Longitudinal Voice Patterns in Bipolar Disorder

描述(由申请人提供)：这项拟议的研究将利用创新的基于手机的技术，确定声学语音参数的变化，以预测双相情感障碍患者临床上显著的情绪状态转变。中心假设是，在临床观察到的情绪变化之前，声学语音模式存在量化变化。这些变化是，可以使用计算方法识别语音模式，而不是对生态收集的语音数据进行纵向监控，这需要来自被观察个体的最小输入。这些通过计算确定的变化对人类观察是不可察觉的，但假设是为了预测临床上有意义的情绪转变。为了验证这一假设，我们将对50名患有I型和II型双相情感障碍的快速循环患者和10名健康对照进行为期6个月的研究，记录他们使用手机时的语音(而不是词汇内容)的声学特征。通过这种方式，我们收集的数据没有观察者的偏见。我们还将使用标准化工具(汉密尔顿抑郁评定量表和青年躁狂评定量表)收集每周临床评估，其中我们还将记录他们的身体声音模式。双相情感障碍是精神病理学评估中对言语模式进行初步研究的理想障碍。这是一种情绪、认知和运动能力出现病理性紊乱的疾病。疾病模式有一个周期性，它在 躁狂使情绪充沛的状态充满活力，并迫使快速言语进入行动迟缓的抑郁情绪阶段，抑制说话的质量和数量。双相情感障碍患者的成功治疗需要对精神状态进行持续的临床监测。目前，很少有技术能够解决以生态方式长期监测个体的挑战。语音模式识别技术将允许进行不引人注目的监测，这些监测可以无缝地整合到手机的日常使用中，以预测未来疾病状态的变化。这项拟议的研究测试了一种高度创新的方法，通过开发一种实用的解决方案来协助双相情感障碍患者的纵向管理。分析语音模式的计算算法将使用统计(高斯混合模型和支持向量机)和动态(隐马尔可夫模型)建模。这个项目有可能在精神疾病的管理方面取得变革性的进展，因为言语模式及其变化很可能反映出 当代和新兴的精神病理学。如果成功，这项技术将在临床可观察到之前，根据对语音和语言变化模式的计算检测，为患者提供医疗和精神护理的优先顺序。

DESCRIPTION (provided by applicant): The proposed research study will identify changes in acoustic speech parameters, using innovative cell phone based technology, in order to predict clinically significant mood state transitions in individuals with bipolar disorder. The central hypothesis is that there are quantitative changes in acoustic speech patterns that occur in advance of clinically observed mood changes. These changes is speech patterns can be identified using computational methods over longitudinal monitoring of ecologically gathered voice data that requires minimal input from the individual being observed. These computationally determined changes are imperceptible to human observation but are hypothesized to predict clinically significant mood transitions. To test this hypothesis we will study 50 rapid cycling individuals with bipolar I and II disorder and 10 healthy controls for 6 months by recording their acoustic characteristics of speech (not lexical content) while using a mobile "smart- phone". In this manner we are gathering data free of observer bias. We will also gather weekly clinical assessments with standardized instruments (Hamilton Depression Rating Scale and Young Mania Rating Scale) in which we will record their physical voice patterns as well. Bipolar disorder is an ideal disorder for the initial study of speech patterns in the assessment of psychopathology. It is an illness with pathological disruptions of emotion, cognitive and motor capacity. There is a periodicity of the illness pattern that oscillates between manic energized states with charged emotions and pressured rapid speech to depressed emotional phases with retarded movements and inhibited quality and quantity of speech. The successful management of patients with bipolar disorder requires ongoing clinical monitoring of mental states. Currently there are few technologies that address the challenge of monitoring individuals long-term in an ecological manner. Speech pattern recognition technology would allow for unobtrusive monitoring that can be seamlessly integrated into daily routine of mobile phone usage to predict future changes in illness states. The proposed study tests a highly innovative approach by developing a practical solution to assist in the longitudinal management of bipolar patients. Computational algorithms of analyzed speech patterns will use statistic (Gausian Mixture Models and Support Vector Machines) and dynamic (Hidden Markov Models) modeling. This project has the potential of transformative advances in the management of psychiatric disease, as speech patterns, and changes therein, are highly likely to be reflective of current and emerging psychopathology. If successful this technology will provide for the prioritization of patients for medical and psychiatric care based on computational detection of change patterns in voice and speech before they are clinically observable.