubramanian, 2017a,b). Further, ETC has shown to reliably capture complexity of very short time series where even LZ fails (Nagaraj and Balasubramanian, 2017a), and for analyzing short RR tachograms from healthy young and old subjects (Balasubramanian and Nagaraj, 2016).…”

Section: /19mentioning

confidence: 99%

“…• Infotheoretic-based causality measures such as TE and others need to estimate joint probability densities which are very difficult to reliably estimate with short and noisy time series. On the other hand, CCC uses Effort-To-Compress (ETC) complexity measure over short windows to capture time-varying causality and it is well established in literature that ETC outperforms infotheoretic measures for short and noisy data (Nagaraj and Balasubramanian, 2017a;Balasubramanian and Nagaraj, 2016).…”

Section: /19mentioning

confidence: 99%

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Supplemental Information 2: MATLAB Toolbox for Computation of Compression-Complexity Causality Measure

View full text Add to dashboard Cite

Causality testing methods are being widely used in various disciplines of science. Modelfree methods for causality estimation are very useful as the underlying model generating the data is often unknown. However, existing model-free measures assume separability of cause and effect at the level of individual samples of measurements and unlike modelbased methods do not perform any intervention to learn causal relationships. These measures can thus only capture causality which is by the associational occurrence of 'cause' and 'effect' between well separated samples. In real-world processes, often 'cause' and 'effect' are inherently inseparable or become inseparable in the acquired measurements. We propose a novel measure that uses an adaptive interventional scheme to capture causality which is not merely associational. The scheme is based on characterizing complexities associated with the dynamical evolution of processes on short windows of measurements. The formulated measure, Compression-Complexity Causality is rigorously tested on simulated and real datasets and its performance is compared with that of existing measures such as Granger Causality and Transfer Entropy. The proposed measure is robust to presence of noise, long-term memory, filtering and decimation, low temporal resolution (including aliasing), non-uniform sampling, finite length signals and presence of common driving variables. Our measure outperforms existing state-of-the-art measures, establishing itself as an effective tool for causality testing in real world applications.

show abstract

Aging and cardiovascular complexity: effect of the length of RR tachograms

Cited by 22 publications

References 52 publications

Data-based intervention approach for Complexity-Causality measure

Data-based intervention approach for Complexity-Causality measure

Peer Review #2 of "Data-based intervention approach for Complexity-Causality measure (v0.2)"

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