Artificial neural networks applied to cancer detection in a breast screening programme

Nonlinear optical phase shift in blood plasmas for neoplasia diagnosis

Detecting cancer at an early stage is crucial for timely treatment and better chances of survival. This research focuses on a scanning method for detecting cancer by examining the nonlinear optical characteristics of blood plasma samples. The study used both cancerous and noncancerous plasma samples and presented the results statistically by utilizing an incident laser power-dependent nonlinear optical phase shift variable called ζ in the Z-scan technique. The results showed a clear difference between the cancerous and non-cancerous samples with an accuracy of 92%. Furthermore, the study suggests the potential for measuring the cancer staging from the cancerous plasma. The study also confirmed a significant difference in ζ for plasma samples undergoing chemotherapy. A red laser with high power (above 18mW) was used to avoid the involvement of fluorophores or other chemical reagents in the plasma samples during the measurement.

Non-Intrusive Load Monitoring (NILM) for Energy Disaggregation Using Soft Computing Techniques

Non-intrusive load monitoring (NILM) has become an important subject of study, since it provides benefits to both consumers and utility companies. The analysis of smart meter signals is useful for identifying consumption patterns and user behaviors, in order to make predictions and optimizations to anticipate the use of electrical appliances at home. However, the problem with this kind of analysis rests in how to isolate individual appliances from an aggregated consumption signal. In this work, we propose an unsupervised disaggregation method based on a controlled dataset obtained using smart meters in a standard household. By using soft computing techniques, the proposed methodology can identify the behavior of each of the devices from aggregated consumption records. In the approach developed in this work, it is possible to detect changes in power levels and to build a box model, consisting of a sequence of rectangles of different heights (power) and widths (time), which is highly adaptable to the real-life working conditions of household appliances. The system was developed and tested using data collected at households in France and the UK (UK-domestic appliance-level electricity (DALE) dataset). The proposed analysis method serves as a basis to be applied to large amounts of data collected by distribution companies with smart meters.

Identification of gender differences in the factors influencing shoulders, neck and upper limb MSD by means of multivariate adaptive regression splines (MARS)

In the present research, models based on multivariate adaptive regression splines (MARS) are proposed to study the influence of gender in the factors affecting the development of shoulders, neck and upper limb MSD. Two different MARS models, corresponding to men and women, are constructed to identify variables with the strongest effect on the target MSD. Both models are capable to predict successfully the occurrence of the studied disorders. Men seem to be more vulnerable to physical risk factors and some other working conditions, whereas women appear to be more affected by psychosocial risk factors and activities carried out outside their working hours. According to the results, gender needs to be considered to ensure the success and effectiveness of ergonomic interventions on the whole working population.

Deep convolutional neural networks for estimating porous material parameters with ultrasound tomography

The feasibility of data based machine learning applied to ultrasound tomography is studied to estimate water-saturated porous material parameters. In this work, the data to train the neural networks is simulated by solving wave propagation in coupled poroviscoelastic-viscoelastic-acoustic media. As the forward model, a high-order discontinuous Galerkin method is considered, while deep convolutional neural networks are used to solve the parameter estimation problem. In the numerical experiment, the material porosity and tortuosity is estimated, while the remaining parameters which are of less interest are successfully marginalized in the neural networks-based inversion. Computational examples confirm the feasibility and accuracy of this approach.

Hybrid Intelligent System to Perform Fault Detection on BIS Sensor During Surgeries

This paper presents a new fault detection system in hypnotic sensors used for general anesthesia during surgery. Drug infusion during surgery is based on information received from patient monitoring devices; accordingly, faults in sensor devices can put patient safety at risk. Our research offers a solution to cope with these undesirable scenarios. We focus on the anesthesia process using intravenous propofol as the hypnotic drug and employing a Bispectral Index (BIS^TM) monitor to estimate the patient’s unconsciousness level. The method developed identifies BIS episodes affected by disturbances during surgery with null clinical value. Thus, the clinician—or the automatic controller—will not take those measures into account to calculate the drug dose. Our method compares the measured BIS signal with expected behavior predicted by the propofol dose provider and the electromyogram (EMG) signal. For the prediction of the BIS signal, a model based on a hybrid intelligent system architecture has been created. The model uses clustering combined with regression techniques. To validate its accuracy, a dataset taken during surgeries with general anesthesia was used. The proposed fault detection method for BIS sensor measures has also been verified using data from real cases. The obtained results prove the method’s effectiveness.