Deep learning based smart health monitoring for automated prediction of epileptic seizures using spectral analysis of scalp EEG

2021 ◽  
Author(s):  
Kuldeep Singh ◽  
Jyoteesh Malhotra
Keyword(s):  
Spectral Analysis ◽  
Deep Learning ◽  
Health Monitoring ◽  
Epileptic Seizures ◽  
Scalp Eeg ◽  
Smart Health ◽  
Automated Prediction

scholarly journals Systematic review of smart health monitoring using deep learning and Artificial intelligence

2021 ◽  
pp. 100028
Author(s):  
A.V.L.N. Sujith ◽  
Guna Sekhar Sajja ◽  
V. Mahalakshmi ◽  
Shibili Nuhmani ◽  
B. Prasanalakshmi
Keyword(s):  
Artificial Intelligence ◽  
Systematic Review ◽  
Deep Learning ◽  
Health Monitoring ◽  
Smart Health

Smart Healthcare for ECG Telemonitoring System

2021 ◽  
Vol 2 (2) ◽  
Author(s):  
Jwan Najeeb Saeed ◽  
◽  
Siddeeq Y. Ameen ◽  
Keyword(s):  
Health Care ◽  
Deep Learning ◽  
Activity Recognition ◽  
Human Activity ◽  
Health Monitoring ◽  
Feature Learning ◽  
Human Activity Recognition ◽  
Smart Healthcare ◽  
Learning Techniques ◽  
Smart Health

Cardiovascular disorders are one of the major causes of sad death among older and middle-aged people. Over the past two decades, health monitoring services have evolved quickly and had the ability to change the way health care is currently provided. However, the most challenging aspect of the mobile and wearable sensor-based human activity recognition pipeline is the extraction of the related features. Feature extraction decreases both computational complexity and time. Deep learning techniques are used for automatic feature learning in a variety of fields, including health, image classification, and, most recently, for the extraction and classification of complex and straightforward human activity recognition in smart health care. This paper reviews the recent state of the art in electrocardiogram (ECG) smart health monitoring systems based on the Internet of things with the machine and deep learning techniques. Moreover, the paper provided possible research and challenges that can help researchers advance state of art in future work.

scholarly journals Smart Health Monitoring Bodyarea Network Using Raspberry Pi

2021 ◽  
Vol 1916 (1) ◽  
pp. 012218
Author(s):  
K Mohil Varmaa ◽  
K Prendra ◽  
K V Ranjith ◽  
T Robinsingh ◽  
V Nandalal ◽  
...  
Keyword(s):  
Health Monitoring ◽  
Raspberry Pi ◽  
Smart Health

scholarly journals Introducing the Special Issue on “Ubiquitous Sensing for Smart Health Monitoring”

Information ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 74
Author(s):  
Yusuf A. Bhagat
Keyword(s):  
Health Monitoring ◽  
Special Issue ◽  
Ubiquitous Sensing ◽  
Smart Health

Sensors continue to pervade our surroundings in undiminished ways [...]

scholarly journals A Recent Investigation on Detection and Classification of Epileptic Seizure Techniques Using EEG Signal

2021 ◽  
Vol 11 (5) ◽  
pp. 668
Author(s):  
Sani Saminu ◽  
Guizhi Xu ◽  
Zhang Shuai ◽  
Isselmou Abd El Kader ◽  
Adamu Halilu Jabire ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Algorithms ◽  
Epileptic Seizures ◽  
Seizure Detection ◽  
Performance Comparison ◽  
Eeg Signal ◽  
Detection Algorithms ◽  
Advantages And Disadvantages ◽  
Monitoring And Diagnosis

The benefits of early detection and classification of epileptic seizures in analysis, monitoring and diagnosis for the realization and actualization of computer-aided devices and recent internet of medical things (IoMT) devices can never be overemphasized. The success of these applications largely depends on the accuracy of the detection and classification techniques employed. Several methods have been investigated, proposed and developed over the years. This paper investigates various seizure detection algorithms and classifications in the last decade, including conventional techniques and recent deep learning algorithms. It also discusses epileptiform detection as one of the steps towards advanced diagnoses of disorders of consciousness (DOCs) and their understanding. A performance comparison was carried out on the different algorithms investigated, and their advantages and disadvantages were explored. From our survey, much attention has recently been paid to exploring the efficacy of deep learning algorithms in seizure detection and classification, which are employed in other areas such as image processing and classification. Hybrid deep learning has also been explored, with CNN-RNN being the most popular.

scholarly journals Data-Driven Structural Health Monitoring and Damage Detection through Deep Learning: State-of-the-Art Review

Sensors ◽  
2020 ◽  
Vol 20 (10) ◽  
pp. 2778 ◽  
Author(s):  
Mohsen Azimi ◽  
Armin Eslamlou ◽  
Gokhan Pekcan
Keyword(s):  
Deep Learning ◽  
Structural Health Monitoring ◽  
Health Monitoring ◽  
High Speed ◽  
Deep Neural Networks ◽  
State Of The Art ◽  
Data Driven ◽  
Structural Health ◽  
Promising Tool ◽  
Significant Attention

Data-driven methods in structural health monitoring (SHM) is gaining popularity due to recent technological advancements in sensors, as well as high-speed internet and cloud-based computation. Since the introduction of deep learning (DL) in civil engineering, particularly in SHM, this emerging and promising tool has attracted significant attention among researchers. The main goal of this paper is to review the latest publications in SHM using emerging DL-based methods and provide readers with an overall understanding of various SHM applications. After a brief introduction, an overview of various DL methods (e.g., deep neural networks, transfer learning, etc.) is presented. The procedure and application of vibration-based, vision-based monitoring, along with some of the recent technologies used for SHM, such as sensors, unmanned aerial vehicles (UAVs), etc. are discussed. The review concludes with prospects and potential limitations of DL-based methods in SHM applications.

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