Analysis and recognition of post-exercise cardiac state based on heart sound features and cardiac troponin I

PURPOSE

Excessive intensity exercises can bring irreversible damage to the heart. We explore whether heart sounds can evaluate cardiac function after high-intensity exercise and hope to prevent overtraining through the changes of heart sound in future training.

METHODS

The study population consisted of 25 male athletes and 24 female athletes. All subjects were healthy and had no history of cardiovascular disease or family history of cardiovascular disease. The subjects were required to do high-intensity exercise for 3 days, with their blood sample and heart sound (HS) signals being collected and analysed before and after exercise. We then developed a Kernel extreme learning machine (KELM) model that can distinguish the state of heart by using the pre- and post-exercise data.

RESULTS

There was no significant change in serum cardiac troponin I after 3 days of load cross-country running, which indicates that there was no myocardial injury after the race. The statistical analysis of time-domain characteristics and multi-fractal characteristic parameters of HS showed that the cardiac reserve capacity of the subjects was enhanced after the cross-country running, and the KELM is an effective classifier to recognize HS and the state of the heart after exercise.

CONCLUSION

Through the results, we can draw the conclusion that this intensity of exercise will not cause profound damage to the athlete's heart. The findings of this study are of great significance for evaluating the condition of the heart with the proposed index of heart sound and prevention of excessive training that causes damage to the heart.

Heart function grading evaluation based on heart sounds and convolutional neural networks

Accurate and rapid cardiac function assessment is critical for disease diagnosis and treatment strategy. However, the current cardiac function assessment methods have their adaptability and limitations. Heart sounds (HS) can reflect changes in heart function. Therefore, HS signals were proposed to assess cardiac function, and a specially designed pruning convolutional neural network (CNN) was applied to recognize subjects' cardiac function at different levels in this paper. Firstly, the adaptive wavelet denoising algorithm and logistic regression based hidden semi-Markov model were utilized for signal denoising and segmentation. Then, the continuous wavelet transform (CWT) was employed to convert the preprocessed HS signals into spectra as input to the convolutional neural network, which can extract features automatically. Finally, the proposed method was compared with AlexNet, Resnet50, Xception, GhostNet and EfficientNet to verify the superiority of the proposed method. Through comprehensive comparison, the proposed approach achieves the best classification performance with an accuracy of 94.34%. The study indicates HS analysis is a non-invasive and effective method for cardiac function classification, which has broad research prospects.

A non-invasive approach to investigation of ventricular blood pressure using cardiac sound features

Heart sounds (HSs) are produced by the interaction of the heart valves, great vessels, and heart wall with blood flow. Previous researchers have demonstrated that blood pressure can be predicted by exploring the features of cardiac sounds. These features include the amplitude of the HSs, the ratio of the amplitude, the systolic time interval, and the spectrum of the HSs. A single feature or combinations of several features have been used for prediction of blood pressure with moderate accuracy. Experiments were conducted with three beagles under various levels of blood pressure induced by different doses of epinephrine. The HSs, blood pressure in the left ventricle and electrocardiograph signals were simultaneously recorded. A total of 31 records (18 262 cardiac beats) were collected. In this paper, 91 features in various domains are extracted and their linear correlations with the measured blood pressures are examined. These features are divided into four groups and applied individually at the input of a neural network to predict the left ventricular blood pressure (LVBP). The analysis shows that non-spectral features can track changes of the LVBP with lower standard deviation. Consequently, the non-spectral feature set gives the best prediction accuracy. The average correlation coefficient between the measured and the predicted blood pressure is 0.92 and the mean absolute error is 6.86 mmHg, even when the systolic blood pressure varies in the large range from 90 mmHg to 282 mmHg. Hence, systolic blood pressure can be accurately predicted even when using fewer HS features. This technique can be used as an alternative to real-time blood pressure monitoring and it has promising applications in home health care environments.

Computer-assisted diagnosis for chronic heart failure by the analysis of their cardiac reserve and heart sound characteristics

An innovative computer-assisted diagnosis system for chronic heart failure (CHF) was proposed in this study, based on cardiac reserve (CR) indexes extraction, heart sound hybrid characteristics extraction and intelligent diagnosis model definition. Firstly, the modified wavelet packet-based denoising method was applied to data pre-processing. Then, the CR indexes such as the ratio of diastolic to systolic duration (D/S) and the amplitude ratio of the first to second heart sound (S1/S2) were extracted. The feature set consisting of the heart sound characteristics such as multifractal spectrum parameters, the frequency corresponding to the maximum peak of the normalized PSD curve (fPSDmax) and adaptive sub-band energy fraction (sub_EF) were calculated based on multifractal detrended fluctuation analysis (MF-DFA), maximum entropy spectra estimation (MESE) and empirical mode decomposition (EMD). Statistical methods such as t-test and receiver operating characteristic (ROC) curve analysis were performed to analyze the difference of each parameter between the healthy and CHF patients. Finally, least square support vector machine (LS-SVM) was employed for the implementation of intelligent diagnosis. The result indicates the achieved diagnostic accuracy, sensitivity and specificity of the proposed system are 95.39%, 96.59% and 93.75% for the detection of CHF, respectively. The selected cutoff values of the diagnosis features are D/S=1.59, S1/S2=1.31, Δα=1.34 and fPSDmax=22.49, determined by ROC curve analysis. This study suggests the proposed methodology could provide a technical clue for the CHF point-of-care system design and be a supplement for CHF diagnosis.