A multi-spot exploration of the topological structures of the reconstructed phase-space for the detection of cardiac murmurs

Oliveira, Jorge; Oliveira, Carolina; Cardoso, Bruna Lopes; Sultan, Malik Saad; Coimbra, Miguel

doi:10.1109/embc.2015.7319319

Cited by 5 publications

(4 citation statements)

References 8 publications

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“…For heart sound segmentation, the methods can be divided depending on which domain they are applied: the time domain (Shannon energy [2]), the frequency domain (homomorphic filter [3]) and the information domain (entropy gradient [4]). For heart sound classification different classifiers have been proposed like Artificial Neural Networks (ANN) [5], k-Nearest Neighbors (k-NN) [6], Support Vector Machines (SVM) [7] and Hidden Markov Models (HMM). HMMs seems to be the ideal statistical model for the highly dynamic and non-stationary nature of the cardiac system, since it is assumed that a sequence of events happens sequentially, and they are highly correlated both temporally and spatially.…”

Section: Introductionmentioning

confidence: 99%

Why should you model time when you use Markov models for heart sound analysis

Oliveira

Mantadelis

Coimbra

2016

2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

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Auscultation is a widely used technique in clinical activity to diagnose heart diseases. However, heart sounds are difficult to interpret because a) of events with very short temporal onset between them (tens of milliseconds) and b) dominant frequencies that are out of the human audible spectrum. In this paper, we propose a model to segment heart sounds using a semi-hidden Markov model instead of a hidden Markov model. Our model in difference from the state-of-the-art hidden Markov models takes in account the temporal constraints that exist in heart cycles. We experimentally confirm that semi-hidden Markov models are able to recreate the "true" continuous state sequence more accurately than hidden Markov models. We achieved a mean error rate per sample of 0.23.

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Section: Introductionmentioning

confidence: 99%

Why should you model time when you use Markov models for heart sound analysis

Oliveira

Mantadelis

Coimbra

2016

2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

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“…In this subspace the heart sounds dynamics are assessed, and the results (in the different auscultation spots) show an increase in accuracy when compared to our previous results [4] and only using one topological feature ( ). We can conclude that the heart murmur trajectories in the embedding subspace are substantially different when compared to healthy patients.…”

Section: Resultsmentioning

confidence: 99%

“…This fact it is explained by the presence of long-range (fractal) correlation along with distinct classes of non-linear interactions [3]. The feature set described in our previous work [4] is a combination of time-frequency domain, perceptual and fractal analysis. We also proposed: 1) the dimension correlation curve; 2) the exponential decay of the false nearest neighbor integral as new features.…”

