[Diagnosis of pulmonary hypertension associated with congenital heart disease based on statistical features of the second heart sound].

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.

[Research on bark-frequency spectral coefficients heart sound classification algorithm based on multiple window time-frequency reassignment].

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.

[Heart sound classification algorithm based on time-frequency combination feature and adaptive fuzzy neural network].

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.

Application of Artificial Intelligence-Based Auxiliary Diagnosis in Congenital Heart Disease Screening.

BACKGROUND:To evaluate the application value of artificial intelligence-based auxiliary diagnosis for congenital heart disease.METHODS:From May 2017 to December 2019, 1892 cases of congenital heart disease heart sounds were collected for learning- and memory-assisted diagnosis. The diagnosis rate and classification recognition were verified in 326 congenital heart disease cases. Auscultation and artificial intelligence-assisted diagnosis were used in 518 258 congenital heart disease screenings, and the detection accuracies of congenital heart disease and pulmonary hypertension were compared.RESULTS:Female sex and age > 14 years were predominant in atrial septal defect (P <.001) compared with ventricular septal defect/patent ductus arteriosus cases. Family history was more prominent in patent ductus arteriosus patients (P <.001). Compared with no pulmonary arterial hypertension, a male predominance was seen in cases of congenital heart disease-pulmonary arterial hypertension (P <.001), and age was significantly associated with pulmonary arterial hypertension (P =.008). A high prevalence of extracardiac anomalies was found in the pulmonary arterial hypertension group. A total of 326 patients were examined by artificial intelligence. The detection rate of atrial septal defect was 73.8%, which was different from that of auscultation (P =.008). The detection rate of ventricular septal defect was 78.8, and the detection rate of patent ductus arte-riosus was 88.9%. A total of 518 258 people from 82 towns and 1220 schools were screened including 15 453 suspected and 3930 (7.58%) confirmed cases. The detection accuracy of artificial intelligence in ventricular septal defect (P =.007) and patent ductus arteriosus (P =.021) classification was higher than that of auscultation. For normal cases, the recurrent neural network had a high accuracy of 97.77% in congenital heart disease-pulmonary arterial hypertension diagnosis (P =.032).CONCLUSION:Artificial intelligence-based diagnosis is an effective assistance method for congenital heart disease screening.

Application of time-frequency domain and deep learning fusion feature in non-invasive diagnosis of congenital heart disease-related pulmonary arterial hypertension.

Pulmonary arterial hypertension associated with congenital heart disease (CHD-PAH) is a fatal cardiovascular disease. A novel method for non-invasive initial diagnosis of the CHD-PAH was put forward in this work. First, original heart sounds were segmented into each cardiac cycle by using double-threshold adaptive method. According to clinical auscultation, the pathological information of CHD-PAH is concentrated in S2, so the time-frequency features in both of an entire cardiac cycle and S2 were extracted. Then the time-frequency features combine with the deep learning features to form a feature vector. It is the fusion feature, which will be input into a classifier. Finally, the majority voting algorithm was used to obtain the optimal classification results. A classification accuracy of 88.61% was achieved using this novel method. Three points are essential: •A double-threshold adaptive method is used to segment heart sound into each cardiac cycle.•The time-frequency domain features in both of an entire cardiac cycle and S2 were extracted, which are combined with deep learning features to form the fusion feature.•The XGBoost was used as three-class classifier for the classification of normal, CHD and CHD-PAH. The majority voting algorithm was used to obtain the optimal classification results.

Strategies to Address Extreme Aortic Tortuosity During Transcatheter Aortic Valve Replacement.

[Heart sound classification based on improved mel frequency cepstrum coefficient and integrated decision network method].

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.

Hybrid technique for large abnormal aorta-pulmonary artery fistula: a simple, safe, and effective method.

A novel heart sound segmentation algorithm via multi-feature input and neural network with attention mechanism.

Objective. Heart sound segmentation (HSS), which aims to identify the exact positions of the first heart sound(S1), second heart sound(S2), the duration of S1, systole, S2, and diastole within a cardiac cycle of phonocardiogram (PCG), is an indispensable step to find out heart health. Recently, some neural network-based methods for heart sound segmentation have shown good performance.Approach. In this paper, a novel method was proposed for HSS exactly using One-Dimensional Convolution and Bidirectional Long-Short Term Memory neural network with Attention mechanism (C-LSTM-A) by incorporating the 0.5-order smooth Shannon entropy envelope and its instantaneous phase waveform (IPW), and third intrinsic mode function (IMF-3) of PCG signal to reduce the difficulty of neural network learning features.Main results. An average F1-score of 96.85 was achieved in the clinical research dataset (Fuwai Yunnan Cardiovascular Hospital heart sound dataset) and an average F1-score of 95.68 was achieved in 2016 PhysioNet/CinC Challenge dataset using the novel method.Significance. The experimental results show that this method has advantages for normal PCG signals and common pathological PCG signals, and the segmented fundamental heart sound(S1, S2), systole, and diastole signal components are beneficial to the study of subsequent heart sound classification.

[Heart sound classification based on sub-band envelope and convolution neural network].

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.

