Longitudinal Voice Patterns in Bipolar Disorder

双相情感障碍的纵向声音模式

• 批准号: 8658149
• 负责人: MELVIN G MCINNIS
• 金额: $ 27.21万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-05-02 至 2016-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8658149
• 关键词: Accent; Acoustics; Address; Algorithms; Anxiety Disorders; Behavior; Bipolar Depression; Bipolar Disorder; Bipolar I; Bipolar II; Car Phone; Cellular Phone; Characteristics; Charge; Clinical; Clinical Research; Clinical assessments; Cognitive; Computational algorithm; Computer Simulation; Computers; Computing Methodologies; Data; Data Collection; Depressed mood; Detection; Devices; Disease; Early Diagnosis; Emotional; Emotions; Environmental Monitoring; Frequencies; Future; Goals; Hamilton Rating Scale for Depression; Health; Human; Individual; Intervention; Interview; Knowledge; Longitudinal Studies; Loudness; Machine Learning; Manic; Measurable; Measures; Medical; Mental Depression; Mental disorders; Modeling; Monitor; Mood Disorders; Moods; Motor; Movement; Neurocognitive; Observer Variation; Outcome; Participant; Patients; Pattern; Pattern Recognition; Perception; Periodicity; Personality; Phase; Population; Prevention; Process; Property; Psychiatric therapeutic procedure; Psychopathology; Psychotic Disorders; Recording of previous events; Recruitment Activity; Secure; Sensory; Shapes; Solutions; Speech; Speech Acoustics; Stress; Structure; Technology; Telephone; Testing; Time; Variant; Voice; Work; base; bipolar mania; clinically significant; digital; heuristics; innovation; insight; instrument; lexical; markov model; mental state; pressure; programs; psychologic; public health relevance; research study; speech processing; statistics; tool

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.

描述（由申请人提供）：拟议的研究将使用基于手机的创新技术识别声学语音参数的变化，以预测双相情感障碍患者的临床显著情绪状态转变。中心假设是，在临床观察到的情绪变化之前，声学语音模式发生了定量变化。这些变化是语音模式可以使用计算方法通过对生态收集的语音数据进行纵向监测来识别，这需要来自被观察个体的最小输入。这些计算确定的变化是人类观察不到的，但被假设为预测临床上显著的情绪转变。为了验证这一假设，我们将研究50名患有双相I型和II型障碍的快速骑车人和10名健康对照者6个月，通过记录他们使用移动的“智能手机”时的语音（而不是词汇内容）的声学特征。通过这种方式，我们正在收集没有观察者偏见的数据。我们还将收集每周的临床评估与标准化工具（汉密尔顿抑郁量表和青年躁狂量表），我们将记录他们的身体语音模式以及。双相情感障碍是一个理想的障碍，初步研究的语言模式，在精神病理学的评估。它是一种情感、认知和运动能力受到病理性破坏的疾病。有一个周期性的疾病模式，振荡之间 从情绪激动、说话急促的狂躁兴奋状态到动作迟缓、说话质量和数量受到抑制的抑郁情绪阶段。双相情感障碍患者的成功管理需要持续的精神状态临床监测。目前，很少有技术能够解决以生态方式长期监测个人的挑战。语音模式识别技术将允许不引人注目的监测，可以无缝地集成到日常的移动的电话使用，以预测未来的变化，疾病状态。拟议的研究通过开发一种实用的解决方案来帮助双相情感障碍患者的纵向管理，测试了一种高度创新的方法。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，因为语言模式及其变化很可能反映出 新兴的精神病理学如果成功，这项技术将提供优先考虑的病人的医疗和精神病护理的基础上计算检测的变化模式，在语音和语言之前，他们是临床观察。