The invention discloses a method and device for detection of a
sleep apnea fragment based on unsupervised
feature learning. The method includes the steps: collecting sleeping electrocardiosignals (ECG), performing analog-
digital conversion on the ECG signals so as to obtain electrocardio digital signals, performing segmentation on the electrocardio digital signals according to minutes so as to obtain ECG segments, extracting a RR
interphase sequence for correction according to the ECG segments, performing cubic spline interpolation and
fast Fourier transform so as to obtain a frequency domainsequence, making a
train set, constructing a stack-type sparse self-coding model, adopting an unlabeled
data set to perform pre-training of a sparse self-
encoder, performing
unsupervised learning andfeature extraction on the
frequency domain sequence, performing
fine tuning on the stack-type sparse self-coding model by using a labeled
train set, constructing a Softmax-hidden markov and time-dependent-cost-sensitive classification model, using the sparse self-coding model to obtain features and corresponding labels in the labeled
train set, and training the Softmax-hidden markov and time-dependent-cost-sensitive classification model by using the features and corresponding labels in the labeled train set so as to obtain a
sleep apnea classification model.