January 10, 2026Bioengineering0 citationsOpen Access

Pragmatic Models for Detection of Hypertension Using Ballistocardiograph Signals and Machine Learning

Q: What does this research mean for the field?

The proposed models achieve a classification accuracy of 96.89% for detecting hypertension using ballistocardiograph signals and machine learning techniques. Novelty: ClaimNovelty.NOVEL_FINDING. Consensus alignment: ConsensusAlignment.NEUTRAL.

SPSunil Kumar PrabhakarHallym University DWDong-Ok WonHallym University

Key Result

The model achieved a classification accuracy of 96.89% for detecting hypertension using ballistocardiograph signals and machine learning techniques.

Key Points

The aim is to develop models for accurately detecting hypertension using ballistocardiograph signals and machine learning techniques.
Proposed two models for hypertension detection using BCG signals.
Model 1 employed K-means clustering, MODWT, and EWT for feature extraction, followed by BTSA and IG for feature selection.
Model 2 utilized PCA, KPCA, and RFM for feature extraction, followed by IG and AOA for feature selection.
Best results achieved with KPCA and AOA combined with Hybrid ARIMA–AdaBoost classifier.
Reported classification accuracy of 96.89%.

Structured PICO

Can machine learning models using ballistocardiograph signals accurately detect hypertension?

Population

Publicly available Ballistocardiograph (BCG) dataset for hypertension detection

Intervention

Machine learning models for hypertension detection using BCG signals (Model 1: K-means/MODWT/EWT with BTSA/IG and AdaBoost-MULDA/AdaBoost-RF; Model 2: PCA/KPCA/RFM with IG/AOA and ARIMA-AdaBoost/TW-HASVM)

Comparator

Comparison among different feature extraction, selection, and classification algorithms

Outcome

Classification accuracy for hypertension detection

A machine learning model utilizing ballistocardiograph signals and a Hybrid ARIMA-AdaBoost classifier can detect hypertension with high accuracy (96.89%).

Main Result

Absolute Event Rate: 0% vs 0%

Abstract

To identify hypertension, Ballistocardiograph (BCG) signals can be primarily utilized. The BCG signal must be thoroughly understood and interpreted so that its application in the classification process could become clearer and more distinct. Various unhealthy habits such as excess consumption of alcohol and tobacco, accompanied by a lack of good diet and a sedentary lifestyle, lead to hypertension. Common symptoms of hypertension include chest pain, shortness of breath, blurred vision, mood swings, frequent urination, etc. In this work, two pragmatic models are proposed for the detection of hypertension using BCG signals and machine learning models. The first model uses K-means clustering, the maximum overlap discrete wavelet transform (MODWT) and the Empirical Wavelet Transform (EWT) techniques for feature extraction, followed by the Binary Tunicate Swarm Algorithm (BTSA) and Information Gain (IG) for feature selection, as well as two efficient hybrid classifiers such as the Hybrid AdaBoost–-Maximum Uncertainty Linear Discriminant Analysis (MULDA) classifier and the Hybrid AdaBoost–Random Forest (RF) classifier for the classification of BCG signals. The second model uses Principal Component Analysis (PCA), Kernel Principal Component Analysis (KPCA) and the Random Feature Mapping (RFM) technique for feature extraction, followed by IG and the Aquila Optimization Algorithm (AOA) for feature selection, as well as two versatile hybrid classifiers such as the Hybrid AutoRegressive Integrated Moving Average (ARIMA)–AdaBoost classifier and the Time-weighted Hybrid AdaBoost–Support Vector Machine (TW-HASVM) classifier for the classification of BCG signals. The proposed methodology was tested on a publicly available BCG dataset, and the best results were obtained when the KPCA feature extraction technique was used with the AOA feature selection technique and classified using the Hybrid ARIMA–AdaBoost classifier, reporting a good classification accuracy of 96.89%.

Ask AI

Helpful

Bookmark

View Full Paper