scholarly journals Recurrence Plot and Machine Learning for Signal Quality Assessment of Photoplethysmogram in Mobile Environment

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2188
Author(s):  
Donggeun Roh ◽  
Hangsik Shin
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Convolutional Neural Network ◽  
Quality Assessment ◽  
Feature Detection ◽  
Layer Structure ◽  
Recurrence Plot ◽  
Classification Model ◽  
Signal Quality ◽  
Predictive Values

The purpose of this study was to develop a machine learning model that could accurately evaluate the quality of a photoplethysmogram based on the shape of the photoplethysmogram and the phase relevance in a pulsatile waveform without requiring complicated pre-processing. Photoplethysmograms were recorded for 76 participants (5 min for each participant). All recorded photoplethysmograms were segmented for each beat to obtain a total of 49,561 pulsatile segments. These pulsatile segments were manually labeled as ‘good’ and ‘poor’ classes and converted to a two-dimensional phase space trajectory image using a recurrence plot. The classification model was implemented using a convolutional neural network with a two-layer structure. As a result, the proposed model correctly classified 48,827 segments out of 49,561 segments and misclassified 734 segments, showing a balanced accuracy of 0.975. Sensitivity, specificity, and positive predictive values of the developed model for the test dataset with a ‘poor’ class classification were 0.964, 0.987, and 0.848, respectively. The area under the curve was 0.994. The convolutional neural network model with recurrence plot as input proposed in this study can be used for signal quality assessment as a generalized model with high accuracy through data expansion. It has an advantage in that it does not require complicated pre-processing or a feature detection process.

Recurrence plot and convolutional neural network-based signal quality assessment method for using photoplethysmogram in mobile environment (Preprint)

2020 ◽  
Author(s):  
Hangsik Shin
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Quality Assessment ◽  
Feature Detection ◽  
Layer Structure ◽  
Recurrence Plot ◽  
Classification Model ◽  
Signal Quality ◽  
Predictive Values

BACKGROUND In clinical use of photoplethysmogram, waveform distortion due to motion noise or low perfusion may lead to inaccurate analysis and diagnostic results. Therefore, it is necessary to find an appropriate analysis method to evaluate the signal quality of the photoplethysmogram so that its wide use in mobile healthcare can be further increased. OBJECTIVE The purpose of this study was to develop a machine learning model that could accurately evaluate the quality of a photoplethysmogram based on the shape of the photoplethysmogram and the phase relevance in a pulsatile waveform without requiring a complicated pre-processing. Its performance was then verified. METHODS Photoplethysmograms were recorded for 76 participants (5 minutes for each participant). All recorded photoplethysmograms were segmented for each beat to obtain a total of 49,561 pulsatile segments. These pulsatile segments were manually labeled as 'good' and 'bad' classes and converted to a two-dimensional phase space trajectory image with size of 124 × 124 using a recurrence plot. The classification model was implemented using a convolutional neural network with a two-layer structure. It was verified through a five-fold cross validation. RESULTS As a result, the proposed model correctly classified 48,827 segments out of 49,561 segments and misclassified 734 segments, showing a balanced accuracy of 0.975. Sensitivity, specificity, and positive predictive values of the developed model for the test dataset with a ‘bad’ class classification were 0.964, 0.987, and 0.848, respectively. The area under the curve was 0.994. CONCLUSIONS The convolutional neural network model with recurrence plot as input proposed in this study can be used for signal quality assessment as a generalized model with high accuracy through data expansion. It has an advantage in that it does not require a complicated pre-processing or feature detection process. CLINICALTRIAL KCT0002080

Recurrence plot and machine learning for signal quality assessment of photoplethysmogram in mobile environment (Preprint)

2020 ◽  
Author(s):  
Donggeun Roh ◽  
Hangsik Shin
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Convolutional Neural Network ◽  
Quality Assessment ◽  
Feature Detection ◽  
Recurrence Plot ◽  
Classification Model ◽  
Signal Quality ◽  
Predictive Values

