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.
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.
Objective To investigate the incidence of urolithiasis in infants aged 0-3 years old fed by milk powder tainted with melamine in the middle area of the Anhui province, as well as its relationship to gender, age, milk powder consumption and drinking water. Methods Questionnaires were distributed to 1079 infants who were screened for urolithiasis by ultrasound examination from September 25, 2008 to October 17, 2008. Data was collected by Epidata and analyzed by SPSS 13.0 software. Results A total of 646 (59.87%) male infants and 433 (40.13%) female infants underwent ultrasound examination in Anhui Provincial Hospital. Of these, 86 infants were diagnosed with urolithiasis with an incidence of 7.97%, including 62 males (72.09%) and 24 females (27.91%). The mean age of those infants with urolithiasis was 1.85±0.77, and all of calculus was located in kidney. The relationship between the incidence of urolithiasis and gender, age, drinking water, feeding bottle sanitation, birth status, as well as the amount of milk powder intake was assessed by using the Pearson Chi-square test. Results showed that significant differences were noted in the incidence of urolithiasis among infants of different genders or with different drinking water sources (Plt;0.05). The result of multiple logistic regression analyses indicated that gender was related to the incidence of urolithiasis (Plt;0.05). The incidence of urolithiasis in female infants was only 58.7% of that in male infants (OR 0.587, 95%CI 0.359 to 0.959). Conclusion The incidence of urolithiasis in infants aged 0-3 years of old in the middle area of Anhui province is relatively high and has anatomical specificity. Further data during the follow-up of these cases should be collected.
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 S1, systole, S2 and diastole. 500 heart sound samples were used to test the performance of our algorithm. The average evaluation accuracy score (F1) was 0.933, the average sensitivity was 0.930, and the average accuracy rate was 0.936. Compared with other algorithms, the performance of our algorithm was more superior. It is proved that the algorithm has high robustness and anti-noise performance, which might provide a novel method for the feature extraction and analysis of heart sound signals collected in clinical environments.
Mitral regurgitation (MR) is the most common valvular heart disease, however, majority of patients are not suitable for open heart surgery due to comorbidity such as organ and heart dysfunction. Transcatheter edge-to-edge mitral valve repair has become an effective treatment option for high-risk patients with MR. Two patients were enrolled in this study inlcuding one 60-year degenerative mitral regurgitation patient and one 72-year functional mitral regurgitation patient. Transcatheter repair procedure was successfully done for the two patients without postoperative complication.
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.
Feature extraction methods and classifier selection are two critical steps in heart sound classification. To capture the pathological features of heart sound signals, this paper introduces a feature extraction method that combines mel-frequency cepstral coefficients (MFCC) and power spectral density (PSD). Unlike conventional classifiers, the adaptive neuro-fuzzy inference system (ANFIS) was chosen as the classifier for this study. In terms of experimental design, we compared different PSDs across various time intervals and frequency ranges, selecting the characteristics with the most effective classification outcomes. We compared four statistical properties, including mean PSD, standard deviation PSD, variance PSD, and median PSD. Through experimental comparisons, we found that combining the features of median PSD and MFCC with heart sound systolic period of 100–300 Hz yielded the best results. The accuracy, precision, sensitivity, specificity, and F1 score were determined to be 96.50%, 99.27%, 93.35%, 99.60%, and 96.35%, respectively. These results demonstrate the algorithm’s significant potential for aiding in the diagnosis of congenital heart disease.
The multi-window time-frequency reassignment helps to improve the time-frequency resolution of bark-frequency spectral coefficient (BFSC) analysis of heart sounds. For this purpose, a new heart sound classification algorithm combining feature extraction based on multi-window time-frequency reassignment BFSC with deep learning was proposed in this paper. Firstly, the randomly intercepted heart sound segments are preprocessed with amplitude normalization, the heart sounds were framed and time-frequency rearrangement based on short-time Fourier transforms were computed using multiple orthogonal windows. A smooth spectrum estimate is calculated by arithmetic averaging each of the obtained independent spectra. Finally, the BFSC of reassignment spectrum is extracted as a feature by the Bark filter bank. In this paper, convolutional network and recurrent neural network are used as classifiers for model comparison and performance evaluation of the extracted features. Eventually, the multi-window time-frequency rearrangement improved BFSC method extracts more discriminative features, with a binary classification accuracy of 0.936, a sensitivity of 0.946, and a specificity of 0.922. These results present that the algorithm proposed in this paper does not need to segment the heart sounds and randomly intercepts the heart sound segments, which greatly simplifies the computational process and is expected to be used for screening of congenital heart disease.
Aiming at the problems of obscure clinical auscultation features of pulmonary hypertension associated with congenital heart disease and the complexity of existing machine-aided diagnostic algorithms, an algorithm based on the statistical characteristics of the high-frequency components of the second heart sound signal is proposed. Firstly, an endpoint detection adaptive segmentation method is employed to extract the second heart sounds. Subsequently, the high-frequency component of the heart sound is decomposed using the discrete wavelet transform. Statistical features including the Hurst exponent, Lempel-Ziv information and sample entropy are extracted from this component. Finally, the extracted features are utilized to train an extreme gradient boosting algorithm (XGBoost) classifier, which achieves an accuracy of 80.45% in triple classification. Notably, this method eliminates the need for a noise reduction algorithm, allows for swift feature extraction, and achieves effective multi-classification using only three features. It is promising for early screening of pulmonary hypertension associated with congenital heart disease.
ObjectiveTo investigate the early clinical results of MitraClip system in domestic patients. Methods We retrospectively analyzed the clinical data of 36 patients who underwent transcatheter edge-to-edge repair procedure using MitraClip system in Beijing Fuwai Hospital, Shenzhen Fuwai Hospital and Fuwai Yunnan Cardiovascular Hospital between January and June 2021. There were 24 males and 12 females, with a median age of 70 (47-86) years. Ten (27.8%) patients had 3+ mitral regurgitation (MR) and 26 (72.2%) patients had 4+ MR preoperatively. ResultsAll procedures were successfully performed. The reduction in MR was 2+ at least immediately after surgery, and 91.7% of patients had MR≤2+ at 3 days postoperatively. There was no statistical difference in left ventricular ejection fraction change postoperatively. Forward velocity and peak gradient of mitral valve were increased after the procedure. Mean gradient of mitral valve were increased at 3 days postoperatively than immediately after surgery (P<0.001). Two patients had acute pericardial effusion intraoperatively, and received pericardial puncture and drainage immediately. ConclusionMitraClip system has been applied well in domestic patients and can significantly improve MR. This sutdy has a good consistency with foreign studies, and the early results are satisfactory.