# A Review of Converging Technologies in eHealth Pertaining to Artificial Intelligence
## 2 Overview of Healthcare Applications
### eHealth
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Authors** & **Work Description** & **Results** & **Technologies Employed** \\ \hline \multirow{3}{*}{[15]} & Decentralized, patient-centric & Interoperability between healthcare platform & \multirow{3}{*}{Artificial intelligence, blockchain} \\  & healthcare system framework & & \\  & security. & & \\ \hline \multirow{3}{*}{[16]} & \multirow{3}{*}{Virtual coaching system for older adults} & Increasing elders’ engagement with a conversational & \multirow{3}{*}{Functional demonstrator of eHealth application COUCH} \\  & & agent-based eHealth platform to provide modern & \\  & Virtual coaching system for & healthcare services to less tech-savvy patients; study & \\  & older adults & limitations include selection bias, lack of & \\  & & personalized content and testing remotely because & \\  & & of COVID-19 restrictions. & \\ \hline \multirow{3}{*}{[17]} & \multirow{3}{*}{Epileptic seizure prediction embedded system using EEG} & Good accuracy with reduced number of electrodes; & \multirow{3}{*}{Texas Instruments MSP432 low-power device, ECG, IoT} \\  & & low power consumption; running on IoT devices; & \\  & Epileptic seizure prediction & measurements of energy consumption and execution & \\  & embedded system using EEG & time for processing EEG data segments; EEG data & \\  & & from ambulatory monitoring system with 16 & \\  & electrodes, 400 Hz sampling rate in 10 min clips. & \\ \hline \multirow{3}{*}{[18]} & System used to identify & \multirow{3}{*}{High accuracy of classifier (92.86\%); data consisted of 46 recordings with 63 channels, 4–6 min of EEG data; data analysis was challenging.} & Artificial intelligence/deep learning model based on long short-term memory (LSTM) \\  & & & \\ \hline \multirow{3}{*}{[19]} & Feature extraction for motor & \multirow{3}{*}{New method with good classification accuracy} & \multirow{3}{*}{Brain–computer interface, novel filters, EEG} \\  & imagery brain–computer & & \\  & interface & & \\  & & IV dataset 2a and 2b). & \\ \hline \multirow{3}{*}{[20]} & \multirow{3}{*}{Stress monitoring system for everyday use} & Hierarchical edge-cloud obtains lower response time & \multirow{3}{*}{Artificial intelligence, CNN, IoT} \\  & & by 77.89\% and energy consumption by 78.56\%; & \\  & everyday use & models in the cloud; high computational effort and & \\  & & missing data proved challenging. & \\ \hline \multirow{3}{*}{[21]} & IoT smart eHealth system & \multirow{3}{*}{Improved transmission rate resulting in more active users; verified by simulations with NS-3 tool.} & Cryptosystem, MAC verification \\  & & & \\ \hline \multirow{3}{*}{[22]} & Body area network-based & \multirow{3}{*}{Efficient system that can analyze substantial amounts of data in real-time; data recorded from an ECG sensor with 3 channels and 4 accelerometer nodes.} & Body area network (BAN), acceleration, ECG sensors \\  & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: eHealth applications. By combining filter banks and Riemannian tangent space (FBRTS), a fusion method for feature extraction in motor imagery brain-computer interfaces (MI-BCIs) is proposed in [19], with good classification accuracy in both datasets it was tested in, and great applicability in eHealth by interpreting EEG signals. A wearable Internet of Things (WIoT) hybrid edge-cloud online stress-monitoring solution [20] can achieve a much higher accuracy when offloading data to the cloud models compared to the edge ones. The importance of data confidentiality has led researchers to work on creating privacy-preserving seamless authentication frameworks, optimizing authentication processes by reducing the number of computations and offering users anonymity through dynamic identities in smart Internet of Things (IoT) implementations [21], attempting to enhance the security of healthcare systems and to secure the transmission of data between intended parties.