scholarly journals A Deep Learning Approach to Estimate the Respiratory Rate from Photoplethysmogram

Ingenius ◽  
2021 ◽  
Author(s):  
Lucas C. Lampier ◽  
Yves L. Coelho ◽  
Eliete M. O. Caldeira ◽  
Teodiano Bastos-Filho
Keyword(s):  
Neural Network ◽  
Heart Rate ◽  
Deep Learning ◽  
Open Access ◽  
Respiratory Rate ◽  
Analytical Methods ◽  
Deep Neural Network ◽  
Learning Approach ◽  
Genetic Optimization ◽  
Median Error

This article describes the methodology used to train and test a Deep Neural Network (DNN) with Photoplethysmography (PPG) data performing a regression task to estimate the Respiratory Rate (RR). The DNN architecture is based on a model used to infer the heart rate (HR) from noisy PPG signals, which is optimized to the RR problem using genetic optimization. Two open-access datasets were used in the tests, the BIDMC and the CapnoBase. With the CapnoBase dataset, the DNN achieved a median error of 1.16 breaths/min, which is comparable with analytical methods in the literature, in which the best error found is 1.1 breaths/min (excluding the 8 % noisiest data). The BIDMC dataset seems to be more challenging, as the minimum median error of the literature’s methods is 2.3 breaths/min (excluding 6 % of the noisiest data), and the DNN based approach achieved a median error of 1.52 breaths/min with the whole dataset.

scholarly journals A Deep Learning Approach to Estimate the Respiratory Rate from Photoplethysmogram

Ingenius ◽  
2021 ◽  
Author(s):  
Lucas C. Lampier ◽  
Yves L. Coelho ◽  
Eliete M. O. Caldeira ◽  
Teodiano Bastos-Filho
Keyword(s):  
Neural Network ◽  
Heart Rate ◽  
Deep Learning ◽  
Open Access ◽  
Respiratory Rate ◽  
Analytical Methods ◽  
Deep Neural Network ◽  
Learning Approach ◽  
Genetic Optimization ◽  
Median Error

This article describes the methodology used to train and test a Deep Neural Network (DNN) with Photoplethysmography (PPG) data performing a regression task to estimate the Respiratory Rate (RR). The DNN architecture is based on a model used to infer the heart rate (HR) from noisy PPG signals, which is optimized to the RR problem using genetic optimization. Two open-access datasets were used in the tests, the BIDMC and the CapnoBase. With the CapnoBase dataset, the DNN achieved a median error of 1.16 breaths/min, which is comparable with analytical methods in the literature, in which the best error found is 1.1 breaths/min (excluding the 8 % noisiest data). The BIDMC dataset seems to be more challenging, as the minimum median error of the literature’s methods is 2.3 breaths/min (excluding 6 % of the noisiest data), and the DNN based approach achieved a median error of 1.52 breaths/min with the whole dataset.

scholarly journals GAIT RECOGNITION BASED ON CONVOLUTIONAL NEURAL NETWORKS

2017 ◽  
Vol XLII-2/W4 ◽  
pp. 207-212 ◽  
Author(s):  
A. Sokolova ◽  
A. Konushin
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Feature Extraction ◽  
Deep Learning ◽  
Deep Neural Network ◽  
Gait Recognition ◽  
Learning Approach ◽  
Network Architectures ◽  
Motion Information ◽  
Advantages And Disadvantages

In this work we investigate the problem of people recognition by their gait. For this task, we implement deep learning approach using the optical flow as the main source of motion information and combine neural feature extraction with the additional embedding of descriptors for representation improvement. In order to find the best heuristics, we compare several deep neural network architectures, learning and classification strategies. The experiments were made on two popular datasets for gait recognition, so we investigate their advantages and disadvantages and the transferability of considered methods.

Deep Learning Approach for Extracting Catch Phrases from Legal Documents

2020 ◽  
pp. 143-158
Author(s):  
Kayalvizhi S. ◽  
Thenmozhi D.
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Learning ◽  
Statistical Methods ◽  
Deep Neural Network ◽  
Mean Average Precision ◽  
Machine Learning Techniques ◽  
Learning Approach ◽  
Legal Documents ◽  
Learning Techniques

Catch phrases are the important phrases that precisely explain the document. They represent the context of the whole document. They can also be used to retrieve relevant prior cases by the judges and lawyers for assuring justice in the domain of law. Currently, catch phrases are extracted using statistical methods, machine learning techniques, and deep learning techniques. The authors propose a sequence to sequence (Seq2Seq) deep neural network to extract catch phrases from legal documents. They have employed several layers, namely embedding layer, encoder-decoder layer, projection layer, and loss layer to build the deep neural network. The methodology is evaluated on IRLeD@FIRE-2017 dataset and the method has obtained 0.787 and 0.607 as mean average precision and recall scores respectively. Results show that the proposed method outperforms the existing systems.

scholarly journals A deep learning approach for detecting the behavior of people having personality disorders towards Covid-19 from Twitter

2021 ◽  
Author(s):  
Mourad Ellouze ◽  
Seifeddine Mechti ◽  
Moez Krichen ◽  
vinayakumar R ◽  
Lamia Hadrich Belguith
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Deep Learning ◽  
Text Mining ◽  
Personality Disorders ◽  
Deep Neural Network ◽  
Selection Task ◽  
Learning Approach ◽  
Features Selection ◽  
Normal Behavior

