Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The objective of this study is to develop a simple and high-performance algorithm for detecting myocardial ischemic beats (MI beats) and episodes.  first applied preprocessing algorithms to reduce noises and artifacts coming from the environmental or human movements. Then,  simplified the procedures of identifying the important points and defined these points based only on heart rate and the R peak which is relatively unaffected by the noises. Finally, a correcting window was employed to re-evaluate the results for more accurately identify the MI beats and episodes. The results show that, when  applied the direct method to detect MI beats, high recognition rates of 89.08%, 85.41%, and 86.05% in sensitivity, specificity, and accuracy, respectively, re achieved. The employment of the correcting window method raised the recognition rates to 95.16%, 90.52%, and 91.80%, respectively. MI episode detection based on the window method results in faithfully detecting all MI episodes with a high 97.56% consistence in episode overlapping and a low false positive rate of 3/10 false alarms/record. The simplicity and effectiveness of the methods demonstrate the potential to be implemented on portable devices and smartphones for myocardial ischemia detection.
['Sung-Nien Yu', 'Po-Chuan Tsai']
I am writing a research paper on a idea Myocardial ischemic beat and episode detection based on morphology and correcting window method
[{"from": "human", "value": "I am writing a research paper on a idea Myocardial ischemic beat and episode detection based on morphology and correcting window method"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The objective of this study is to develop a simple and high-performance algorithm for detecting myocardial ischemic beats (MI beats) and episodes.  first applied preprocessing algorithms to reduce noises and artifacts coming from the environmental or human movements. Then,  simplified the procedures of identifying the important points and defined these points based only on heart rate and the R peak which is relatively unaffected by the noises. Finally, a correcting window was employed to re-evaluate the results for more accurately identify the MI beats and episodes. The results show that, when  applied the direct method to detect MI beats, high recognition rates of 89.08%, 85.41%, and 86.05% in sensitivity, specificity, and accuracy, respectively, re achieved. The employment of the correcting window method raised the recognition rates to 95.16%, 90.52%, and 91.80%, respectively. MI episode detection based on the window method results in faithfully detecting all MI episodes with a high 97.56% consistence in episode overlapping and a low false positive rate of 3/10 false alarms/record. The simplicity and effectiveness of the methods demonstrate the potential to be implemented on portable devices and smartphones for myocardial ischemia detection."}]