Longitudinal Voice Patterns in Bipolar Disorder

双相情感障碍的纵向声音模式

• 批准号: 8494970
• 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/8494970
• 关键词: Accent; Acoustics; Address; Algorithms; Anxiety Disorders; Behavior; Bipolar Disorder; 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; 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）：拟议的研究将识别声学语音参数的变化，使用创新的基于手机的技术，以预测双相情感障碍患者临床上重要的情绪状态转变。中心假设是，在临床观察到的情绪变化之前，声学语音模式发生了定量变化。这些语音模式的变化可以通过对生态收集的语音数据进行纵向监测的计算方法来识别，这需要从被观察的个体中获得最小的输入。这些计算确定的变化对人类观察来说是难以察觉的，但假设可以预测临床显着的情绪转变。为了验证这一假设，我们将对50名快速循环的双相I和II型患者和10名健康对照者进行为期6个月的研究，在使用移动“智能手机”时记录他们的语音声学特征（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。我们还将收集每周一次的临床评估，使用标准化的工具（汉密尔顿抑郁评定量表和年轻躁狂评定量表），我们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。这是一种病理性的情绪、认知和运动能力紊乱的疾病。疾病模式有一个周期性，在两者之间振荡