Neuromorphic on-chip recognition of saliva samples of COPD and healthy controls using memristive devices

Zarrin, Pouya Soltani; Zahari, Finn; Mahadevaiah, Mamathamba Kalishettyhalli; Pérez, Eduardo; Kohlstedt, H.; Wenger, Christian

doi:10.1038/s41598-020-76823-7

Cited by 14 publications

(14 citation statements)

References 50 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These results represent that increasing the value of k in the k-fold CV strategy can increase the performance rates of classification algorithms. Nonetheless, Zarrin et al (10,11) failed to investigate this issue in their studies.…”

Section: Discussionmentioning

confidence: 99%

“…The present system was evaluated on the publicly available data, namely, the Exasens dataset (9)(10)(11). The database includes some attributes of 4 sample groups.…”

Section: Datamentioning

confidence: 99%

“…The smoking status, weight, age, gender, cytokine level, and pathogen load are some important issues in this regard (9)(10)(11).…”

mentioning

confidence: 99%

“…In this respect, the current study sought to evaluate the performance of several ML algorithms in the classification of the saliva samples of COPD and healthy volunteers. For this purpose, the works by Zarrin et al (10,11) were extended by implementing different structures of the DT algorithm, Feed-forward neural network (FNN), probabilistic neural network (PNN), layer recurrent neural network (LRNN), Elman neural network (ENN), a generalized regression neural network (GRNN), different ensemble methods (EMs), k-nearest neighbor (kNN), SVM, and Naive Bayes (NB). Figure 1 shows the proposed diagnostic framework.…”

mentioning

confidence: 99%

See 3 more Smart Citations

The Potential of Machine Learning Algorithms in Discriminating Chronic Obstructive Pulmonary Disease and Healthy Saliva Samples

Goshvarpour

2021

Dis Diagn

View full text Add to dashboard Cite

Background: Today, with the spread of tobacco use and increased environmental pollutions, respiratory diseases are considered important factors threatening human life. Chronic obstructive pulmonary disease (COPD) is a kind of inflammatory lung disease. Clinically, COPD is currently diagnosed and monitored by spirometry as the gold-standard technique although spirometry systems encounter some limitations. Thanks to the economical handling and sampling, practicality, and non-invasiveness of saliva biomarkers, it is promising for the testing environment. Accordingly, the current analytic observational study aimed to propose an intelligent system for COPD detection. Materials and Methods: To this end, 40 COPD (8 females and 32 males in the age range of 71.67±8.27 years) and 40 controls (17 females and 23 males within the age range of 38.23±14.05 years) were considered in this study. The samples were characterized by absolute minimum value and the average value of the real and imaginary parts of saliva permittivity. Additionally, the age, gender, and smoking status of the participants were determined, and then the performance of various classifiers was evaluated by adjusting k in k-fold cross-validation (CV) and classifier parameterization. Results: The results showed that the k-nearest neighbor outperformed other classifiers. Using both 8- and 10-fold CV, the maximum classification rates of 100% were achieved for all k values. On the other hand, increasing the k in k-fold CV improved classification performances. The positive role of parameterization was revealed as well. Conclusions: Overall, these findings authenticated the potential of machine learning (ML) algorithms in the diagnosis of COPD using subjects’ saliva features and demographic information.

show abstract

Section: Discussionmentioning

confidence: 99%

“…The present system was evaluated on the publicly available data, namely, the Exasens dataset (9)(10)(11). The database includes some attributes of 4 sample groups.…”

Section: Datamentioning

confidence: 99%

“…The smoking status, weight, age, gender, cytokine level, and pathogen load are some important issues in this regard (9)(10)(11).…”

mentioning

confidence: 99%

mentioning

confidence: 99%

See 2 more Smart Citations

The Potential of Machine Learning Algorithms in Discriminating Chronic Obstructive Pulmonary Disease and Healthy Saliva Samples

