A heart sound classification method based on complete ensemble empirical modal decomposition with adaptive noise permutation entropy and support vector machine / 生物医学工程学杂志

Heart sound signal is a kind of physiological signal with nonlinear and nonstationary features. In order to improve the accuracy and efficiency of the phonocardiogram (PCG) classification, a new method was proposed by means of support vector machine (SVM) in which the complete ensemble empirical modal decomposition with adaptive noise (CEEMDAN) permutation entropy was as the eigenvector of heart sound signal. Firstly, the PCG was decomposed by CEEMDAN into a number of intrinsic mode functions (IMFs) from high to low frequency. Secondly, the IMFs were sifted according to the correlation coefficient, energy factor and signal-to-noise ratio. Then the instantaneous frequency was extracted by Hilbert transform, and its permutation entropy was constituted into eigenvector. Finally, the accuracy of the method was verified by using a hundred PCG samples selected from the 2016 PhysioNet/CinC Challenge. The results showed that the accuracy rate of the proposed method could reach up to 87%. In comparison with the traditional EMD and EEMD permutation entropy methods, the accuracy rate was increased by 18%-24%, which demonstrates the efficiency of the proposed method.

Heart sound classification based on improved mel frequency cepstrum coefficient and integrated decision network method / 生物医学工程学杂志

Heart sound analysis is significant for early diagnosis of congenital heart disease. A novel method of heart sound classification was proposed in this paper, in which the traditional mel frequency cepstral coefficient (MFCC) method was improved by using the Fisher discriminant half raised-sine function (F-HRSF) and an integrated decision network was used as classifier. It does not rely on segmentation of the cardiac cycle. Firstly, the heart sound signals were framed and windowed. Then, the features of heart sounds were extracted by using improved MFCC, in which the F-HRSF was used to weight sub-band components of MFCC according to the Fisher discriminant ratio of each sub-band component and the raised half sine function. Three classification networks, convolutional neural network (CNN), long and short-term memory network (LSTM), and gated recurrent unit (GRU) were combined as integrated decision network. Finally, the two-category classification results were obtained through the majority voting algorithm. An accuracy of 92.15%, sensitivity of 91.43%, specificity of 92.83%, corrected accuracy of 92.01%, and F score of 92.13% were achieved using the novel signal processing techniques. It shows that the algorithm has great potential in early diagnosis of congenital heart disease.

Diseño, elaboración y validación de un simulador realista y de bajo costo para exploración cardiaca / Design, assembly and validation of a low-cost, high-fidelity simulator for heart exploration

Resumen Introducción: La exploración cardiaca es una competencia clínica fundamental que requiere exposición o entrenamiento continuo. La baja disponibilidad y accesibilidad de pacientes con patología cardiaca constituye una barrera para adquirir esta competencia. Se han documentado inadecuadas habilidades de auscultación cardiaca en estudiantes de medicina, residentes y médicos graduados. Objetivo: Elaborar y validar un simulador de alta fidelidad y bajo costo para exploración cardiaca. Métodos: Se diseñó y elaboró un simulador para exploración cardiaca, realista y de bajo costo capaz de reproducir ruidos cardiacos normales. Posteriormente se realizó la validación del simulador por un grupo de expertos que emitieron su opinión de acuerdo con una escala tipo Likert. Resultados: El 94 % afirmó que el simulador motiva el aprendizaje de la exploración cardiaca y 92 % lo consideró un modelo realista; 91 % consideró que el simulador es una herramienta atractiva para fortalecer el aprendizaje y 98 % recomendó seguir utilizándolo. Conclusiones: El uso del simulador facilita la adquisición de competencias y estimula el aprendizaje en el estudiante, lo cual puede ser atribuido a la práctica deliberada, a un mayor tiempo de exposición y a la interacción cognitiva.

