Digital Image Processing (3rd Edition)
Digital Image Processing (3rd Edition)
Neural network classification of homomorphic segmented heart sounds
Applied Soft Computing
Phonocardiogram segmentation by using hidden Markov models
BIEN '07 Proceedings of the fifth IASTED International Conference: biomedical engineering
Activity-aware ECG-based patient authentication for remote health monitoring
Proceedings of the 2009 international conference on Multimodal interfaces
Pseudo affine Wigner distributions: definition and kernelformulation
IEEE Transactions on Signal Processing
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Having in mind the availability of electronic stethoscopes, phonocardiograms (PCGs) have become popular for cardiovascular functionality monitoring and signal processing applications. Detection of fundamental heart sounds (HSs), S1s and S2s, is considered to be a crucial step in PCG analysis. Electrocardiogram (ECG), noted as a reference signal, is often synchronously recorded in order to simplify the S1/S2 detection process. Nevertheless, electronic stethoscopes are frequently used without additional ECG equipment. We propose a new algorithm for automatic fundamental HSs detection via: joint time-frequency representation based on pseudo affine Wigner-Ville distribution (PAWVD), Haar wavelet lifting scheme (Haar-LS), normalized average Shannon energy (NASE) and autocorrelation. The performance of the proposed algorithm was calculated on both normal (50) and pathological (75) PCG recordings, eight seconds long each, contributed by 125 different pediatric patients. The algorithm showed relatively high recall (90.41%) and precision (96.39%) rates of S1/S2 detection procedure in a variety of PCG signals, without ECG as a reference. Furthermore, it indicated the ability to overcome splitting within the S1/S2 heart sounds.