Nonlinear Analysis of Physiological Signals: A Review

Faust, Oliver; Bairy, Muralidhar G.

doi:10.1142/s0219519412400155

Cited by 84 publications

(91 citation statements)

References 111 publications

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“…These regularity-based metrics are easy to compute and work successfully even for very short and noisy time series [38]. Moreover, they have proven to be useful in numerous clinical scenarios [33,34]. However, these entropy measures ignore the temporal order of data.…”

Section: Shock Outcome Predictors Based On Entropy Measuresmentioning

confidence: 99%

“…Indeed, entropy can be defined as a measure of disorder or uncertainty in a system, and biological systems are characterized by complex dynamics [33]. Thus, these metrics have been able to reveal useful clinical information about how the brain, the heart and other complex systems work under very common diseases [34]. Our main hypothesis is that measures of entropy, appropriately chosen and characterized to quantify VF regularity/complexity, may serve as reliable shock outcome predictors.…”

Section: Introductionmentioning

confidence: 99%

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Application of Entropy-Based Features to Predict Defibrillation Outcome in Cardiac Arrest

Chicote

Irusta

Alcaraz

et al. 2016

Entropy

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Prediction of defibrillation success is of vital importance to guide therapy and improve the survival of patients suffering out-of-hospital cardiac arrest (OHCA). Currently, the most efficient methods to predict shock success are based on the analysis of the electrocardiogram (ECG) during ventricular fibrillation (VF), and recent studies suggest the efficacy of waveform indices that characterize the underlying non-linear dynamics of VF. In this study we introduce, adapt and fully characterize six entropy indices for VF shock outcome prediction, based on the classical definitions of entropy to measure the regularity and predictability of a time series. Data from 163 OHCA patients comprising 419 shocks (107 successful) were used, and the performance of the entropy indices was characterized in terms of embedding dimension (m) and matching tolerance (r). Six classical predictors were also assessed as baseline prediction values. The best prediction results were obtained for fuzzy entropy (FuzzEn) with m = 3 and an amplitude-dependent tolerance of r = 80 µV. This resulted in a balanced sensitivity/specificity of 80.4%/76.9%, which improved by over five points the results obtained for the best classical predictor. These results suggest that a FuzzEn approach for a joint quantification of VF amplitude and its non-linear dynamics may be a promising tool to optimize OHCA treatment.

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Section: Shock Outcome Predictors Based On Entropy Measuresmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Application of Entropy-Based Features to Predict Defibrillation Outcome in Cardiac Arrest

Chicote

Irusta

Alcaraz

et al. 2016

Entropy

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show abstract

“…The features extract as well as condense the information and present it as a parameter value [121]. Statistical methods, such as Analysis Of Variance (ANOVA) with p-and F -values, can be used to select and rank the features.…”

Section: Feature Extraction and Assessmentmentioning

confidence: 99%

Computer aided diagnosis of Coronary Artery Disease, Myocardial Infarction and carotid atherosclerosis using ultrasound images: A review

et al. 2017

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“…Hence, it is necessary to translate that link into tangible improvements for patients as well as cost savings for society. CAD systems are a well-known strategy to realize the diagnostic potential of physiological measurements, such as ECG [34,35].…”

Section: Computer Aided Diagnosis Systemsmentioning

confidence: 99%

A Review of Ecg-Based Diagnosis Support Systems for Obstructive Sleep Apnea

Faust

Acharya

et al. 2016

J. Mech. Med. Biol.

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Humans need sleep. It is important for physical and psychological recreation. During sleep our consciousness is suspended or least altered. Hence, our ability to avoid or react to disturbances is reduced. These disturbances can come from external sources or from disorders within the body. Obstructive Sleep Apnea (OSA) is such a disorder. It is caused by obstruction of the upper airways which causes periods where the breathing ceases. In many cases, periods of reduced breathing, known as hypopnea, precede OSA events. The medical background of OSA is well understood, but the traditional diagnosis is expensive, as it requires sophisticated measurements and human interpretation of potentially large amounts of physiological data. Electrocardiogram (ECG) measurements have the potential to reduce the cost of OSA diagnosis by simplifying the measurement process. On the down side, detecting OSA events based on ECG data is a complex task which requires highly skilled practitioners. Computer algorithms can help to detect the subtle signal changes which indicate the presence of a disorder. That approach has the following advantages: computers never tire, processing resources are economical and progress, in the form of better algorithms, can be easily disseminated as updates over the internet. Furthermore, Computer-Aided Diagnosis (CAD) reduces intra- and inter-observer variability. In this review, we adopt and support the position that computer based ECG signal interpretation is able to diagnose OSA with a high degree of accuracy.

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Nonlinear Analysis of Physiological Signals: A Review

Cited by 84 publications

References 111 publications

Application of Entropy-Based Features to Predict Defibrillation Outcome in Cardiac Arrest

Application of Entropy-Based Features to Predict Defibrillation Outcome in Cardiac Arrest

Computer aided diagnosis of Coronary Artery Disease, Myocardial Infarction and carotid atherosclerosis using ultrasound images: A review

A Review of Ecg-Based Diagnosis Support Systems for Obstructive Sleep Apnea

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