Abstract Introduction: Heart exploration is an essential clinical competence that requires continuous training and exposure. Low availability and accessibility to patients with heart disease constitutes a barrier to acquiring this competence. Inadequate cardiac auscultation skills in medical students, residents, and graduate physicians have been documented. Objective: To develop and validate a low-cost, high-fidelity simulator for heart exploration. Methods: A low-cost, high-fidelity heart examination simulator capable of reproducing normal cardiac sounds was designed and developed. Subsequently, the simulator was validated by a group of experts who gave their opinion according to a Likert scale. Results: Ninety-four percent agreed that the simulator motivates the learning of heart exploration, and 92 % considered it to be a realistic model; 91 % considered that the simulator is an attractive tool to reinforce learning and 98 % recommended its further use. Conclusions: The use of the simulator facilitates the acquisition of skills and stimulates learning in the student, which can be attributed to repeated practice, longer exposure time and cognitive interaction.

Recognition of S1 and S2 heart sounds with two-stream convolutional neural networks / 生物医学工程学杂志

Auscultation of heart sounds is an important method for the diagnosis of heart conditions. For most people, the audible component of heart sound are the first heart sound (S1) and the second heart sound (S2). Different diseases usually generate murmurs at different stages in a cardiac cycle. Segmenting the heart sounds precisely is the prerequisite for diagnosis. S1 and S2 emerges at the beginning of systole and diastole, respectively. Locating S1 and S2 accurately is beneficial for the segmentation of heart sounds. This paper proposed a method to classify the S1 and S2 based on their properties, and did not take use of the duration of systole and diastole. S1 and S2 in the training dataset were transformed to spectra by short-time Fourier transform and be feed to the two-stream convolutional neural network. The classification accuracy of the test dataset was as high as 91.135%. The highest sensitivity and specificity were 91.156% and 92.074%, respectively. Extracting the features of the input signals artificially can be avoid with the method proposed in this article. The calculation is not complicated, which makes this method effective for distinguishing S1 and S2 in real time.

A heart sound classification method based on joint decision of extreme gradient boosting and deep neural network / 生物医学工程学杂志

Heart sound is one of the common medical signals for diagnosing cardiovascular diseases. This paper studies the binary classification between normal or abnormal heart sounds, and proposes a heart sound classification algorithm based on the joint decision of extreme gradient boosting (XGBoost) and deep neural network, achieving a further improvement in feature extraction and model accuracy. First, the preprocessed heart sound recordings are segmented into four status, and five categories of features are extracted from the signals based on segmentation. The first four categories of features are sieved through recursive feature elimination, which is used as the input of the XGBoost classifier. The last category is the Mel-frequency cepstral coefficient (MFCC), which is used as the input of long short-term memory network (LSTM). Considering the imbalance of the data set, these two classifiers are both improved with weights. Finally, the heterogeneous integrated decision method is adopted to obtain the prediction. The algorithm was applied to the open heart sound database of the PhysioNet Computing in Cardiology(CINC) Challenge in 2016 on the PhysioNet website, to test the sensitivity, specificity, modified accuracy and F score. The results were 93%, 89.4%, 91.2% and 91.3% respectively. Compared with the results of machine learning, convolutional neural networks (CNN) and other methods used by other researchers, the accuracy and sensibility have been obviously improved, which proves that the method in this paper could effectively improve the accuracy of heart sound signal classification, and has great potential in the clinical auxiliary diagnosis application of some cardiovascular diseases.

Heart sound classification based on sub-band envelope and convolution neural network / 生物医学工程学杂志

Automatic classification of heart sounds plays an important role in the early diagnosis of congenital heart disease. A kind of heart sound classification algorithms based on sub-band envelope feature and convolution neural network was proposed in this paper, which did not need to segment the heart sounds according to cardiac cycle accurately. Firstly, the heart sound signal was divided into some frames. Then, the frame level heart sound signal was filtered with Gammatone filter bank to obtain the sub-band signals. Next, the sub-band envelope was extracted by Hilbert transform. After that, the sub-band envelope was stacked into a feature map. Finally, type Ⅰ and type Ⅱ convolution neural network were selected as classifier. The result shown that the sub-band envelope feature was better in type Ⅰ than type Ⅱ. The algorithm is tested with 1 000 heart sound samples. The test results show that the overall performance of the algorithm proposed in this paper is significantly improved compared with other similar algorithms, which provides a new method for automatic classification of congenital heart disease, and speeds up the process of automatic classification of heart sounds applied to the actual screening.

