Sch-net: a deep learning architecture for automatic detection of schizophrenia.
Sch-net: a deep learning architecture for automatic detection of schizophrenia.
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DOI：
10.1186/s12938-021-00915-2
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发表时间：
2021-08-03
影响因子：
3.9
通讯作者：
Xiong X
中科院分区：
文献类型：
--
作者：
Fu J;Yang S;He F;He L;Li Y;Zhang J;Xiong X
精神分裂症是一种慢性、严重的精神疾病，严重影响患者的日常生活和工作。临床上，阴性症状的精神分裂症常被误诊。诊断也依赖于临床医生的经验。因此，迫切需要一种客观、有效的方法来诊断阴性症状的精神分裂症。最近的研究表明，言语障碍可被视为诊断精神分裂症的一个指标。精神分裂症语音检测的文献主要是基于特征工程的，由于语音信号的多变性，有效的特征提取是困难的。这项工作设计了一种基于卷积神经网络的新型Sch-net神经网络，这是第一个使用深度学习技术进行端到端精神分裂症语音检测的工作。Sch-net在卷积骨干架构中增加了两个组件：跳过连接和卷积块注意模块（CBAM）。跳跃连接通过出现低级和高级特征来丰富用于分类的信息。CBAM通过提供可学习的权重来突出有效特征。Sch-net结合了两者的优点，避免了人工特征提取和选择的过程。我们通过对包含28名精神分裂症患者和28名健康对照的精神分裂症语音数据集进行消融实验来验证我们的Sch-net。与基于特征工程和深度神经网络的模型进行了比较。实验结果表明，Sch-net在精神分裂症语音检测任务上有很好的性能，在精神分裂症语音数据集上的准确率可以达到97.68%。为了进一步验证我们的模型的泛化能力，我们在开放访问的LANNA儿童语音数据库上对Sch-net进行了测试，用于特定的语言障碍检测。结果表明，我们的模型在分类SLI患者和健康对照方面达到了99.52%的准确率。我们的代码将在https://github.com/Scu-sen/Sch-net上提供。大量的实验表明，所提出的Sch-net可以为精神分裂症和特定语言障碍的诊断提供辅助信息。
Schizophrenia is a chronic and severe mental disease, which largely influences the daily life and work of patients. Clinically, schizophrenia with negative symptoms is usually misdiagnosed. The diagnosis is also dependent on the experience of clinicians. It is urgent to develop an objective and effective method to diagnose schizophrenia with negative symptoms. Recent studies had shown that impaired speech could be considered as an indicator to diagnose schizophrenia. The literature about schizophrenic speech detection was mainly based on feature engineering, in which effective feature extraction is difficult because of the variability of speech signals. This work designs a novel Sch-net neural network based on a convolutional neural network, which is the first work for end-to-end schizophrenic speech detection using deep learning techniques. The Sch-net adds two components, skip connections and convolutional block attention module (CBAM), to the convolutional backbone architecture. The skip connections enrich the information used for the classification by emerging low- and high-level features. The CBAM highlights the effective features by giving learnable weights. The proposed Sch-net combines the advantages of the two components, which can avoid the procedure of manual feature extraction and selection. We validate our Sch-net through ablation experiments on a schizophrenic speech data set that contains 28 patients with schizophrenia and 28 healthy controls. The comparisons with the models based on feature engineering and deep neural networks are also conducted. The experimental results show that the Sch-net has a great performance on the schizophrenic speech detection task, which can achieve 97.68% accuracy on the schizophrenic speech data set. To further verify the generalization of our model, the Sch-net is tested on open access LANNA children speech database for specific language impairment detection. The results show that our model achieves 99.52% accuracy in classifying patients with SLI and healthy controls. Our code will be available at https://github.com/Scu-sen/Sch-net. Extensive experiments show that the proposed Sch-net can provide aided information for the diagnosis of schizophrenia and specific language impairment.
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