DigiScope &amp;#x2014; Unobtrusive collection and annotating of auscultations in real hospital environments

Pereira, Daniel; Hedayioglu, Fabio de Lima; Correia, Ricardo; Silva, Tomé S.; Dutra, Inês; Almeida, Fernando; Mattos, Sandra S.; Coimbra, Miguel

doi:10.1109/iembs.2011.6090280

Cited by 23 publications

(17 citation statements)

References 7 publications

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“…The dataset we use in this study (PASCAL challenge Dataset B) consists of 312 auscultations gathered using the DigiScope ( Figure 2) Collector system [18] deployed in the Maternal and Fetal Cardiology Unit of the Real Hospital Português (RHP) in Recife Brazil, led by Dra. Sandra Mattos who coordinated this data collection task.…”

Section: Datasetmentioning

confidence: 99%

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Classifying heart sounds using multiresolution time series motifs

Gomes

Jorge

Azevedo

2013

Proceedings of the International C* Conference on Computer Science and Software Engineering

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The aim of this work is to describe an exploratory study on the use of a SAX-based Multiresolution Motif Discovery method for Heart Sound Classification. The idea of our work is to discover relevant frequent motifs in the audio signals and use the discovered motifs and their frequency as characterizing attributes. We also describe different configurations of motif discovery for defining attributes and compare the use of a decision tree based algorithm with random forests on this kind of data. Experiments were performed with a dataset obtained from a clinic trial in hospitals using the digital stethoscope DigiScope. This exploratory study suggests that motifs contain valuable information that can be further exploited for Heart Sound Classification.

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Section: Datasetmentioning

confidence: 99%

“…The approach has been tested on a dataset obtained from a clinic trial in hospitals using the digital stethoscope DigiScope [18]. This dataset has been used as Dataset B for the PASCAL Classifying Heart Sounds Challenge [2].…”

Section: Introductionmentioning

confidence: 99%

Classifying heart sounds using multiresolution time series motifs

Gomes

Jorge

Azevedo

2013

Proceedings of the International C* Conference on Computer Science and Software Engineering

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“…The heart sounds used have been collected in Real Hospital Português (Recife, Brasil) [9]. The auscultation spots (aortic-AV, pulmonary-PV, tricuspid-TV and mitral-MV) were auscultate sequentially for about 15 seconds at 4000Hz sampling frequency.…”

Section: Methodsmentioning

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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“…Heart sounds were collected from 312 auscultations gathered using the DigiScope Collector system [9] deployed in the Maternal and Fetal Cardiology Unit of the Real Hospital Português (Recife, Brazil). Each auscultation consists of 6 to 10 seconds recorded for each of the four standard cardiac auscultation spots in children.…”

Section: Introductionmentioning

confidence: 99%

Classifying heart sounds using SAX motifs, random forests and text mining techniques

Gomes

Jorge

Azevedo

2014

Proceedings of the 18th International Database Engineering &Amp; Applications Symposium on - IDEAS '14

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In this paper we describe an approach to classifying heart sounds (classes Normal, Murmur and Extra-systole) that is based on the discretization of sound signals using the SAX (Symbolic Aggregate Approximation) representation. The ability of automatically classifying heart sounds or at least support human decision in this task is socially relevant to spread the reach of medical care using simple mobile devices or digital stethoscopes. In our approach, sounds are first pre-processed using signal processing techniques (decimate, low-pass filter, normalize, Shannon envelope). Then the pre-processed symbols are transformed into sequences of discrete SAX symbols. These sequences are subject to a process of motif discovery. Frequent sequences of symbols (motifs) are adopted as features. Each sound is then characterized by the frequent motifs that occur in it and their respective frequency. This is similar to the term frequency (TF) model used in text mining. In this paper we compare the TF model with the application of the TFIDF (Term frequency -Inverse Document Frequency) and the use of bi-grams (frequent size two sequences of motifs). Results show the ability of the motifs based TF approach to separate classes and the relative value of the TFIDF and the bi-grams variants. The separation of the Extra-systole class is overly difficult and much better results are obtained for separating the Murmur class. Empirical validation is conducted using real data collected in noisy environments. We have also assessed the cost-reduction potential of the proposed methods by considering a fixed cost model and using a cost sensitive meta algorithm.

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DigiScope — Unobtrusive collection and annotating of auscultations in real hospital environments

Cited by 23 publications

References 7 publications

Classifying heart sounds using multiresolution time series motifs

Classifying heart sounds using multiresolution time series motifs

Exploratory Study of the Cardiac Dynamic Trajectory in the Embedding Space

Classifying heart sounds using SAX motifs, random forests and text mining techniques

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