Messner Elmar, Fediuk Melanie, Swatek Paul, Scheidl Stefan, Smolle-Juttner Freyja-Maria, Olschewski Horst, Pernkopf Franz
Annu Int Conf IEEE Eng Med Biol Soc. 2018 Jul;2018:356-359. doi: 10.1109/EMBC.2018.8512237.
In this paper, we present a method for event detection in single-channel lung sound recordings. This includes the detection of crackles and breathing phase events (inspiration/expiration). Therefore, we propose an event detection approach with spectral features and bidirectional gated recurrent neural networks (BiGRNNs). In our experiments, we use multichannel lung sound recordings from lung-healthy subjects and patients diagnosed with idiopathic pulmonary fibrosis, collected within a clinical trial. We achieve an event-based F-score of F ≈ 86% for breathing phase events and F ≈ 72% for crackles. The proposed method shows robustness regarding the contamination of the lung sound recordings with noise, bowel and heart sounds.
在本文中，我们提出了一种用于单通道肺部声音记录中事件检测的方法。这包括对啰音和呼吸阶段事件（吸气/呼气）的检测。因此，我们提出了一种结合频谱特征和双向门控循环神经网络（BiGRNN）的事件检测方法。在我们的实验中，我们使用了来自肺部健康受试者和被诊断患有特发性肺纤维化的患者的多通道肺部声音记录，这些记录是在一项临床试验中收集的。对于呼吸阶段事件，我们基于事件的F分数约为86%，对于啰音约为72%。所提出的方法在肺部声音记录受到噪声、肠鸣音和心音污染的情况下表现出鲁棒性。
