Measuring Complexity of Biomedical Signals

Schlotthauer, Gastón; Humeau-Heurtier, Anne; Escudero, Javier; Rufiner, Hugo Leonardo

doi:10.1155/2018/5408254

Cited by 3 publications

(3 citation statements)

References 10 publications

(10 reference statements)

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“…From this, these measures were proposed in order to exploit this difference in the regularity or complexity of the data. These measures have proven useful in several works on biomedical signals [50]. Further, our previous work [51] showed the potential of these features for classification.…”

Section: Methodsmentioning

confidence: 94%

Sleep-wake stages classification using heart rate signals from pulse oximetry

Casal

Persia

Schlotthauer

2019

Heliyon

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The most important index of obstructive sleep apnea/hypopnea syndrome (OSAHS) is the apnea/hyponea index (AHI). The AHI is the number of apnea/hypopnea events per hour of sleep. Algorithms for the screening of OSAHS from pulse oximetry estimate an approximation to AHI counting the desaturation events without consider the sleep stage of the patient. This paper presents an automatic system to determine if a patient is awake or asleep using heart rate (HR) signals provided by pulse oximetry. In this study, 70 features are estimated using entropy and complexity measures, frequency domain and time-scale domain methods, and classical statistics. The dimension of feature space is reduced from 70 to 40 using three different schemes based on forward feature selection with support vector machine and feature importance with random forest. The algorithms were designed, trained and tested with 5000 patients from the Sleep Heart Health Study database. In the test stage, 10-fold cross validation method was applied obtaining performances up to 85.2% accuracy, 88.3% specificity, 79.0% sensitivity, 67.0% positive predictive value, and 91.3% negative predictive value. The results are encouraging, showing the possibility of using HR signals obtained from the same oximeter to determine the sleep stage of the patient, and thus potentially improving the estimation of AHI based on only pulse oximetry.

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Section: Methodsmentioning

confidence: 94%

Sleep-wake stages classification using heart rate signals from pulse oximetry

Casal

Persia

Schlotthauer

2019

Heliyon

Self Cite

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“…pathological conditions of the underlying physiological system (Angelini et al 2007, Schlotthauer et al 2018. Indeed, subjects with IPD without medication exhibited less complexity than HC, which in turn showed less complexity than medicated IPD subjects.…”

Section: Hc-onmentioning

confidence: 99%

“…Of these, the analysis of signal entropy and complexity has been gaining popularity as it is becoming increasingly clear that most physiological systems are complex dynamical systems, a quality that allows living systems to adapt to external perturbations and preserve homeostasis (Castiglioni et al 2020). There are many examples in which measurements of entropy and/or complexity of biomedical signals have helped to detect underlying changes in physiology that were, in turn, associated with pathological events (Gao et al 2012, Schlotthauer et al 2018. Although fewer in number, some studies have also shown that the approach can be applied to short signals (Wu et al 2013, Yentes et al 2013, Borin et al 2020.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

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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.

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