Long short-term memory for apnea detection based on Heart Rate Variability - Novák, D; Mucha, K; Al-Ani, T (Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, 2008)
The main drive force in apnea current diagnostic is to reduce overwhelming number of sleep disorders candidates by means of very simple-to-use, comfortable and cheap methodology. The proposed framework is based only on automatic analysis of electrocardiogram signal. The feature extraction stage was performed using methods of Heart Rate Variability and Detrended Fluctuation analysis. Feature-spaces formed using these two methods were used as input to a Long Short-Term Memory Artificial Neural Network chosen for its capability to find temporally dependencies in the data. The framework was evaluated on Challenge 2000 Physionet database yielding successful rate 82.1%, sensitivity 85.5% and specificity 80.1%.
Keywords: reprodukovatelnost výsledků; Memory; Sleep Apnea Syndromes; Pattern Recognition, Automated; Reproducibility of Results; Humans; počítačové zpracování signálu; paměť; lidé; neuronové sítě; Polysomnography; polysomnografie; senzitivita a specificita; Diagnosis, Computer-Assisted; rozpoznávání automatizované; algoritmy; srdeční frekvence; syndromy spánkové apnoe; Sensitivity and Specificity; Neural Networks, Computer; Heart Rate; Algorithms; Signal Processing, Computer-Assisted; diagnóza počítačová