scholarly journals Application of convolutional neural networks for prediction of disinfection by-products

2022 ◽  
Vol 12 (1) ◽  
Author(s):  
Nicolás M. Peleato
Keyword(s):  
Neural Networks ◽  
Fluorescence Spectroscopy ◽  
Convolutional Neural Networks ◽  
Chemical Characterization ◽  
Drinking Water Treatment ◽  
Individual Species ◽  
Monitoring And Control ◽  
Significant Difference ◽  
Process Monitoring And Control ◽  
By Products

AbstractFluorescence spectroscopy can provide high-level chemical characterization and quantification that is suitable for use in online process monitoring and control. However, the high-dimensionality of excitation–emission matrices and superposition of underlying signals is a major challenge to implementation. Herein the use of Convolutional Neural Networks (CNNs) is investigated to interpret fluorescence spectra and predict the formation of disinfection by-products during drinking water treatment. Using deep CNNs, mean absolute prediction error on a test set of data for total trihalomethanes, total haloacetic acids, and the major individual species were all < 6 µg/L and represent a significant difference improved by 39–62% compared to multi-layer perceptron type networks. Heat maps that identify spectral areas of importance for prediction showed unique humic-like and protein-like regions for individual disinfection by-product species that can be used to validate models and provide insight into precursor characteristics. The use of fluorescence spectroscopy coupled with deep CNNs shows promise to be used for rapid estimation of DBP formation potentials without the need for extensive data pre-processing or dimensionality reduction. Knowledge of DBP formation potentials in near real-time can enable tighter treatment controls and management efforts to minimize the exposure of the public to DBPs.

scholarly journals Application of Convolutional Neural Networks for Prediction of 1 Disinfection By-Products

2021 ◽  
Author(s):  
Nicolás M. Peleato
Keyword(s):  
Neural Networks ◽  
Fluorescence Spectroscopy ◽  
Convolutional Neural Networks ◽  
Chemical Characterization ◽  
Drinking Water Treatment ◽  
Individual Species ◽  
Monitoring And Control ◽  
Significant Difference ◽  
Process Monitoring And Control ◽  
By Products

Abstract Fluorescence spectroscopy can provide high-level chemical characterization and quantification that is suitable for use in online process monitoring and control. However, the high-dimensionality of excitation-emission matrices and superposition of underlying signals is a major challenge to implementation. Herein the use of Convolutional Neural Networks (CNNs) is investigated to interpret fluorescence spectra and predict the formation of disinfection by-products during drinking water treatment. Using deep CNNs, mean absolute prediction error on a test set of data for total trihalomethanes, total haloacetic acids, and the major individual species were all < 6 µg/L and represent a significant difference improved by 39% - 62% compared to dense neural networks. Heat maps that identify spectral areas of importance for prediction showed unique humic-like and protein-like regions for individual disinfection by-product species that can be used to validate models and provide insight into precursor characteristics. The use of fluorescence spectroscopy coupled with deep CNNs shows promise to be used for rapid estimation of DBP formation potentials without the need for extensive data pre-processing or dimensionality reduction. Knowledge of DBP formation potentials in near real-time can enable tighter treatment controls and management efforts to minimize the exposure of the public to DBPs.

scholarly journals Convolutional Neural Networks for Decoding of Covert Attention Focus and Saliency Maps for EEG Feature Visualization

2019 ◽  
Author(s):  
Amr Farahat ◽  
Christoph Reichert ◽  
Catherine M. Sweeney-Reed ◽  
Hermann Hinrichs
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Cognitive Task ◽  
Event Related Potentials ◽  
Eeg Signal ◽  
Linear Discriminant ◽  
Saliency Maps ◽  
Significant Difference ◽  
Related Potentials ◽  
The Impact

