Query: heart sound segmentation

title: A Markov Switching Model Approach to Heart Sound Segmentation and Classification
abstract: We consider challenges in accurate segmentation of heart sound signals recorded under noisy clinical environments for subsequent classification of pathological events. Existing state of the art solutions to heart sound segmentation use probabilistic models such as hidden Markov models (HMMs) which, however, are limited by its observation independence assumption and rely on pre extraction of noise robust features.  We propose a Markov switching autoregressive (MSAR) process to model the raw heart sound signals directly, which allows efficient segmentation of the cyclical heart sound states according to the distinct dependence structure in each state. To enhance robustness, we extend the MSAR model to a switching linear dynamic system (SLDS) that jointly model both the switching AR dynamics of underlying heart sound signals and the noise effects. We introduce a novel algorithm via fusion of switching Kalman filter and the duration dependent Viterbi algorithm, which incorporates the duration of heart sound states to improve state decoding.  Evaluated on Physionet/CinC Challenge 2016 dataset, the proposed MSAR SLDS approach significantly outperforms the hidden semi Markov model (HSMM) in heart sound segmentation based on raw signals and comparable to a feature based HSMM. The segmented labels were then used to train Gaussian mixture HMM classifier for identification of abnormal beats, achieving high average precision of 86.1% on the same dataset including very noisy recordings.  The proposed approach shows noticeable performance in heart sound segmentation and classification on a large noisy dataset.  It is potentially useful in developing automated heart monitoring systems for pre screening of heart pathologies.

title: An efficient heart sound segmentation approach using kurtosis and zero frequency filter features
abstract: This paper proposes an efficient heart sound segmentation method for automatic detection of heart sounds. In this method, the abrupt change at the heart sound locations is considered as a cue factor for segmentation. The phonocardiogram signal is analysed by passing kurtosis of the signal envelope through zero frequency filter (ZFF) The impulses at the locations of S1 and S2 in the filtered signal are used for the localization of heart sound. The performance of proposed method is evaluated on a real clinical dataset PhysioNet/CinC Challenge Heart Sound (PhysioNet/CinC) A set of 120 heart sound recordings, consisting of normal heart sound as well as pathological heart sound, is considered for evaluation. The experimental result shows that the proposed algorithm achieves an average sensitivity of 98.61% average positive prediction of 99.11% and average overall accuracy of 98.07% The robustness of the proposed algorithm is verified using additive white Gaussian noise and respiratory noise.

title: Heart Sound Segmentation Using Bidirectional LSTMs With Attention
abstract: This paper proposes a novel framework for the segmentation of phonocardiogram (PCG) signals into heart states, exploiting the temporal evolution of the PCG as well as considering the salient information that it provides for the detection of the heart state.  We propose the use of recurrent neural networks and exploit recent advancements in attention based learning to segment the PCG signal. This allows the network to identify the most salient aspects of the signal and disregard uninformative information.  The proposed method attains state of the art performance on multiple benchmarks including both human and animal heart recordings. Furthermore, we empirically analyse different feature combinations including envelop features, wavelet and Mel Frequency Cepstral Coefficients (MFCC) and provide quantitative measurements that explore the importance of different features in the proposed approach.  We demonstrate that a recurrent neural network coupled with attention mechanisms can effectively learn from irregular and noisy PCG recordings. Our analysis of different feature combinations shows that MFCC features and their derivatives offer the best performance compared to classical wavelet and envelop features.  Heart sound segmentation is a crucial pre processing step for many diagnostic applications. The proposed method provides a cost effective alternative to labour extensive manual segmentation, and provides a more accurate segmentation than existing methods. As such, it can improve the performance of further analysis including the detection of murmurs and ejection clicks. The proposed method is also applicable for detection and segmentation of other one dimensional biomedical signals.

title: Understanding the Importance of Heart Sound Segmentation for Heart Anomaly Detection
abstract: Traditionally, abnormal heart sound classification is framed as a three stage process. The first stage involves segmenting the phonocardiogram to detect fundamental heart sounds; after which features are extracted and classification is performed. Some researchers in the field argue the segmentation step is an unwanted computational burden, whereas others embrace it as a prior step to feature extraction. When comparing accuracies achieved by studies that have segmented heart sounds before analysis with those who have overlooked that step, the question of whether to segment heart sounds before feature extraction is still open. In this study, we explicitly examine the importance of heart sound segmentation as a prior step for heart sound classification, and then seek to apply the obtained insights to propose a robust classifier for abnormal heart sound detection. Furthermore, recognizing the pressing need for explainable Artificial Intelligence (AI) models in the medical domain, we also unveil hidden representations learned by the classifier using model interpretation techniques. Experimental results demonstrate that the segmentation plays an essential role in abnormal heart sound classification. Our new classifier is also shown to be robust, stable and most importantly, explainable, with an accuracy of almost 100% on the widely used PhysioNet dataset.

title: Heart sound segmentation via Duration Long Short Term Memory neural network
abstract: Heart sound segmentation, which aims at detecting the first and second heart sound in phonocardiogram, is an essential step to automatically analyze heart valve diseases. Recently, the neural network based methods have demonstrated their promising performance in segmenting the heart sound data. However, the methods also suffer from serious limitations due to the used envelope features. The reason is largely due to that the envelope features cannot effectively model the intrinsic sequential characteristic, resulting in the poor utilization of the duration information of heart cycles. In this paper, we propose a Duration Long Short Term Memory network (Duration LSTM) to effectively address this problem by incorporating the duration features. The proposed method is investigated in the real world phonocardiogram dataset (Massachusetts Institute of Technology heart sounds database) and compared with the two representatives of the existing state of the art methods, the experimental results demonstrate that the proposed method has the promising performance on different tolerance windows. In addition, the proposed model also has some advantages in the impact of recording length and the phenomenon of the end effect.