Goshvarpour

2021

Dis Diagn

View full text Add to dashboard Cite

show abstract

“…In general the use of memristors represents an interesting possibility for the fabrication of systems able to encode synaptic weights directly in their conductance and thus achieve substantial speedup and power reduction compared to standard digital hardware [15,24]. Complex ANN based on perceptrons organized in crossbar array configurations [25] or with a single-memristor-layer input, using backpropagation algorithms, have been reported [15,24].…”

Section: Introductionmentioning

confidence: 99%

A binary classifier based on a reconfigurable dense network of metallic nanojunctions

Mirigliano

Paroli

Martini

et al. 2021

Neuromorph. Comput. Eng.

View full text Add to dashboard Cite

Major efforts to reproduce the brain performances in terms of classification and pattern recognition have been focused on the development of artificial neuromorphic systems based on topdown lithographic technologies typical of highly integrated components of digital computers. Unconventional computing has been proposed as an alternative exploiting the complexity and collective phenomena originating from various classes of physical substrates. Materials composed of a large number of non-linear nanoscale junctions are of particular interest: these systems, obtained by the self-assembling of nano-objects like nanoparticles and nanowires, results in non-linear conduction properties characterized by spatiotemporal correlation in their electrical activity. This appears particularly useful for classification of complex features: nonlinear projection into a high-dimensional space can make data linearly separable, providing classification solutions that are computationally very expensive with digital computers. Recently we reported that nanostructured Au films fabricated from the assembling of gold clusters by supersonic cluster beam deposition show a complex resistive switching behaviour. Their non-linear electric behaviour is remarkably stable and reproducible allowing the facile training of the devices on precise resistive states. Here we report about the fabrication and characterization of a device that allows the binary classification of Boolean functions by exploiting the properties of cluster-assembled Au films interconnecting a generic pattern of electrodes. This device, that constitutes a generalization of the perceptron, can receive inputs from different electrode configurations and generate a complete set of Boolean functions of n variables for classification tasks. We also show that the non-linear and non-local electrical conduction of clusterassembled gold films, working at room temperature, allows the classification of non-linearly separable functions without previous training of the device.

show abstract

Metaverse Wearables for Immersive Digital Healthcare: A Review

Kim,

Yang,

Lee

et al. 2023

Advanced Science

View full text Add to dashboard Cite

The recent exponential growth of metaverse technology has been instrumental in reshaping a myriad of sectors, not least digital healthcare. This comprehensive review critically examines the landscape and future applications of metaverse wearables toward immersive digital healthcare. The key technologies and advancements that have spearheaded the metamorphosis of metaverse wearables are categorized, encapsulating all‐encompassed extended reality, such as virtual reality, augmented reality, mixed reality, and other haptic feedback systems. Moreover, the fundamentals of their deployment in assistive healthcare (especially for rehabilitation), medical and nursing education, and remote patient management and treatment are investigated. The potential benefits of integrating metaverse wearables into healthcare paradigms are multifold, encompassing improved patient prognosis, enhanced accessibility to high‐quality care, and high standards of practitioner instruction. Nevertheless, these technologies are not without their inherent challenges and untapped opportunities, which span privacy protection, data safeguarding, and innovation in artificial intelligence. In summary, future research trajectories and potential advancements to circumvent these hurdles are also discussed, further augmenting the incorporation of metaverse wearables within healthcare infrastructures in the post‐pandemic era.

show abstract

Neuromorphic on-chip recognition of saliva samples of COPD and healthy controls using memristive devices

Cited by 14 publications

References 50 publications

The Potential of Machine Learning Algorithms in Discriminating Chronic Obstructive Pulmonary Disease and Healthy Saliva Samples

The Potential of Machine Learning Algorithms in Discriminating Chronic Obstructive Pulmonary Disease and Healthy Saliva Samples

A binary classifier based on a reconfigurable dense network of metallic nanojunctions

Metaverse Wearables for Immersive Digital Healthcare: A Review

Contact Info

Product

Resources

About