Heart sound denoising by dynamic noise estimation / 生物医学工程学杂志

Denoising methods based on wavelet analysis and empirical mode decomposition cannot essentially track and eliminate noise, which usually cause distortion of heart sounds. Based on this problem, a heart sound denoising method based on improved minimum control recursive average and optimally modified log-spectral amplitude is proposed in this paper. The proposed method uses a short-time window to smoothly and dynamically track and estimate the minimum noise value. The noise estimation results are used to obtain the optimal spectrum gain function, and to minimize the noise by minimizing the difference between the clean heart sound and the estimated clean heart sound. In addition, combined with the subjective analysis of spectrum and the objective analysis of contribution to normal and abnormal heart sound classification system, we propose a more rigorous evaluation mechanism. The experimental results show that the proposed method effectively improves the time-frequency features, and obtains higher scores in the normal and abnormal heart sound classification systems. The proposed method can help medical workers to improve the accuracy of their diagnosis, and also has great reference value for the construction and application of computer-aided diagnosis system.

Segmentation of heart sound signals based on duration hidden Markov model / 生物医学工程学杂志

Heart sound segmentation is a key step before heart sound classification. It refers to the processing of the acquired heart sound signal that separates the cardiac cycle into systolic and diastolic, etc. To solve the accuracy limitation of heart sound segmentation without relying on electrocardiogram, an algorithm based on the duration hidden Markov model (DHMM) was proposed. Firstly, the heart sound samples were positionally labeled. Then autocorrelation estimation method was used to estimate cardiac cycle duration, and Gaussian mixture distribution was used to model the duration of sample-state. Next, the hidden Markov model (HMM) was optimized in the training set and the DHMM was established. Finally, the Viterbi algorithm was used to track back the state of heart sounds to obtain S

Design and Implementation of Heart Sound Detection Device Based on MEMS MIC / 中国医疗器械杂志

The paper describes how to develop a digital heart sound signal detection device based on high gain MEMS MIC that can accurately collect and store human heart sounds. According to the method of collecting heart sound signal by traditional stethoscope, the system improves the traditional stethoscope, and a composite probe equipped with a MEMS microphone sensor is designed. The MEMS microphone sensor converts the sound pressure signal into a voltage signal, and then amplifies, converts with Sigma Delta, extracts and filters the collected signal. After the heart sound signal is uploaded to the PC, the Empirical Mode Decomposition (EMD) is carried out to reconstruct the signal, and then the Independent Component Analysis (ICA) method is used for blind source separation and finally the heart rate is calculated by autocorrelation analysis. At the end of the paper, a preliminary comparative analysis of the performance of the system was carried out, and the accuracy of the heart sound signal was verified.

Classification of heart sound signals in congenital heart disease based on convolutional neural network / 生物医学工程学杂志

Cardiac auscultation is the basic way for primary diagnosis and screening of congenital heart disease(CHD). A new classification algorithm of CHD based on convolution neural network was proposed for analysis and classification of CHD heart sounds in this work. The algorithm was based on the clinically collected diagnosed CHD heart sound signal. Firstly the heart sound signal preprocessing algorithm was used to extract and organize the Mel Cepstral Coefficient (MFSC) of the heart sound signal in the one-dimensional time domain and turn it into a two-dimensional feature sample. Secondly, 1 000 feature samples were used to train and optimize the convolutional neural network, and the training results with the accuracy of 0.896 and the loss value of 0.25 were obtained by using the Adam optimizer. Finally, 200 samples were tested with convolution neural network, and the results showed that the accuracy was up to 0.895, the sensitivity was 0.910, and the specificity was 0.880. Compared with other algorithms, the proposed algorithm has improved accuracy and specificity. It proves that the proposed method effectively improves the robustness and accuracy of heart sound classification and is expected to be applied to machine-assisted auscultation.

