Multimodal ECG and biometric data fusion for improved detection of obstructive sleep apnea hypopnea syndrome

Obstructive Sleep Apnea Hypopnea Syndrome (OSAHS) can cause excessive daytime sleepiness and cognitive decline due to long-term nocturnal hypoxia. Without timely treatment, it may increase the risk of obesity, coronary heart disease, stroke, and other serious disorders. However, OSAHS is often underdiagnosed because the standard detection method, overnight polysomnography (PSG), is expensive and available only in limited medical facilities. This study aimed to develop a lower-cost and more accurate approach for detecting OSAHS using electrocardiogram (ECG) signals and biometric data.

We proposed a multimodal feature fusion framework that integrated ECG features extracted through a long short-term memory (LSTM) network with biometric features obtained via support vector machines (SVM). The fused features were classified through a fully connected layer to detect OSAHS. Two independent databases were used to evaluate the performance of the proposed method.

Experimental results showed that the LSTM-SVM fusion model achieved an accuracy of 97.1%, outperforming conventional classification models. In addition, it achieved 92% accuracy on a separate dataset, demonstrating strong generalization ability and potential for practical clinical application.

By combining LSTM-extracted ECG features with SVM-based biometric features, the proposed multimodal fusion method provided highly effective OSAHS detection. The findings suggest considerable potential for the use of this approach in real medical environments.