Using complexity–entropy planes to detect Parkinson’s disease from short segments of haemodynamic signals

Abstract

Objective. There is emerging evidence that analysing the entropy and complexity of biomedical signals can detect underlying changes in physiology which may be reflective of disease pathology. This approach can be used even when only short recordings of biomedical signals are available. This study aimed to determine whether entropy and complexity measures can detect differences between subjects with Parkinsons disease and healthy controls (HCs). Approach. A method based on a diagram of entropy versus complexity, named complexity–entropy plane, was used to re-analyse a dataset of cerebral haemodynamic signals from subjects with Parkinsons disease and HCs obtained under poikilocapnic conditions. A probability distribution for a set of ordinal patterns, designed to capture regularities in a time series, was computed from each signal under analysis. Four types of entropy and ten types of complexity measures were estimated from these distributions. Mean values of entropy and complexity were compared and their classification power was assessed by evaluating the best linear separator on the corresponding complexity–entropy planes. Main results. Few linear separators obtained significantly better classification, evaluated as the area under the receiver operating characteristic curve, than signal mean values. However, significant differences in both entropy and complexity were detected between the groups of participants. Significance. Measures of entropy and complexity were able to detect differences between healthy volunteers and subjects with Parkinson's disease, in poikilocapnic conditions, even though only short recordings were available for analysis. Further work is needed to refine this promising approach, and to help understand the findings in the context of specific pathophysiological changes.

A primer on entropy in neuroscience

“…Since then, entropy has been adopted as a measure of the information processing capacity of neural systems (Bergström and Nevanlinna, 1972;Borst and Theunissen, 1999;Hancock et al, 2022;Keshmiri, 2020;Saxe et al, 2018;Strong et al, 1998;Wang et al, 2014;Yuan et al, 2003). Furthermore, entropy is used to quantify the repertoire of brain states (Ahmed et al, 2011;Monteforte and Wolf, 2010;Song and Zhang, 2016;Tagliazucchi et al, 2014;Vivot et al, 2020), the effect of disease (Drachman, 2006;Gómez and Hornero, 2010;Jara et al, 2021;Kannathal et al, 2005;Morabito et al, 2012;Sharanreddy and Kulkarni, 2013;Takahashi, 2013;Thomas et al, 2015;van Drongelen et al, 2003;Wang et al, 2017;Wu et al, 2017;Zhou et al, 2016), and more recently the influence of psychedelics (Carhart-Harris, 2018;Carhart-Harris et al, 2014;Herzog et al, 2020;Lebedev et al, 2016;Viol et al, 2017).…”

Multiscale entropy analysis of retinal signals reveals reduced complexity in a mouse model of Alzheimer’s disease

“…Recent evidence supports this theory as, for instance, entropy measures of cerebral blood flow velocity decayed in altered states 27 , actigraphy data exhibited lower complexity in depressed subjects as compared to non-depressed controls 28 , and complexity of EEG signals from subjects with AD correlated well with performance in working memory tasks 29 . MSE analysis can have significant implications in biomedical research as several studies have demonstrated its application to detection of heartbeat irregularity 22 , 30 , postural control alterations 31 and postoperative neurocognitive dysfunction 32 , and diagnosis of depression 28 , Parkinson’s disease 33 , schizophrenia 34 , and AD 35 .…”

Multiscale Entropy Analysis of Retinal Signals Reveals Reduced Complexity in a Mouse Model of Alzheimer’s Disease

“…Recent evidence seems to support this theory as, for instance, entropy measures of cerebral blood flow velocity decayed in altered states 27 , actigraphy data exhibited lower complexity in depressed subjects as compared to non-depressed controls 28 , and complexity of EEG signals from subjects with AD correlated well with performance in working memory tasks 29 . MSE analysis can have significant implications in biomedical research as several studies have demonstrated its application to detection of heartbeat irregularity 22,30 , postural control alterations 31 and postoperative neurocognitive dysfunction 32 , and diagnosis of depression 28 , Parkinson's disease 33 , schizophrenia 34 , and AD 35 .…”