A novel method based on convolutional neural networks for deriving standard 12-lead ECG from serial 3-lead ECG

2019 ◽  
Vol 20 (3) ◽  
pp. 405-413 ◽  
Author(s):  
Lu-di Wang ◽  
Wei Zhou ◽  
Ying Xing ◽  
Na Liu ◽  
Mahmood Movahedipour ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Novel Method

scholarly journals Driving Stress Detection Using Multimodal Convolutional Neural Networks with Nonlinear Representation of Short-Term Physiological Signals

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2381
Author(s):  
Jaewon Lee ◽  
Hyeonjeong Lee ◽  
Miyoung Shin
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Traffic Accidents ◽  
Physiological Signals ◽  
Superior Performance ◽  
Short Term ◽  
Input Signals ◽  
Skin Response ◽  
Novel Method ◽  
Fully Connected

Mental stress can lead to traffic accidents by reducing a driver’s concentration or increasing fatigue while driving. In recent years, demand for methods to detect drivers’ stress in advance to prevent dangerous situations increased. Thus, we propose a novel method for detecting driving stress using nonlinear representations of short-term (30 s or less) physiological signals for multimodal convolutional neural networks (CNNs). Specifically, from hand/foot galvanic skin response (HGSR, FGSR) and heart rate (HR) short-term input signals, first, we generate corresponding two-dimensional nonlinear representations called continuous recurrence plots (Cont-RPs). Second, from the Cont-RPs, we use multimodal CNNs to automatically extract FGSR, HGSR, and HR signal representative features that can effectively differentiate between stressed and relaxed states. Lastly, we concatenate the three extracted features into one integrated representation vector, which we feed to a fully connected layer to perform classification. For the evaluation, we use a public stress dataset collected from actual driving environments. Experimental results show that the proposed method demonstrates superior performance for 30-s signals, with an overall accuracy of 95.67%, an approximately 2.5–3% improvement compared with that of previous works. Additionally, for 10-s signals, the proposed method achieves 92.33% classification accuracy, which is similar to or better than the performance of other methods using long-term signals (over 100 s).

scholarly journals An Efficient Algorithm for Cardiac Arrhythmia Classification Using Ensemble of Depthwise Separable Convolutional Neural Networks

2020 ◽  
Vol 10 (2) ◽  
pp. 483 ◽  
Author(s):  
Eko Ihsanto ◽  
Kalamullah Ramli ◽  
Dodi Sudiana ◽  
Teddy Surya Gunawan
Keyword(s):  
Neural Networks ◽  
Feature Extraction ◽  
Cardiac Arrhythmia ◽  
Convolutional Neural Networks ◽  
Computational Cost ◽  
Training Data ◽  
Qrs Detection ◽  
Convolutional Network ◽  
Novel Method ◽  
Electrocardiogram Ecg

Many algorithms have been developed for automated electrocardiogram (ECG) classification. Due to the non-stationary nature of the ECG signal, it is rather challenging to use traditional handcraft methods, such as time-based analysis of feature extraction and classification, to pave the way for machine learning implementation. This paper proposed a novel method, i.e., the ensemble of depthwise separable convolutional (DSC) neural networks for the classification of cardiac arrhythmia ECG beats. Using our proposed method, the four stages of ECG classification, i.e., QRS detection, preprocessing, feature extraction, and classification, were reduced to two steps only, i.e., QRS detection and classification. No preprocessing method was required while feature extraction was combined with classification. Moreover, to reduce the computational cost while maintaining its accuracy, several techniques were implemented, including All Convolutional Network (ACN), Batch Normalization (BN), and ensemble convolutional neural networks. The performance of the proposed ensemble CNNs were evaluated using the MIT-BIH arrythmia database. In the training phase, around 22% of the 110,057 beats data extracted from 48 records were utilized. Using only these 22% labeled training data, our proposed algorithm was able to classify the remaining 78% of the database into 16 classes. Furthermore, the sensitivity ( S n ), specificity ( S p ), and positive predictivity ( P p ), and accuracy ( A c c ) are 99.03%, 99.94%, 99.03%, and 99.88%, respectively. The proposed algorithm required around 180 μs, which is suitable for real time application. These results showed that our proposed method outperformed other state of the art methods.

