Intelligent methodologies for cardiac sound signals analysis and characterization in cepstrum and time‐scale domains. (15th November 2019)

• Record Type: Journal Article
• Authors: El‐Dahshan, El‐Sayed A.; Bassiouni, Mahmoud M.
• Is Part Of: Computational intelligence. Volume 36:Number 2(2020)
• Page Start: 427
• Page End: 458
• Publication Date: 2019-11-15
• Subjects: cardiac sound signals -- cepstrum analysis -- identification and authentication -- intelligent methodologies -- time‐scale representation; Artificial intelligence -- Periodicals; Computational linguistics -- Periodicals; 006.3
• DOI: 10.1111/coin.12244
• Languages: English
• ISSNs: 0824-7935

Abstract

Abstract: Biometric authentication is the process that allows an individual to be identified based on a set of unique biological features data. In this study, we present different experiments to use the cardiac sound signals (phonocardiogram "PCG") as a biometric authentication trait. We have applied different features extraction approaches and different classification techniques to use the PCG as a biometric trait. Through all experiments, data acquisition is based on collecting the cardiac sounds from HSCT‐11 and PASCAL CHSC2011 datasets, while preprocessing is concerned with de‐noising of cardiac sounds using multiresolution‐decomposition and multiresolution‐reconstruction (MDR‐MRR). The de‐noised signal is then segmented based on frame‐windowing and Shanon energy (SE) methods. For feature extraction, Cepstral (Cp) domain (based on mel‐frequency) and time‐scale (T‐S) domain (based on Wavelet Transform) features are extracted from the de‐noised signal after segmentation. The features, extracted from the Cp‐domain and the T‐S domain, are fed to four different classifiers: Artificial neural networks (ANN), support vector machine (SVM), random forest (RF) and K‐nearest neighbor (KNN). The performance of the classifications is assessed based on the k‐fold cross validation. The computation complexity of the feature extraction domains is expressed using the Big‐O measurements. The T‐S features are superior to PCG heart signals in terms of the classification accuracy. The experiments' results give the highest classification accuracy with lowest computation complexity for RF in the Cp domain and SVM and ANN in the T‐S domain.