arlenejohn/cnn_sleep_apnea

Code for the paper `A 1D-CNN based deep learning technique for sleep apnea detection in IoT sensors'

CNN_sleep_apnea

The code and models in the article "A 1D-CNN Based Deep Learning Technique for Sleep Apnea Detection in Wearable Sensors."

Please cite the article if using this resource: A. John, B. Cardiff and D. John, "A 1D-CNN Based Deep Learning Technique for Sleep Apnea Detection in IoT Sensors," 2021 IEEE International Symposium on Circuits and Systems (ISCAS), 2021, pp. 1-5, doi: 10.1109/ISCAS51556.2021.9401300.

Code for model M1 in the article is shared in the jupyter noebook.

The raw data and processed data is hosted on https://drive.google.com/drive/folders/12bksbKpkro5s4RljIe8nF1gbC0Dl-d3Z?usp=sharing. Please request access.

cnn_sleep_apnea's Issues

Need the code urgently, thanks!

Hello, I am currently doing some experiments about apnea detection with the UCDDB data set, but the result is awful. I think it may be related to the preprocessing step. So I searched many articles until I found that the accuracy result in your newest article can reach 100%. That's amazing! Then I found your github homepage and wanted to see how you deal with the data, but I haven't waited until the update, so I'm urgently need the relevant code , thanks very much!!!

Hello? I have some problems on your sleep_apnea code process

I downloaded for all your upload code about matlab code and python data.
And I preprocessed sleep apnea data on your matlab code.
Also I activated on your [sleep_apnea_ecg.ipynb] code for checking result.
However I tried 3 times because your upload [sleep_apnea_ecg.ipynb] training data size didnt match on my training data. Also if, I continued training [sleep_apnea_ecg.ipynb] on my training data, it wouldnt match on your loss and accuracy results.
Example
1.1. My training data shape
(817234, 1408, 1)
1.2. Your training data shape
(805926, 1408, 1)
2.1 My loss and accuracy time series
Epoch 1/500 - 87s 3ms/step - loss: 0.6935 - accuracy: 0.5000 - val_loss: 7.5324 - val_accuracy: 0.4974
Epoch 2/500 - 87s 3ms/step - loss: 0.6935 - accuracy: 0.4989 - val_loss: 7.5596 - val_accuracy: 0.4953
Epoch 3/500 - 86s 3ms/step - loss: 0.6935 - accuracy: 0.5001 - val_loss: 0.6931 - val_accuracy: 0.4995
........ Epoch 55/500 - 86s 3ms/step - loss: 0.6935 - accuracy: 0.4993 - val_loss: 0.6939 - val_accuracy: 0.5000
2.2 Your loss and accuracy time series
Epoch 1/500 - 119s 5ms/step - loss: 0.2138 - accuracy: 0.9156 - val_loss: 0.1518 - val_accuracy: 0.9488
Epoch 2/500 - 120s 5ms/step - loss: 0.1051 - accuracy: 0.9677 - val_loss: 0.1378 - val_accuracy: 0.9590
Epoch 3/500 - 120s 5ms/step - loss: 0.0849 - accuracy: 0.9756 - val_loss: 0.1264 - val_accuracy: 0.9633

So. would you mind if I received all data for train, valid, and test data.mat?