This paper proposes an architecture taking advantage of artificial intelligence and text mining techniques in order to: (i) detect paranoid people by classifying their set of tweets into two classes (Paranoid/not-Paranoid), (ii) ensure the surveillance of these people by classifying their tweets about Covid-19 into two classes (person with normal behavior, person with inappropriate behavior). These objectives are achieved using an approach that takes advantage of different information related to the textual part, user and tweets for features selection task and deep neural network for the classification task. We obtained as an F-score rate 70% for the detection of paranoid people and 73% for the detection of the behavior of these people towards Covid-19. The obtained results are motivating and encouraging researchers to improve them given the interest and the importance of this research axis.

scholarly journals Deep Neural Network Architecture Search for Wearable Heart Rate Estimations

2021 ◽  
Author(s):  
Daniel Ray ◽  
Tim Collins ◽  
Prasad Ponnapalli
Keyword(s):  
Neural Network ◽  
Heart Rate ◽  
Deep Learning ◽  
Network Architecture ◽  
Deep Neural Network ◽  
State Of The Art ◽  
Data Driven ◽  
Network Architectures ◽  
Learning Approaches ◽  
Neural Network Architecture

Extracting accurate heart rate estimations from wrist-worn photoplethysmography (PPG) devices is challenging due to the signal containing artifacts from several sources. Deep Learning approaches have shown very promising results outperforming classical methods with improvements of 21% and 31% on two state-of-the-art datasets. This paper provides an analysis of several data-driven methods for creating deep neural network architectures with hopes of further improvements.

scholarly journals A deep learning approach for detecting the behavior of people having personality disorders towards Covid-19 from Twitter

2021 ◽  
Author(s):  
Mourad Ellouze ◽  
Seifeddine Mechti ◽  
Moez Krichen ◽  
vinayakumar R ◽  
Lamia Hadrich Belguith
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Deep Learning ◽  
Text Mining ◽  
Personality Disorders ◽  
Deep Neural Network ◽  
Selection Task ◽  
Learning Approach ◽  
Features Selection ◽  
Normal Behavior

This paper proposes an architecture taking advantage of artificial intelligence and text mining techniques in order to: (i) detect paranoid people by classifying their set of tweets into two classes (Paranoid/not-Paranoid), (ii) ensure the surveillance of these people by classifying their tweets about Covid-19 into two classes (person with normal behavior, person with inappropriate behavior). These objectives are achieved using an approach that takes advantage of different information related to the textual part, user and tweets for features selection task and deep neural network for the classification task. We obtained as an F-score rate 70% for the detection of paranoid people and 73% for the detection of the behavior of these people towards Covid-19. The obtained results are motivating and encouraging researchers to improve them given the interest and the importance of this research axis.

scholarly journals When Image Denoising Meets High-Level Vision Tasks: A Deep Learning Approach

2018 ◽  
Author(s):  
Ding Liu ◽  
Bihan Wen ◽  
Xianming Liu ◽  
Zhangyang Wang ◽  
Thomas Huang
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Image Denoising ◽  
Deep Neural Network ◽  
Mutual Influence ◽  
State Of The Art ◽  
Back Propagation ◽  
Learning Approach ◽  
Network Solution ◽  
High Level

Conventionally, image denoising and high-level vision tasks are handled separately in computer vision. In this paper, we cope with the two jointly and explore the mutual influence between them. First we propose a convolutional neural network for image denoising which achieves the state-of-the-art performance. Second we propose a deep neural network solution that cascades two modules for image denoising and various high-level tasks, respectively, and use the joint loss for updating only the denoising network via back-propagation. We demonstrate that on one hand, the proposed denoiser has the generality to overcome the performance degradation of different high-level vision tasks. On the other hand, with the guidance of high-level vision information, the denoising network can generate more visually appealing results. To the best of our knowledge, this is the first work investigating the benefit of exploiting image semantics simultaneously for image denoising and high-level vision tasks via deep learning.

scholarly journals Robust Approach to Supervised Deep Neural Network Training for Real-Time Object Classification in Cluttered Indoor Environment

2021 ◽  
Vol 11 (15) ◽  
pp. 7148
Author(s):  
Bedada Endale ◽  
Abera Tullu ◽  
Hayoung Shi ◽  
Beom-Soo Kang
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Real Time ◽  
Network Architecture ◽  
Input Data ◽  
Deep Neural Network ◽  
Data Augmentation ◽  
Object Classification ◽  
Training Data ◽  
Gradient Descent Algorithm

Unmanned aerial vehicles (UAVs) are being widely utilized for various missions: in both civilian and military sectors. Many of these missions demand UAVs to acquire artificial intelligence about the environments they are navigating in. This perception can be realized by training a computing machine to classify objects in the environment. One of the well known machine training approaches is supervised deep learning, which enables a machine to classify objects. However, supervised deep learning comes with huge sacrifice in terms of time and computational resources. Collecting big input data, pre-training processes, such as labeling training data, and the need for a high performance computer for training are some of the challenges that supervised deep learning poses. To address these setbacks, this study proposes mission specific input data augmentation techniques and the design of light-weight deep neural network architecture that is capable of real-time object classification. Semi-direct visual odometry (SVO) data of augmented images are used to train the network for object classification. Ten classes of 10,000 different images in each class were used as input data where 80% were for training the network and the remaining 20% were used for network validation. For the optimization of the designed deep neural network, a sequential gradient descent algorithm was implemented. This algorithm has the advantage of handling redundancy in the data more efficiently than other algorithms.

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