Investigation of automatic spindle detection in sleep EEG signals contaminated with noise and artifact sources

Abstract Electroencephalography (EEG) serves as the gold standard for noninvasive diagnosis of different types of sleep disorders such as sleep apnea, insomnia, narcolepsy, restless leg syndrome, and parasomnias. In this study, a novel automated cascade filter is introduced as a preprocessing tool for suppressing all noise and artifact interferences from sleep EEG signals before detecting sleep spindles. The multi-stage filter employs the Multi-Kernel Normalized Least Mean Square with Coherence-based Sparsification (MKNLMS-CS) algorithm in the first step to remove all artifact interferences while applying the 1-D patch-based Non-Local Means (NLM) algorithm in the subsequent step to remove all noise components. Three state-of-the-art automated spindle detection algorithms, namely Mc-Sleep, Spinky, and Spindler, are examined in EEG signals contaminated with noise and artifact components individually and concurrently. The spindle detection performance is investigated with real EEG data taken from the well-known DREAMS database, and the experimental results demonstrate the importance of the proposed multi-stage filter in enhancing the performance of spindle detection using the three spindle detection algorithms. This elucidates the robustness of the suggested multi-stage filter in providing high-resolution sleep EEG data from noisy EEG recordings. Also, experimental results reveal that Spinky algorithm outperforms Mc-Sleep and Spindler methods in detecting spindles for filtered EEG signals using several evaluation metrics, including accuracy (94.8% versus 92.0% and 94.6%), precision (53.4% versus 36.4% and 47.5%), specificity (97.3% versus 93.9% and 96.1%) and F1-score (58.2% versus 41.3% and 50.9%), respectively. This shows that combining the proposed multi-stage filter with Spinky algorithm outperforms the other two methods in detecting spindles in EEG signals, and it represents an efficient automated spindle detection system that achieves high diagnosis performance in terms of accuracy (94.8%), specificity (97.3%), and F1-score (58.2%).