A Robust and Real-Time Capable Envelope-Based Algorithm for Heart Sound Classification: Validation under Different Physiological Conditions

Sun

et al. 2021

Entropy

The automated classification of heart sounds plays a significant role in the diagnosis of cardiovascular diseases (CVDs). With the recent introduction of medical big data and artificial intelligence technology, there has been an increased focus on the development of deep learning approaches for heart sound classification. However, despite significant achievements in this field, there are still limitations due to insufficient data, inefficient training, and the unavailability of effective models. With the aim of improving the accuracy of heart sounds classification, an in-depth systematic review and an analysis of existing deep learning methods were performed in the present study, with an emphasis on the convolutional neural network (CNN) and recurrent neural network (RNN) methods developed over the last five years. This paper also discusses the challenges and expected future trends in the application of deep learning to heart sounds classification with the objective of providing an essential reference for further study.

Section: Denoisingmentioning

confidence: 99%

Deep Learning Methods for Heart Sounds Classification: A Systematic Review

Sun

et al. 2021

Entropy

“…Viterbi algorithms [17] Hilbert transform and STFT Threshold method for peaks Experience of the amplitudes and remove invalid time intervals [18] Homomorphic envelope, Hilbert envelope, wavelet envelope, power spectral density envelope, and MFCCs…”

Section: Sequential Max Temporal Modeling and Several Differentmentioning

confidence: 99%

Temporal Convolutional Network Connected with an Anti-Arrhythmia Hidden Semi-Markov Model for Heart Sound Segmentation

Yin

Liu

2020

Applied Sciences

Heart sound segmentation (HSS) is a critical step in heart sound processing, where it improves the interpretability of heart sound disease classification algorithms. In this study, we aimed to develop a real-time algorithm for HSS by combining the temporal convolutional network (TCN) and the hidden semi-Markov model (HSMM), and improve the performance of HSMM for heart sounds with arrhythmias. We experimented with TCN and determined the best parameters based on spectral features, envelopes, and one-dimensional CNN. However, the TCN results could contradict the natural fixed order of S1-systolic-S2-diastolic of heart sound, and thereby the Viterbi algorithm based on HSMM was connected to correct the order errors. On this basis, we improved the performance of the Viterbi algorithm when detecting heart sounds with cardiac arrhythmias by changing the distribution and weights of the state duration probabilities. The public PhysioNet Computing in Cardiology Challenge 2016 data set was employed to evaluate the performance of the proposed algorithm. The proposed algorithm achieved an F1 score of 97.02%, and this result was comparable with the current state-of-the-art segmentation algorithms. In addition, the proposed enhanced Viterbi algorithm for HSMM corrected 30 out of 30 arrhythmia errors after checking one by one in the dataset.

“…Envelope/envelogram based methods were mostly used for both segmentation and classification purposes of the PCG signals. Shannon energy envelogram [11], Shannon envelogram on wavelet decomposed signal [12], Shannon envelogram on S-transformed signal [13], envelogram from Hilbert transformed signal [14], moment waveform envelogram [15], squared energy envelogram [16] etc. are the most commonly used available methods.…”

Section: Introductionmentioning

confidence: 99%

Automated Heart Valve Disorder Detection Based on PDF Modeling of Formant Variation Pattern in PCG Signal

2022

Heart valve disorder (HVD) analysis from heart sound is being well known for a long period of time, and use of digital stethoscope gives opportunity to diagnose HVDs from phonocardiographic (PCG) signal. An automated HVD detection technique from PCG signal can play a key role as a firsthand diagnostic tool for the physicians. In this paper, in order to classify different HVDs, we propose to utilize the formant characteristic of the PCG signal, which is an acoustic property of the heart sound. PCG signals exhibit significant variations depending on different types of HVDs and thus conventional time frequency domain features or statistical features are extracted from PCG signal for disease classification. However, direct PCG signals are also used in sequential networks to classify HVDs. Similar to the formant peaks of voiced speech signal, the spectrum corresponding to the PCG signal exhibits distinguishable peaks, especially in the voiced part of the heart sound (lub-dub). Keeping this notable key point in consideration, Burg's autoregressive model is used to find the parametric spectrum of the PCG signal. The first two formants of the PCG signal, that carry the most informative acoustic properties of the heart sound, are estimated from the Burg's spectrum, and are used for feature extraction. The magnitude, frequency and phase of each formant are considered to evaluate these features. Instead of considering a long duration of PCG signal at a time, we consider the overlapping sub-frames, and extract formants from each subframe, which generates a temporal variation of the formants. Finally, we propose a PDF model fitting of the formant variation, and utilize the estimated model parameters along with some statistical features to classify the HVDs. Two famous publicly available PCG datasets are used to demonstrate the performance of the proposed method, that efficiently classify the binary/five classes of heart sounds. The results reveal that the proposed method has the overall accuracy values of 93.46% and 99.28% for the two datasets, which is better in comparison to other previously reported state-of-the-art techniques.