Tian Fuze, Zhang Haojie, Tan Yang, Zhu Lixian, Shen Lin, Qian Kun, Hu Bin, Schuller Bjorn W, Yamamoto Yoshiharu
IEEE J Biomed Health Inform. 2025 Jan;29(1):152-165. doi: 10.1109/JBHI.2024.3487012. Epub 2025 Jan 7.
The development of affective computing and medical electronic technologies has led to the emergence of Artificial Intelligence (AI)-based methods for the early detection of depression. However, previous studies have often overlooked the necessity for the AI-assisted diagnosis system to be wearable and accessible in practical scenarios for depression recognition. In this work, we present an on-board executable multi-feature transfer-enhanced fusion model for our custom-designed wearable three-lead Electroencephalogram (EEG) sensor, based on EEG data collected from 73 depressed patients and 108 healthy controls. Experimental results show that the proposed model exhibits low-computational complexity (65.0 K parameters), promising Floating-Point Operations (FLOPs) performance (25.6 M), real-time processing (1.5 s/execution), and low power consumption (320.8 mW). Furthermore, it requires only 202.0 KB of Random Access Memory (RAM) and 279.6 KB of Read-Only Memory (ROM) when deployed on the EEG sensor. Despite its low computational and spatial complexity, the model achieves a notable classification accuracy of 95.2%, specificity of 94.0%, and sensitivity of 96.9% under independent test conditions. These results underscore the potential of deploying the model on the wearable three-lead EEG sensor for assisting in the diagnosis of depression.
情感计算和医学电子技术的发展催生了基于人工智能（AI）的抑郁症早期检测方法。然而，以往的研究往往忽视了人工智能辅助诊断系统在抑郁症识别实际场景中需具备可穿戴性和可及性的必要性。在这项工作中，我们基于从73名抑郁症患者和108名健康对照者收集的脑电图（EEG）数据，为我们定制设计的可穿戴三导联脑电图传感器提出了一种板载可执行多特征转移增强融合模型。实验结果表明，所提出的模型具有低计算复杂度（65.0K参数）、良好的浮点运算（FLOPs）性能（25.6M）、实时处理能力（1.5秒/次执行）和低功耗（320.8毫瓦）。此外，当部署在脑电图传感器上时，它仅需要202.0KB的随机存取存储器（RAM）和279.6KB的只读存储器（ROM）。尽管其计算和空间复杂度较低，但该模型在独立测试条件下实现了95.2%的显著分类准确率、94.0%的特异性和96.9%的灵敏度。这些结果凸显了将该模型部署在可穿戴三导联脑电图传感器上辅助诊断抑郁症的潜力。
