Mutual information and phase dependencies: measures of reduced nonlinear cardiorespiratory interactions after myocardial infarction

Hoyer, Dirk; Leder, U.; Hoyer, Heike; Pompe, Bernd; Sommer, Michael; Zwiener, U.

doi:10.1016/s1350-4533(01)00120-5

Cited by 92 publications

(104 citation statements)

References 18 publications

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“…This expression represents the cross mutual information between ( ) (Hoyer et al 2002, Jeong et al 2001, Na et al 2002, Xu et al 1997. Similarly,…”

Section: Auto Mutual Information Function Rate Of Decrease (Amifrd)mentioning

confidence: 99%

“…where, as mentioned earlier, I denotes the number of partitions used to estimate the histogram of ( ) t X (Hoyer et al 2002(Hoyer et al , 2005(Hoyer et al , 2006.…”

Section: Auto Mutual Information Function Rate Of Decrease (Amifrd)mentioning

confidence: 99%

“…This metric also shows differences between the EEG of schizophrenic patients and that recorded from control subjects (Na et al 2002). In addition, different AMIF decay parameters can help to detect several cardiomyopathies from cardiac data (Hoyer et al 2002, Palacios et al 2007.…”

Section: Introductionmentioning

confidence: 99%

“…Several physiological signals, including cardiovascular and brain activity recordings, exhibit a partially non-linear behaviour (Andrzejak et al 2001, Hoyer et al 2002, Palacios et al 2007. Additionally, some authors have suggested that healthy systems have non-linear complex relationships that fail with ageing and disease (Costa et al 2005, Goldberger et al 2002.…”

Section: Introductionmentioning

confidence: 99%

“…It is worth mentioning that several studies have illustrated the behaviour of the CMIF for some test sequences in contrast to the correlation function (David et al 2004, Hoyer et al 2002, Paluš et al 1993, Pompe et al 1998, Quian Quiroga et al 2002. Moreover, the decay of the AMIF has been compared with other entropic measures (Abásolo et al 2008, Hoyer et al 2005.…”

Section: Introductionmentioning

confidence: 99%

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Interpretation of the auto-mutual information rate of decrease in the context of biomedical signal analysis. Application to electroencephalogram recordings

2009

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The mutual information (MI) is a measure of both linear and non-linear dependences. It can be applied to a time series and a time-delayed version of the same sequence to compute the auto mutual information function (AMIF). Moreover, the AMIF rate of decrease (AMIFRD) with increasing time delay in a signal is correlated with its entropy and has been used to characterise biomedical data. In this paper, we aimed at gaining insight into the dependence of the AMIFRD on several signal processing concepts and at illustrating its application to biomedical time series analysis. Thus, we have analysed a set of synthetic sequences with the AMIFRD. The results show that the AMIF decreases more quickly as bandwidth increases and that the AMIFRD becomes more negative as there is more white noise contaminating the time series. Additionally, this metric detected changes in the non-linear dynamics of a signal. Finally, in order to illustrate the analysis of real biomedical signals with the AMIFRD, this metric was applied to electroencephalogram (EEG) signals acquired with eyes open and closed and to ictal and non-ictal intracranial EEG recordings.

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“…This expression represents the cross mutual information between ( ) (Hoyer et al 2002, Jeong et al 2001, Na et al 2002, Xu et al 1997. Similarly,…”

Section: Auto Mutual Information Function Rate Of Decrease (Amifrd)mentioning

confidence: 99%

“…where, as mentioned earlier, I denotes the number of partitions used to estimate the histogram of ( ) t X (Hoyer et al 2002(Hoyer et al , 2005(Hoyer et al , 2006.…”

Section: Auto Mutual Information Function Rate Of Decrease (Amifrd)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Interpretation of the auto-mutual information rate of decrease in the context of biomedical signal analysis. Application to electroencephalogram recordings

2009

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Autocorrelation and Cross‐Correlation Methods

Kohn

2006

Wiley Encyclopedia of Biomedical Engineering

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Nonlinear dynamic analysis of oscillatory repetitive movements in Parkinson's disease and essential tremor

et al. 2010

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Uncertainty exists on whether Parkinson's disease (PD) and essential tremor (ET) patients have similar degree of impairment during motor tasks. We investigated this problem by analyzing nonlinear dynamics of repetitive movements in 21 control subjects, 33 mild-moderate PD patients, and 18 ET patients. Accelerometer signals were recorded during finger tapping and unbounded forearm movements between two points, and processed with moving average filtering to generate a new signal consisting of the temporal distance between consecutive cycles. We calculated: mean interpeak interval (slowness), interpeak interval variability (irregularity), and beat decay (BD) of the auto mutual information (AMI) value, which estimates signal predictability by measuring the loss of signal information over a timescale. Both PD and ET had longer interpeak interval (except for finger tapping), higher interpeak interval variability, and higher BD-AMI values than controls (P ≤ 0.007, all comparisons). ET patients had higher BD-AMI values than PD (P = 0.003). BD-AMI was the parameter that discriminated better between subjects (diagnosis accuracies about 80%). No differences existed between PD patients with and without tremor or between PD or ET patients with different disease stages, for any parameter. Evaluation of nonlinear dynamics of oscillatory repetitive movements is a feasible and promising tool for studying movement physiology. Movement performance is more predictable in PD and ET than in controls, even in early disease stages. Slowness and irregularity of movement in PD and ET cannot be fully explained by tremor. Some common pathogenic mechanisms leading to bradykinesia may contribute to this impairment.

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Mutual information and phase dependencies: measures of reduced nonlinear cardiorespiratory interactions after myocardial infarction

Cited by 92 publications

References 18 publications

Interpretation of the auto-mutual information rate of decrease in the context of biomedical signal analysis. Application to electroencephalogram recordings

Interpretation of the auto-mutual information rate of decrease in the context of biomedical signal analysis. Application to electroencephalogram recordings

Autocorrelation and Cross‐Correlation Methods

Nonlinear dynamic analysis of oscillatory repetitive movements in Parkinson's disease and essential tremor

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