Extending the Sana Mobile Healthcare Platform with Features Providing ECG Analysis

2017 ◽  
pp. 289-321 ◽  
Author(s):  
Katerina Tsampi ◽  
Spyros Panagiotakis ◽  
Elias Hatzakis ◽  
Emmanouil Lakiotakis ◽  
Georgia Atsali ◽  
...  
Keyword(s):  
Ecg Analysis ◽  
Mobile Healthcare

Is it time to abandon cardiotocographic ECG analysis?

The Lancet ◽  
2000 ◽  
Vol 355 (9197) ◽  
pp. 422 ◽  
Author(s):  
M Whittle
Keyword(s):  
Ecg Analysis

ECG Analysis: A Brief Review

2019 ◽  
Vol 12 ◽  
Author(s):  
Saumendra Kumar Mohapatra ◽  
Mihir Narayan Mohanty
Keyword(s):  
Technology Development ◽  
Accurate Result ◽  
Early Stage ◽  
Heart Diseases ◽  
Short Review ◽  
Disease Diagnosis ◽  
Accurate Analysis ◽  
Ecg Analysis ◽  
Proper Analysis ◽  
Living Being

Background: In recent years cardiac problems found proportional to technology development. Cardiac signal (Electrocardiogram) relates to the electrical activity of the heart of a living being and it is an important tool for diagnosis of heart diseases. Method: Accurate analysis of ECG signal can provide support for detection, classification, and diagnosis. Physicians can detect the disease and start the diagnosis at an early stage. Apart from cardiac disease diagnosis ECG can be used for emotion recognition, heart rate detection, and biometric identification. Objective: The objective of this paper is to provide a short review of earlier techniques used for ECG analysis. It can provide support to the researchers in a new direction. The review is based on preprocessing, feature extraction, classification, and different measuring parameters for accuracy proof. Also, different data sources for getting the cardiac signal is presented and various application area of the ECG analysis is presented. It explains the work from 2008 to 2018. Conclusion: Proper analysis of the cardiac signal is essential for better diagnosis. In automated ECG analysis, it is essential to get an accurate result. Numerous techniques were addressed by the researchers for the analysis of ECG. It is important to know different steps related to ECG analysis. A review is done based on different stages of ECG analysis and its applications in society.

scholarly journals Reinforcement Learning to Send Reminders at Right Moments in Smartphone Exercise Application: A Feasibility Study

2021 ◽  
Vol 18 (11) ◽  
pp. 6059
Author(s):  
Shihan Wang ◽  
Karlijn Sporrel ◽  
Herke van Hoof ◽  
Monique Simons ◽  
Rémi D. D. de Boer ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Physical Activities ◽  
Lessons Learned ◽  
Just In Time ◽  
Healthcare Intervention ◽  
Adaptive Intervention ◽  
Mobile Healthcare ◽  
Crucial Component ◽  
Application Data ◽  
The Right

Just-in-time adaptive intervention (JITAI) has gained attention recently and previous studies have indicated that it is an effective strategy in the field of mobile healthcare intervention. Identifying the right moment for the intervention is a crucial component. In this paper the reinforcement learning (RL) technique has been used in a smartphone exercise application to promote physical activity. This RL model determines the ‘right’ time to deliver a restricted number of notifications adaptively, with respect to users’ temporary context information (i.e., time and calendar). A four-week trial study was conducted to examine the feasibility of our model with real target users. JITAI reminders were sent by the RL model in the fourth week of the intervention, while the participants could only access the app’s other functionalities during the first 3 weeks. Eleven target users registered for this study, and the data from 7 participants using the application for 4 weeks and receiving the intervening reminders were analyzed. Not only were the reaction behaviors of users after receiving the reminders analyzed from the application data, but the user experience with the reminders was also explored in a questionnaire and exit interviews. The results show that 83.3% reminders sent at adaptive moments were able to elicit user reaction within 50 min, and 66.7% of physical activities in the intervention week were performed within 5 h of the delivery of a reminder. Our findings indicated the usability of the RL model, while the timing of the moments to deliver reminders can be further improved based on lessons learned.

The relationship between patient donation and satisfaction with online healthcare service quality

2019 ◽  
Vol 47 (12) ◽  
pp. 1-10
Author(s):  
Yuanrong Hu ◽  
Shengkang Lu ◽  
Zhongming Tang
Keyword(s):  
Patient Satisfaction ◽  
Healthcare Service ◽  
Outcome Quality ◽  
Healthcare Applications ◽  
Mobile Healthcare ◽  
Process And Outcome ◽  
Patient Consultation ◽  
The Impact ◽  
The Relationship

We explored how donation relates to patient satisfaction with the quality of process and outcome in an online healthcare service. Using a dataset of 496,723 patient consultation records collected from ChunyuDoctor, which is among the largest of the Chinese mobile healthcare applications, we conducted a multiple regression and found that patient satisfaction with both process and outcome jointly influenced their donation. We also found that higher quality satisfaction levels meant paying patients were more likely to donate than were free patients. Our results also showed satisfaction with the quality of the process and the outcome had an equal impact on patient donation for the free patients, but the impact of process quality was greater than that of outcome quality for the paying patients, suggesting the importance of enhancing the quality of the process in an online healthcare service. Implications of the findings are discussed.

A Secure Robust and Privacy Enhanced Mobile Healthcare Framework

2018 ◽  
Vol 15 (3) ◽  
pp. 61-81
Author(s):  
Hisham M. Alsaghier ◽  
Shaik Shakeel Ahamad
Keyword(s):  
Health Systems ◽  
Mobile Health ◽  
Integrated Circuit ◽  
Cost Effective ◽  
Healthcare Services ◽  
Healthcare Industry ◽  
Continuous Transition ◽  
Trusted Platform Module ◽  
Mobile Healthcare ◽  
Mobile Health Systems

This article describes how the exponential growth of mobile applications has changed the way healthcare services function, and mobile healthcare using the Cloud is the most promising technology for healthcare industry. The mobile healthcare industry is in a continuous transition phase that requires continual innovation. There has been identified some of the challenges in the area of security protocols for mobile health systems which still need to be addressed in the future to enable cost-effective, secure and robust mobile health systems. This article addresses these challenges by proposing a secure robust and privacy-enhanced mobile healthcare framework (SRPF) by adopting a Community Cloud (CC), WPKI cryptosystems, Universal Integrated Circuit Cards (UICCs) and a Trusted Platform Module (TPM). All the security properties are provided within this framework. SRPF overcomes replay attacks, Man in the Middle (MITM) Attacks, Impersonation attacks and Multi-Protocol attacks as SRPF was successfully verified using a scyther tool and by BAN logic.

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