May 29, 2021International Journal of Environmental Research and Public HealthOpen Access

Automated Detection of Hypertension Using Physiological Signals: A Review

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Why the study?

Manual detection of hypertension using physiological signals is time consuming and prone to human errors, prompting the development of computer-aided diagnosis systems.

Does automated analysis of physiological signals (ECG, HRV, PPG, BCG) using machine learning accurately detect hypertension?

Structured PICO

Does automated analysis of physiological signals (ECG, HRV, PPG, BCG) using machine learning accurately detect hypertension?

Population

23 studies on automated detection of hypertension using ECG, HRV, PPG, and BCG signals, plus a proposed computational analysis using 3,694 ECG segments (3,172 low-risk hypertension, 442 high-risk hypertension, 80 healthy controls) from public databases (SHAREE and PTB).

Intervention

Automated detection of hypertension using machine learning and deep learning methods based on physiological signals (ECG, HRV, PPG, BCG), specifically a proposed SVM classifier using HOS bispectrum, cumulant, and RQA features.

Outcome

Diagnostic performance (accuracy, sensitivity, specificity, AUC) for detecting and classifying hypertensionsurrogate

Machine learning methods based on ECG and HRV signals, particularly using non-linear features like HOS bispectrum and RQA, can accurately detect and classify hypertension risk levels, offering potential for continuous cuffless remote monitoring.

Implication

May support cuffless remote hypertension monitoring; extends systematic evidence favoring ECG/HRV-based ML methods.

Abstract

Arterial hypertension (HT) is a chronic condition of elevated blood pressure (BP), which may cause increased incidence of cardiovascular disease, stroke, kidney failure and mortality. If the HT is diagnosed early, effective treatment can control the BP and avert adverse outcomes. Physiological signals like electrocardiography (ECG), photoplethysmography (PPG), heart rate variability (HRV), and ballistocardiography (BCG) can be used to monitor health status but are not directly correlated with BP measurements. The manual detection of HT using these physiological signals is time consuming and prone to human errors. Hence, many computer-aided diagnosis systems have been developed. This paper is a systematic review of studies conducted on the automated detection of HT using ECG, HRV, PPG and BCG signals. In this review, we have identified 23 studies out of 250 screened papers, which fulfilled our eligibility criteria. Details of the study methods, physiological signal studied, database used, various nonlinear techniques employed, feature extraction, and diagnostic performance parameters are discussed. The machine learning and deep learning based methods based on ECG and HRV signals have yielded the best performance and can be used for the development of computer-aided diagnosis of HT. This work provides insights that may be useful for the development of wearable for continuous cuffless remote monitoring of BP based on ECG and HRV signals.