Single lead ECG and CPAP titration may help manage Sleep apnea at home

Obstructive Sleep Apnea (OSA) is a potentially serious sleep disorder which is caused when breathing repeatedly stops and starts. Snoring loudly and feeling tired even after a full night's sleep, is a concern as it hampers most activities of the day.

Continuous positive airway pressure (CPAP) is effective in treating OSA, as the air flow is introduced into the airways to maintain a continuous pressure to constantly stent the airways open, in people who are breathing spontaneously but adherence to it is often inadequate.

The effective way is to detect sleep apnea events in advance, and to adjust the pressure accordingly, which could improve the long-term use of CPAP treatment. A recent study aimed to develop a machine-learning algorithm using retrospective electrocardiogram (ECG) data and CPAP titration to forecast sleep apnea events before they happen. The study is published in the The Journal of Sleep Research.

Researchers used a support vector machine (SVM), k-nearest neighbour (KNN), decision tree (DT), and linear discriminative analysis (LDA) to detect sleep apnea events 30–90 s in advance. Preprocessed 30 s segments were time–frequency transformed to spectrograms using continuous wavelet transform, followed by feature generation using the bag-of-features technique.

The key findings of the study are

• Specific frequency bands of 0.5–50 Hz, 0.8–10 Hz, and 8–50 Hz were also extracted to detect the most detected band.

• Our results indicated that SVM outperformed KNN, LDA, and DT across frequency bands and leading time segments.

• The 8–50 Hz frequency band gave the best accuracy of 98.2%, and a F1-score of 0.93. Segments 60 s before sleep events seemed to exhibit better performance than other pre-OSA segments.

Researchers concluded that “Our findings demonstrate the feasibility of detecting sleep apnea events in advance using only a single-lead ECG signal at CPAP titration, making our proposed framework a novel and promising approach to managing obstructive sleep apnea at home.”

Reference: Linh, T. T. D., Trang, N. T. H., Lin, S.-Y., Wu, D., Liu, W.-T., & Hu, C.-J. (2023). Detection of preceding sleep apnea using ECG spectrogram during CPAP titration night: A novel machine-learning and bag-of-features framework. Journal of Sleep Research, 1– 12. https://doi.org/10.1111/jsr.13991.