Combining Algorithms in Automatic Detection of QRS Complexes in ECG Signals
IEEE Transactions on Information Technology in Biomedicine
Sleep-wake stages classification and sleep efficiency estimation using single-lead electrocardiogram
Expert Systems with Applications: An International Journal
Development of a joint space width measurement method based on radiographic hand images
Computers in Biology and Medicine
An R-peak detection method that uses an SVD filter and a search back system
Computer Methods and Programs in Biomedicine
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An in-home sleep monitoring system was developed previously in our laboratory for monitoring electrocardiography (ECG) and respiratory signals. However, the ECG signal acquired with this system is prone to high-grade noise caused by motion artifact. Since the detection of the QRS complexes with high accuracy is very important in a computer-based analysis of the ECG, a high accuracy QRS detection algorithm is developed and based on the combination of heart rate indicators and morphological ECG features. The proposed algorithm is tested both on 16h data acquired using the two sensors of our cardiorespiratory belt system, i.e., the polyvinylidene fluoride (PVDF) film and the conductive fabric sheets, and on all 48 records of the MIT/BIH Arrhythmia Database. Satisfying results are obtained for both databases, the sensitivity S"e and positive predictivity P"+ were calculated for each case and results show S"e=[96.98%, 93.76%] and P"+=[97.81%, 99.48%] for conductive fabric and PVDF film sensors, respectively, and S"e=99.77% and P"+=99.64% in the case of the MIT/BIH Arrhythmia Database. Further, heart rate variability (HRV) measures were calculated using our system and a commercial system. A comparison between systems' results is done to show the usefulness of our developed algorithm used with our cardiorespiratory belt sensor.