Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The Electrocardiogram (ECG) is widely used for diagnosis of heart diseases. Therefore, the quality of information extracted from the ECG has a vital role. In real recordings, ECG is corrupted by artifacts such as prolonged repolarization, respiration, changes of electrode position, muscle contraction, and por line interface. , a denoising technique for ECG signals based on Empirical Mode Decomposition (EMD) is proposed.  use Ensemble Empirical Mode Decomposition (EEMD) to overcome the limitations of EMD. Moreover, to overcome the limitations of thresholding methods  use the combination of mutual information and two EMD based interval thresholding approaches. Our new method is evaluated on ECG signals available in MIT-BIH database. This method is compared with two EEMD based interval thresholding methods. The results show that our proposed method has a better Signal to Noise Ratio improvement (SNRimp) and a lor Mean Square Error (MSE) than the other two methods.
['Marjaneh Taghavi', 'Mohammad Bagher Shamsollahi', 'Lotfi Senhadji']
I am writing a research paper on a idea ECG denoising using mutual information based classification of IMFs and interval thresholding
[{"from": "human", "value": "I am writing a research paper on a idea ECG denoising using mutual information based classification of IMFs and interval thresholding"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The Electrocardiogram (ECG) is widely used for diagnosis of heart diseases. Therefore, the quality of information extracted from the ECG has a vital role. In real recordings, ECG is corrupted by artifacts such as prolonged repolarization, respiration, changes of electrode position, muscle contraction, and por line interface. , a denoising technique for ECG signals based on Empirical Mode Decomposition (EMD) is proposed.  use Ensemble Empirical Mode Decomposition (EEMD) to overcome the limitations of EMD. Moreover, to overcome the limitations of thresholding methods  use the combination of mutual information and two EMD based interval thresholding approaches. Our new method is evaluated on ECG signals available in MIT-BIH database. This method is compared with two EEMD based interval thresholding methods. The results show that our proposed method has a better Signal to Noise Ratio improvement (SNRimp) and a lor Mean Square Error (MSE) than the other two methods."}]