Key result
Machine learning models utilizing smartphone sensors and demographic data show promise in estimating blood pressure, but require improved reliability across diverse conditions to meet regulatory standards.
Why the study?
Cuff-based blood pressure measurement requires equipment that is not always readily available, prompting the development of smartphone and machine learning techniques for blood pressure monitoring.
Do machine learning models using only a smartphone accurately assess blood pressure and detect hypertension compared to standard cuff-based measures?
Systematic Review
Do machine learning models using only a smartphone accurately assess blood pressure and detect hypertension compared to standard cuff-based measures?
Smartphone-based machine learning models show promise for non-invasive blood pressure assessment and hypertension detection, but require improved reliability and standardized public datasets for validation before widespread clinical use.
Smartphone ML models for BP estimation show feasibility but lack reliability across conditions; leaves open clinical adoption pending broader validation.
Regular monitoring of blood pressure (BP) allows for early detection of hypertension and symptoms related to cardiovascular disease. Measuring BP with a cuff requires equipment that is not always readily available and it may be impractical for some patients. Smartphones are an integral part of the lives of most people; thus, detecting and monitoring hypertension with a smartphone is likely to increase the ability to monitor BP due to the convenience of use for many patients. Smartphones lend themselves to assessing cardiovascular health because their built-in sensors and cameras provide a means of detecting arterial pulsations. To this end, several image processing and machine learning (ML) techniques for predicting BP using a smartphone have been developed. Several ML models that utilize smartphones are discussed in this literature review. Of the 53 papers identified, seven publications were evaluated. The performance of the ML models was assessed based on their accuracy for classification, the mean error measure, and the standard deviation of error for regression. It was found that artificial neural networks and support vector machines were often used. Because a variety of influencing factors determines the performance of an ML model, no clear preference could be determined. The number of input features ranged from five to 233, with the most commonly used being demographic data and the features extracted from photoplethysmogram signals. Each study had a different number of participants, ranging from 17 to 5,992. Comparisons of the cuff-based measures were mostly used to validate the results. Some of these ML models are already used to detect hypertension and BP but, to satisfy possible regulatory demands, improved reliability is needed under a wider range of conditions, including controlled and uncontrolled environments. A discussion of the advantages of various ML techniques and the selected features is offered at the end of this systematic review.
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Haugg et al. (2022) conducted a systematic review in Hypertension. Smartphone and Machine Learning Techniques vs. Cuff-based blood pressure measurement was evaluated on Accuracy for classification, mean error, and standard deviation of error for regression. Machine learning models utilizing smartphone sensors and demographic data show promise in estimating blood pressure, but require improved reliability across diverse conditions to meet regulatory standards.
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