Tapotosh Ghosh

Recent advancements in the manufacturing and commercialisation of miniaturised sensors and low-cost wearables have enabled an effortless monitoring of lifestyle by detecting and analysing physiological signals. Heart rate variability (HRV) denotes the time interval between consecutive heartbeats.The HRV signal, as detected by the sensors and devices, has been popularly used as an indicative measure to estimate the level of stress, depression, and anxiety. For years, artificial intelligence (AI)-based learning systems have been known for their predictive capabilities, and in recent years, AI models with deep learning (DL) architectures have been successfully applied to achieve unprecedented accuracy. In order to determine effective methodologies applied to the collection, processing, and prediction of stress from HRV data, this work presents an in depth analysis of 43 studies reporting the application of various AI algorithms. The methods are summarised in tables and thoroughly evaluated to ensure the completeness of their findings and reported results. To make the work comprehensive, a detailed review has been conducted on sensing technologies, pre-processing methods applied on multi-modal data, and employed prediction models. This is followed by a critical examination of how various Machine Learning (ML) models, have been utilised in predicting stress from HRV data. In addition, the reported reseults from the selected studies have been carefully analysed to identify features that enable the models to perform better. Finally, the challenges of using HRV to predict stress are listed, along with some possible mitigation strategies. This work aims to highlight the impact of AI-based stress prediction methodologies from HRV data, and is expected to aid the development of more meticulous techniques.

Alzheimer’s disease has become a major concern in the healthcare domain as it is growing rapidly. Much research has been conducted to detect it from MRI images through various deep learning approaches.However, the problems of the availability of medical data and preserving the privacy of patients still exists. To mitigate this issue in Alzheimer’s disease detection, we implement the federated approach, which is found to be more efficient, robust, and consistent compared with the conventional approach. For this, we need deep excavation on various orientations of MRI images and transfer learning architectures. Then, we utilize two publicly available datasets (OASIS and ADNI) and design various cases to evaluate the performance of the federated approach. The federated approach achieves better accuracy and sensitivity compared with the conventional approaches in most of the cases. Moreover, the robustness of the proposed approach is also found to be better than the conventional approach. In our federated approach, MobileNet, a low-cost transfer learning architecture, achieves the highest 95.24%, 81.94%, and 83.97% accuracy in the OASIS, ADNI, and merged (ADNI + OASIS) test sets, which is much higher than the achieved performance in the conventional approach. Furthermore, in the proposed approach, only the weights of the model are shared, which keeps the original MRI images in their respective hospital or institutions, preserving privacy in the healthcare domain.

Alzheimer’s disease has become a major concern in the healthcare domain as it is growing rapidly. Many researches have been conducted to detect it from MRI images through various deep-learning approaches. However, the problem of the availability of medical data and the privacy of the patients still exists. To mitigate this issue in Alzheimer’s disease detection, we have implemented the federated approach which is found to be more efficient, robust, and consistent compared to the conventional approach. For this, we needed deep excavation on various orientations of MRI images and transfer learning architectures. Then, we utilized the publicly available two datasets (OASIS, and ADNI), and designed various cases to evaluate the performance of the federated approach. In almost all of them, the federated approach achieved better accuracy and sensitivity. In distinguishing between two classes equally, the capability of the models was better in the found model when trained with federated approach compared to the conventional approach. In this approach. MobileNet, which is a low-cost transfer learning architecture, achieved the highest 95.24%, 81.94%, and 83.97% accuracy in OASIS, ADNI, and merged (ADNI+OASIS) test set, which was way higher than the achieved performance in Conventional approach. Moreover, in this approach, only the weights of the model were shared which keeps the original MRI images in their respective hospital or institutions, mitigating the privacy concern in the healthcare domain.