Ensemble-Based Machine Learning Algorithms for Classifying Breast Tissue Based on Electrical Impedance Spectroscopy

2019 ◽  
pp. 260-266
Author(s):  
Sam Matiur Rahman ◽  
Md Asraf Ali ◽  
Omar Altwijri ◽  
Mahdi Alqahtani ◽  
Nasim Ahmed ◽  
...  
Keyword(s):  
Machine Learning ◽  
Impedance Spectroscopy ◽  
Electrical Impedance ◽  
Breast Tissue ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Electrical Impedance Spectroscopy

scholarly journals Comparison of Selected Machine Learning Algorithms for Industrial Electrical Tomography

Sensors ◽  
2019 ◽  
Vol 19 (7) ◽  
pp. 1521 ◽  
Author(s):  
Tomasz Rymarczyk ◽  
Grzegorz Kłosowski ◽  
Edward Kozłowski ◽  
Paweł Tchórzewski
Keyword(s):  
Machine Learning ◽  
Electrical Impedance Tomography ◽  
Newton Method ◽  
Electrical Impedance ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Electrical Tomography ◽  
Learning Methods ◽  
Impedance Tomography ◽  
Machine Learning Methods

The main goal of this work was to compare the selected machine learning methods with the classic deterministic method in the industrial field of electrical impedance tomography. The research focused on the development and comparison of algorithms and models for the analysis and reconstruction of data using electrical tomography. The novelty was the use of original machine learning algorithms. Their characteristic feature is the use of many separately trained subsystems, each of which generates a single pixel of the output image. Artificial Neural Network (ANN), LARS and Elastic net methods were used to solve the inverse problem. These algorithms have been modified by a corresponding increase in equations (multiply) for electrical impedance tomography using the finite element method grid. The Gauss-Newton method was used as a reference to machine learning methods. The algorithms were trained using learning data obtained through computer simulation based on real models. The results of the experiments showed that in the considered cases the best quality of reconstructions was achieved by ANN. At the same time, ANN was the slowest in terms of both the training process and the speed of image generation. Other machine learning methods were comparable with the deterministic Gauss-Newton method and with each other.

scholarly journals Machine learning for stem cell differentiation and proliferation classification on electrical impedance spectroscopy

2019 ◽  
Vol 10 (1) ◽  
pp. 124-132
Author(s):  
André B. Cunha ◽  
Jie Hou ◽  
Christin Schuelke
Keyword(s):  
Machine Learning ◽  
Stem Cell ◽  
Impedance Spectroscopy ◽  
Electrical Impedance ◽  
Stem Cell Differentiation ◽  
Electrical Impedance Spectroscopy ◽  
Stem Cell Lines ◽  
Eis Measurements ◽  
Experimental Treatments ◽  
Differentiation And Proliferation

Abstract Electrical impedance spectroscopy (EIS) measurements on cells is a proven method to assess stem cell proliferation and differentiation. Cell regenerative medicine (CRM) is an emerging field where the need to develop and deploy stem cell assessment techniques is paramount as experimental treatments reach pre-clinical and clinical stages. However, EIS measurements on cells is a method requiring extensive post-processing and analysis. As a contribution to address this concern, we developed three machine learning models for three different stem cell lines able to classify the measured data as proliferation or differentiation laying the stone for future studies on using machine learning to profile EIS measurements on stem cells spectra.

scholarly journals Comparative Study of Machine Learning Algorithms to Classify Hand Gestures from Deployable and Breathable Kirigami-Based Electrical Impedance Bracelet

2020 ◽  
Vol 4 (3) ◽  
pp. 47
Author(s):  
Godwin Ponraj Joseph Vedhagiri ◽  
Xin Zhi Wang ◽  
Kirthika Senthil Kumar ◽  
Hongliang Ren
Keyword(s):  
Machine Learning ◽  
Comparative Study ◽  
Electrical Impedance ◽  
Learning Algorithms ◽  
The Body ◽  
Machine Learning Algorithms ◽  
Skin Impedance ◽  
Primary Data ◽  
Support Vector ◽  
Hand Gestures

Wearable devices are gaining recognition for their use as a biosensor platform. Electrical impedance tomography (EIT) is one of the sensing techniques that utilizes wearable sensors as its primary data acquisition system. It measures the impedance or resistance at the peripheral (skin) level and calculates the conductivity distribution throughout the body. Even though the technology has existed for several decades, modern-day EIT devices are still costly and bulky. The paper proposes a novel low-cost kirigami-based wearable device that has soft PEDOT: PSS electrodes for sensing skin impedances. Simulation results show that the proposed kirigami structure for the bracelet has a large deformation during actuation while experiencing relatively lower stress. The paper also presents a comparative study on a few machine learning algorithms to classify hand gestures, based on the measured skin impedance. The best classification accuracy (91.49%) was observed from the quadratic support vector machine (SVM) algorithm with 48 principal components.

scholarly journals Classification of Wood Chips Using Electrical Impedance Spectroscopy and Machine Learning

Sensors ◽  
2020 ◽  
Vol 20 (4) ◽  
pp. 1076 ◽  
Author(s):  
Markku Tiitta ◽  
Valtteri Tiitta ◽  
Jorma Heikkinen ◽  
Reijo Lappalainen ◽  
Laura Tomppo
Keyword(s):  
Machine Learning ◽  
Impedance Spectroscopy ◽  
Electrical Impedance ◽  
Nearest Neighbor ◽  
Electrical Impedance Spectroscopy ◽  
Wood Chips ◽  
Raw Material ◽  
Support Vector ◽  
Birch Bark ◽  
K Nearest Neighbor

Wood chips are extensively utilised as raw material for the pulp and bio-fuel industry, and advanced material analyses may improve the processes in utilizing these products. Electrical impedance spectroscopy (EIS) combined with machine learning was used in order to analyse heartwood content of pine chips and bark content of birch chips. A novel electrode system integrated in a sampling container was developed for the testing using frequency range 42 Hz–5 MHz. Three electrode pairs were used to measure the samples in x-, y- and z-direction. Three machine learning methods were used: K-nearest neighbor (KNN), decision tree (DT) and support vector machines (SVM). The heartwood content of pine chips and bark content of birch chips were classified with an accuracy of 91% using EIS from pure materials combined with a k-nearest neighbour classifier. When using mixed materials and multiple classes, 73% correct classification for pine heartwood content (four groups) and 64% for birch bark content (five groups) were achieved.

scholarly journals Comparisons Among Multiple Machine Learning Based Classifiers for Breast Cancer Risk Stratification Using Electrical Impedance Spectroscopy

2020 ◽  
Vol 4 (4) ◽  
Author(s):  
Md. Toukir Ahmed ◽  
Md. Rayhanul Masud ◽  
Abdullah Al Mamun
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Random Forest ◽  
Impedance Spectroscopy ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Risk Stratification ◽  
Electrical Impedance ◽  
Performance Metrics ◽  
Electrical Impedance Spectroscopy

Nowadays, women worldwide are affected greatly by Breast cancer. The consequences of the disease and the number of affected are so alarming that it requires immediate attention. Prediction of the disease is the primary and most significant route to prevention of it. This study aims to have a comparison among multiple machine learning based classifiers for breast cancer risk stratification using resonance-frequency electrical impedance spectroscopy. Five machine learning based classifiers namely- Naïve Bayes, Multilayer perceptron, J48, Bagging and Random Forest were applied to the dataset and a comparison was made based on different performance metrics. The study demonstrated that Random Forest classifier performed slightly better than the others in both splitting and folding of the dataset.

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