Section: Introductionmentioning

confidence: 99%

Exploratory Study of the Cardiac Dynamic Trajectory in the Embedding Space

Oliveira

Cardoso

Coimbra

2016

2016 Computing in Cardiology Conference (CinC)

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In this paper, the topological and dynamical properties of the heart sounds are assessed. The signal is preprocessed and projected into an embedding subspace, which is more suitable to detect the irregularities and the unstable trajectories registered during the cardiac murmurs than the original heart sound signal.We present a method for heart murmur classification divided into five major steps: a) signal is divided into heart beats; b) entropy gradient envelogram is computed from the pre-processed signal; c) the orbital trajectories are reconstructed using the embedding theory; d) n orbits in the embedding subspace are extracted (per heart beat); e) the median of the n orbits is used as an input to K-Nearest Neighbors (KNN) classifier.The experimental results achieved are in agreement with the current state of art for heart murmur classification. IntroductionHeart sound auscultation using a traditional stethoscope is the simplest, fastest and cheapest method for heart examination. Although the importance of the traditional auscultation methods has decreased due to its inherent restrictions. The phonocardiogram (PCG) has preserved its importance in pediatric cardiology, cardiology, and internal diseases, evaluating congenital cardiac defects. The phonocardiogram is divided into heart cycle (S11) components such as: S1 (first heart sound) and S2 (second heart sound) [1]. These establish the boundaries of the other two fundamental components of a heart cycle: the systole (S21), and the diastole (S12). The S1 and S2 are generated by the opening and closing of the heart valves, in pathogenic situations additional sounds such as S3, S4 or murmurs are listened [1]. Heart murmurs are turbulence phenomena characterized by momentum diffusion, high momentum convection, and rapid variation of pressure and velocity both in space and time [1]. The automatic detection of heart murmurs strongly depends on the extraction of an appropriate set of features, which are hopefully capable of splitting the data into two or more categories: normal and different types of abnormal cases.Ahlstrom proposed a set of 207 features composed by Shannon energy, wavelet coefficients, fractal dimensions and recurrence quantification analysis. A subset of 14 features was derived using a Pudil's sequential floating forward selection algorithm. Using a neural network classifier, this subset achieved 86% of accuracy [2]. Delgado-Trejos compared three types of features: spectral, perceptual and fractal features. Using a K-nearest neighbor's classifier they observed that fractal features provide the best accuracy (97, 17%) followed by spectral (95, 28%) and perceptual features (88,7%). This fact it is explained by the presence of long-range (fractal) correlation along with distinct classes of non-linear interactions [3]. The feature set described in our previous work [4] is a combination of time-frequency domain, perceptual and fractal analysis. We also proposed: 1) the dimension correlation curve; 2) the exponential decay of the false nearest...

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“…Other research studies proposed the use of KNN algorithms to classify abnormal heart sounds. Oliveira et al [53] utilized KNN algorithms to detect cardiac murmurs using a combination of time-frequency domain and perceptual and fractal analysis. Hamidi et al [54] suggested two techniques to distinguish between normal and abnormal heart sound signals.…”

Section: Pcg Signal Feature Extraction and Classificationmentioning

confidence: 99%

Cardiovascular Disease Recognition Based on Heartbeat Segmentation and Selection Process

Boulares

Alotaibi

Almansour

et al. 2021

IJERPH

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Assessment of heart sounds which are generated by the beating heart and the resultant blood flow through it provides a valuable tool for cardiovascular disease (CVD) diagnostics. The cardiac auscultation using the classical stethoscope phonological cardiogram is known as the most famous exam method to detect heart anomalies. This exam requires a qualified cardiologist, who relies on the cardiac cycle vibration sound (heart muscle contractions and valves closure) to detect abnormalities in the heart during the pumping action. Phonocardiogram (PCG) signal represents the recording of sounds and murmurs resulting from the heart auscultation, typically with a stethoscope, as a part of medical diagnosis. For the sake of helping physicians in a clinical environment, a range of artificial intelligence methods was proposed to automatically analyze PCG signal to help in the preliminary diagnosis of different heart diseases. The aim of this research paper is providing an accurate CVD recognition model based on unsupervised and supervised machine learning methods relayed on convolutional neural network (CNN). The proposed approach is evaluated on heart sound signals from the well-known, publicly available PASCAL and PhysioNet datasets. Experimental results show that the heart cycle segmentation and segment selection processes have a direct impact on the validation accuracy, sensitivity (TPR), precision (PPV), and specificity (TNR). Based on PASCAL dataset, we obtained encouraging classification results with overall accuracy 0.87, overall precision 0.81, and overall sensitivity 0.83. Concerning Micro classification results, we obtained Micro accuracy 0.91, Micro sensitivity 0.83, Micro precision 0.84, and Micro specificity 0.92. Using PhysioNet dataset, we achieved very good results: 0.97 accuracy, 0.946 sensitivity, 0.944 precision, and 0.946 specificity.

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A multi-spot exploration of the topological structures of the reconstructed phase-space for the detection of cardiac murmurs

Cited by 5 publications

References 8 publications

Why should you model time when you use Markov models for heart sound analysis

Why should you model time when you use Markov models for heart sound analysis

Exploratory Study of the Cardiac Dynamic Trajectory in the Embedding Space

Cardiovascular Disease Recognition Based on Heartbeat Segmentation and Selection Process

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