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

05/09/2020
by   Xingyao Wang, et al.
0

Objective: This paper presents a novel heart sound segmentation algorithm based on Temporal-Framing Adaptive Network (TFAN), including state transition loss and dynamic inference for decoding the most likely state sequence. Methods: In contrast to previous state-of-the-art approaches, the TFAN-based method does not require any knowledge of the state duration of heart sounds and is therefore likely to generalize to non sinus rhythm. The TFAN-based method was trained on 50 recordings randomly chosen from Training set A of the 2016 PhysioNet/Computer in Cardiology Challenge and tested on the other 12 independent training and test databases (2099 recordings and 52180 beats). The databases for segmentation were separated into three levels of increasing difficulty (LEVEL-I, -II and -III) for performance reporting. Results: The TFAN-based method achieved a superior F1 score for all 12 databases except for `Test-B', with an average of 96.7 method. Moreover, the TFAN-based method achieved an overall F1 score of 99.2 94.4 88.54 TFAN-based method therefore provides a substantial improvement, particularly for more difficult cases, and on data sets not represented in the public training data. Significance: The proposed method is highly flexible and likely to apply to other non-stationary time series. Further work is required to understand to what extent this approach will provide improved diagnostic performance, although it is logical to assume superior segmentation will lead to improved diagnostics.

READ FULL TEXT

page 1

page 8

research
04/02/2020

Heart Sound Segmentation using Bidirectional LSTMs with Attention

This paper proposes a novel framework for the segmentation of phonocardi...
research
04/15/2022

Deep CardioSound-An Ensembled Deep Learning Model for Heart Sound MultiLabelling

Heart sound diagnosis and classification play an essential role in detec...
research
04/04/2016

Recurrent Neural Networks for Polyphonic Sound Event Detection in Real Life Recordings

In this paper we present an approach to polyphonic sound event detection...
research
11/22/2022

Coreference Resolution through a seq2seq Transition-Based System

Most recent coreference resolution systems use search algorithms over po...
research
01/15/2023

Training one model to detect heart and lung sound events from single point auscultations

Objective: This work proposes a semi-supervised training approach for de...
research
06/27/2023

MAE-GEBD:Winning the CVPR'2023 LOVEU-GEBD Challenge

The Generic Event Boundary Detection (GEBD) task aims to build a model f...
research
09/25/2021

Adaptive video transmission using QUBO method and Digital Annealer based on Ising machine

With the dramatically increasing video streaming in the total network tr...

Please sign up or login with your details

Forgot password? Click here to reset