title: Research on automatic heart sound segmentation algorithm based on feature fusion envelope
abstract: Aiming at the problem of effective segmentation of heart sounds, a heart sound segmentation algorithm based on multi envelope feature fusion was proposed in this paper, which used interval search combined with threshold judgment to locate S1and S2. Firstly, the PCG signal was denoised by multi scale wavelet soft threshold. Then the normalized Shannon energy envelope and the Viola integral envelope were extracted for the denoised signal. The Pearson correlation coefficients between the two and the original signal were calculated respectively. And then the fusion was carried out according to the correlation. Next, the envelope peak points were searched in large interval and between minizones by interval search. After that, the variance of search results was compared and the result with smaller variance was used to compensate for the peak value. Finally, the pseudo peak was removed according to the maximum duration of S1 and S2. Through the evaluation of PhysioNet 2016 data set, the F1 score was 0.9015, which proved that this method can effectively supplement the current heart sound segmentation algorithm.

title: Deep Convolutional Neural Networks for Heart Sound Segmentation
abstract: This paper studies the use of deep convolutional neural networks to segment heart sounds into their main components. The proposed methods are based on the adoption of a deep convolutional neural network architecture, which is inspired by similar approaches used for image segmentation. Different temporal modeling schemes are applied to the output of the proposed neural network, which induce the output state sequence to be consistent with the natural sequence of states within a heart sound signal (S1, systole, S2, diastole) In particular, convolutional neural networks are used in conjunction with underlying hidden Markov models and hidden semi Markov models to infer emission distributions. The proposed approaches are tested on heart sound signals from the publicly available PhysioNet dataset, and they are shown to outperform current state of the art segmentation methods by achieving an average sensitivity of 93.9% and an average positive predictive value of 94% in detecting S1 and S2 sounds.

title: Logistic Regression HSMM Based Heart Sound Segmentation
abstract: The identification of the exact positions of the first and second heart sounds within a phonocardiogram (PCG) or heart sound segmentation, is an essential step in the automatic analysis of heart sound recordings, allowing for the classification of pathological events. While threshold based segmentation methods have shown modest success, probabilistic models, such as hidden Markov models, have recently been shown to surpass the capabilities of previous methods. Segmentation performance is further improved when apriori information about the expected duration of the states is incorporated into the model, such as in a hidden semiMarkov model (HSMM) This paper addresses the problem of the accurate segmentation of the first and second heart sound within noisy real world PCG recordings using an HSMM, extended with the use of logistic regression for emission probability estimation. In addition, we implement a modified Viterbi algorithm for decoding the most likely sequence of states, and evaluated this method on a large dataset of 10 172 s of PCG recorded from 112 patients (including 12 181 first and 11 627 second heart sounds) The proposed method achieved an average F1 score of 95.63 0.85% while the current state of the art achieved 86.28 1.55% when evaluated on unseen test recordings. The greater discrimination between states afforded using logistic regression as opposed to the previous Gaussian distribution based emission probability estimation as well as the use of an extended Viterbi algorithm allows this method to significantly outperform the current state of the art method based on a two sided paired t test.

title: Adaptive Sojourn Time HSMM for Heart Sound Segmentation
abstract: Heart sounds are difficult to interpret due to events with very short temporal onset between them (tens of milliseconds) and dominant frequencies that are out of the human audible spectrum. Computer assisted decision systems may help but they require robust signal processing algorithms. In this paper, we propose a new algorithm for heart sound segmentation using a hidden semi Markov model. The proposed algorithm infers more suitable sojourn time parameters than those currently suggested by the state of the art, through a maximum likelihood approach. We test our approach over three different datasets, including the publicly available PhysioNet and Pascal datasets. We also release a pediatric dataset composed of 29 heart sounds. In contrast with any other dataset available online, the annotations of the heart sounds in the released dataset contain information about the beginning and the ending of each heart sound event. Annotations were made by two cardiopulmonologists. The proposed algorithm is compared with the current state of the art. The results show a significant increase in segmentation performance, regardless the dataset or the methodology presented. For example, when using the PhysioNet dataset to train and to evaluate the HSMMs, our algorithm achieved average an F score of \text{92} compared to \text{89} achieved by the algorithm described in [D.B. Springer, L. Tarassenko, and G. D. Clifford, "Logistic regressionHSMM based heart sound segmentation,"  2016] In this sense, the proposed approach to adapt sojourn time parameters represents an effective solution for heart sound segmentation problems, even when the training data does not perfectly express the variability of the testing data.

title: Multi centroid diastolic duration distribution based HSMM for heart sound segmentation
abstract: This paper presents a multi centroid diastolic duration model for the hidden semi Markov model (HSMM) based heart sound segmentation. The centroids are calculated by hierarchical agglomerative clustering of the neighboring diastolic duration values using Ward's method until center of clusters are found at least a systolic duration apart. The multiple peak distribution yields a sharper gradient of likelihood around the expected centroids and improves the discriminability of similar observations. The peak density at each centroid acts as a reference point for the HSMM to determine the origin of the hidden state and adjust the corresponding state duration based on the maximum likelihood criterion. This model overcomes the limitation of the single peak mean value model that may overfit the duration distribution when the heart rate variation is relatively large. An extended logistic regression HSMM algorithm using the proposed duration model is presented for the heart sound segmentation. In addition, the total variation filter is used to attenuate the effect of noises and emphasize the fundamental heart sounds, S1 and S2. The proposed method is evaluated on the training set a of 2016 Physionet/Computing in Cardiology Challenge and yields an average F1 score of 98.36 0.43.