A hybrid method for fundamental heart sound segmentation using group-sparsity denoising and variational mode decomposition

Segmentation of fundamental heart sounds–S1 and S2 is important for automated monitoring of cardiac activity including diagnosis of the heart diseases. This pa-per proposes a novel hybrid method for S1 and S2 heart sound segmentation using group sparsity denoising and variation mode decomposition (VMD) technique. In the proposed method, the measured phonocardiogram (PCG) signals are denoised using group sparsity algorithm by exploiting the group sparse (GS) property of PCG signals. The denoised GS-PCG signals are then decomposed into subsequent modes with specific spectral characteristics using VMD algorithm. The appropriate mode for further processing is selected based on mode central frequencies and mode energy. It is then followed by the extraction of Hilbert envelope (HEnv) and a thresholding on the selected mode to segment S1 and S2 heart sounds. The performance advantage of the proposed method is verified using PCG signals from benchmark databases namely eGeneralMedical, Littmann, Washington, and Michigan. The proposed hybrid algorithm has achieved a sensitivity of 100%, positive predictivity of 98%, accuracy of 98% and detection error rate of 1.5%. The promising results obtained suggest that proposed approach can be considered for automated heart sound segmentation.

Design and Implementation of a New Heart Sound Detecting Device / 中国医疗器械杂志

This article describes how to develop a practical new type of digital heart sound signal detection device that can achieve quantitative and accurate capture of human heart sounds and records. According to the mechanism and characteristics of the heart sound signal, the goal of this system design is to set the platform. The system uses a contact-type piezoelectric film microphone, which can effectively pick up the effective frequency band of the heart sound, then amplify and filter the collected original signal, and perform preliminary verification on the system to obtain the desired heart sound signal.

Automatic heart activity diagnosis based on Gram polynomials and probabilistic neural networks

The paper proposes a new approach to heart activity diagnosis based on Gram polynomials and probabilistic neural networks (PNN). Heart disease recognition is based on the analysis of phonocardiogram (PCG) digital sequences. The PNN provides a powerful tool for proper classification of the input data set. The novelty of the proposed approach lies in a powerful feature extraction based on Gram polynomials and the Fourier transform. The proposed system presents good performance obtaining overall sensitivity of 93%, specificity of 91% and accuracy of 94%, using a public database of over 3000 heart beat sound recordings, classified as normal and abnormal heart sounds. Thus, it can be concluded that Gram polynomials and PNN prove to be a very efficient technique using the PCG signal for characterizing heart diseases.

Usefulness of the second heart sound for predicting pulmonary hypertension in patients with interstitial lung disease / Utilidade da segunda bulha cardíaca na predição de hipertensão pulmonar em portadores de doenças intersticiais pulmonares

CONTEXT AND OBJECTIVE: P2 hyperphonesis is considered to be a valuable finding in semiological diagnoses of pulmonary hypertension (PH). The aim here was to evaluate the accuracy of the pulmonary component of second heart sounds for predicting PH in patients with interstitial lung disease. DESIGN AND SETTING: Cross-sectional study at the University of Brasilia and Hospital de Base do Distrito Federal. METHODS: Heart sounds were acquired using an electronic stethoscope and were analyzed using phonocardiography. Clinical signs suggestive of PH, such as second heart sound (S2) in pulmonary area louder than in aortic area; P2 > A2 in pulmonary area and P2 present in mitral area, were compared with Doppler echocardiographic parameters suggestive of PH. Sensitivity (S), specificity (Sp) and positive (LR+) and negative (LR-) likelihood ratios were evaluated. RESULTS: There was no significant correlation between S2 or P2 amplitude and PASP (pulmonary artery systolic pressure) (P = 0.185 and 0.115; P= 0.13 and 0.34, respectively). Higher S2 in pulmonary area than in aortic area, compared with all the criteria suggestive of PH, showed S = 60%, Sp= 22%; LR+ = 0.7; LR- = 1.7; while P2> A2 showed S= 57%, Sp = 39%; LR+ = 0.9; LR- = 1.1; and P2 in mitral area showed: S= 68%, Sp = 41%; LR+ = 1.1; LR- = 0.7. All these signals together showed: S= 50%, Sp = 56%. CONCLUSIONS: The semiological signs indicative of PH presented low sensitivity and specificity levels for clinically diagnosing this comorbidity.

