The electronic stethoscope combined with artificial intelligence (AI) technology has realized the digital acquisition of heart sounds and intelligent identification of congenital heart disease, which provides objective basis for heart sound auscultation and improves the accuracy of congenital heart disease diagnosis. At the present stage, the AI based cardiac auscultation technique mainly focuses on the research of AI algorithms, and the researchers have designed and summarized a variety of effective algorithms based on the characteristics of cardiac audio data, among which the mel-frequency cepstral coefficients (MFCC) is the most effective one, and widely used in the cardiac auscultation. However, the current cardiac sound analysis techniques are based on specific data sets, and have not been validated in clinic, so the performance of algorithms need to be further verified. The lack of heart sound data, especially the high-quality, standardized, publicly available heart sound database with disease labeling, further restricts the development of heart sound diagnostic analysis and its application in screening. Therefore, expert consensus is necessary in establishing an authoritative heart sound database and standardizing the heart sound auscultation screening process for congenital heart disease. This paper provides an overview of the research and application status of auscultation algorithm and hardware equipment based on AI in auscultation screening of congenital heart disease, and puts forward the problems to be solved in clinical application of AI auscultation screening technology.
Algorithms ; Artificial Intelligence ; Heart Auscultation/trends* ; Heart Defects, Congenital/diagnosis* ; Humans ; Mass Screening/methods*

An 11-year-old, castrated male dog presented with a 3-month history of cough and depression. Auscultation revealed systolic murmur and thoracic radiographs showing enlargement of both the atrium and left ventricle. Echocardiography showed thickened mitral valve and moderate-to-severe left atrial enlargement. Additionally, M-mode echocardiography showed symmetric left ventricular wall thickening and systolic anterior motion of the mitral valve, while Doppler imaging revealed high velocity turbulent flow through the left ventricular outflow tract. Based on echocardiography, this case was diagnosed with hypertrophic obstructive cardiomyopathy. After 5 months, the dog was clinically static in radiography and echocardiography.
Animals ; Auscultation ; Cardiomyopathy, Hypertrophic ; Child ; Cough ; Depression ; Dogs ; Echocardiography ; Heart Ventricles ; Humans ; Male ; Mitral Valve ; Radiography ; Systolic Murmurs

OBJECTIVE: This article investigated the changes of some biochemical markers and cardiac function in chronic heart failure (CHF), and provided the basis for the diagnosis of CHF. METHODS: New Zealand rabbit CHF model was established using adriamycin (ADR). Twenty New Zealand rabbits were randomly divided into model group (=15) and control group (=5), injected with ADR and saline solution the ear vein respectively, 2 times a week, lasting for 8 weeks. After that, myocardial enzymes, carotid artery pressure, echocardiogram (ECG) and phonocardiogram (PCG) of all New Zealand rabbits were detected and recorded. RESULTS: Compared with control group, all parameters of the model group were changed significantly (<0.05). CONCLUSIONS CHF leads to myocardial damage in New Zealand rabbits, decreased systolic and diastolic function, cardiac reserve index can be used to assess cardiac function.
Animals ; Biomarkers ; analysis ; Blood Pressure ; Carotid Arteries ; physiopathology ; Chronic Disease ; Doxorubicin ; Electrocardiography ; Heart Failure ; chemically induced ; physiopathology ; Myocardium ; enzymology ; Phonocardiography ; Rabbits ; Random Allocation

BACKGROUND: Confirming a successful caudal block is challenging in the pediatric population. Pulse transit time (PTT) may reflect the decrease in arterial resistance and may act as a potential indicator for confirming successful peripheral nerve or axial block. Heart rate variability (HRV) is also a possible candidate because it may be influenced by variation in sympathetic tone. We expected an increasing PTT pattern and change in HRV parameters after caudal block. METHODS: We enrolled 27 male patients (range, 1–4 years old) who were scheduled for urological surgeries. Caudal block was performed with 1 ml/kg of 0.25% ropivacaine and 1 : 200,000 epinephrine under sevoflurane anesthesia after the surgery. Successful block was confirmed by auscultation and ultrasonography. PTT and HRV parameters, such as standard deviation of normal-to-normal intervals, root mean square of successive differences, very low-frequency power, low-frequency power (LF), high-frequency power (HF), LF/HF ratio, approximate entropy (ApEn) were calculated based on electrocardiography from 1 min before to 5 min after the block. Those variables were analyzed by repeated measures analysis of variance. RESULTS: No significant change was found in PTT with time interval after caudal block. Heart rate and ApEn of the R-R interval decreased with time interval (P = 0.001, 0.033, respectively). Some HRV parameters showed notable changes, although statistically insignificant. CONCLUSIONS: The PTT pattern may not be an indicator for successful caudal block. However, heart rate with parameters of HRV analysis may be alternatives.
Anesthesia ; Anesthesia, Caudal ; Anesthesia, General* ; Auscultation ; Child* ; Electrocardiography ; Entropy ; Epinephrine ; Heart Rate* ; Heart* ; Humans ; Male ; Peripheral Nerves ; Pulse Wave Analysis* ; Ultrasonography

