Istrate Dan, Castelli Eric, Vacher Michel, Besacier Laurent, Serignat Jean-François
Ecole Supérieure d'Informatique et Genie des Telecommunication (ESIGETEL), Avon-Fontainebleau, France.
IEEE Trans Inf Technol Biomed. 2006 Apr;10(2):264-74. doi: 10.1109/titb.2005.859889.
Today, the growth of the aging population in Europe needs an increasing number of health care professionals and facilities for aged persons. Medical telemonitoring at home (and, more generally, telemedicine) improves the patient's comfort and reduces hospitalization costs. Using sound surveillance as an alternative solution to video telemonitoring, this paper deals with the detection and classification of alarming sounds in a noisy environment. The proposed sound analysis system can detect distress or everyday sounds everywhere in the monitored apartment, and is connected to classical medical telemonitoring sensors through a data fusion process. The sound analysis system is divided in two stages: sound detection and classification. The first analysis stage (sound detection) must extract significant sounds from a continuous signal flow. A new detection algorithm based on discrete wavelet transform is proposed in this paper, which leads to accurate results when applied to nonstationary signals (such as impulsive sounds). The algorithm presented in this paper was evaluated in a noisy environment and is favorably compared to the state of the art algorithms in the field. The second stage of the system is sound classification, which uses a statistical approach to identify unknown sounds. A statistical study was done to find out the most discriminant acoustical parameters in the input of the classification module. New wavelet based parameters, better adapted to noise, are proposed in this paper. The telemonitoring system validation is presented through various real and simulated test sets. The global sound based system leads to a 3% missed alarm rate and could be fused with other medical sensors to improve performance.
如今，欧洲老龄化人口的增长需要越来越多的医疗保健专业人员和老年护理设施。家庭医疗远程监测（更广泛地说，远程医疗）提高了患者的舒适度并降低了住院成本。本文将声音监测作为视频远程监测的替代解决方案，探讨了在嘈杂环境中对报警声音的检测和分类。所提出的声音分析系统能够检测被监测公寓内任何位置的遇险声音或日常声音，并通过数据融合过程与传统医疗远程监测传感器相连。声音分析系统分为两个阶段：声音检测和分类。第一个分析阶段（声音检测）必须从连续的信号流中提取重要声音。本文提出了一种基于离散小波变换的新检测算法，该算法应用于非平稳信号（如脉冲声音）时能得出准确结果。本文所提出的算法在嘈杂环境中进行了评估，与该领域的现有算法相比具有优势。系统的第二阶段是声音分类，它采用统计方法来识别未知声音。通过统计研究找出分类模块输入中最具判别力的声学参数。本文提出了基于小波的新参数，这些参数更适合噪声环境。通过各种真实和模拟测试集展示了远程监测系统的验证情况。基于声音的整体系统误报率为3%，并且可以与其他医疗传感器融合以提高性能。
