Kim Hyeyoon, Kim Seoyoung, Lee Subin, Lee Kyogu, Kim Euitae
Department of Brain and Cognitive Sciences, College of Natural Sciences, Seoul National University, Seoul, Republic of Korea.
Department of Neuropsychiatry, Seoul National University Bundang Hospital, Seongnam, Republic of Korea.
Psychiatry Investig. 2024 Aug;21(8):822-831. doi: 10.30773/pi.2023.0417. Epub 2024 Aug 8.
Extrapyramidal symptoms (EPS) are common side effects of antipsychotic drugs. Despite the growing interest in exploring objective biomarkers for EPS prevention and the potential use of voice in detecting clinical disorders, no studies have demonstrated the relationships between vocal changes and EPS. Therefore, we aimed to determine the associations between voice changes and antipsychotic dosage, and further investigated whether speech characteristics could be used as predictors of EPS.
Forty-two patients receiving or expected to receive antipsychotic drugs were recruited. Drug-induced parkinsonism of EPS was evaluated using the Simpson-Angus Scale (SAS). Participants' voice data consisted of 16 neutral sentences and 2 second-long /Ah/utterances. Thirteen voice features were extracted from the obtained voice data. Each voice feature was compared between groups categorized based on SAS total score of below and above "0.6." The associations between antipsychotic dosage and voice characteristics were examined, and vocal trait variations according to the presence of EPS were explored.
Significant associations were observed between specific vocal characteristics and antipsychotic dosage across both datasets of 1-16 sentences and /Ah/utterances. Notably, Mel-Frequency Cepstral Coefficients (MFCC) exhibited noteworthy variations in response to the presence of EPS. Specifically, among the 13 MFCC coefficients, MFCC1 (t=-4.47, p<0.001), MFCC8 (t=-4.49, p<0.001), and MFCC12 (t=-2.21, p=0.029) showed significant group differences in the overall statistical values.
Our results suggest that MFCC may serve as a predictor of detecting drug-induced parkinsonism of EPS. Further research should address potential confounding factors impacting the relationship between MFCC and antipsychotic dosage, possibly improving EPS detection and reducing antipsychotic medication side effects.
锥体外系症状(EPS)是抗精神病药物常见的副作用。尽管人们越来越关注探索用于预防EPS的客观生物标志物以及声音在检测临床疾病中的潜在用途,但尚无研究证明声音变化与EPS之间的关系。因此,我们旨在确定声音变化与抗精神病药物剂量之间的关联,并进一步研究语音特征是否可作为EPS的预测指标。
招募了42名正在接受或预期接受抗精神病药物治疗的患者。使用辛普森-安格斯量表(SAS)评估药物引起的EPS帕金森综合征。参与者的语音数据包括16个中性句子和2秒长的/Ah/发音。从获得的语音数据中提取了13个语音特征。根据SAS总分低于和高于“0.6”对组进行分类,比较了每组之间的每个语音特征。研究了抗精神病药物剂量与语音特征之间的关联,并探讨了根据EPS的存在情况的语音特征变化。
在1 - 16个句子和/Ah/发音这两个数据集的特定语音特征与抗精神病药物剂量之间均观察到显著关联。值得注意的是,梅尔频率倒谱系数(MFCC)对EPS的存在表现出显著变化。具体而言,在13个MFCC系数中,MFCC1(t = -4.47,p < 0.001)、MFCC8(t = -4.49,p < 0.001)和MFCC12(t = -2.21,p = 0.029)在总体统计值上显示出显著的组间差异。
我们的结果表明,MFCC可能作为检测药物引起的EPS帕金森综合征的预测指标。进一步的研究应解决影响MFCC与抗精神病药物剂量之间关系的潜在混杂因素,这可能会改善EPS的检测并减少抗精神病药物的副作用。