title: Automatic detection system for cough sounds as a symptom of abnormal health condition.
abstract: The problem of attending to the health of the aged who live alone has became an important issue in developed countries. One way of solving the problem is to check their health condition by a remote-monitoring technique and support them with well-timed treatment. The purpose of this study is to develop an automatic system that can monitor a health condition in real time using acoustical information and detect an abnormal symptom. In this study, cough sound was chosen as a representative acoustical symptom of abnormal health conditions. For the development of the system distinguishing a cough sound from other environmental sounds, a hybrid model was proposed that consists of an artificial neural network (ANN) model and a hidden Markov model (HMM). The ANN model used energy cepstral coefficients obtained by filter banks based on human auditory characteristics as input parameters representing a spectral feature of a sound signal. Subsequently, an output of this ANN model and a filtered envelope of the signal were used for making an input sequence for the HMM that deals with the temporal variation of the sound signal. Compared with the conventional HMM using Mel-frequency cepstral coefficients, the proposed hybrid model improved recognition rates on low SNR from 5 dB down to -10 dB. Finally, a preliminary prototype of the automatic detection system was simply illustrated.
authors.affiliation: Department of Electrical and Mechanical Engineering, Seikei University, Tokyo 180-8633, Japan. soulshin@gmail.com
authors.forename: Sung-Hwan
authors.initials: SH
authors.lastname: Shin
authors.forename: Takeo
authors.initials: T
authors.lastname: Hashimoto
authors.forename: Shigeko
authors.initials: S
authors.lastname: Hatano
mesh_terms: D000368:Aged; D003371:Cough / Q000503:physiopathology*; D006801:Humans; D008390:Markov Chains; D008954:Models, Biological; D008991:Monitoring, Physiologic / Q000379:methods*; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods*; D012135:Respiratory Sounds / Q000503:physiopathology*