Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Through an investigation of various clinical cases, heart sounds are found to be quasi-cyclostationary. Nonlinear time scaling from cycle-to-cycle is proposed to enhance cyclic stationarity, where nonlinear time scaling is approximated by a piecewise linear function. The techniques of cyclostationary signal processing are employed in this paper to reduce noise and disturbance in the cycle-frequency domain. Heart sounds can be theoretically recovered in the presence of additive, zero mean noise, and disturbance (perhaps non-Gaussian, nonstationary, or colored). The experimental tests in various conditions confirm the theoretical results.
['Hong Tang', 'Ting Li', 'Tianshuang Qiu']
I am writing a research paper on a idea Noise and Disturbance Reduction for Heart Sounds in Cycle-Frequency Domain Based on Nonlinear Time Scaling
[{"from": "human", "value": "I am writing a research paper on a idea Noise and Disturbance Reduction for Heart Sounds in Cycle-Frequency Domain Based on Nonlinear Time Scaling"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Through an investigation of various clinical cases, heart sounds are found to be quasi-cyclostationary. Nonlinear time scaling from cycle-to-cycle is proposed to enhance cyclic stationarity, where nonlinear time scaling is approximated by a piecewise linear function. The techniques of cyclostationary signal processing are employed in this paper to reduce noise and disturbance in the cycle-frequency domain. Heart sounds can be theoretically recovered in the presence of additive, zero mean noise, and disturbance (perhaps non-Gaussian, nonstationary, or colored). The experimental tests in various conditions confirm the theoretical results."}]