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A key application for IoT based technologies in the field of healthcare is wireless medical sensors that can be used to monitor patients’ physiological information such as heartbeat, bowel activity and lung sounds. Real-time detection of bowel motility after major abdominal surgery has significant importance for the patients’ healing process. Due to temporal cessation of intestinal motility after the surgery, a period of fasting is commonly practiced, and patients are fed with fluids following the recovery of bowel motility. Many studies have been conducted to monitor intestinal motility and automatically detect bowel activity. Detection and identification are challenging because of the ambient noise in clinical environments. Active noise cancellation methods remove unwanted signals by using adaptive filters. In this paper, active noise cancellation simulations were performed in order to remove ambient noise from gastrointestinal auscultation recordings. The simulation setup was created based on a previously developed IoT-driven electronic stethoscope by our group. Five widely used adaptive filter algorithms: Least Mean Squares, Normalized Least Mean Squares, Affine Projection, Recursive Least Squares, and Adaptive Lattice were tested, and performance evaluations are reported. |
