Automatic detection system for cough sounds as a symptom of abnormal health condition

IEEE Transactions on Information Technology in Biomedicine

Volumn 13, Issue 4, 2009, Pages 486-493

  Shin, Sung Hwan ^a   Hashimoto, Takeo ^a   Hatano, Shigeko ^a  

^a SEIKEI UNIVERSITY   (Japan)

Author keywords

Automatic detection; Cough sound; Health monitoring; Hybrid model

Indexed keywords

ACOUSTICAL INFORMATION; ARTIFICIAL NEURAL NETWORK MODELS; AUTOMATIC DETECTION; AUTOMATIC SYSTEMS; CEPSTRAL COEFFICIENTS; COUGH SOUND; COUGH SOUNDS; DEVELOPED COUNTRIES; ENVIRONMENTAL SOUNDS; HEALTH CONDITION; HEALTH MONITORING; HYBRID MODEL; INPUT PARAMETER; INPUT SEQUENCE; LOW SNR; MEL-FREQUENCY CEPSTRAL COEFFICIENTS; MONITORING TECHNIQUES; REAL TIME; RECOGNITION RATES; SOUND SIGNAL; SPECTRAL FEATURE; TEMPORAL VARIATION;

BACKPROPAGATION; FILTER BANKS; HEALTH; HIDDEN MARKOV MODELS; HYBRID SENSORS; NEURAL NETWORKS; STRUCTURES (BUILT OBJECTS);

SIGNAL DETECTION;

ABNORMAL RESPIRATORY SOUND; AGED; ARTICLE; ARTIFICIAL NEURAL NETWORK; AUTOMATED PATTERN RECOGNITION; BIOLOGICAL MODEL; COUGHING; HUMAN; METHODOLOGY; MONITORING; PATHOPHYSIOLOGY; PROBABILITY;

AGED; COUGH; HUMANS; MARKOV CHAINS; MODELS, BIOLOGICAL; MONITORING, PHYSIOLOGIC; NEURAL NETWORKS (COMPUTER); PATTERN RECOGNITION, AUTOMATED; RESPIRATORY SOUNDS;

EID: 67749104176     PISSN: 10897771     EISSN: None     Source Type: Journal    
DOI: 10.1109/TITB.2008.923771     Document Type: Conference Paper

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