BACKGROUND In clinical use of photoplethysmogram, waveform distortion due to motion noise or low perfusion may lead to inaccurate analysis and diagnostic results. Therefore, it is necessary to find an appropriate analysis method to evaluate the signal quality of the photoplethysmogram so that its wide use in mobile healthcare can be further increased. OBJECTIVE The purpose of this study was to develop a machine learning model that could accurately evaluate the quality of a photoplethysmogram based on the shape of the photoplethysmogram and the phase relevance in a pulsatile waveform without requiring a complicated pre-processing. Its performance was then verified. METHODS Photoplethysmograms were recorded for 76 participants (5 minutes for each participant). All recorded photoplethysmograms were segmented for each beat to obtain a total of 49,561 pulsatile segments. These pulsatile segments were manually labeled as 'good' and 'bad' classes and converted to a two-dimensional phase space trajectory image with size of 124 × 124 using a recurrence plot. The classification model was implemented using a convolutional neural network with a two-layer structure. It was verified through a five-fold cross validation. RESULTS As a result, the proposed model correctly classified 48,827 segments out of 49,561 segments and misclassified 734 segments, showing a balanced accuracy of 0.975. Sensitivity, specificity, and positive predictive values of the developed model for the test dataset with a ‘bad’ class classification were 0.964, 0.987, and 0.848, respectively. The area under the curve was 0.994. CONCLUSIONS The convolutional neural network model with recurrence plot as input proposed in this study can be used for signal quality assessment as a generalized model with high accuracy through data expansion. It has an advantage in that it does not require a complicated pre-processing or feature detection process. CLINICALTRIAL KCT0002080

scholarly journals Improving Water Quality Assessment through Anomaly Detection Using Hybrid Convolutional Neural Network Approach

2021 ◽  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Water Quality ◽  
Deep Learning ◽  
Anomaly Detection ◽  
Convolutional Neural Network ◽  
Quality Assessment ◽  
Water Quality Assessment ◽  
Machine Learning Algorithms ◽  
Quality Analysis

<p>Water being a precious commodity for every person around the world needs to be quality monitored continuously for ensuring safety whilst usage. The water data collected from sensors in water plants are used for water quality assessment. The anomaly present in the water data seriously affects the performance of water quality assessment. Hence it needs to be addressed. In this regard, water data collected from sensors have been subjected to various anomaly detection approaches guided by Machine Learning (ML) and Deep Learning framework. Standard machine learning algorithms have been used extensively in water quality analysis and these algorithms in general converge quickly. Considering the fact that manual feature selection has to be done for ML algorithms, Deep Learning (DL) algorithm is proposed which involve implicit feature learning. A hybrid model is formulated that takes advantage of both and presented it is data invariant too. This novel Hybrid Convolutional Neural Network (CNN) and Extreme Learning Machine (ELM) approach is used to detect presence of anomalies in sensor collected water data. The experiment of the proposed CNN-ELM model is carried out using the publicly available dataset GECCO 2019. The findings proved that the model has improved the water quality assessment of the sensor water data collected by detecting the anomalies efficiently and achieves F1 score of 0.92. This model can be implemented in water quality assessment.</p>

Detection of Epileptic Seizure Using Pretrained Deep Convolutional Neural Network and Transfer Learning

2020 ◽  
Vol 83 (6) ◽  
pp. 602-614
Author(s):  
Hidir Selcuk Nogay ◽  
Hojjat Adeli
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Convolutional Neural Network ◽  
Transfer Learning ◽  
Epileptic Seizure ◽  
Binary Classification ◽  
Classification Model ◽  
Automatic Identification ◽  
Eeg Signal ◽  
Data Set

<b><i>Introduction:</i></b> The diagnosis of epilepsy takes a certain process, depending entirely on the attending physician. However, the human factor may cause erroneous diagnosis in the analysis of the EEG signal. In the past 2 decades, many advanced signal processing and machine learning methods have been developed for the detection of epileptic seizures. However, many of these methods require large data sets and complex operations. <b><i>Methods:</i></b> In this study, an end-to-end machine learning model is presented for detection of epileptic seizure using the pretrained deep two-dimensional convolutional neural network (CNN) and the concept of transfer learning. The EEG signal is converted directly into visual data with a spectrogram and used directly as input data. <b><i>Results:</i></b> The authors analyzed the results of the training of the proposed pretrained AlexNet CNN model. Both binary and ternary classifications were performed without any extra procedure such as feature extraction. By performing data set creation from short-term spectrogram graphic images, the authors were able to achieve 100% accuracy for binary classification for epileptic seizure detection and 100% for ternary classification. <b><i>Discussion/Conclusion:</i></b> The proposed automatic identification and classification model can help in the early diagnosis of epilepsy, thus providing the opportunity for effective early treatment.

scholarly journals Impact of Training Sample Size on the Effects of Regularization in a Convolutional Neural Network-based Dental X-ray Artifact Prediction Model

