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Hypertension affects an estimated 1.4 billion people and is a major cause of morbidity and mortality worldwide. Early diagnosis and intervention can potentially decrease cardiovascular events later in life. However, blood pressure (BP) measurements take time and require training for health care professionals. The measurements are also inconvenient for patients to access, numerous daily variables affect BP values, and only a few BP readings can be collected per session. This leads to an unmet need for an accurate, 24-h continuous, and portable BP measurement system. Electrocardiograms (ECGs) have been considered as an alternative way to measure BP and may meet this need. This review summarizes the literature published from January 1, 2010, to January 1, 2020, on the use of only ECG wave morphology to monitor BP or identify hypertension. From 35 articles analyzed (9 of those with no listed comorbidities and confounders), the P wave, QTc intervals and TpTe intervals may be promising for this purpose. Unfortunately, with the limited number of articles and the variety of participant populations, we are unable to make conclusions about the effectiveness of ECG-only BP monitoring. We provide 13 recommendations for future ECG-only BP monitoring studies and highlight the limited findings in pregnant and pediatric populations. With the advent of convenient and portable ECG signal recording in smart devices and wearables such as watches, understanding how to apply ECG-only findings to identify hypertension early is crucial to improving health outcomes worldwide.

Remote condition monitoring systems for rural infrastructure lack "intelligent" analysis and advanced insights offered by recent Internet of Things devices. This is because the extreme and inaccessible operating locations necessitate the conservative use of limited resources, such as battery life and data transmission. Present implementations are often limited to usage data loggers, which are informative of general usage but postprocessed advanced insights lag real-time system changes. A lightweight novelty filter is implemented onboard rural handpumps to identify subsets of data as potential infrastructure failure. The "intelligent" summaries of these data subsets are sent to a cloud-based system, where more advanced machine learning approaches are applied to increase the fidelity of potential failure predictions. The proposed method was tested on three independent data sets and found that the on-pump novelty filter could predict failure with up to 61.6% in situ. Incorporating more advanced machine learning methods on the cloud-based platform increased the classifiers' positive predictive value by at least an additional 10%-73%. This novel method has proven that it is possible for rural operating, resource-constrained devices to use lightweight, onboard machine learning approaches to perform anomaly detection in the embedded system. Distributed inference between the embedded system at the rural node and powerful cloud-based machine learning algorithms offers robust information without the need for expensive hardware or sensors embedded in situ-making the possibility of a large-scale (and perhaps even continent-wide) monitoring system feasible.

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