Chaotic Function Based ECG Encryption System

2017 ◽  
pp. 205-221
Author(s):  
Butta Singh ◽  
Manjit Singh ◽  
Dixit Sharma
Keyword(s):  
Health Care ◽  
Security Analysis ◽  
Medical Data ◽  
Monitoring Systems ◽  
Challenging Issue ◽  
Health Care Monitoring ◽  
Electrocardiogram Ecg ◽  
Ecg Encryption ◽  
Remote Health

Remote health-care monitoring systems communicate biomedical information (e.g. Electrocardiogram (ECG)) over insecure networks. Protection of the integrity, authentication and confidentiality of the medical data is a challenging issue. This chapter proposed an encryption process having a 4-round five steps -encryption structure includes: the random pixel insertion, row separation, substitution of each separated row, row combination and rotation. Accuracy and security analysis of proposed method for 2D ECG encryption is evaluated on MIT-BIH arrhythmia database.

scholarly journals Chaotic Function Based ECG Encryption System

Cryptography ◽  
2020 ◽  
pp. 22-38 ◽  
Author(s):  
Butta Singh ◽  
Manjit Singh ◽  
Dixit Sharma
Keyword(s):  
Health Care ◽  
Security Analysis ◽  
Medical Data ◽  
Monitoring Systems ◽  
Challenging Issue ◽  
Health Care Monitoring ◽  
Electrocardiogram Ecg ◽  
Ecg Encryption ◽  
Remote Health

Remote health-care monitoring systems communicate biomedical information (e.g. Electrocardiogram (ECG)) over insecure networks. Protection of the integrity, authentication and confidentiality of the medical data is a challenging issue. This chapter proposed an encryption process having a 4-round five steps -encryption structure includes: the random pixel insertion, row separation, substitution of each separated row, row combination and rotation. Accuracy and security analysis of proposed method for 2D ECG encryption is evaluated on MIT-BIH arrhythmia database.

Review on the wearable health-care monitoring system with robust motion artifacts reduction techniques

Sensor Review ◽  
2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Aarthy Prabakaran ◽  
Elizabeth Rufus
Keyword(s):  
Health Care ◽  
Monitoring System ◽  
Learning Algorithm ◽  
Motion Artifacts ◽  
Sensor Data ◽  
Monitoring Systems ◽  
Design Modification ◽  
Content Type ◽  
Wearable Monitoring Systems ◽  
Health Care Monitoring

Purpose Wearables are gaining prominence in the health-care industry and their use is growing. The elderly and other patients can use these wearables to monitor their vitals at home and have them sent to their doctors for feedback. Many studies are being conducted to improve wearable health-care monitoring systems to obtain clinically relevant diagnoses. The accuracy of this system is limited by several challenges, such as motion artifacts (MA), power line interference, false detection and acquiring vitals using dry electrodes. This paper aims to focus on wearable health-care monitoring systems in the literature and provides the effect of MA on the wearable system. Also presents the problems faced while tracking the vitals of users. Design/methodology/approach MA is a major concern and certainly needs to be suppressed. An analysis of the causes and effects of MA on wearable monitoring systems is conducted. Also, a study from the literature on motion artifact detection and reduction is carried out and presented here. The benefits of a machine learning algorithm in a wearable monitoring system are also presented. Finally, distinct applications of the wearable monitoring system have been explored. Findings According to the study reduction of MA and multiple sensor data fusion increases the accuracy of wearable monitoring systems. Originality/value This study also presents the outlines of design modification of dry/non-contact electrodes to minimize the MA. Also, discussed few approaches to design an efficient wearable health-care monitoring system.

scholarly journals Review on Smart Health Care Monitoring Systems

2021 ◽  
pp. 418-422
Author(s):  
Vasishth V. Katre ◽  
Dr. P. N. Chatur
Keyword(s):  
Machine Learning ◽  
Health Care ◽  
Health System ◽  
Monitoring Systems ◽  
Learning Approach ◽  
Health Issues ◽  
Medical Field ◽  
Machine Learning Approach ◽  
Smart Health ◽  
Health Care Monitoring

Document IoT is leading in smart health care system. Using different sensors it's possible to monitor the patients healthcare remotely. This is unimagined and leads to a spatial longitude amalgamated with machine learning approach. Leading to smart health care, and headway in medical field. It may lead to know severe health issues ahead of time which would be tranquil to the health system. Which would benefit the hospital administration and management. This paper elucidates on the distinct sort of IoT based health care monitoring systems. The aim is to juxtapose the present health care IoT systems.

scholarly journals A Classical Method for Health Monitoring on IoT: From Architecture, Security & Application

2019 ◽  
Vol 8 (11S) ◽  
pp. 1043-1046
Keyword(s):  
Health Care ◽  
Intelligent System ◽  
Classical Method ◽  
Real Life ◽  
Health Data ◽  
Monitoring Systems ◽  
Healthcare Monitoring ◽  
Multiple Sensor ◽  
Iot Devices ◽  
Health Care Monitoring

The development of smart health care monitoring systems, and services is driven by the development of the Internet of Things (IoT). IoT devices produce major role in todays healthcare monitoring systems large number of systems are in this field developing to track health data. The data generated by these devices has to face numerous security related requirements in order to be useful in real life. Among these one of the requirements is provide a detail approach to meet complete intelligent system to monitor health records, The theme proposed here is collect data from multiple sensor use and filter useful information about present state for identifying and integrating the health status of a person. Apart from this how to share health data in secure platform and authenticated architecture node in IoT. In proposed work describes the usage of IoT in health care data in a classical manner which in bounded in an architecture and system devices.

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