2020 ◽  
Vol 14 (1) ◽  
pp. 5
Author(s):  
Adam Adli ◽  
Pascal Tyrrell
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Convolutional Neural Network ◽  
Sample Size ◽  
Training Sample ◽  
Training Data ◽  
Classification Model ◽  
Sample Sizes ◽  
X Ray ◽  
Training Sample Size

Introduction: Advances in computers have allowed for the practical application of increasingly advanced machine learning models to aid healthcare providers with diagnosis and inspection of medical images. Often, a lack of training data and computation time can be a limiting factor in the development of an accurate machine learning model in the domain of medical imaging. As a possible solution, this study investigated whether L2 regularization moderate s the overfitting that occurs as a result of small training sample sizes.Methods: This study employed transfer learning experiments on a dental x-ray binary classification model to explore L2 regularization with respect to training sample size in five common convolutional neural network architectures. Model testing performance was investigated and technical implementation details including computation times and hardware considerations as well as performance factors and practical feasibility were described.Results: The experimental results showed a trend that smaller training sample sizes benefitted more from regularization than larger training sample sizes. Further, the results showed that applying L2 regularization did not apply significant computational overhead and that the extra rounds of training L2 regularization were feasible when training sample sizes are relatively small.Conclusion: Overall, this study found that there is a window of opportunity in which the benefits of employing regularization can be most cost-effective relative to training sample size. It is recommended that training sample size should be carefully considered when forming expectations of achievable generalizability improvements that result from investing computational resources into model regularization.

scholarly journals A Deep Convolutional Neural Network-Based Multi-Class Image Classification for Automatic Wafer Map Failure Recognition in Semiconductor Manufacturing

2021 ◽  
Vol 11 (20) ◽  
pp. 9769
Author(s):  
Huilin Zheng ◽  
Syed Waseem Abbas Sherazi ◽  
Sang Hyeok Son ◽  
Jong Yun Lee
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Manufacturing Process ◽  
Semiconductor Manufacturing ◽  
Prediction Models ◽  
Deep Convolutional Neural Network ◽  
Classification Model ◽  
Boosted Decision Trees

Wafer maps provide engineers with important information about the root causes of failures during the semiconductor manufacturing process. Through the efficient recognition of the wafer map failure pattern type, the semiconductor manufacturing process and its product performance can be improved, as well as reducing the product cost. Therefore, this paper proposes an accurate model for the automatic recognition of wafer map failure types using a deep learning-based convolutional neural network (DCNN). For this experiment, we use WM811K, which is an open-source real-time wafer map dataset containing wafer map images of nine failure classes. Our research contents can be briefly summarized as follows. First, we use random sampling to extract 500 images from each class of the original image dataset. Then we propose a deep convolutional neural network model to generate a multi-class classification model. Lastly, we evaluate the performance of the proposed prediction model and compare it with three other popular machine learning-based models—logistic regression, random forest, and gradient boosted decision trees—and several well-known deep learning models—VGGNet, ResNet, and EfficientNet. Consequently, the comprehensive analysis showed that the performance of the proposed DCNN model outperformed those of other popular machine learning and deep learning-based prediction models.

scholarly journals Effectiveness of transfer learning for enhancing tumor classification with a convolutional neural network on frozen sections

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Young-Gon Kim ◽  
Sungchul Kim ◽  
Cristina Eunbee Cho ◽  
In Hye Song ◽  
Hee Jin Lee ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Transfer Learning ◽  
Frozen Section ◽  
Medical Center ◽  
External Validation ◽  
Model Performance ◽  
Classification Model ◽  
Training Dataset

AbstractFast and accurate confirmation of metastasis on the frozen tissue section of intraoperative sentinel lymph node biopsy is an essential tool for critical surgical decisions. However, accurate diagnosis by pathologists is difficult within the time limitations. Training a robust and accurate deep learning model is also difficult owing to the limited number of frozen datasets with high quality labels. To overcome these issues, we validated the effectiveness of transfer learning from CAMELYON16 to improve performance of the convolutional neural network (CNN)-based classification model on our frozen dataset (N = 297) from Asan Medical Center (AMC). Among the 297 whole slide images (WSIs), 157 and 40 WSIs were used to train deep learning models with different dataset ratios at 2, 4, 8, 20, 40, and 100%. The remaining, i.e., 100 WSIs, were used to validate model performance in terms of patch- and slide-level classification. An additional 228 WSIs from Seoul National University Bundang Hospital (SNUBH) were used as an external validation. Three initial weights, i.e., scratch-based (random initialization), ImageNet-based, and CAMELYON16-based models were used to validate their effectiveness in external validation. In the patch-level classification results on the AMC dataset, CAMELYON16-based models trained with a small dataset (up to 40%, i.e., 62 WSIs) showed a significantly higher area under the curve (AUC) of 0.929 than those of the scratch- and ImageNet-based models at 0.897 and 0.919, respectively, while CAMELYON16-based and ImageNet-based models trained with 100% of the training dataset showed comparable AUCs at 0.944 and 0.943, respectively. For the external validation, CAMELYON16-based models showed higher AUCs than those of the scratch- and ImageNet-based models. Model performance for slide feasibility of the transfer learning to enhance model performance was validated in the case of frozen section datasets with limited numbers.

