Faculty Opinions recommendation of Permutation entropy of the electroencephalogram: a measure of anaesthetic drug effect.

2008 ◽  
Author(s):  
Michael Avidan
Keyword(s):  
Drug Effect ◽  
Permutation Entropy ◽  
Anaesthetic Drug

scholarly journals Permutation entropy of the electroencephalogram: a measure of anaesthetic drug effect

2008 ◽  
Vol 101 (6) ◽  
pp. 810-821 ◽  
Author(s):  
E. Olofsen ◽  
J.W. Sleigh ◽  
A. Dahan
Keyword(s):  
Drug Effect ◽  
Permutation Entropy ◽  
Anaesthetic Drug

Parameter selection in permutation entropy for an electroencephalographic measure of isoflurane anesthetic drug effect

2012 ◽  
Vol 27 (2) ◽  
pp. 113-123 ◽  
Author(s):  
Duan Li ◽  
Zhenhu Liang ◽  
Yinghua Wang ◽  
Satoshi Hagihira ◽  
Jamie W. Sleigh ◽  
...  
Keyword(s):  
Drug Effect ◽  
Parameter Selection ◽  
Permutation Entropy ◽  
Anesthetic Drug

Comparison of BIS and AAI as measures of anaesthetic drug effect during desflurane-remifentanil anaesthesia

2004 ◽  
Vol 48 (9) ◽  
pp. 1168-1173 ◽  
Author(s):  
S. Kreuer ◽  
J. Bruhn ◽  
R. Larsen ◽  
C. Bauer ◽  
W. Wilhelm
Keyword(s):  
Drug Effect ◽  
Anaesthetic Drug

Using Permutation Entropy to Measure the Electroencephalographic Effects of Sevoflurane

2008 ◽  
Vol 109 (3) ◽  
pp. 448-456 ◽  
Author(s):  
Xiaoli Li ◽  
Suyuan Cui ◽  
Logan J. Voss
Keyword(s):  
Dose Response ◽  
Drug Effect ◽  
Brain Activity ◽  
Approximate Entropy ◽  
Permutation Entropy ◽  
Pharmacodynamic Modeling ◽  
Anesthetic Drug ◽  
Effect Site ◽  
Prediction Probability ◽  
Effect Site Concentration

Background Approximate entropy (AE) has been proposed as a measure of anesthetic drug effect in electroencephalographic data. Recently, a new method called permutation entropy (PE) based on symbolic dynamics was also proposed to measure the complexity in an electroencephalographic series. In this study, the AE and PE were applied to electroencephalographic recordings for revealing the effect of sevoflurane on brain activity. The dose-response relation of PE during sevoflurane anesthesia was compared with that of AE. Methods Nineteen patients' electroencephalographic data were collected during the induction of general anesthesia with sevoflurane. PE and AE were applied to the electroencephalographic recordings, and the performance of both measures was assessed by pharmacokinetic-pharmacodynamic modeling and prediction probability. To ensure an accurate complexity measure of electroencephalographic recordings, a wavelet-based preprocessor was built in advance. Results Both PE and AE could distinguish between the awake and anesthetized states and were highly correlated to each other (r = 0.8, P = 0.004). The pharmacokinetic-pharmacodynamic model adequately described the dose-response relation between PE and AE and sevoflurane effect site concentration. The coefficient R between PE and effect site concentration was 0.89 +/- 0.07 for all patients, compared with 0.60 +/- 0.14 for AE. Prediction probabilities of 0.86 +/- 0.04 and 0.79 +/- 0.09 for PE and AE showed that PE has a stronger ability to differentiate between the awake and anesthetic states. Conclusion The results show that PE can estimate the sevoflurane drug effect more effectively than AE. This method could be applied to design a new electroencephalographic monitoring system to estimate sevoflurane anesthetic drug effect.

