scholarly journals RETRACTION NOTICE: Atrial fibrillation classification using deep learning algorithm in Internet of Things–based smart healthcare system

2020 ◽  
pp. 146045822096875
Keyword(s):  
Atrial Fibrillation ◽  
Deep Learning ◽  
Internet Of Things ◽  
Healthcare System ◽  
Learning Algorithm ◽  
Deep Learning Algorithm ◽  
Smart Healthcare ◽  
Retraction Notice

scholarly journals Deep Learning and Internet of Things for Beach Monitoring: An Experimental Study of Beach Attendance Prediction at Castelldefels Beach

2021 ◽  
Vol 11 (22) ◽  
pp. 10735
Author(s):  
Mari Carmen Domingo
Keyword(s):  
Experimental Study ◽  
Deep Learning ◽  
Internet Of Things ◽  
Cyber Security ◽  
Learning Algorithm ◽  
Absolute Error ◽  
K Nearest Neighbors ◽  
Smart Transportation ◽  
Deep Learning Algorithm ◽  
Smart Healthcare

Smart seaside cities can fully exploit the capabilities brought by Internet of Things (IoT) and artificial intelligence to improve the efficiency of city services in traditional smart city applications: smart home, smart healthcare, smart transportation, smart surveillance, smart environment, cyber security, etc. However, smart coastal cities are characterized by their specific application domain, namely, beach monitoring. Beach attendance prediction is a beach monitoring application of particular importance for coastal managers to successfully plan beach services in terms of security, rescue, health and environmental assistance. In this paper, an experimental study that uses IoT data and deep learning to predict the number of beach visitors at Castelldefels beach (Barcelona, Spain) was developed. Images of Castelldefels beach were captured by a video monitoring system. An image recognition software was used to estimate beach attendance. A deep learning algorithm (deep neural network) to predict beach attendance was developed. The experimental results prove the feasibility of Deep Neural Networks (DNNs) for beach attendance prediction. For each beach, a classification of occupancy was estimated, depending on the number of beach visitors. The proposed model outperforms other machine learning models (decision tree, k-nearest neighbors, and random forest) and can successfully classify seven beach occupancy levels with the Mean Absolute Error (MAE), accuracy, precision, recall and F1-score of 0.03, 92.7%, 92.9%, 92.7%, and 92.7%, respectively.

scholarly journals DEEP LEARNING-BASED OBJECT RECOGNITION ROBOT CONTROL VIA WEB AND MOBILE USING AN INTERNET OF THINGS (IoT) CONNECTION

Kursor ◽  
2020 ◽  
Vol 10 (4) ◽  
Author(s):  
Basuki Rahmat ◽  
Budi Nugroho
Keyword(s):  
Deep Learning ◽  
Internet Of Things ◽  
Robot Control ◽  
Learning Algorithm ◽  
Control Program ◽  
Robotic Control ◽  
Intelligent Surveillance ◽  
Deep Learning Algorithm ◽  
Electronic Board ◽  
Mobile Robot Control

The paper presents the intelligent surveillance robotic control techniques via web and mobile via an Internet of Things (IoT) connection. The robot is equipped with a Kinect Xbox 360 camera and a Deep Learning algorithm for recognizing objects in front of it. The Deep Learning algorithm used is OpenCV's Deep Neural Network (DNN). The intelligent surveillance robot in this study was named BNU 4.0. The brain controlling this robot is the NodeMCU V3 microcontroller. Electronic board based on the ESP8266 chip. With this chip, NodeMCU V3 can connect to the cloud Internet of Things (IoT). Cloud IoT used in this research is cloudmqtt (https://www.cloudmqtt.com). With the Arduino program embedded in the NodeMCU V3 microcontroller, it can then run the robot control program via web and mobile. The mobile robot control program uses the Android MQTT IoT Application Panel.

scholarly journals Detection of Atrial Fibrillation Using a Ring-Type Wearable Device (CardioTracker) and Deep Learning Analysis of Photoplethysmography Signals: Prospective Observational Proof-of-Concept Study (Preprint)

2019 ◽  
Author(s):  
Soonil Kwon ◽  
Joonki Hong ◽  
Eue-Keun Choi ◽  
Byunghwan Lee ◽  
Changhyun Baik ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Deep Learning ◽  
Diagnostic Accuracy ◽  
Predictive Value ◽  
Learning Algorithm ◽  
Wearable Device ◽  
Control Group ◽  
Ring Type ◽  
Deep Learning Algorithm ◽  
Learning Analysis