scholarly journals Real Time Multipurpose Smart Waste Classification Model for Efficient Recycling in Smart Cities Using Multilayer Convolutional Neural Network and Perceptron

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4916
Author(s):  
Ali Usman Gondal ◽  
Muhammad Imran Sadiq ◽  
Tariq Ali ◽  
Muhammad Irfan ◽  
Ahmad Shaf ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Real Time ◽  
Multilayer Perceptron ◽  
Smart Cities ◽  
Classification Model ◽  
Rapid Urbanization ◽  
Metal Waste ◽  
Waste Classification ◽  
The City

Urbanization is a big concern for both developed and developing countries in recent years. People shift themselves and their families to urban areas for the sake of better education and a modern lifestyle. Due to rapid urbanization, cities are facing huge challenges, one of which is waste management, as the volume of waste is directly proportional to the people living in the city. The municipalities and the city administrations use the traditional wastage classification techniques which are manual, very slow, inefficient and costly. Therefore, automatic waste classification and management is essential for the cities that are being urbanized for the better recycling of waste. Better recycling of waste gives the opportunity to reduce the amount of waste sent to landfills by reducing the need to collect new raw material. In this paper, the idea of a real-time smart waste classification model is presented that uses a hybrid approach to classify waste into various classes. Two machine learning models, a multilayer perceptron and multilayer convolutional neural network (ML-CNN), are implemented. The multilayer perceptron is used to provide binary classification, i.e., metal or non-metal waste, and the CNN identifies the class of non-metal waste. A camera is placed in front of the waste conveyor belt, which takes a picture of the waste and classifies it. Upon successful classification, an automatic hand hammer is used to push the waste into the assigned labeled bucket. Experiments were carried out in a real-time environment with image segmentation. The training, testing, and validation accuracy of the purposed model was 0.99% under different training batches with different input features.

scholarly journals Machine learning for image-based detection of patients with obstructive sleep apnea: an exploratory study

2021 ◽  
Author(s):  
Satoru Tsuiki ◽  
Takuya Nagaoka ◽  
Tatsuya Fukuda ◽  
Yuki Sakamoto ◽  
Fernanda R. Almeida ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
Convolutional Neural Network ◽  
Deep Convolutional Neural Network ◽  
Apnea Hypopnea Index ◽  
Cephalometric Analysis ◽  
Obstructive Sleep ◽  
Main Region

Abstract Purpose In 2-dimensional lateral cephalometric radiographs, patients with severe obstructive sleep apnea (OSA) exhibit a more crowded oropharynx in comparison with non-OSA. We tested the hypothesis that machine learning, an application of artificial intelligence (AI), could be used to detect patients with severe OSA based on 2-dimensional images. Methods A deep convolutional neural network was developed (n = 1258; 90%) and tested (n = 131; 10%) using data from 1389 (100%) lateral cephalometric radiographs obtained from individuals diagnosed with severe OSA (n = 867; apnea hypopnea index > 30 events/h sleep) or non-OSA (n = 522; apnea hypopnea index < 5 events/h sleep) at a single center for sleep disorders. Three kinds of data sets were prepared by changing the area of interest using a single image: the original image without any modification (full image), an image containing a facial profile, upper airway, and craniofacial soft/hard tissues (main region), and an image containing part of the occipital region (head only). A radiologist also performed a conventional manual cephalometric analysis of the full image for comparison. Results The sensitivity/specificity was 0.87/0.82 for full image, 0.88/0.75 for main region, 0.71/0.63 for head only, and 0.54/0.80 for the manual analysis. The area under the receiver-operating characteristic curve was the highest for main region 0.92, for full image 0.89, for head only 0.70, and for manual cephalometric analysis 0.75. Conclusions A deep convolutional neural network identified individuals with severe OSA with high accuracy. Future research on this concept using AI and images can be further encouraged when discussing triage of OSA.

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