The application of deep learning techniques in the medical field,specifically for Atrial Fibrillation(AFib)detection through Electrocardiogram(ECG)signals,has witnessed significant interest.Accurate and timely diagnos...The application of deep learning techniques in the medical field,specifically for Atrial Fibrillation(AFib)detection through Electrocardiogram(ECG)signals,has witnessed significant interest.Accurate and timely diagnosis increases the patient’s chances of recovery.However,issues like overfitting and inconsistent accuracy across datasets remain challenges.In a quest to address these challenges,a study presents two prominent deep learning architectures,ResNet-50 and DenseNet-121,to evaluate their effectiveness in AFib detection.The aim was to create a robust detection mechanism that consistently performs well.Metrics such as loss,accuracy,precision,sensitivity,and Area Under the Curve(AUC)were utilized for evaluation.The findings revealed that ResNet-50 surpassed DenseNet-121 in all evaluated categories.It demonstrated lower loss rate 0.0315 and 0.0305 superior accuracy of 98.77%and 98.88%,precision of 98.78%and 98.89%and sensitivity of 98.76%and 98.86%for training and validation,hinting at its advanced capability for AFib detection.These insights offer a substantial contribution to the existing literature on deep learning applications for AFib detection from ECG signals.The comparative performance data assists future researchers in selecting suitable deep-learning architectures for AFib detection.Moreover,the outcomes of this study are anticipated to stimulate the development of more advanced and efficient ECG-based AFib detection methodologies,for more accurate and early detection of AFib,thereby fostering improved patient care and outcomes.展开更多
目的:探讨腔内心电图(ECG)定位导管尖端技术在新生儿经外周置入中心静脉导管(peripherally inserted central catheters,PICC)中的应用及效果。方法:采用回顾性分析方法,将2014年1月至2017年12月经下肢静脉置入PICC的39例新生儿设为对照...目的:探讨腔内心电图(ECG)定位导管尖端技术在新生儿经外周置入中心静脉导管(peripherally inserted central catheters,PICC)中的应用及效果。方法:采用回顾性分析方法,将2014年1月至2017年12月经下肢静脉置入PICC的39例新生儿设为对照组,2018年1月至2020年12月经下肢外周静脉置入PICC的42例新生儿设为观察组。对照组置管成功后固定PICC导管,拍摄X线片;观察组将导管送至比预定长度长2 cm时,患儿穿刺侧肢体呈外展屈曲位,连接心电监护仪,推注0.9%氯化钠溶液,心电监护仪上出现P波与QRS波之比40%~50%,再缓慢外拔导管至P波与体表心电图的P波一致,固定PICC导管,拍摄X线片。根据X线片结果,观察2组一次置管到位率、完成治疗率。结果:观察组PICC导管一次置管到位率为95.23%(40/42),高于对照组的76.92%(30/39)(P<0.05);2组完成治疗率差异无统计学意义(P>0.05)。结论:ECG可用于辅助定位新生儿经下肢置入PICC导管尖端的位置,且操作简单,容易掌握,方法安全、可行,值得临床推广应用。展开更多
文摘The application of deep learning techniques in the medical field,specifically for Atrial Fibrillation(AFib)detection through Electrocardiogram(ECG)signals,has witnessed significant interest.Accurate and timely diagnosis increases the patient’s chances of recovery.However,issues like overfitting and inconsistent accuracy across datasets remain challenges.In a quest to address these challenges,a study presents two prominent deep learning architectures,ResNet-50 and DenseNet-121,to evaluate their effectiveness in AFib detection.The aim was to create a robust detection mechanism that consistently performs well.Metrics such as loss,accuracy,precision,sensitivity,and Area Under the Curve(AUC)were utilized for evaluation.The findings revealed that ResNet-50 surpassed DenseNet-121 in all evaluated categories.It demonstrated lower loss rate 0.0315 and 0.0305 superior accuracy of 98.77%and 98.88%,precision of 98.78%and 98.89%and sensitivity of 98.76%and 98.86%for training and validation,hinting at its advanced capability for AFib detection.These insights offer a substantial contribution to the existing literature on deep learning applications for AFib detection from ECG signals.The comparative performance data assists future researchers in selecting suitable deep-learning architectures for AFib detection.Moreover,the outcomes of this study are anticipated to stimulate the development of more advanced and efficient ECG-based AFib detection methodologies,for more accurate and early detection of AFib,thereby fostering improved patient care and outcomes.
文摘目的:探讨腔内心电图(ECG)定位导管尖端技术在新生儿经外周置入中心静脉导管(peripherally inserted central catheters,PICC)中的应用及效果。方法:采用回顾性分析方法,将2014年1月至2017年12月经下肢静脉置入PICC的39例新生儿设为对照组,2018年1月至2020年12月经下肢外周静脉置入PICC的42例新生儿设为观察组。对照组置管成功后固定PICC导管,拍摄X线片;观察组将导管送至比预定长度长2 cm时,患儿穿刺侧肢体呈外展屈曲位,连接心电监护仪,推注0.9%氯化钠溶液,心电监护仪上出现P波与QRS波之比40%~50%,再缓慢外拔导管至P波与体表心电图的P波一致,固定PICC导管,拍摄X线片。根据X线片结果,观察2组一次置管到位率、完成治疗率。结果:观察组PICC导管一次置管到位率为95.23%(40/42),高于对照组的76.92%(30/39)(P<0.05);2组完成治疗率差异无统计学意义(P>0.05)。结论:ECG可用于辅助定位新生儿经下肢置入PICC导管尖端的位置,且操作简单,容易掌握,方法安全、可行,值得临床推广应用。
