A Patient-Specific Machine Learning based EEG Processor for Accurate Estimation of Depth of Anesthesia

Khan, Fatima; Ashraf, Usman; Altaf, Muhammad Awais Bin; Saadeh, Wala

doi:10.1109/biocas.2018.8584828

Cited by 17 publications

(3 citation statements)

References 11 publications

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“…There are dozens of algorithms in ML, including deep learning, decision trees, clustering, and Bayesian. For example, the use of decision trees to monitor the depth of anesthesia is a type of ML [72]. Artificial neural networks (ANNs) are mathematical models of information processing based on structures similar to the brain's synaptic connections.…”

Section: Ai For Extracting and Quantitating The Feature Information O...mentioning

confidence: 99%

Integrated Optical Coherence Tomography and Deep Learning for Evaluating of the Injectable Hydrogel on Skin Wound Healing

Zhao¹,

Chen²

2023

Wound Healing - Recent Advances and Future Opportunities

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Recently hydrogels and the treatment of skin wounds based on hydrogel dressings have become one of the research hotspots in the field of skin trauma. In this chapter, we focus on the materials and methods of hydrogel preparation, and discuss the properties that hydrogels should possess for the treatment of wounds. Moreover, we discuss the potential of non-invasive optical imaging techniques in the assessment of cutaneous wound healing. The research results of the application of non-invasive optical techniques such as diffuse reflectance spectroscopy (DRS) and optical coherence tomography (OCT) in scar identification, skin bruising, and skin and vascular structure identification are reviewed. Furthermore, we further discuss the superiority and potential of current artificial intelligence (AI) technology in dermatological diagnosis, and analyze the application status of hydrogel in skin wound treatment. Finally, we believe that the combination of AI and optical imaging technology in the development and efficacy monitoring of hydrogels will be a promising research direction in the future.

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Section: Ai For Extracting and Quantitating The Feature Information O...mentioning

confidence: 99%

Integrated Optical Coherence Tomography and Deep Learning for Evaluating of the Injectable Hydrogel on Skin Wound Healing

Zhao¹,

Chen²

2023

Wound Healing - Recent Advances and Future Opportunities

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“…Therefore, EEGs have been widely used in emotion recognition, depression detection, DoA estimation, etc. [ 4 , 5 , 6 , 7 , 8 , 9 , 10 ].…”

Section: Introductionmentioning

confidence: 99%

Estimating the Depth of Anesthesia from EEG Signals Based on a Deep Residual Shrinkage Network

Shi

Huang

Xiao

et al. 2023

Sensors

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The reliable monitoring of the depth of anesthesia (DoA) is essential to control the anesthesia procedure. Electroencephalography (EEG) has been widely used to estimate DoA since EEG could reflect the effect of anesthetic drugs on the central nervous system (CNS). In this study, we propose that a deep learning model consisting mainly of a deep residual shrinkage network (DRSN) and a 1 × 1 convolution network could estimate DoA in terms of patient state index (PSI) values. First, we preprocessed the four raw channels of EEG signals to remove electrical noise and other physiological signals. The proposed model then takes the preprocessed EEG signals as inputs to predict PSI values. Then we extracted 14 features from the preprocessed EEG signals and implemented three conventional feature-based models as comparisons. A dataset of 18 patients was used to evaluate the models’ performances. The results of the five-fold cross-validation show that there is a relatively high similarity between the ground-truth PSI values and the predicted PSI values of our proposed model, which outperforms the conventional models, and further, that the Spearman’s rank correlation coefficient is 0.9344. In addition, an ablation experiment was conducted to demonstrate the effectiveness of the soft-thresholding module for EEG-signal processing, and a cross-subject validation was implemented to illustrate the robustness of the proposed method. In summary, the procedure is not merely feasible for estimating DoA by mimicking PSI values but also inspired us to develop a precise DoA-estimation system with more convincing assessments of anesthetization levels.

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“…The more advanced subset of AI algorithms in the form of machine learning (ML) addressed the complex problems of AI and showed a promising way forward because of its ability to make autonomous decisions based on previous learning and the scenarios at hand [9]. The algorithms used in ML can find applications in diverse use cases, such as the use of decision trees to monitor the depth of anesthesia [10] and support vector machines (SVM) in financial research [11]. Artificial neural networks (ANN) have not only outperformed other ML algorithms but also surpassed human intelligence in specific tasks, e.g., image classification on an ImageNet dataset [12].…”

Section: Introductionmentioning

confidence: 99%

Advancements in Microprocessor Architecture for Ubiquitous AI—An Overview on History, Evolution, and Upcoming Challenges in AI Implementation

2021

Self Cite

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Artificial intelligence (AI) has successfully made its way into contemporary industrial sectors such as automobiles, defense, industrial automation 4.0, healthcare technologies, agriculture, and many other domains because of its ability to act autonomously without continuous human interventions. However, this capability requires processing huge amounts of learning data to extract useful information in real time. The buzz around AI is not new, as this term has been widely known for the past half century. In the 1960s, scientists began to think about machines acting more like humans, which resulted in the development of the first natural language processing computers. It laid the foundation of AI, but there were only a handful of applications until the 1990s due to limitations in processing speed, memory, and computational power available. Since the 1990s, advancements in computer architecture and memory organization have enabled microprocessors to deliver much higher performance. Simultaneously, improvements in the understanding and mathematical representation of AI gave birth to its subset, referred to as machine learning (ML). ML includes different algorithms for independent learning, and the most promising ones are based on brain-inspired techniques classified as artificial neural networks (ANNs). ANNs have subsequently evolved to have deeper and larger structures and are often characterized as deep neural networks (DNN) and convolution neural networks (CNN). In tandem with the emergence of multicore processors, ML techniques started to be embedded in a range of scenarios and applications. Recently, application-specific instruction-set architecture for AI applications has also been supported in different microprocessors. Thus, continuous improvement in microprocessor capabilities has reached a stage where it is now possible to implement complex real-time intelligent applications like computer vision, object identification, speech recognition, data security, spectrum sensing, etc. This paper presents an overview on the evolution of AI and how the increasing capabilities of microprocessors have fueled the adoption of AI in a plethora of application domains. The paper also discusses the upcoming trends in microprocessor architectures and how they will further propel the assimilation of AI in our daily lives.

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A Patient-Specific Machine Learning based EEG Processor for Accurate Estimation of Depth of Anesthesia

Cited by 17 publications

References 11 publications

Integrated Optical Coherence Tomography and Deep Learning for Evaluating of the Injectable Hydrogel on Skin Wound Healing

Integrated Optical Coherence Tomography and Deep Learning for Evaluating of the Injectable Hydrogel on Skin Wound Healing

Estimating the Depth of Anesthesia from EEG Signals Based on a Deep Residual Shrinkage Network

Advancements in Microprocessor Architecture for Ubiquitous AI—An Overview on History, Evolution, and Upcoming Challenges in AI Implementation

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