Fig 1.
Proposed 2D-CNN network for OSA event detection.
“Conv(k,s,f)” denotes a convolutional layer where k, s, and f are the kernel size, stride size, and number of filters, respectively. “Max(p,s)” denotes a max-pooling layer where p and s are the pool size and stride size, respectively. The values for the filter sizes “f” in the four residual blocks are 32, 64, 96, and 128.
Fig 2.
(a) Part of an original ECG segment. (b) The denoised and scaled version.
Fig 3.
Image dataset creation.
Fig 4.
One-minute ECG segments transformed into (128, 128, 3) RGB images.
(a) Scalogram image of a normal ECG segment. (b) Spectrogram image of the normal segment. (c) Scalogram image of an apnea ECG segment. (d) Spectrogram image of the apnea segment.
Fig 5.
Fusing the scalogram and spectrogram for an apnea ECG segment.
(a) Gray-scaled scalogram and spectrogram images. (b) RGB components of the modified image. (c) Fused image. (d) Fused image of a normal ECG segment. (e) Fused image of an apnea ECG segment.
Fig 6.
Schematic diagram of the training procedure for the proposed 2D-CNN model with 10-fold cross validation.
Fig 7.
Distributions of validation accuracy for TFR images and fused images over 10 folds.
Fig 8.
Confusion matrices for per-segment apnea detection, with classwise PR and RE shown in the bottom and right-hand boxes, respectively: (a) Wigner–Ville distribution images, (b) scalogram images, (c) spectrogram images, and (d) fused images.
Fig 9.
IQR plots of PR, RE, and F1 for apnea detection obtained across all folds.
The center line indicates the median, the box limits indicate the upper and lower quartiles, the whiskers indicate 1.5 × IQR, and × indicates the mean. The images are Wigner–Ville distribution images (wg), scalogram images (sc), spectrogram images (sp), or fused images (fu).
Table 1.
Overall performance in per-segment apnea detection TFR images and fused images.
Fig 10.
Accuracy-loss graph of the proposed CNN (for the lowest-performing model).
Fig 11.
Overall 10-fold cross-validation results for per-segment apnea detection with Wigner–Ville distribution, scalogram, spectrogram, and fused images.
Black lines indicate the corresponding 95% confidence interval.
Table 2.
Performance comparison of proposed and previous methods for per-segment apnea detection.