Palanisamy Sivamani, Rajaguru Harikumar
Department of Electronics and Communication Engineering, Jansons Institute of Technology, Coimbatore 641659, India.
Department of Electronics and Communication Engineering, Bannari Amman Institute of Technology, Sathyamangalam 638401, India.
Diagnostics (Basel). 2024 Oct 14;14(20):2287. doi: 10.3390/diagnostics14202287.
BACKGROUND/OBJECTIVES: Photoplethysmography (PPG) signals, which measure blood volume changes through light absorption, are increasingly used for non-invasive cardiovascular disease (CVD) detection. Analyzing PPG signals can help identify irregular heart patterns and other indicators of CVD.
This research involves a total of 41 subjects sourced from the CapnoBase database, consisting of 21 normal subjects and 20 CVD cases. In the initial stage, heuristic optimization algorithms, such as ABC-PSO, the Cuckoo Search algorithm (CSA), and the Dragonfly algorithm (DFA), were applied to reduce the dimension of the PPG data. Next, these Dimensionally Reduced (DR) PPG data are then fed into various classifiers such as Linear Regression (LR), Linear Regression with Bayesian Linear Discriminant Classifier (LR-BLDC), K-Nearest Neighbors (KNN), PCA-Firefly, Linear Discriminant Analysis (LDA), Kernel LDA (KLDA), Probabilistic LDA (ProbLDA), SVM-Linear, SVM-Polynomial, and SVM-RBF, to identify CVD. Classifier performance is evaluated using Accuracy, Kappa, MCC, F1 Score, Good Detection Rate (GDR), Error rate, and Jaccard Index (JI).
The SVM-RBF classifier for ABC PSO dimensionality reduced values outperforms other classifiers, achieving the highest accuracy of 95.12% along with the minimum error rate of 4.88%. In addition to that, it provides an MCC and kappa value of 0.90, a GDR and F1 score of 95%, and a Jaccard Index of 90.48%.
This study demonstrated that heuristic-based optimization and machine learning classification of PPG signals are highly effective for the non-invasive detection of cardiovascular disease.
背景/目的：光电容积脉搏波描记法（PPG）信号通过光吸收来测量血容量变化，越来越多地用于无创心血管疾病（CVD）检测。分析PPG信号有助于识别不规则的心脏模式和其他CVD指标。
本研究共纳入41名受试者，来自CapnoBase数据库，其中包括21名正常受试者和20例CVD病例。在初始阶段，应用启发式优化算法，如ABC-PSO、布谷鸟搜索算法（CSA）和蜻蜓算法（DFA），对PPG数据进行降维。接下来，将这些降维后的PPG数据输入到各种分类器中，如线性回归（LR）、带贝叶斯线性判别分类器的线性回归（LR-BLDC）、K近邻（KNN）、主成分分析-萤火虫算法、线性判别分析（LDA）、核线性判别分析（KLDA）、概率线性判别分析（ProbLDA）、支持向量机-线性、支持向量机-多项式和支持向量机-径向基函数，以识别CVD。使用准确率、卡帕值、马修斯相关系数（MCC）、F1分数（F1 Score）、良好检测率（GDR）、错误率和杰卡德指数（JI）来评估分类器性能。
用于ABC PSO降维值的支持向量机-径向基函数分类器优于其他分类器，达到了95.12%的最高准确率以及4.88%的最低错误率。除此之外，它的MCC和卡帕值为0.90，GDR和F1分数为95%，杰卡德指数为90.48%。
本研究表明，基于启发式的PPG信号优化和机器学习分类对于心血管疾病的无创检测非常有效。
