Ankit Parekh1, Ivan W Selesnick2, David M Rapoport3, Indu Ayappa3. 1. Department of Mathematics, School of Engineering, New York University, USA. Electronic address: ankit.parekh@nyu.edu. 2. Department of Electrical and Computer Engineering, School of Engineering, New York University, USA. 3. Department of Medicine, Division of Pulmonary, Critical Care and Sleep Medicine, School of Medicine, New York University, USA.
Abstract
BACKGROUND: This paper addresses the problem of detecting sleep spindles and K-complexes in human sleep EEG. Sleep spindles and K-complexes aid in classifying stage 2 NREM human sleep. NEW METHOD: We propose a non-linear model for the EEG, consisting of a transient, low-frequency, and an oscillatory component. The transient component captures the non-oscillatory transients in the EEG. The oscillatory component admits a sparse time-frequency representation. Using a convex objective function, this paper presents a fast non-linear optimization algorithm to estimate the components in the proposed signal model. The low-frequency and oscillatory components are used to detect K-complexes and sleep spindles respectively. RESULTS AND COMPARISON WITH OTHER METHODS: The performance of the proposed method is evaluated using an online EEG database. The F1 scores for the spindle detection averaged 0.70 ± 0.03 and the F1 scores for the K-complex detection averaged 0.57 ± 0.02. The Matthews Correlation Coefficient and Cohen's Kappa values were in a range similar to the F1 scores for both the sleep spindle and K-complex detection. The F1 scores for the proposed method are higher than existing detection algorithms. CONCLUSIONS: Comparable run-times and better detection results than traditional detection algorithms suggests that the proposed method is promising for the practical detection of sleep spindles and K-complexes.
BACKGROUND: This paper addresses the problem of detecting sleep spindles and K-complexes in human sleep EEG. Sleep spindles and K-complexes aid in classifying stage 2 NREM human sleep. NEW METHOD: We propose a non-linear model for the EEG, consisting of a transient, low-frequency, and an oscillatory component. The transient component captures the non-oscillatory transients in the EEG. The oscillatory component admits a sparse time-frequency representation. Using a convex objective function, this paper presents a fast non-linear optimization algorithm to estimate the components in the proposed signal model. The low-frequency and oscillatory components are used to detect K-complexes and sleep spindles respectively. RESULTS AND COMPARISON WITH OTHER METHODS: The performance of the proposed method is evaluated using an online EEG database. The F1 scores for the spindle detection averaged 0.70 ± 0.03 and the F1 scores for the K-complex detection averaged 0.57 ± 0.02. The Matthews Correlation Coefficient and Cohen's Kappa values were in a range similar to the F1 scores for both the sleep spindle and K-complex detection. The F1 scores for the proposed method are higher than existing detection algorithms. CONCLUSIONS: Comparable run-times and better detection results than traditional detection algorithms suggests that the proposed method is promising for the practical detection of sleep spindles and K-complexes.
Authors: Daniel J Levendowski; Luigi Ferini-Strambi; Charlene Gamaldo; Mindy Cetel; Robert Rosenberg; Philip R Westbrook Journal: J Clin Sleep Med Date: 2017-06-15 Impact factor: 4.062
Authors: Ankit Parekh; Korey Kam; Anna E Mullins; Bresne Castillo; Asem Berkalieva; Madhu Mazumdar; Andrew W Varga; Danny J Eckert; David M Rapoport; Indu Ayappa Journal: Sleep Date: 2021-07-09 Impact factor: 5.849
Authors: Anna E Mullins; Masrai K Williams; Korey Kam; Ankit Parekh; Omonigho M Bubu; Bresne Castillo; Zachary J Roberts; David M Rapoport; Indu Ayappa; Ricardo S Osorio; Andrew W Varga Journal: J Clin Sleep Med Date: 2021-05-01 Impact factor: 4.062