Heart sound classification algorithm based on time-frequency combination feature and adaptive fuzzy neural network / 生物医学工程学杂志

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.

Research of Assistant Diagnosis System for Arrhythmias / 中国医学物理学杂志

Objective: Develop an assistant diagnosis system for arrhythinias which can reduce doctors' workload and improve their veracity of diagnosing ECG. Methods: Firstly, this paper achieves filter and detection of waves using wavelet transform and extracts parameters; Secondly, it reduces parameters using rough set and identifies arrhythmias according to relevant rules; At last, it gets the membership of abnormal heart beat by fuzzy neural network. Results: The main modules such as filter, detection of waves and identification of arrhythmias are achieved well, and a complete system is formed. Conclusions: This system can identify nineteen arrhythmaias and get their membership and position. It could assist doctors in making the proper diagnosis on ECG potentially.