Key result
Novel ML model estimates blood pressure from 16-second ECG epochs within ~2 mmHg error.
Why the study?
There is a need for advanced noninvasive artificial intelligence-based blood pressure monitoring systems to help prevent diseases like stroke and cardiovascular disease caused by hypertension.
Does a Gaussian process regression machine learning algorithm accurately estimate blood pressure from short-duration ECG signals in individuals during daily activity?
Observational (n=5)
Does a Gaussian process regression machine learning algorithm accurately estimate blood pressure from short-duration ECG signals in individuals during daily activity?
Systolic and diastolic blood pressure values can be calculated with high performance using a Gaussian process regression exponential machine learning algorithm on 16-second ECG signals.
May support cuffless BP monitoring development; hypothesis-generating in N=5 and requires larger validation.
Objective. Measurement and monitoring of blood pressure are of great importance for preventing diseases such as cardiovascular and stroke caused by hypertension. Therefore, there is a need for advanced artificial intelligence-based systolic and diastolic blood pressure systems with a new technological infrastructure with a noninvasive process. The study is aimed at determining the minimum ECG time required for calculating systolic and diastolic blood pressure based on the Electrocardiography (ECG) signal. Methodology. The study includes ECG recordings of five individuals taken from the IEEE database, measured during daily activity. For the study, each signal was divided into epochs of 2-4-6-8-10-12-14-16-18-20 seconds. Twenty-five features were extracted from each epoched signal. The dimension of the dataset was reduced by using Spearman’s feature selection algorithm. Analysis based on metrics was carried out by applying machine learning algorithms to the obtained dataset. Gaussian process regression exponential (GPR) machine learning algorithm was preferred because it is easy to integrate into embedded systems. Results. The MAPE estimation performance values for diastolic and systolic blood pressure values for 16-second epochs were 2.44 mmHg and 1.92 mmHg, respectively. Conclusion. According to the study results, it is evaluated that systolic and diastolic blood pressure values can be calculated with a high-performance ratio with 16-second ECG signals.
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Nour et al. (2022) conducted an observational in Blood pressure monitoring (n=5). Machine learning (Gaussian process regression) on 16-second ECG signals vs. Other machine learning models and epoch durations was evaluated on Mean absolute percentage error (MAPE) for systolic and diastolic blood pressure estimation. A Gaussian process regression machine learning model calculated systolic and diastolic blood pressure from 16-second ECG signal epochs with mean estimation errors of 1.92 mmHg and 2.44 mmHg, respectively.
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