Alzheimer’s disease, a commonly prevalent neurological disorder in elderly patients, causes a cognitive decline in memory and as the disease progresses impacts patient’s daily activities. Alzheimer’s is one of the most grueling neurological diseases which can significantly change and impact patient’s life. A strong correlation between Epilepsy and Alzheimer’s disease was proved by eminent researchers over the past decades. Epilepsy may be prevalent in Alzheimer’s disease patients from the early stages of disease condition. Medical practitioners alter the dosage of anti-seizure medications on a short-term basis when there is a high risk of epilepsy in patients. Hence continuous monitoring of the patients can alert the patient’s caregiver regarding the risk of seizure attacks. An Internet of medical things framework for early warning for epileptic seizures is still a challenging issue in the field of health care as there are very few clinical datasets available. Seizure warnings in elderly patients with biosensors is a milestone in the research of Internet of Medical Things. In this scenario, an extensive review study of the biosensors that monitor and measure biomarkers for detection of epilepsy is indispensable. The research study indicates the challenges in seizure detection in Alzheimer’s disease patients and identifies it as a promising area for future research. The paper includes recommended architecture for IoMT framework for an epilepsy alert system for Alzheimer’s disease patients. This research study articulates highly effective architectural choices and protocols for biosensor-based patient monitoring and seizure alert systems for Alzheimer’s disease patients within the framework of Internet of Medical Things (IoMT). This study recommends the design of caregiver integrated seizure alert system for alzheimer’s patients including wearable devices with built-in biosensors, edge-fog-cloud architecture, light-weight Artificial Intelligence model, and apt protocols for each layer. The challenges in implementing seizure alert system in a multisensory smart home environment are identified as low latency, high reliability, fault tolerance, continuous data acquisition, multi sensor data fusion, scalability, and security.
Vedu et al. (Wed,) studied this question.