scholarly journals PlanetScope Radiometric Normalization and Sentinel-2 Super-Resolution (2.5 m): A Straightforward Spectral-Spatial Fusion of Multi-Satellite Multi-Sensor Images Using Residual Convolutional Neural Networks

2020 ◽  
Vol 12 (15) ◽  
pp. 2366
Author(s):  
Nicolas Latte ◽  
Philippe Lejeune
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Network Architecture ◽  
State Of The Art ◽  
Super Resolution ◽  
Neural Network Architecture ◽  
Research Areas ◽  
Novel Method ◽  
Spatial Fusion ◽  
Sentinel 2

Sentinel-2 (S2) imagery is used in many research areas and for diverse applications. Its spectral resolution and quality are high but its spatial resolutions, of at most 10 m, is not sufficient for fine scale analysis. A novel method was thus proposed to super-resolve S2 imagery to 2.5 m. For a given S2 tile, the 10 S2 bands (four at 10 m and six at 20 m) were fused with additional images acquired at higher spatial resolution by the PlanetScope (PS) constellation. The radiometric inconsistencies between PS microsatellites were normalized. Radiometric normalization and super-resolution were achieved simultaneously using state-of–the-art super-resolution residual convolutional neural networks adapted to the particularities of S2 and PS imageries (including masks of clouds and shadows). The method is described in detail, from image selection and downloading to neural network architecture, training, and prediction. The quality was thoroughly assessed visually (photointerpretation) and quantitatively, confirming that the proposed method is highly spatially and spectrally accurate. The method is also robust and can be applied to S2 images acquired worldwide at any date.

Traditional Classifiers vs. Deep Learning for Cyberbullying Detection

2018 ◽  
pp. 133-149
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Search Algorithm ◽  
Learning Approach ◽  
Brute Force ◽  
Brute Force Search ◽  
Novel Method ◽  
Objective View ◽  
Cyberbullying Detection

In this chapter, the authors present their approach to cyberbullying detection with the use of various traditional classifiers, including a deep learning approach. Research has tackled the problem of cyberbullying detection during recent years. However, due to complexity of language used in cyberbullying, the results obtained with traditional classifiers has remained only mildly satisfying. In this chapter, the authors apply a number of traditional classifiers, used also in previous research, to obtain an objective view on to what extent each of them is suitable to the task. They also propose a novel method to automatic cyberbullying detection based on convolutional neural networks and increased feature density. The experiments performed on actual cyberbullying data showed a major advantage of the presented approach to all previous methods, including the two best performing methods so far based on SO-PMI-IR and brute-force search algorithm, presented in previous two chapters.

Classification of Human Metaspread Images Using Convolutional Neural Networks

2020 ◽  
pp. 2150033
Author(s):  
Tanvi Arora
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Genetic Information ◽  
Medical Condition ◽  
Genetic Defect ◽  
Ground Truth ◽  
Genetic Defects ◽  
Division Process ◽  
Novel Method

Chromosomes are the genetic information carriers. Any modification to the structure or the number of chromosomes results in a medical condition termed as genetic defect. In order to uncover the genetic defects, the chromosomes are imaged during the cell division process. The images thus generated are termed as metaspread images and are used for identifying the genetic defects. It has been observed that the metaspread images generally suffer from intensity inhomogeneity and the chromosomes are also present in varied orientations, and as a result finding genetic defects from such images is a tedious process. Therefore, cytogeneticists manually select the images that can be used for the purpose of uncovering the genetic defects and the generation of the karyotype. In the proposed approach, a novel method is being presented using DenseNet architecture of the convolutional neural networks-based classifier, which classifies the human metaspread images into two distinct categories, namely, analyzable and non-analyzable based on the orientation of the chromosomes present in the metaspread images. This classification process will help to select the most prominent metaspread images for karyotype generation that has least amount of touching and overlapping chromosomes. The proposed method is novel in comparison to the earlier methods as it works on any type of image, be it G band images, MFISH images or the Q-banded images. The proposed method has been trained by using a ground truth of 156 750 metaspread images. The proposed classifier has been able to achieve an error rate of 1.46%.

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