In this paper, an improved empirical mode decomposition (EMD) algorithm for phonocardiogram (PCG) signal de-noising is proposed. Based on PCG signal processing theory, the S1/S2 components can be extracted by combining the improved EMD-Wavelet algorithm and Shannon energy envelope algorithm. Firstly, by applying EMD-Wavelet algorithm for pre-processing, the PCG signal was well filtered. Then, the filtered PCG signal was saved and applied in the following processing steps. Secondly, time domain features, frequency domain features and energy envelope of the each intrinsic mode function's (IMF) were computed. Based on the time frequency domain features of PCG's IMF components which were extracted from the EMD algorithm and energy envelope of the PCG, the S1/S2 components were pinpointed accurately. Meanwhile, a detecting fixed method, which was based on the time domain processing, was proposed to amend the detection results. Finally, to test the performance of the algorithm proposed in this paper, a series of experiments was contrived. The experiments with thirty samples were tested for validating the effectiveness of the new method. Results of test experiments revealed that the accuracy for recognizing S1/S2 components was as high as 99.75%. Comparing the results of the method proposed in this paper with those of traditional algorithm, the detection accuracy was increased by 5.56%. The detection results showed that the algorithm described in this paper was effective and accurate. The work described in this paper will be utilized in the further studying on identity recognition.
Algorithms ; Humans ; Phonocardiography ; Signal Processing, Computer-Assisted

OBJECTIVEExtraction of cepstral coefficients combined with Gaussian Mixture Model (GMM) is used to propose a biometric method based on heart sound signal.

METHODSFirstly, the original heart sounds signal was preprocessed by wavelet denoising. Then, Linear Prediction Cepstral Coefficients (LPCC) and Mel Frequency Cepstral Coefficients (MFCC) are compared to extract representative features and develops hidden Markov model (HMM) for signal classification. At last, the experiment collects 100 heart sounds from 50 people to test the proposed algorithm.

RESULTSThe comparative experiments prove that LPCC is more suitable than MFCC for heart sound biometric, and by wavelet denoising in each piece of heart sound signal, the system achieves higher recognition rate than traditional GMM.

CONCLUSIONThose results show that this method can effectively improve the recognition performance of the system and achieve a satisfactory effect.

Algorithms ; Biometry ; Heart ; physiology ; Humans ; Markov Chains ; Models, Biological ; Phonocardiography ; methods ; Wavelet Analysis

Heart Failure ; diagnosis ; therapy ; Humans ; Phonocardiography ; methods

In this paper, a new method based on the nonlinear chaos theory was proposed to study the arrhythmia with the combination of the correlation dimension and largest Lyapunov exponent, through computing and analyzing these two parameters of 30 cases normal heart sound and 30 cases with arrhythmia. The results showed that the two parameters of the heart sounds with arrhythmia were higher than those with the normal, and there was significant difference between these two kinds of heart sounds. That is probably due to the irregularity of the arrhythmia which causes the decrease of predictability, and it's more complex than the normal heart sound. Therefore, the correlation dimension and the largest Lyapunov exponent can be used to analyze the arrhythmia and for its feature extraction.
Arrhythmias, Cardiac ; diagnosis ; physiopathology ; Heart Sounds ; physiology ; Humans ; Logistic Models ; Nonlinear Dynamics ; Phonocardiography ; Signal Processing, Computer-Assisted

Auscultation, the act of listening for heart sounds to aid in the diagnosis of various heart diseases, is a widely used efficient technique by cardiologists. Since the mechanical prosthetic heart valves are widely used today, it is important to develop a simple and efficient method to detect abnormal mechanical valves. The study on five different mechanical valves showed that only the case of perivalvular leakage could be detected by spectral estimation. Though it is possible to classify different mechanical valves by using time-frequency components of the signal directly, the recognition rate is merely 84%. However, with the improved local discriminant bases (LDB) algorithm to extract features from heart sounds, the recognition rate is 97.3%. Experimental results demonstrated that the improved LDB algorithm could improve classification rate and reduce computational complexity in comparison with original LDB algorithm.
Algorithms ; Heart Sounds ; physiology ; Heart Valve Diseases ; physiopathology ; surgery ; Heart Valve Prosthesis ; Heart Valves ; physiopathology ; Humans ; Pattern Recognition, Automated ; Phonocardiography ; Signal Processing, Computer-Assisted ; Spectrum Analysis ; methods

Heart sound signals acquisition is the primary basis for achieving non-invasive diagnosis of coronary heart disease. In this paper, a digital signal processor (DSP)-based on miniaturized circuit of heart sound signals acquisition and analysis platform was designed to achieve the functions of filtering, collecting, processing, displaying and the communicating with PC. With the self-developed experimental platform, we collected 228 cases of heart sounds of clinical data, and processed the signals using de-noising method with wavelet transform. These experimental results indicated that the db6 wavelet has the most obvious de-noising effect among the four most commonly used wavelets, i.e., haar, db6, sym8, and coif5. One wavelet at different levels possessed different de-noising effects, with level-5 db6 decomposition obtaining the most desirable result.
Algorithms ; Auscultation ; Heart Sounds ; Humans ; Phonocardiography ; methods ; Signal Processing, Computer-Assisted ; Wavelet Analysis