Automated Cough Assessment on a Mobile Platform.
Automated Cough Assessment on a Mobile Platform.
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DOI：
10.1155/2014/951621
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发表时间：
2014-01-01
期刊：
影响因子：
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通讯作者：
Bocko, Mark
中科院分区：
文献类型：
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作者：
Sterling, Mark;Rhee, Hyekyun;Bocko, Mark
本文介绍了一种自动哮喘监测系统（ADAM）的研制。这包括一个运行自定义应用程序的消费电子移动的平台。应用程序从连接到设备模数转换器的外部用户佩戴麦克风（麦克风输入）获取音频信号。该信号被处理以确定咳嗽声的存在或不存在。记录症状记录和原始音频波形，并使其易于访问，以供医疗保健提供者稍后查看。症状检测算法基于标准语音识别和机器学习范例，并且包括音频特征提取步骤，随后是基于隐马尔可夫模型的维特比解码器，该维特比解码器已经在来自各种主题的音频示例的大型数据库上训练。研究了多重隐马尔可夫模型的拓扑结构和阶数。识别器的性能提出在交叉验证测试中确定的灵敏度和虚警率。
The development of an Automated System for Asthma Monitoring (ADAM) is described. This consists of a consumer electronics mobile platform running a custom application. The application acquires an audio signal from an external user-worn microphone connected to the device analog-to-digital converter (microphone input). This signal is processed to determine the presence or absence of cough sounds. Symptom tallies and raw audio waveforms are recorded and made easily accessible for later review by a healthcare provider. The symptom detection algorithm is based upon standard speech recognition and machine learning paradigms and consists of an audio feature extraction step followed by a Hidden Markov Model based Viterbi decoder that has been trained on a large database of audio examples from a variety of subjects. Multiple Hidden Markov Model topologies and orders are studied. Performance of the recognizer is presented in terms of the sensitivity and the rate of false alarm as determined in a cross-validation test.