Predictors of onset and offset of drug effect

2001 ◽  
Vol 18 (Suppl. 23) ◽  
pp. 26-31 ◽  
Author(s):  
T. W. Schnider ◽  
C. F. Minto
Keyword(s):  
Drug Effect

Predictive biomarkers for linking disease pathology and drug effect

2016 ◽  
Vol 22 (999) ◽  
pp. 1-1
Author(s):  
Bernd Mayer ◽  
Andreas Heinzel ◽  
Arno Lukas ◽  
Paul Perco
Keyword(s):  
Drug Effect ◽  
Predictive Biomarkers

How Do People Taking Psychiatric Drugs Explain Their “Chemical Imbalance?”

2012 ◽  
Vol 13 (3) ◽  
pp. 176-189 ◽  
Author(s):  
David Cohen ◽  
Shannon Hughes
Keyword(s):  
Drug Effect ◽  
Symptom Relief ◽  
Institutional Support ◽  
Mental Illnesses ◽  
Personal Meaning ◽  
Mental Health Practitioners ◽  
Psychiatric Drugs ◽  
Health Practitioners ◽  
Chemical Imbalance ◽  
Post Hoc

Many people believe that chemical imbalances cause mental illnesses, despite the absence of evidence to ascertain this. This study describes the reasoning that people use in their own case to justify this belief. Data come from recorded medication histories with 22 adults aged 23–68 years, taking different psychiatric drugs for various problems and varying durations, asked directly if they thought their problem was caused by a chemical imbalance and to explain their answer. About two-thirds expressed belief that they had a chemical imbalance; and the rest that they did not have one, did not or could not know, or that their medication had caused one. Reasoning backward from positive drug experiences (ex juvantibus or post hoc) and appeals to authority and convention characterized most answers expressing belief in an imbalance. Experiencing improvement while taking drugs and acquiescing in mental health practitioners’ views instills or reinforces people’s belief that they are or were chemically imbalanced, which suggests viewing the belief as a drug effect. The chemical imbalance notion is likely to persist, as its appeal to give personal meaning to symptom relief and its unfalsifiability ensure institutional support that neutralizes the absence of scientific support.

scholarly journals Employing entropy measures to identify visitors in multi-occupancy environments

2020 ◽  
Author(s):  
Aadel Howedi ◽  
Ahmad Lotfi ◽  
Amir Pourabdollah
Keyword(s):  
Home Environment ◽  
Daily Living ◽  
Wearable Sensors ◽  
Permutation Entropy ◽  
Care Workers ◽  
Entropy Measures ◽  
Novel Method ◽  
High Degree ◽  
Motion Detectors ◽  
To Receive

AbstractHuman activity recognition (HAR) is used to support older adults to live independently in their own homes. Once activities of daily living (ADL) are recognised, gathered information will be used to identify abnormalities in comparison with the routine activities. Ambient sensors, including occupancy sensors and door entry sensors, are often used to monitor and identify different activities. Most of the current research in HAR focuses on a single-occupant environment when only one person is monitored, and their activities are categorised. The assumption that home environments are occupied by one person all the time is often not true. It is common for a resident to receive visits from family members or health care workers, representing a multi-occupancy environment. Entropy analysis is an established method for irregularity detection in many applications; however, it has been rarely applied in the context of ADL and HAR. In this paper, a novel method based on different entropy measures, including Shannon Entropy, Permutation Entropy, and Multiscale-Permutation Entropy, is employed to investigate the effectiveness of these entropy measures in identifying visitors in a home environment. This research aims to investigate whether entropy measures can be utilised to identify a visitor in a home environment, solely based on the information collected from motion detectors [e.g., passive infra-red] and door entry sensors. The entropy measures are tested and evaluated based on a dataset gathered from a real home environment. Experimental results are presented to show the effectiveness of entropy measures to identify visitors and the time of their visits without the need for employing extra wearable sensors to tag the visitors. The results obtained from the experiments show that the proposed entropy measures could be used to detect and identify a visitor in a home environment with a high degree of accuracy.

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