Background The symptom of chest pain is one of the most common presenting complaints seen in primary and secondary care and is the leading cause of emergency department visits. PQRST pain assessment method might be us...Background The symptom of chest pain is one of the most common presenting complaints seen in primary and secondary care and is the leading cause of emergency department visits. PQRST pain assessment method might be useful, but contemporary researches of its feasibility for chest pain patients are limited. Methods Between March 2017 and August 2017, 533 consecutive patients as control group and 657 cases as treatment group were retrospectively recruited in the Emergency Department of our center. We compared the time took for the first cardiogram, the time spent in the emergency department, triage accuracy and patient stratification rate between two groups. Results In treatment group, statistically less time was spent to take the first cardiogram（5.3±1.2 vs. 11.2±2.5, P 〈 0.001）. This group had higher triage accuracy（92.34% vs. 86.91%, P = 0.003）and patient stratification（95.51% vs. 91.48%, P = 0.006）. Conclusions PQRST pain assessment method is useful and feasible for increasing triage accuracy and patient stratification rate of non-Traumatic chest pain patients in emergency department.展开更多
This paper presents a Novel Windowing Algorithm for Electrocardiogram Feature Extraction and Pattern Recognition. The work presented here deals with a simple and efficient way of detecting ECG features that are P, Q, ...This paper presents a Novel Windowing Algorithm for Electrocardiogram Feature Extraction and Pattern Recognition. The work presented here deals with a simple and efficient way of detecting ECG features that are P, Q, R, S and T waves. Windowing method is used to select these waves. Windows are based on varying R-R intervals. It has been tested on ECG simulator data and also on different records of the MIT-BIH arrhythmia database, producing satisfactory results. ECG timing intervals are also required for monitoring the cardiac condition of patients. Hence after feature detections ECG timing intervals like the PR interval, QRS duration, the QT interval, the QT corrected interval and Vent Rate are efficiently calculated using proposed Formulae.展开更多
The PQRST segment which include the major information in a heart beatis detected and used as the input pattern to cluster by ART2 model. The parametersof pacemaker which consist of pulse, QRS characteristics, clusteri...The PQRST segment which include the major information in a heart beatis detected and used as the input pattern to cluster by ART2 model. The parametersof pacemaker which consist of pulse, QRS characteristics, clustering results andprogrammed parameters are combined in analyzing paced ECG (PECG) synthetically.展开更多
文摘Background The symptom of chest pain is one of the most common presenting complaints seen in primary and secondary care and is the leading cause of emergency department visits. PQRST pain assessment method might be useful, but contemporary researches of its feasibility for chest pain patients are limited. Methods Between March 2017 and August 2017, 533 consecutive patients as control group and 657 cases as treatment group were retrospectively recruited in the Emergency Department of our center. We compared the time took for the first cardiogram, the time spent in the emergency department, triage accuracy and patient stratification rate between two groups. Results In treatment group, statistically less time was spent to take the first cardiogram（5.3±1.2 vs. 11.2±2.5, P 〈 0.001）. This group had higher triage accuracy（92.34% vs. 86.91%, P = 0.003）and patient stratification（95.51% vs. 91.48%, P = 0.006）. Conclusions PQRST pain assessment method is useful and feasible for increasing triage accuracy and patient stratification rate of non-Traumatic chest pain patients in emergency department.
文摘This paper presents a Novel Windowing Algorithm for Electrocardiogram Feature Extraction and Pattern Recognition. The work presented here deals with a simple and efficient way of detecting ECG features that are P, Q, R, S and T waves. Windowing method is used to select these waves. Windows are based on varying R-R intervals. It has been tested on ECG simulator data and also on different records of the MIT-BIH arrhythmia database, producing satisfactory results. ECG timing intervals are also required for monitoring the cardiac condition of patients. Hence after feature detections ECG timing intervals like the PR interval, QRS duration, the QT interval, the QT corrected interval and Vent Rate are efficiently calculated using proposed Formulae.
文摘The PQRST segment which include the major information in a heart beatis detected and used as the input pattern to cluster by ART2 model. The parametersof pacemaker which consist of pulse, QRS characteristics, clustering results andprogrammed parameters are combined in analyzing paced ECG (PECG) synthetically.
