Segmentation and identification of some pathological phonocardiogram signals using time-frequency analysis

Multistage decision‐based heart sound delineation method for automated analysis of heart sounds and murmurs

“…The other normal and abnormal heart sounds (S3 and S4), high-pitched clicks, and different types of heart murmurs may occur in the systolic and diastolic pause intervals [5]. Heart murmurs are often characterised by the timing (early, mid, or late), intensity, duration, pitch (low, medium, or high), quality (blowing, rumbling, or musical), and configurations of crescendo, decrescendo, crescendo-decrescendo [1][2][3][4][5][6][7][8]. Thus, an automated delineation method for accurate measurements of sound parameters including amplitude, frequency content, duration, systolic, and diastolic intervals, timing, and configuration of murmurs is most important for effective diagnosis of cardiovascular diseases.…”

“…Heart murmurs are often characterised by the timing (early, mid, or late), intensity, duration, pitch (low, medium, or high), quality (blowing, rumbling, or musical), and configurations of crescendo, decrescendo, crescendo-decrescendo [1][2][3][4][5][6][7][8]. Thus, an automated delineation method for accurate measurements of sound parameters including amplitude, frequency content, duration, systolic, and diastolic intervals, timing, and configuration of murmurs is most important for effective diagnosis of cardiovascular diseases.Many heart sound segmentation (HSS) methods were reported based on the reference electrocardiogram (ECG) and/or carotid pulse (CP) signals [3], empirical mode decomposition (EMD) [4,5], hidden Markov models [7], wavelet transform (WT), and wavelet packet transform [8][9][10], temporal-spectral features [11][12][13][14], homomorphic envelogram, and self-organising probabilistic model [15], Hilbert transform [16,17], support vector machine [18], and artificial neural network [19]. On the basis of the feature extraction approaches, the HSS methods can be categorised into four major groups: (i) ECG and/or CP waveform-based methods, (ii) temporal-spectral feature-based methods, (iii) envelope-based methods, and (iv) hybrid methods.…”

“…Many heart sound segmentation (HSS) methods were reported based on the reference electrocardiogram (ECG) and/or carotid pulse (CP) signals [3], empirical mode decomposition (EMD) [4,5], hidden Markov models [7], wavelet transform (WT), and wavelet packet transform [8][9][10], temporal-spectral features [11][12][13][14], homomorphic envelogram, and self-organising probabilistic model [15], Hilbert transform [16,17], support vector machine [18], and artificial neural network [19]. On the basis of the feature extraction approaches, the HSS methods can be categorised into four major groups: (i) ECG and/or CP waveform-based methods, (ii) temporal-spectral feature-based methods, (iii) envelope-based methods, and (iv) hybrid methods.…”

“…Introduction: Phonocardiography provides vital information for diagnosis of various cardiovascular diseases [1][2][3][4][5][6][7][8]. The normal PCG signal consists of the first heart sound (S1), the systolic pause after the sound S1, the second heart sound (S2), and the diastolic pause after the sound S2 [3][4][5].…”

“…In pathological cases, the extra sounds including clicks, snaps, rubs, and various kinds of systolic and diastolic murmurs (including, early, mid, and late) may occur between the two loudest sounds S1 and S2. Most clinical studies show that different types of abnormal sounds and murmurs are often characterised by using their morphological parameters including timing, duration, intensity, frequency content, and configuration [1][2][3][4][5][6][7][8]. To the best of our knowledge, there are no systematic heart sound delineation methods that can automatically determine the boundaries and peaks of both heart sounds and murmurs present in the PCG signal.…”