Fetal Heart Sounds Classification Using Time-Cyclic Frequency Spectrogram and Hybrid Attention Network

Fetal heart monitoring is a crucial method for assessing fetal health status. However, commonly used techniques may pose potential medical risks and are not suitable for long-term monitoring. In this paper, we propose utilizing fetal heart sounds (FHS) for classifying fetal health status, taking advantage of its non-invasive, safe, straightforward, and cost-effective properties. Firstly, we introduce a novel acoustic feature for fetal heart sounds, termed the time-cyclic frequency spectrogram. This feature emphasizes the periodicity of heartbeats and effectively captures the changes in fetal heart rate. Additionally, we implement a frequency band energy-weighted algorithm to mitigate interference from periodic noises. Secondly, we propose a hybrid attention network that integrates both global-local attention and time-cyclic frequency attention. This network leverages medical prior knowledge to focus on the most critical aspects of the time-cyclic frequency spectrogram. Experimental results demonstrate that the proposed feature effectively characterizes variations in fetal heart rate, and the hybrid attention network can accurately capture spectral line variations, leading to improved classification of fetal health conditions.