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Objective To investigate the clinical effect of remimazolam combined with remifentanil in patients undergoing laryngoscope vocal cord surgery under general anesthesia.Methods A total of 180 patients undergoing laryngoscope vocal cord surgery under general anesthesia from January to August 2022,77 males and 103 females,aged 18-64 years,BMI 18-30 kg/m2,ASA physical status Ⅰ-Ⅲ were select-ed.The patients were divided into four groups using a random number table method:propofol group(group C),remimazolam 1.0 mg·kg-1·h-1 group(group R1),remimazolam 1.5 mg·kg-1·h-1 group(group R2),and remimazolam 2.0 mg·kg-1·h-1 group(group R3),45 patients in each group.Group C main-tained by intravenous infusion of propofol 5 mg·kg-1·h-1,groups R1,R2,and R3 were maintained by intravenous infusion of remimazolam 1.0,1.5,and 2.0 mg·kg-1·h-1,respectively.All patients were combined with remifentanil 0.2 μg·kg-1·min-1.HR,MAP,and BIS were recorded before anesthesia in-duction(T1),immediately after laryngoscope insertion(T2),immediately at the end of anesthesia mainte-nance(T3),and at tracheal extubation(T4).The onset time of sedation,awakening time,sedation-agita-tion score at extubation and Ramsay score 5 minutes after extubation were recorded.The intraoperative use of ephedrine and nitroglycerin were recorded.The number of injection pain and remedy sedations were recor-ded,the occurrence of adverse reactions such as nausea and vomiting,respiratory depression within 1 hour after extubation,and intraoperative awareness were recorded.Results Compared with group C,MAP at T3,BIS at T2 and T3 were significantly increased,MAP at T4 was significantly decreased,the onset time of sedation was significantly prolonged,the use of ephedrine and the incidence of injection pain were signifi-cantly decreased in group R1(P＜0.05),HR and MAP were significantly decreased at T2 and T4,MAP was significantly increased at T3,the onset time of sedation,awakening time,extubation time were signifi-cantly prolonged,the use of ephedrine and the incidence of injection pain were significantly reduced in group R2(P＜0.05),HR and MAP were significantly decreased at T2 and T4,the onset time of sedation,awakening time,extubation time were significantly prolonged,Ramsay score was significantly increased in group R3(P＜0.05).Compared with group R1,HR and MAP were significantly decreased at T2 and T4,BIS was significantly decreased at T2 and T3,the awakening time and extubation time were significantly pro-longed in group R2(P＜0.05),HR at T2 and T4,MAP at T2-T4,BIS at T2 and T3 were significantly de-creased,the awakening time and extubation time were significantly prolonged,Ramsay score was significant-ly increased in group R3(P＜0.05).Compared with group R2,MAP at T3 was significantly decreased and Ramsay score was significantly increased in group R3(P＜0.05).There were no significantly differences between the rates of nitroglycerin usage,rescue sedation,nausea and vomiting,and respiratory depression in the four groups.Conclusion Remimazolam can be safely used for anesthesia induction and maintenance in laryngoscope vocal cord surgery.The maintenance of remimazolam 1.5 mg·kg-1·h-1 combined with remifentanil can better maintain the hemodynamics stability during the surgery than remimazolam 1.0 and 2.0 mg·kg-1·h-1.

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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.

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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.

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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.

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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

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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.

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In this work, a new method of heart sound signal preprocessing is presented. First, the heart sound signals are decomposed by using multilayer wavelet transform. And then double parameters as thresholds are used in processing each layer after decomposition for denoising. Next, reconstruction of heart sound signals could be done after processing last layer. Four methods, i.e. wavelet transform, Hilbert-Huang transform (HHT), mathematical morphology, and normalized average Shannon energy, were used to extract the envelop of the heart sound signals respectively after reconstruction of heart sounds. All methods were improved in this study. We finally in our study chose 30 cases of raw heart sound signals, which were selected randomly from a database comed from The Clinical Medicine Institute of Montreal, and processed them by using the improved methods. The results were satisfactory. It showed that the extracted envelope with the original signal has a high degree of matching, whether it is a low frequency portion or high frequency portion. Most of all information of heart sound has been maintained in the envelope.

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Independent component analysis (ICA) is a novel method developed in recent years for Blind Source Separation. In this paper, the phonocardiogram (PCG) was separated into three components by applying ICA. The basic principle of ICA was introduced in this paper. A fast and robust fixed-point algorithm for ICA was used to analyze PCG signals in this study. The experiments showed that ICA could separate the components of heart sounds from PCG signals successfully.

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Heart sounds are highly valuable to the clinical diagnoses of most cardiovascular diseases, so the analysis of phonocardiographic signals is helpful to diagnosing cardiovascular diseases clinically. Phonocardiographic signals are non-stable, so it is necessary to choose appropriate method in time-frequency analysis. The traditional method such as Fourier Transform is dissatisfactory. Continuous Wavelet Transform (CWT) and Matching (MPM) Pursuit Method are both effective methods. They can be used to extract and cluster the characteristics of the signals. By analysis and comparison, the two methods showed the advantages over traditional methods. Additionally, their respective merits and demerits are indicated.

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According to the valvular theory, the vibrations affected by the mitral and tricuspid valves closure in early systole produce the first heart sound (S1). S1 usually includes many frequency components. In this paper, a method using the multi-resolution analysis of wavelet transformation is recommended for detecting the frequency range of S1. First, S1 was decomposed into different levels on frequency. Then the normalized Shannon energy of the different levels was calculated. The level containing the maximum energy is the major components' level of S1. The frequency range of this level is the major frequency range of S1. The frequency range of S1 was successfully detected by the method.

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