[Diagnosis of pulmonary hypertension associated with congenital heart disease based on statistical features of the second heart sound].

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.

[Research on bark-frequency spectral coefficients heart sound classification algorithm based on multiple window time-frequency reassignment].

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.

[Heart sound classification algorithm based on time-frequency combination feature and adaptive fuzzy neural network].

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.

Application of Artificial Intelligence-Based Auxiliary Diagnosis in Congenital Heart Disease Screening.

BACKGROUND:To evaluate the application value of artificial intelligence-based auxiliary diagnosis for congenital heart disease.METHODS:From May 2017 to December 2019, 1892 cases of congenital heart disease heart sounds were collected for learning- and memory-assisted diagnosis. The diagnosis rate and classification recognition were verified in 326 congenital heart disease cases. Auscultation and artificial intelligence-assisted diagnosis were used in 518 258 congenital heart disease screenings, and the detection accuracies of congenital heart disease and pulmonary hypertension were compared.RESULTS:Female sex and age > 14 years were predominant in atrial septal defect (P <.001) compared with ventricular septal defect/patent ductus arteriosus cases. Family history was more prominent in patent ductus arteriosus patients (P <.001). Compared with no pulmonary arterial hypertension, a male predominance was seen in cases of congenital heart disease-pulmonary arterial hypertension (P <.001), and age was significantly associated with pulmonary arterial hypertension (P =.008). A high prevalence of extracardiac anomalies was found in the pulmonary arterial hypertension group. A total of 326 patients were examined by artificial intelligence. The detection rate of atrial septal defect was 73.8%, which was different from that of auscultation (P =.008). The detection rate of ventricular septal defect was 78.8, and the detection rate of patent ductus arte-riosus was 88.9%. A total of 518 258 people from 82 towns and 1220 schools were screened including 15 453 suspected and 3930 (7.58%) confirmed cases. The detection accuracy of artificial intelligence in ventricular septal defect (P =.007) and patent ductus arteriosus (P =.021) classification was higher than that of auscultation. For normal cases, the recurrent neural network had a high accuracy of 97.77% in congenital heart disease-pulmonary arterial hypertension diagnosis (P =.032).CONCLUSION:Artificial intelligence-based diagnosis is an effective assistance method for congenital heart disease screening.

Application of time-frequency domain and deep learning fusion feature in non-invasive diagnosis of congenital heart disease-related pulmonary arterial hypertension.

Pulmonary arterial hypertension associated with congenital heart disease (CHD-PAH) is a fatal cardiovascular disease. A novel method for non-invasive initial diagnosis of the CHD-PAH was put forward in this work. First, original heart sounds were segmented into each cardiac cycle by using double-threshold adaptive method. According to clinical auscultation, the pathological information of CHD-PAH is concentrated in S2, so the time-frequency features in both of an entire cardiac cycle and S2 were extracted. Then the time-frequency features combine with the deep learning features to form a feature vector. It is the fusion feature, which will be input into a classifier. Finally, the majority voting algorithm was used to obtain the optimal classification results. A classification accuracy of 88.61% was achieved using this novel method. Three points are essential: •A double-threshold adaptive method is used to segment heart sound into each cardiac cycle.•The time-frequency domain features in both of an entire cardiac cycle and S2 were extracted, which are combined with deep learning features to form the fusion feature.•The XGBoost was used as three-class classifier for the classification of normal, CHD and CHD-PAH. The majority voting algorithm was used to obtain the optimal classification results.

Strategies to Address Extreme Aortic Tortuosity During Transcatheter Aortic Valve Replacement.

[Heart sound classification based on improved mel frequency cepstrum coefficient and integrated decision network method].

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.

Hybrid technique for large abnormal aorta-pulmonary artery fistula: a simple, safe, and effective method.

A novel heart sound segmentation algorithm via multi-feature input and neural network with attention mechanism.

Objective. Heart sound segmentation (HSS), which aims to identify the exact positions of the first heart sound(S1), second heart sound(S2), the duration of S1, systole, S2, and diastole within a cardiac cycle of phonocardiogram (PCG), is an indispensable step to find out heart health. Recently, some neural network-based methods for heart sound segmentation have shown good performance.Approach. In this paper, a novel method was proposed for HSS exactly using One-Dimensional Convolution and Bidirectional Long-Short Term Memory neural network with Attention mechanism (C-LSTM-A) by incorporating the 0.5-order smooth Shannon entropy envelope and its instantaneous phase waveform (IPW), and third intrinsic mode function (IMF-3) of PCG signal to reduce the difficulty of neural network learning features.Main results. An average F1-score of 96.85 was achieved in the clinical research dataset (Fuwai Yunnan Cardiovascular Hospital heart sound dataset) and an average F1-score of 95.68 was achieved in 2016 PhysioNet/CinC Challenge dataset using the novel method.Significance. The experimental results show that this method has advantages for normal PCG signals and common pathological PCG signals, and the segmented fundamental heart sound(S1, S2), systole, and diastole signal components are beneficial to the study of subsequent heart sound classification.

[Heart sound classification based on sub-band envelope and convolution neural network].

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.