RESUMO CONTEXTO E OBJETIVO: Hiperfonese de P2 tem sido considerada como achado valoroso no diagnóstico semiológico de hipertensão pulmonar (HP). O objetivo foi de avaliar a acurácia do componente pulmonar da segunda bulha cardíaca em predizer HP nos pacientes portadores de doenças intersticiais pulmonares. TIPO DE ESTUDO E LOCAL: Estudo transversal na Universidade de Brasília e Hospital de Base do Distrito Federal. MÉTODOS: Os sons cardíacos foram adquiridos com estetoscópio eletrônico e analisados por fonocardiografia. Os sinais clínicos sugestivos de HP, como B2 mais intensamente audível em área pulmonar que aórtica, P2 > A2 na área pulmonar e P2 presente em área mitral foram confrontados com parâmetros cardiográficos no exame de Doppler sugestivos de HP. Sensibilidade (S), especificidade (E), razões de verossimilhança positiva (RV+) e negativa (RV-) foram avaliados. RESULTADOS: Não houve correlação significativa entre amplitude de B2 e P2 e a PSAP (pressão sistólica arterial pulmonar) (P = 0,185 e 0,115; P = 0,13 e 0,34; respectivamente). A análise da presença de B2 mais intensa na área pulmonar que aórtica, quando comparada a todos os critérios sugestivos de HP, mostrou S = 60%; E = 22%; RV+ = 0,7; RV- = 1,7; enquanto P2 > A2 mostrou: S = 57%; E = 39%; RV+ = 0,9; RV- = 1,1; e P2 no foco mitral mostrou: S = 68%; E = 41%; RV+ = 1,1; RV- = 0,7. Todos os sinais juntos mostraram S = 50%; E = 56%. CONCLUSÃO: Os sinais semiológicos indicativos de HP apresentam baixos valores de especificidade e sensibilidade para diagnóstico clínico dessa comorbidade.

Segmentação automática para classificação digital de sinais de fonocardiograma / Automatic segmentation for signal classification of digital phonocardiogram

Com o avanço tecnológico surgem novas ferramentas que auxiliam os médicos no diagnóstico de diversas doenças. Na área cardiovascular, após permanecer por um longo período em segundo plano, a ausculta cardíaca voltou a ser muito utilizada devido ao surgimento, no mercado, de estetoscópios digitais. Tais aparelhos contam com novos recursos tecnológicos que permitem a captação e a análise de dados de forma automática, oferecendo mais informações ao profissional da área. Levando em conta essa nova ascensão da área de Fonocardiografia,o presente trabalho se dedicou à separação das bulhas S1 e S2 por meio de ferramentas computacionais, com o propósito de auxiliar médicos não especialistas em Cardiologia a verificar a existência de possíveis anormalidades no som cardíaco. Acreditando na possibilidade de este procedimento vir a ser utilizado posteriormente para auxiliar no reconhecimento de padrões dos sons cardíacos, este trabalho se propôs a criar um algoritmo para detecção automática de anormalidades que afetam as bulhas S1 e S2. Assim, aplicou-se a técnica de Wavelet sobre uma base de dados de sons cardíacos constituída de 1209 bulhas...

A Guide Book for Children and Adolescents to Experience Anatomy and Clinics / 체질인류학회지

The purpose of this study is to enable children and adolescents to experience anatomy and clinics. For the purpose, the ways to use the anatomy educational resources (comics, 3-dimensional images, and 2-dimensional images) and diagnostic tools (stethoscope, sphygmomanometer, pen light, and reflex hammer) were described in a guide book. Following the guide book, students experienced anatomy and clinics in a course of the science museum. They learned anatomy with the comics, then did virtual dissection with the 3-dimensional and 2-dimensional images. Sequentially, with the diagnostic tools, they listened to heart sound, measured blood pressure, and performed light reflex and knee jerk. Through this study, we have found that anatomy and clinics should be experienced pleasantly. The complimentary guide book is expected to be further improved in future, so as to achieve better experience at home, science museum, and school.

