Liyanage Erandhi M, Lan Kun-Chan, Ha Quang, Ling Sai Ho
School of Electrical and Data Engineering, University of Technology Sydney, 15 Broadway, Ultimo, Sydney, NSW 2007, Australia.
Department of Computer Science and Information Engineering, National Cheng Kung University, 1, Dasyue Rd, East District, Tainan City 701, Taiwan.
Sensors (Basel). 2025 Aug 11;25(16):4968. doi: 10.3390/s25164968.
Extrapyramidal symptoms encompass features of Parkinsonism, including bradykinesia, cogwheel rigidity, and resting tremors, which contribute to motor impairments hindering handwriting and speech. In this study, we analyzed voice data captured using a voice sensor setup from 94 patients exhibiting varying levels of EPS and 30 unaffected controls. Each participant provided 13 recordings of repeated vowel and consonant sounds. The Drug-Induced Extrapyramidal Side Effect Scale and Glasgow Antipsychotic Side Effect Scales were used when grading patients into mild, moderate, and severe extrapyramidal symptoms, both administered by trained clinicians. To develop an objective assessment tool, we employed a transfer learning approach using a DenseNet architecture for feature extraction and classification. Its architecture enables the hierarchical concatenation of features at each layer. In this study, we identified that key acoustic features, MFCC, chroma, and spectral contrast vary significantly with the severity of extrapyramidal symptoms. Based on these findings, we developed a DenseNet-based model capable of predicting extrapyramidal symptoms from voice data. This model can classify with an accuracy of 81.9% and a precision of 82.0%. To the best of our knowledge, this is the first study to introduce a voice-based model for assessing the severity of extrapyramidal symptoms.
锥体外系症状包括帕金森氏症的特征,如运动迟缓、齿轮样强直和静止性震颤,这些症状会导致运动障碍,影响书写和言语能力。在本研究中,我们分析了使用语音传感器装置采集的94名表现出不同程度锥体外系症状的患者以及30名未受影响的对照者的语音数据。每位参与者提供了13次重复元音和辅音的录音。在将患者分为轻度、中度和重度锥体外系症状时,使用了药物性锥体外系副作用量表和格拉斯哥抗精神病药物副作用量表,均由经过培训的临床医生进行评定。为了开发一种客观的评估工具,我们采用了一种迁移学习方法,使用DenseNet架构进行特征提取和分类。其架构能够在每一层进行特征的分层连接。在本研究中,我们发现关键声学特征,即梅尔频率倒谱系数(MFCC)、色度和频谱对比度,会随着锥体外系症状的严重程度而显著变化。基于这些发现,我们开发了一种基于DenseNet的模型,能够从语音数据中预测锥体外系症状。该模型的分类准确率为81.9%,精确率为82.0%。据我们所知,这是第一项引入基于语音的模型来评估锥体外系症状严重程度的研究。