Journal of Medical & Surgical Pathology
Open Access
ISSN: 2472-4971
+44 1223 790975
ISSN: 2472-4971
+44 1223 790975
Murtadha Kareem
UK
Posters & Accepted Abstracts: J Med Surg Pathol
Statement of the Problem: A stroke is a serious life-threatening medical condition that occurs when the blood supply to part of the brain is cut off. Strokes are a medical emergency and urgent treatment is essential. The sooner a person receives treatment for a stroke, the less damage is likely to happen. Clinical studies showed that Atrial Fibrillation (AF), either permanent or intermittent (paroxysmal), increases the risk of cardio-embolic stroke. The purpose of this study is to establish stroke risk monitoring service which overcomes the difficulties of both the data distribution and the data interpretation problems.
Methodology: The data distribution issues were solved by using Heart Rate (HR) signals which have a low data rate. To be specific, state of the art for Internet of Things (IoT) infrastructure is sufficient to communicate and store HR data. The data interpretation was solved through a combination of algorithmic and human decision making. The proposed system is composed of commercial wearable HR sensors for patient let data collection. IoT platform was applied to store, handle and analysis the received data. An android smartphone transmits the HR measurements to a cloud server for storage and processing. The data interpretation can be done by a medical practitioner or a deep learning algorithm.
Findings: ThingTweet informs patients when their status has changed through appropriate feedback channels. The proposed stroke risk monitoring service has the potential to reduce the workload of medical practitioners. Furthermore, the proposed system protects human life by offering 24/7 monitoring service in order to reduce the stroke risk and thereby it also assists physicians during the early stage disease diagnosis while the subject is still in the home environment.