Realization of Heart Sound Envelope Extraction Implemented on LabVIEW Based on Hilbert-Huang Transform / 生物医学工程学杂志

We proposed a research of a heart sound envelope extraction system in this paper. The system was implemented on LabVIEW based on the Hilbert-Huang transform (HHT). We firstly used the sound card to collect the heart sound, and then implemented the complete system program of signal acquisition, pretreatment and envelope extraction on LabVIEW based on the theory of HHT. Finally, we used a case to prove that the system could collect heart sound, preprocess and extract the envelope easily. The system was better to retain and show the characteristics of heart sound envelope, and its program and methods were important to other researches, such as those on the vibration and voice, etc.

Research on the Method of Blood Pressure Monitoring Based on Multiple Parameters of Pulse Wave / 生物医学工程学杂志

In order to improve the accuracy of blood pressure measurement in wearable devices, this paper presents a method for detecting blood pressure based on multiple parameters of pulse wave. Based on regression analysis between blood pressure and the characteristic parameters of pulse wave, such as the pulse wave transit time (PWTT), cardiac output, coefficient of pulse wave, the average slope of the ascending branch, heart rate, etc. we established a model to calculate blood pressure. For overcoming the application deficiencies caused by measuring ECG in wearable device, such as replacing electrodes and ECG lead sets which are not convenient, we calculated the PWTT with heart sound as reference (PWTT(PCG)). We experimentally verified the detection of blood pressure based on PWTT(PCG) and based on multiple parameters of pulse wave. The experiment results showed that it was feasible to calculate the PWTT from PWTT(PCG). The mean measurement error of the systolic and diastolic blood pressure calculated by the model based on multiple parameters of pulse wave is 1.62 mm Hg and 1.12 mm Hg, increased by 57% and 53% compared to those of the model based on simple parameter. This method has more measurement accuracy.

Denoising of Fetal Heart Sound Based on Empirical Mode Decomposition Method / 生物医学工程学杂志

Fetal heart sound is nonlinear and non-stationary, which contains a lot of noise when it is colleced, so the denoising method is important. We proposed a new denoising method in our study. Firstly, we chose the preprocessing of low-pass filter with a cutoff frequency of 200 Hz and the resampling. Secondly, we decomposed the signal based on empirical mode decomposition method (EMD) of Hilbert-Huang transform, then denoised some selected target components with wavelet soft threshold adaptive noise cancellation algorithm. Finally we got the clean fetal heart sound by combining the target components. In the EMD, we used a mask signal to eliminate the mode mixing problem, used mirroring extension method to eliminate the end effect, and referenced the stopping rule from the research of Rilling. This method eliminated the baseline drift and noise at once. To compare with wavelet transform (WT), mathematical morphology (MM) and the Fourier transform (FT), the SNR was improved obviously, and the RMSE was the minimum, which could satisfy the need of the practical application.

Identification of Staphylococcus pseudintermedius in canine infective endocarditis using PCR-RFLP analysis / 동물의과학연구지

A 5-year-old, 8.95 kg, female Schnauzer presented anorexia with a 3-day history and increased heart sound intensity. Based on the clinical and echocardiographic findings along with the positive blood culture result, the dog was diagnosed with infective endocarditis (IE). Using proper antibiotics treatment, clinical signs were improved within 3 days and resolved within 1 week. For exact identification of the causative agent, multiplex polymerase chain reaction (PCR) and PCR-restriction fragment length polymorphism (RFLP) methods were performed. The etiological agent was confirmed as Staphylococcus pseudintermedius with antibiotics resistance genes such as beta-lactamase (blaZ) and methicilline resistance (mecA). The bacterial virulence factors included pyogenic toxin genes such as staphylococcal enterotoxins A, B, C, D, and E and toxic shock syndrome toxin 1. Diagnosis of IE is challenging due to a variety of non-specific clinical presentations, rapid disease progression, and lack of a confirmative diagnostic technique. This report demonstrated that such molecular diagnostics could be very useful for diagnosing and identifying characteristics of the causative organism for prediction of prognosis and proper treatment. To our knowledge, this is the first report on the isolation of S. pseudintermedius using molecular diagnostics from a clinical case of canine IE.