ABSTRACTObjectiveConvolutional neural networks (CNNs) have proven successful as function approximators and have therefore been used for classification problems including electroencephalography (EEG) signal decoding for brain-computer interfaces (BCI). Artificial neural networks, however, are considered black boxes, because they usually have thousands of parameters, making interpretation of their internal processes challenging. Here we systematically evaluate the use of CNNs for EEG signal decoding and investigate a method for visualizing the CNN model decision process.ApproachWe developed a CNN model to decode the covert focus of attention from EEG event-related potentials during object selection. We compared the CNN and the commonly used linear discriminant analysis (LDA) classifier performance, applied to datasets with different dimensionality, and analyzed transfer learning capacity. Moreover, we validated the impact of single model components by systematically altering the model. Furthermore, we investigated the use of saliency maps as a tool for visualizing the spatial and temporal features driving the model output.Main resultsThe CNN model and the LDA classifier achieved comparable accuracy on the lower-dimensional dataset, but CNN exceeded LDA performance significantly on the higher-dimensional dataset (without hypothesis-driven preprocessing), achieving an average decoding accuracy of 90.7% (chance level = 8.3%). Parallel convolutions, tanh or ELU activation functions, and dropout regularization proved valuable for model performance, whereas the sequential convolutions, ReLU activation function, and batch normalization components, reduced accuracy or yielded no significant difference. Saliency maps revealed meaningful features, displaying the typical spatial distribution and latency of the P300 component expected during this task.SignificanceFollowing systematic evaluation, we provide recommendations for when and how to use CNN models in EEG decoding. Moreover, we propose a new approach for investigating the neural correlates of a cognitive task by training CNN models on raw high-dimensional EEG data and utilizing saliency maps for relevant feature extraction.

scholarly journals Detection of myocardial ischemia by intracoronary ECG using convolutional neural networks

PLoS ONE ◽  
2021 ◽  
Vol 16 (6) ◽  
pp. e0253200
Author(s):  
Marius Reto Bigler ◽  
Christian Seiler
Keyword(s):  
Neural Networks ◽  
Coronary Artery ◽  
Myocardial Ischemia ◽  
Convolutional Neural Networks ◽  
Balloon Occlusion ◽  
Operating Characteristics ◽  
Shift Measurement ◽  
Coronary Syndrome ◽  
St Segment ◽  
Significant Difference

Introduction The electrocardiogram (ECG) is a valuable tool for the diagnosis of myocardial ischemia as it presents distinctive ischemic patterns. Deep learning methods such as convolutional neural networks (CNN) are employed to extract data-derived features and to recognize natural patterns. Hence, CNN enable an unbiased view on well-known clinical phenomenon, e.g., myocardial ischemia. This study tested a novel, hypothesis-generating approach using pre-trained CNN to determine the optimal ischemic parameter as obtained from the highly susceptible intracoronary ECG (icECG). Method This was a retrospective observational study in 228 patients with chronic coronary syndrome. Each patient had participated in clinical trials with icECG recording and ST-segment shift measurement at the beginning (i.e., non-ischemic) and the end (i.e., ischemic) of a one-minute proximal coronary artery balloon occlusion establishing the reference. Using these data (893 icECGs in total), two pre-trained, open-access CNN (GoogLeNet/ResNet101) were trained to recognize ischemia. The best performing CNN during training were compared with the icECG ST-segment shift for diagnostic accuracy in the detection of artificially induced myocardial ischemia. Results Using coronary patency or occlusion as reference for absent or present myocardial ischemia, receiver-operating-characteristics (ROC)-analysis of manually obtained icECG ST-segment shift (mV) showed an area under the ROC-curve (AUC) of 0.903±0.043 (p<0.0001, sensitivity 80%, specificity 92% at a cut-off of 0.279mV). The best performing CNN showed an AUC of 0.924 (sensitivity 93%, specificity 92%). DeLong-Test of the ROC-curves showed no significant difference between the AUCs. The underlying morphology responsible for the network prediction differed between the trained networks but was focused on the ST-segment and the T-wave for myocardial ischemia detection. Conclusions When tested in an experimental setting with artificially induced coronary artery occlusion, quantitative icECG ST-segment shift and CNN using pathophysiologic prediction criteria detect myocardial ischemia with similarly high accuracy.

scholarly journals Detection of myocardial ischemia by intracoronary ECG using convolutional neural networks

2021 ◽  
Vol 42 (Supplement_1) ◽  
Author(s):  
M R Bigler ◽  
C Seiler
Keyword(s):  
Neural Networks ◽  
Coronary Artery ◽  
Myocardial Ischemia ◽  
Convolutional Neural Networks ◽  
Balloon Occlusion ◽  
Operating Characteristics ◽  
Shift Measurement ◽  
Coronary Syndrome ◽  
St Segment ◽  
Significant Difference

