Long short-term memory for apnea detection based on Heart Rate Variability.
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%.
Novák
D
D
Department of Cybernetics, Czech Technical University in Prague, Czech Republic. xnovakd1@labe.felk.cvut.cz
Mucha
K
K
Al-Ani
T
T
D000465:Algorithms; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D006339:Heart Rate; D006801:Humans; D008568:Memory; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated; D017286:Polysomnography / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted; D012891:Sleep Apnea Syndromes / Q000175:diagnosis* / Q000503:physiopathology*