A Linear Classifier For Cough And Pseudo-Cough Sounds In Patients With Cervical Spinal Cord Injury

The quality of respiratory function determines the recovery and survival rate of the patients with cervical spinal cord injury. A cost efficient method of evaluating the respiratory function is to assess the strength of their cough sounds. However, some patients with cervical spinal cord injury fail to develop an effective cough because of pain or nerve damage, and their voice is shout, called pseudo-cough herein, rather than cough. Such samples of pseudo-cough sounds should be weeded out so as to avoid wrong evaluation of respiratory function. In this paper, a linear classifier is proposed to recognize pseudo-cough sounds from cough sounds for patients with cervical spinal cord injury. To alleviate dependence on the number of cough-sound and pseudo-cough-sound samples, a light-weight classifier is constructed by using merely two features: zero-crossing rate and maximal autocorrelation coefficient, and the classifier is trained mainly with unvoiced and voiced sounds rather than cough sounds and pseudo-cough sounds. Experimental results showed that the sensitivity and specificity were 98% and 86.4%, respectively.