Abstract Introduction The electrocardiogram (ECG) is a valuable tool for the diagnosis of myocardial ischemia as it presents distinctive ischemic patterns. Deep learning methods such as convolutional neural networks (CNN) are employed to extract data-derived features and to recognize natural patterns. This study aimed to determine the optimal ischemic parameter as obtained from the highly susceptible intracoronary ECG (icECG) using pre-trained CNN. Method This was a retrospective observational study in 228 patients with chronic coronary syndrome. Each patient had participated in clinical trials with icECG recording and ST-segment shift measurement at the beginning (i.e.,non-ischemic) and the end (i.e.,ischemic) of a one-minute proximal coronary artery balloon occlusion establishing the reference. Using these data (893icECGs in total), two pre-trained, open-access CNN (GoogLeNet/ResNet101) were trained to recognize ischemia. The best performing CNN during training were compared with the icECG ST-segment shift for diagnostic accuracy in the detection of artificially induced myocardial ischemia. Results Using coronary patency or occlusion as reference for absent or present myocardial ischemia, receiver-operating-characteristics (ROC)-analysis of manually obtained icECG ST-segment shift (mV) showed an area under the ROC-curve (AUC) of 0.903±0.043 (p&lt;0.0001, sensitivity 80%, specificity 92% at a cut-off of 0.279mV). The best performing CNN showed an AUC of 0.924 (sensitivity 93%, specificity 92%). DeLong-Test of the ROC-curves showed no significant difference between the AUCs. The underlying morphology responsible for the network prediction differed between the trained networks but was focused on the ST-segment and the T-wave for myocardial ischemia detection. Conclusions When tested in an experimental setting with artificially induced coronary artery occlusion, quantitative icECG ST-segment shift and CNN using pathophysiologic prediction criteria detect myocardial ischemia with similarly high accuracy. FUNDunding Acknowledgement Type of funding sources: None.

scholarly journals Automatic Segmentation of Clinical Target Volumes for Post-Modified Radical Mastectomy Radiotherapy Using Convolutional Neural Networks

2021 ◽  
Vol 10 ◽  
Author(s):  
Zhikai Liu ◽  
Fangjie Liu ◽  
Wanqi Chen ◽  
Xia Liu ◽  
Xiaorong Hou ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Neural Networks ◽  
Chest Wall ◽  
Convolutional Neural Networks ◽  
Automatic Segmentation ◽  
Dice Similarity Coefficient ◽  
Proposed Model ◽  
Significant Difference ◽  
Target Volumes ◽  
Clinical Target Volumes

BackgroundThis study aims to construct and validate a model based on convolutional neural networks (CNNs), which can fulfil the automatic segmentation of clinical target volumes (CTVs) of breast cancer for radiotherapy.MethodsIn this work, computed tomography (CT) scans of 110 patients who underwent modified radical mastectomies were collected. The CTV contours were confirmed by two experienced oncologists. A novel CNN was constructed to automatically delineate the CTV. Quantitative evaluation metrics were calculated, and a clinical evaluation was conducted to evaluate the performance of our model.ResultsThe mean Dice similarity coefficient (DSC) of the proposed model was 0.90, and the 95th percentile Hausdorff distance (95HD) was 5.65 mm. The evaluation results of the two clinicians showed that 99.3% of the chest wall CTV slices could be accepted by clinician A, and this number was 98.9% for clinician B. In addition, 9/10 of patients had all slices accepted by clinician A, while 7/10 could be accepted by clinician B. The score differences between the AI (artificial intelligence) group and the GT (ground truth) group showed no statistically significant difference for either clinician. However, the score differences in the AI group were significantly different between the two clinicians. The Kappa consistency index was 0.259. It took 3.45 s to delineate the chest wall CTV using the model.ConclusionOur model could automatically generate the CTVs for breast cancer. AI-generated structures of the proposed model showed a trend that was comparable, or was even better, than those of human-generated structures. Additional multicentre evaluations should be performed for adequate validation before the model can be completely applied in clinical practice.

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