Adaptive signal processing
Teaching DSP software development: from design to fixed-point implementations
IEEE Transactions on Education
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This article presents an application of a digital signal processor for reducing a power line noise and detecting a swallow signal in surface electromyography (SEMG) from a dysphagic patient, a person who has difficulty in swallowing. The sampling rate at 1000 sample/s was used in SEMG data acquisition. Each sample was collected with 16-bit resolution. We use an adaptive linear neural network (ADALINE) filter and least mean square (LMS) algorithms to reduce power line noise. Parameters used for testing performance of the ADALINE adaptive filter are as follows: number of tapped delay line = 10, delay = 10 and learning rate = 0.0156. Results from the real-time implementation on TMS320VC5509A demonstrate that the system can successfully eliminate both 50-Hz power line noise and its odd harmonic components. In addition, the swallowing detection system based on the digital signal processor functions correctly. In other words, the trigger signal is appropriately generated for 1 second when the beginning point of swallowing signal is detected. When total number of swallows was 40, the system was able to detect 37 times of swallows correctly.