Study and analysis of motion artifacts for ambulatory electroencephalography

Motion artifacts contribute complexity in acquiring clean electroencephalography (EEG) data. It is one of the major challenges for ambulatory EEG. The performance of mobile health monitoring, neurological disorders diagnosis and surgeries can be significantly improved by reducing the motion artifacts. Although different papers have proposed various novel approaches for removing motion artifacts, the datasets used to validate those algorithms are questionable. In this paper, a unique EEG dataset was presented where ten different activities were performed. No such previous EEG recordings using EMOTIV EEG headset are available in research history that explicitly mentioned and considered a number of daily activities that induced motion artifacts in EEG recordings. Quantitative study shows that in comparison to correlation coefficient, the coherence analysis depicted a better similarity measure between motion artifacts and motion sensor data. Motion artifacts were characterized with very low frequency which overlapped with the Delta rhythm of the EEG. Also, a general wavelet transform based approach was presented to remove motion artifacts. Further experiment and analysis with more similarity metrics and longer recording duration for each activity is required to finalize the characteristics of motion artifacts and henceforth reliably identify and subsequently remove the motion artifacts in the contaminated EEG recordings.

Mobile health monitoring based studies for diabetes mellitus: a review

Diabetes as a chronic disease is considered to be a serious problem not only for diabetic patients but also for caregivers, families and countries. Hazardously, as an example, 16% of the Middle East population died every year because of diabetes as it is reported by World Health Organization (WHO). Therefore, it is crucial to utilize the recent advances and technologies to find the best instrument for diabetes monitoring and management. Recently, mobile health (mHealth) technologies have a vital role in the healthcare industrial world. Undoubtedly, mHealth technologies are used to manage, track, monitor, diagnose, and prevent chronic diseases including, diabetes. Certainly, the main advantages of mHealth include a real-time and continuous monitoring with high reliability, accessibility, and availability. In addition to that, mHealth is considered to be a fast, accurate, simple, cheap, comfortable, and safe technology. Hence, the proposed study aims to review existing mHealth studies for managing, diagnosing, tracking, detecting, and predicting diabetic mellitus. Moreover, challenges and future trends of this emerging topic are also discussed.

Abstract P178: Sentinel Platform Reduces Blood Pressure And Crisis Hypertension (2020 Update)

Introduction: We assessed the efficacy of mobile healthhypertension monitoring for patients enrolled inMedicare’s Remote Physiologic Monitoring (RPM)program. Hypothesis: Uncontrolled hypertension is an increasingepidemic associated with cardiovascular disease.Despite many available treatments, the averagetime to blood pressure control is slow. Lack ofaccess to patient information including bloodpressure data outside of the clinic setting meansthat clinicians cannot easily titrate medications. Wehypothesized that mobile health monitoring andcommunication with clinicians in a Medicare cohortwould decrease the hypertension burden andmitigate crisis blood pressure in patients. Methods: 1,544 patients who had contributed ≥ 20 bloodpressure readings in a remote monitoring programwere included in the study population, spanningclinics in Florida, Tennessee, Arizona, Ohio, Texas,New York, and California. Eligible patients carried adiagnosis of hypertension and had been seen bytheir doctor within the year they were referred. Themobile health platform was utilized to aggregateblood pressure data, which was analyzed by aremote care team and provided to clinicians on amonthly basis. Patients’ doctors and their teamsreviewed and managed the patients based on thedata provided by the mobile-cloud platform. Theremote monitoring program provided alerts to clinicstaff for patients who had blood pressures greaterthan 180mm Hg systolic (crisis hypertension) forexpedited decision making. Results: 1,544 patients who provided >20 BP readingsfrom January 2018 to January 2020 wereincluded in the study. A total of 297,731 bloodpressure readings were included in thisanalysis. Patient readings were stratified byepoch chronologically. The first epoch (E1),represented the first 25% of readings in theremote monitoring system, and the fourth epoch(E4) represented the final 25% of readings.From E1 to E4, patients saw an averagedecrease of 3.8 mmHg in systolic bloodpressure (132.9 vs. 129.1; p<0.001). Theproportion of readings in crisis hypertensionrange decreased from 2.3% to 1.1%; p=0.03). Conclusions: RPM offers a scalable solution to resistant hypertension.