Supervised learning methods for pathological arterial pulse wave differentiation: A SVM and neural networks approach - Paiva, Joana S.; Cardoso, João; Pereira, Tânia (International journal of medical informatics (Shannon, Ireland), 2018)
•The arterial pulse pressure waveform (APW) provides an adequate description of the arterial system behaviour..•The development of techniques based on the automatic analysis of biomedical signals could be crucial for a reliable cardiovascular assessment.•An APW database comprising signals from 213 patients acquired with a novel optical system was used here.•Support Vector Machines (SVM) and Neural Networks were compared for differentiating between noisy waveforms, healthy and pathologic APWs.•SVM showed a higher accuracy possibly due to its ability to deal with the non-linearity and high-dimensionality degree of APW signal. The main goal of this study was to develop an automatic method based on supervised learning methods, able to distinguish healthy from pathologic arterial pulse wave (APW), and those two from noisy waveforms (non-relevant segments of the signal), from the data acquired during a clinical examination with a novel optical system. The APW dataset analysed was composed by signals acquired in a clinical environment from a total of 213 subjects, including healthy volunteers and non-healthy patients. The signals were parameterised by means of 39pulse features: morphologic, time domain statistics, cross-correlation features, wavelet features. Multiclass Support Vector Machine Recursive Feature Elimination (SVM RFE) method was used to select the most relevant features. A comparative study was performed in order to evaluate the performance of the two classifiers: Support Vector Machine (SVM) and Artificial Neural Network (ANN). SVM achieved a statistically significant better performance for this problem with an average accuracy of 0.9917±0.0024 and a F-Measure of 0.9925±0.0019, in comparison with ANN, which reached the values of 0.9847±0.0032 and 0.9852±0.0031 for Accuracy and F-Measure, respectively. A significant difference was observed between the performances obtained with SVM classifier using a different number of features from the original set available. The comparison between SVM and NN allowed reassert the higher performance of SVM. The results obtained in this study showed the potential of the proposed method to differentiate those three important signal outcomes (healthy, pathologic and noise) and to reduce bias associated with clinical diagnosis of cardiovascular disease using APW.