Classification of Respiration Episodes using Fuzzy Logic

Respiratory signals collected from young adults using Biopac's abdominal strain gauge were properly filtered, amplified and digitized. An algorithm that combined Autoregressive (AR) and modified zero-crossing models was used to extract signal parameters such as the energy and frequency of the underlying signal. These parameters were used in a classification scheme based on fuzzy logic. Because of the variability of respiration signals, fuzzy logic provides a more natural classification as opposed to threshold based method [3]-[4]. Experimental results show that fuzzy logic presents a flexible and adaptable classificatory mechanism, which shows in percentage to which a segment of respiration signal belongs to one of the following categories: normal respiration, respiration with artifacts or apnea. It can be effectively used to reduce false alarms and improve classification of ambiguous cases.