The apnea-ECG database. Comput Cardiol. 2000; 2000(27):255-8. http://dx.doi.org/10.1109/CIC.2000.898505.Penzel T,Moody GB,Mark RG,et al.The Apnea-ECG database.Computers in Cardiology. 2000Penzel T, Moody GB, Mark RG, Goldberger AL, Peter JH. The apnea-ecg database. In: ...
On the generalizability of ECG-based obstructive sleep apnea monitoring: merits and limitations of the Apnea-ECG database Obstructive sleep apnea syndrome (OSAS) is a sleep disorder that affects a large part of the population and the development of algorithms using cardiovascular features for OSAS ...
At present, there are some SA detection works based on the PhysioNet Apnea-ECG database. To further verify the effectiveness of our proposed method, we compare it with these works. It is worth noting that due to the different data preprocessing used in these works, the samples are slightly...
Data description To validate the proposed algorithm, two publicly available and widely used databases, namely- Physionet's Apnea-ECG database and St. Vincents University Hospital/University College Dublin Sleep Apnea Database have been used. Results and discussions The problem of computer-assisted slee...
The data consist of 70 records, divided into a learning set of 35 records (a01 through a20, b01 through b05, and c01 through c10), and a test set of 35 records (x01 through x35), all of which may be downloaded from this page. Recordings vary in length fr
We use the Apnea-ECG database of PhysioNet to conduct experiments. In terms of segment detection results, the proposed method achieves an accuracy of 91.13%, and the sensitivity and specificity reach 90.32% and 91.63% respectively. In terms of individual screening, the accuracy of the proposed ...
This database consists of 70 ECG recordings. A detailed time- and frequency-domain features and nonlinear features extracted from the RR interval of the ECG signals for observing minutes of sleep apnea are occurred in this work. Time-domain features mean HR (P = 0.0093, r = 0.3593) and RR...
Testing and validation has been performed using MIT BIH online database [1]. The developed technique accurately diagnosed all the sleep apnea cases in MIT BIH sleep apnea database.Syeda Quratulain AlirVarun JeotiSamir Brahim Belhaouari
The results of patient classification were biased toward data from the wearable patch-type device, which may be influenced by different ECG waveforms. The proposed method is tuned with a sample of the data from the device, and the performance result of Cohen's kappa increased from 0.54 to ...
To evaluate the proposed approach, 34 single-lead ECG signals from the Physionet Apnea-ECG database are used. Experimental findings show that using ARIMA-EGARCH coefficients as a feature vector make it possible to classify apneic and normal ECG segments, and the new ARIMA-EGARCH parameter-...