E-Nose System for Anesthetic Dose Level Detection using Artificial Neural Network

Saraoğlu, Hamdi Melih; Edin, Burçak

doi:10.1007/s10916-007-9087-7

Cited by 28 publications

(15 citation statements)

References 13 publications

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“…Saraoğlu et al [447,448] addressed the problem of anesthesia levels during surgeries. The effects of applied anesthesia on a patient, known as the depth of anesthesia, can change according to the chemicals used and characteristics of the patient.…”

Section: Biomedical Research Developments For Electronic Nosesmentioning

confidence: 99%

Advances in Electronic-Nose Technologies Developed for Biomedical Applications

Wilson

Baietto

2011

Sensors

332

236

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The research and development of new electronic-nose applications in the biomedical field has accelerated at a phenomenal rate over the past 25 years. Many innovative e-nose technologies have provided solutions and applications to a wide variety of complex biomedical and healthcare problems. The purposes of this review are to present a comprehensive analysis of past and recent biomedical research findings and developments of electronic-nose sensor technologies, and to identify current and future potential e-nose applications that will continue to advance the effectiveness and efficiency of biomedical treatments and healthcare services for many years. An abundance of electronic-nose applications has been developed for a variety of healthcare sectors including diagnostics, immunology, pathology, patient recovery, pharmacology, physical therapy, physiology, preventative medicine, remote healthcare, and wound and graft healing. Specific biomedical e-nose applications range from uses in biochemical testing, blood-compatibility evaluations, disease diagnoses, and drug delivery to monitoring of metabolic levels, organ dysfunctions, and patient conditions through telemedicine. This paper summarizes the major electronic-nose technologies developed for healthcare and biomedical applications since the late 1980s when electronic aroma detection technologies were first recognized to be potentially useful in providing effective solutions to problems in the healthcare industry.

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Section: Biomedical Research Developments For Electronic Nosesmentioning

confidence: 99%

Advances in Electronic-Nose Technologies Developed for Biomedical Applications

Wilson

Baietto

2011

Sensors

332

236

View full text Add to dashboard Cite

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“…21 Therefore, mean relative absolute error (RAE) is frequently used to test both robustness and appropriateness of the NN architectures. 5,18,21 In this study, 75% of these data is used for training and 25% is used for testing. Four steps are followed in classification as follows: (1) Here, the architectures of X-X-3 and X-X-X-3 architectures are used where X is 5, 10, and 20.…”

Section: Classification Approachesmentioning

confidence: 99%

Log Energy Entropy-Based EEG Classification with Multilayer Neural Networks in Seizure

2009

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In this study, normal EEG series recorded from healthy volunteers and epileptic EEG series recorded from patients within and without seizure are classified by using Multilayer Neural Network (MLNN) architectures with respect to several time domain entropy measures such as Shannon Entropy (ShanEn), Log Energy Entropy (LogEn), and Sample Entropy (Sampen). In tests, the MLNN is performed with several numbers of neurons for both one hidden layer and two hidden layers. The results show that segments in seizure have significantly lower entropy values than normal EEG series. This result indicates an important increase of EEG regularity in epilepsy patients. The LogEn approach, which has not been experienced in EEG classification yet, provides the most reliable features into the EEG classification with very low absolute error as 0.01. In particular, the MLNN can be proposed to distinguish the seizure activity from the seizure-free epileptic series where the LogEn values are considered as signal features that characterize the degree of EEG complexity. The highest classification accuracy is obtained for one hidden layer architecture.

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“…However, the depth of anesthesia may vary depending on patient's condition (age, weight, etc.) and anesthetic agents [2]. It is important to measure accurately anesthetic dose, which applied to the patient during operation.…”

Section: Introductionmentioning

confidence: 99%

“…Saraoglu and Edin [2] have developed e-nose system to estimate the anesthetic dose level. This system determines gas concentration based on steady-state responses of the sensors [27][28][29].…”

Section: Introductionmentioning

confidence: 99%

Determining the Appropriate Amount of Anesthetic Gas Using DWT and EMD Combined with Neural Network

et al. 2014

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The spectrum of EEG has been studied to predict the depth of anesthesia using variety of signal processing methods up to date. Those standard models have used the full spectrum of EEG signals together with the systolic-diastolic pressure and pulse values. As it is generally agreed today that the brain is in stable state and the delta-theta bands of the EEG spectrum remain active during anesthesia. Considering this background, two questions that motivates this paper. First, determining the amount of gas to be administered is whether feasable from the spectrum of EEG during the maintenance stage of surgical operations. Second, more specifically, the delta-theta bands of the EEG spectrum are whether sufficient alone for this aim. This research aims to answer these two questions together. Discrete wavelet transformation (DWT) and empirical mode decomposition (EMD) were applied to the EEG signals to extract delta-theta bands. The power density spectrum (PSD) values of target bands were presented as inputs to multi-layer perceptron (MLP) neural network (NN), which predicted the gas level. The present study has practical implications in terms of using less data, in an effective way and also saves time as well.

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E-Nose System for Anesthetic Dose Level Detection using Artificial Neural Network

Cited by 28 publications

References 13 publications

Advances in Electronic-Nose Technologies Developed for Biomedical Applications

Advances in Electronic-Nose Technologies Developed for Biomedical Applications

Log Energy Entropy-Based EEG Classification with Multilayer Neural Networks in Seizure

Determining the Appropriate Amount of Anesthetic Gas Using DWT and EMD Combined with Neural Network

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