ABSTRACT
This paper addresses the issue of heart rate detection from noisy ECG data, and presents a method with low complexity and low memory requirements that can detect QRS complex in the presence of noise and muscle artifacts. On the MIT-BIH arrhythmia database we were able to detect 99.3% of QRS complexes with 0.47% false detection. This method can also be applied to heart rate detection using phonocardio signals.
Subject(s)
Electrocardiography/statistics & numerical data , Heart Rate , Algorithms , Biomedical Engineering , Electrocardiography/instrumentation , Heart Sounds , Humans , Phonocardiography/instrumentation , Phonocardiography/statistics & numerical data , Signal Processing, Computer-AssistedABSTRACT
Transcribing medical documents accurately into pre-defined formats and within certain time frames is vital for administrative and medical purposes in any hospital. This paper describes quantitative models incorporating available data to represent transcription activities of a medical records department. We forecasted the workload of the department, determined the optimal worker schedule and designed a simulation model to represent the workflow of the transcription function of a medical record department. The findings provided insight into the workflow, staffing and performance of the department.