BACKGROUND Continuous photoplethysmography (PPG) monitoring with a wearable device may aid the early detection of atrial fibrillation (AF). OBJECTIVE We aimed to evaluate the diagnostic performance of a ring-type wearable device (CardioTracker, CART), which can detect AF using deep learning analysis of PPG signals. METHODS Patients with persistent AF who underwent cardioversion were recruited prospectively. We recorded PPG signals at the finger with CART and a conventional pulse oximeter before and after cardioversion over a period of 15 min (each instrument). Cardiologists validated the PPG rhythms with simultaneous single-lead electrocardiography. The PPG data were transmitted to a smartphone wirelessly and analyzed with a deep learning algorithm. We also validated the deep learning algorithm in 20 healthy subjects with sinus rhythm (SR). RESULTS In 100 study participants, CART generated a total of 13,038 30-s PPG samples (5850 for SR and 7188 for AF). Using the deep learning algorithm, the diagnostic accuracy, sensitivity, specificity, positive-predictive value, and negative-predictive value were 96.9%, 99.0%, 94.3%, 95.6%, and 98.7%, respectively. Although the diagnostic accuracy decreased with shorter sample lengths, the accuracy was maintained at 94.7% with 10-s measurements. For SR, the specificity decreased with higher variability of peak-to-peak intervals. However, for AF, CART maintained consistent sensitivity regardless of variability. Pulse rates had a lower impact on sensitivity than on specificity. The performance of CART was comparable to that of the conventional device when using a proper threshold. External validation showed that 94.99% (16,529/17,400) of the PPG samples from the control group were correctly identified with SR. CONCLUSIONS A ring-type wearable device with deep learning analysis of PPG signals could accurately diagnose AF without relying on electrocardiography. With this device, continuous monitoring for AF may be promising in high-risk populations. CLINICALTRIAL ClinicalTrials.gov NCT04023188; https://clinicaltrials.gov/ct2/show/NCT04023188

scholarly journals Smart Healthcare System Based on Cloud-Internet of Things and Deep Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Benzhen Guo ◽  
Yanli Ma ◽  
Jingjing Yang ◽  
Zhihui Wang
Keyword(s):  
Deep Learning ◽  
Health Status ◽  
Internet Of Things ◽  
Healthcare System ◽  
Learning Model ◽  
Gray Scale ◽  
Data Set ◽  
Smart Healthcare ◽  
Cloud Server ◽  
Deep Learning Model

Introduction. Health monitoring and remote diagnosis can be realized through Smart Healthcare. In view of the existing problems such as simple measurement parameters of wearable devices, huge computing pressure of cloud servers, and lack of individualization of diagnosis, a novel Cloud-Internet of Things (C-IOT) framework for medical monitoring is put forward. Methods. Smart phones are adopted as gateway devices to achieve data standardization and preprocess to generate health gray-scale map uploaded to the cloud server. The cloud server realizes the business logic processing and uses the deep learning model to carry out the gray-scale map calculation of health parameters. A deep learning model based on the convolution neural network (CNN) is constructed, in which six volunteers are selected to participate in the experiment, and their health data are marked by private doctors to generate initial data set. Results. Experimental results show the feasibility of the proposed framework. The test data set is used to test the CNN model after training; the forecast accuracy is over 77.6%. Conclusion. The CNN model performs well in the recognition of health status. Collectively, this Smart Healthcare System is expected to assist doctors by improving the diagnosis of health status in clinical practice.

scholarly journals Detection of Atrial Fibrillation Using a Ring-Type Wearable Device (CardioTracker) and Deep Learning Analysis of Photoplethysmography Signals: Prospective Observational Proof-of-Concept Study

10.2196/16443 ◽  
2020 ◽  
Vol 22 (5) ◽  
pp. e16443
Author(s):  
Soonil Kwon ◽  
Joonki Hong ◽  
Eue-Keun Choi ◽  
Byunghwan Lee ◽  
Changhyun Baik ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Deep Learning ◽  
Diagnostic Accuracy ◽  
Predictive Value ◽  
Learning Algorithm ◽  
Wearable Device ◽  
Control Group ◽  
Ring Type ◽  
Deep Learning Algorithm ◽  
Learning Analysis

Background Continuous photoplethysmography (PPG) monitoring with a wearable device may aid the early detection of atrial fibrillation (AF). Objective We aimed to evaluate the diagnostic performance of a ring-type wearable device (CardioTracker, CART), which can detect AF using deep learning analysis of PPG signals. Methods Patients with persistent AF who underwent cardioversion were recruited prospectively. We recorded PPG signals at the finger with CART and a conventional pulse oximeter before and after cardioversion over a period of 15 min (each instrument). Cardiologists validated the PPG rhythms with simultaneous single-lead electrocardiography. The PPG data were transmitted to a smartphone wirelessly and analyzed with a deep learning algorithm. We also validated the deep learning algorithm in 20 healthy subjects with sinus rhythm (SR). Results In 100 study participants, CART generated a total of 13,038 30-s PPG samples (5850 for SR and 7188 for AF). Using the deep learning algorithm, the diagnostic accuracy, sensitivity, specificity, positive-predictive value, and negative-predictive value were 96.9%, 99.0%, 94.3%, 95.6%, and 98.7%, respectively. Although the diagnostic accuracy decreased with shorter sample lengths, the accuracy was maintained at 94.7% with 10-s measurements. For SR, the specificity decreased with higher variability of peak-to-peak intervals. However, for AF, CART maintained consistent sensitivity regardless of variability. Pulse rates had a lower impact on sensitivity than on specificity. The performance of CART was comparable to that of the conventional device when using a proper threshold. External validation showed that 94.99% (16,529/17,400) of the PPG samples from the control group were correctly identified with SR. Conclusions A ring-type wearable device with deep learning analysis of PPG signals could accurately diagnose AF without relying on electrocardiography. With this device, continuous monitoring for AF may be promising in high-risk populations. Trial Registration ClinicalTrials.gov NCT04023188; https://clinicaltrials.gov/ct2/show/NCT04023188

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