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来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着情绪状态转变。中心假设是，在临床观察到的情绪变化之前，声音语音模式发生了定量变化。这些变化是可以使用计算方法对生态收集的语音数据进行纵向监测来识别语音模式，这需要被观察个体的最少输入。这些计算确定的变化对于人类观察来说是难以察觉的，但假设可以预测临床上显着的情绪转变。为了检验这一假设，我们将对 50 名患有 I 型和 II 型双相情感障碍的快速循环个体和 10 名健康对照组进行为期 6 个月的研究，通过使用移动“智能手机”记录他们的语音声学特征（而非词汇内容）。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年轻躁狂评定量表）收集每周的临床评估，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言语模式初步研究的理想障碍。这是一种对情绪、认知和运动能力造成病理性破坏的疾病。疾病模式存在周期性，在 躁狂的、精力充沛的状态，情绪激动，言语急促，情绪低落，动作迟缓，言语的质量和数量受到抑制。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实现不显眼的监控，可以无缝集成到手机使用的日常工作中，以预测疾病状态的未来变化。拟议的研究通过开发实用的解决方案来测试高度创新的方法，以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。该项目具有在精神疾病管理方面取得变革性进步的潜力，因为言语模式及其变化很可能反映了 当前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声音和言语变化模式之前，根据对声音和言语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。