scholarly journals Temporal-Framing Adaptive Network for Heart Sound Segmentation without Prior Knowledge of State Duration

2020 ◽  
pp. 1-1 ◽  
Author(s):  
Xingyao Wang ◽  
Chengyu Liu ◽  
Yuwen Li ◽  
Xianghong Cheng ◽  
Jianqing Li ◽  
...  
Keyword(s):  
Prior Knowledge ◽  
Heart Sound ◽  
Adaptive Network ◽  
Temporal Framing ◽  
State Duration ◽  
Heart Sound Segmentation

scholarly journals Temporal Convolutional Network Connected with an Anti-Arrhythmia Hidden Semi-Markov Model for Heart Sound Segmentation

2020 ◽  
Vol 10 (20) ◽  
pp. 7049
Author(s):  
Yibo Yin ◽  
Kainan Ma ◽  
Ming Liu
Keyword(s):  
Markov Model ◽  
Viterbi Algorithm ◽  
Heart Sound ◽  
Time Algorithm ◽  
Heart Sounds ◽  
Disease Classification ◽  
Convolutional Network ◽  
Data Set ◽  
State Duration ◽  
Heart Sound Segmentation

Heart sound segmentation (HSS) is a critical step in heart sound processing, where it improves the interpretability of heart sound disease classification algorithms. In this study, we aimed to develop a real-time algorithm for HSS by combining the temporal convolutional network (TCN) and the hidden semi-Markov model (HSMM), and improve the performance of HSMM for heart sounds with arrhythmias. We experimented with TCN and determined the best parameters based on spectral features, envelopes, and one-dimensional CNN. However, the TCN results could contradict the natural fixed order of S1-systolic-S2-diastolic of heart sound, and thereby the Viterbi algorithm based on HSMM was connected to correct the order errors. On this basis, we improved the performance of the Viterbi algorithm when detecting heart sounds with cardiac arrhythmias by changing the distribution and weights of the state duration probabilities. The public PhysioNet Computing in Cardiology Challenge 2016 data set was employed to evaluate the performance of the proposed algorithm. The proposed algorithm achieved an F1 score of 97.02%, and this result was comparable with the current state-of-the-art segmentation algorithms. In addition, the proposed enhanced Viterbi algorithm for HSMM corrected 30 out of 30 arrhythmia errors after checking one by one in the dataset.

scholarly journals Best Paper Selection

2017 ◽  
Vol 26 (01) ◽  
pp. 212-213
Keyword(s):  
Logistic Regression ◽  
Private Information ◽  
Statistical Models ◽  
Heart Sound ◽  
Information Leakage ◽  
Scientific Data ◽  
Training Data ◽  
Genotype Data ◽  
Scientific Data Management ◽  
Heart Sound Segmentation

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