Voice analysis as an objective state marker in bipolar disorder.
Voice analysis as an objective state marker in bipolar disorder.
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DOI：
10.1038/tp.2016.123
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发表时间：
2016-07-19
影响因子：
6.8
通讯作者：
Kessing LV
中科院分区：
文献类型：
--
作者：
Faurholt-Jepsen M;Busk J;Frost M;Vinberg M;Christensen EM;Winther O;Bardram JE;Kessing LV
言语变化被认为是双相情感障碍中抑郁和躁狂的敏感而有效的衡量标准。本研究旨在调查（1）在电话中收集的语音特征作为双相情感障碍情感状态的客观标志物，以及（2）是否将语音特征与自动生成的客观智能手机行为数据相结合（例如，每天的短信和电话数量）和电子自我监测数据（情绪）在疾病活动上的活动将增加作为情感状态标记的准确性。使用智能手机，语音功能，自动生成的客观智能手机的行为活动和电子自我监测数据的数据收集从28例门诊患者在自然的设置，每天在12周的时间。由一名对智能手机数据不知情的研究人员分别使用汉密尔顿抑郁量表17项和杨氏躁狂量表评估抑郁和躁狂症状。使用随机森林算法分析数据。情感状态进行了分类，在日常生活中的电话中提取的语音特征。语音特征被认为是更准确，敏感和具体的躁狂或混合状态的分类与曲线下面积（AUC）=0.89相比，AUC=0.78的抑郁状态的分类。将语音特征与自动生成的关于行为活动的客观智能手机数据和电子自我监测数据相结合，略微提高了情感状态分类的准确性、灵敏度和特异性。使用智能手机在自然环境中收集的语音特征可以用作双相情感障碍患者的客观状态标记。
Changes in speech have been suggested as sensitive and valid measures of depression and mania in bipolar disorder. The present study aimed at investigating (1) voice features collected during phone calls as objective markers of affective states in bipolar disorder and (2) if combining voice features with automatically generated objective smartphone data on behavioral activities (for example, number of text messages and phone calls per day) and electronic self-monitored data (mood) on illness activity would increase the accuracy as a marker of affective states. Using smartphones, voice features, automatically generated objective smartphone data on behavioral activities and electronic self-monitored data were collected from 28 outpatients with bipolar disorder in naturalistic settings on a daily basis during a period of 12 weeks. Depressive and manic symptoms were assessed using the Hamilton Depression Rating Scale 17-item and the Young Mania Rating Scale, respectively, by a researcher blinded to smartphone data. Data were analyzed using random forest algorithms. Affective states were classified using voice features extracted during everyday life phone calls. Voice features were found to be more accurate, sensitive and specific in the classification of manic or mixed states with an area under the curve (AUC)=0.89 compared with an AUC=0.78 for the classification of depressive states. Combining voice features with automatically generated objective smartphone data on behavioral activities and electronic self-monitored data increased the accuracy, sensitivity and specificity of classification of affective states slightly. Voice features collected in naturalistic settings using smartphones may be used as objective state markers in patients with bipolar disorder.
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影响因子：
4
作者：
Monteith S;Glenn T;Geddes J;Bauer M
通讯作者：
Bauer M
DOI：
10.1136/eb-2012-100885
发表时间：
2013-06-01
期刊：
Evidence-based medicine
影响因子：
--
作者：
Buijink, Arthur Willem Gerard;Visser, Benjamin Jelle;Marshall, Louise
通讯作者：
Marshall, Louise
影响因子：
3.1
作者：
Faurholt-Jepsen, Maria;Vinberg, Maj;Kessing, Lars Vedel
通讯作者：
Kessing, Lars Vedel
影响因子：
3.6
作者：
KUHS, H;RESCHKE, D
通讯作者：
RESCHKE, D
影响因子：
2.9
作者：
Faurholt-Jepsen M;Vinberg M;Christensen EM;Frost M;Bardram J;Kessing LV
通讯作者：
Kessing LV