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New tool fuses ML and DL features to detect sleep apnea

The New ConCAD model leverages the features of deep learning and traditional machine learning to precisely detect sleep apnea.

Individuals who suspect having sleep apnea and doctors who diagnose them could soon have a more effective way to detect the condition at home. Penn State College of Information Sciences and Technology researchers have developed a ConCAD method that can detect sleep apnea by incorporating expert knowledge into deep learning techniques. This research was presented at the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD), virtually held between Sept. 13-17.

Researchers believe the new tool outperforms all existing baseline methods because deep learning technology is brewed with expert knowledge. This tool automatically learns patterns from electrocardiogram (ECG) data collected by at-home devices, making it a faster and more ideal solution than any other existing sleep apnea diagnostics.

“The standard approach to detect sleep apnea involves polysomnography (sleep study) — that involves a patient staying in hospital overnight — under supervision of a clinical practitioner. This process is time-consuming, tedious, and intrusive,” said Guanje Huang, lead author of the paper.

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Huang explained that a patient’s data is collected through a sleep study that includes — measuring brain waves, blood oxygen levels, heart rate, respiration, and body movements. Later, clinicians devote their time and various resources to analyze data. “It is very crucial to design an accurate model to automatically analyze data, and help doctors detect sleep apnea swiftly,” said Huang

There also exist other tools that automatically detect sleep apnea through at-home devices using computer models. They are either built through traditional machine learning methods that rely on prior knowledge from human experts or through deep learning methods that eliminate the need for such experts. The former requires hand-crafted features, and the latter consists of immense amounts of data resulting in limitations to these standalone approaches.

“The traditional machine learning method usually requires a small amount of data to learn a robust classifier but requires a careful feature extraction and selection process,” Huang explained.“ Whereas the deep learning models usually achieve better performance but require a large dataset.

Huang’s ConCAD (Contrastive Learning-based Cross Attention for Sleep Apnea Detection) model detects sleep apnea precisely by simultaneously leveraging deep learning features and traditional machine learning’s expert knowledge. This model explicitly requires an expert understanding of RR interval (RRI) and R peak envelope (RPE). Existing methods for detecting sleep apnea involve measuring the intervals between and peak of the R wave, which measures cardiac rhythm in a patient’s ventricular walls, in a standard ECG. Whereas ConCAD utilizes a cross-attention mechanism — a deep learning model that assigns weights to each part based on their importance — to fuse the deep learning features with the expert knowledge features, emphasizing the important features automatically.

The working of ConCAD consists of fours steps:

  1. Pass the original raw ECG data through feature extractors to automatically learn patterns from both expert knowledge and deep learning methods that could indicate sleep apnea.
  2. The patterns or features are automatically and synergistically fused and assigned a weight based on each important part.
  3. The model undergoes a contrastive learning process to match similar features closely.
  4. At last, the data is classified based on final features of ECG and corresponding expert knowledge to calculate the patient’s probability of sleep apnea.

Researchers used two publicly available ECG datasets to test the ConCAD model. These datasets comprise more than 26,000 segments of 30-second and two-and-a-half-minute inputs annotated by experts, identifying apnea or regular sleep events. As compared to six existing state-of-the-art sleep apnea detection methods, ConCAD outperforms all the models.  For the first dataset, ConCAD accurately identifies sleep apnea events for 88.75% in one-minute segments, and 91.22% in the five-minute segments, and 82.5% and 83.47% respectively in the second dataset.

Fenglong Ma, assistant professor of information sciences and technology and the principal investigator, said, “If patients use personal ECG devices at home, they may monitor their sleep apnea conditions with the ConCAD model.” “This is a new attempt to assimilate expert knowledge into a deep learning model that will assist doctors in simplifying the diagnostic process of sleep apnea,” added Ma.

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Amit Kulkarni
Engineer | Academician | Data Science | Technical Writer Interested in ML Algorithms, Artificial Intelligence, and the implementation of new technology.

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