睡眠是影響生活品質一個重要環節，不只能維持生命機能，更與健康程度大幅相關，研究及分析睡眠腦波對反映生理現象至關重要，還有助於了解睡眠障礙、找出潛在疾病和評估睡眠品質。
美國睡眠醫學學會的判讀規則制定了特定的標準來解釋和辨識睡眠中的腦波特徵，但腦波判讀仍然在很大程度上依賴睡眠技師的專業知識，過去研究經常依靠頻率對腦電圖譜進行自動偵測，本研究利用k複合波及慢波的波型特徵，開發自動偵測演算法，並充分考慮技師們在日常工作中判讀睡眠圖譜的經驗，目標是開發與人工判讀密切相符的自動偵測演算法，方法具有可解釋性，在k複合波偵測及慢波偵測的F1-score分別達到89%及95%，本研究運用此兩項自動偵測方法，探討服用藥物與k複合波密度的關聯，以及睡眠慢波與年齡的關係，兩項結果皆與過去文獻相符。此外，本研究可作為輔助判讀的工具，降低技師在睡眠階段判讀的不一致性，旨在為預防醫學和腦波研究的廣泛應用提供全面的腦波資訊。

Sleep is a crucial aspect that significantly affects life quality, as it not only maintains vital physiological functions but also correlates significantly with health. Research and analysis of sleep brain waves play a vital role in reflecting physiological phenomena, aiding in understanding sleep disorders, identifying underlying health conditions, and evaluating sleep quality.
Past studies have often utilized frequency-based automatic detection methods on EEG spectra. In this research, we developed an automatic detection algorithm based on the morphological characteristics of K complexes and slow waves. The goal was to create an algorithm closely aligned with manual scoring. The method achieved an F1-score of 89% for K complex detection and 95% for slow wave detection. This study utilized these automatic detection methods to explore the association between medication and K complex density and to examine the relationship between Slow waves and age. Both results were consistent with previous literature. Moreover, this research can serve as a supplementary tool to reduce inconsistencies in sleep stage interpretation among technicians.
This study aims to provide comprehensive brain wave information for the broad application of preventive medicine and brain wave research, contributing to improved sleep analysis and enhancing our understanding of sleep-related disorders and their potential implications for overall health.

Benington, J. H., & Heller, H. C. (1995). Restoration of brain energy metabolism as the function of sleep. Progress in neurobiology, 45(4), 347-360.
Berry, R. B., Brooks, R., Gamaldo, C. E., Harding, S. M., Marcus, C., & Vaughn, B. V. (2012). The AASM manual for the scoring of sleep and associated events. Rules, Terminology and Technical Specifications, Darien, Illinois, American Academy of Sleep Medicine, 176, 2012.
Cash, S. S., Halgren, E., Dehghani, N., Rossetti, A. O., Thesen, T., Wang, C., Devinsky, O., Kuzniecky, R., Doyle, W., & Madsen, J. R. (2009). The human K-complex represents an isolated cortical down-state. Science, 324(5930), 1084-1087.
Devuyst, S., Dutoit, T., Stenuit, P., & Kerkhofs, M. (2010). Automatic K-complexes detection in sleep EEG recordings using likelihood thresholds. 2010 Annual international conference of the ieee engineering in medicine and biology,
Nomenclature, S. E. P. (1991). American electroencephalographic society guidelines for. Journal of clinical Neurophysiology, 8(2), 200-202.
Erdamar, A., Duman, F., & Yetkin, S. (2012). A wavelet and teager energy operator based method for automatic detection of K-Complex in sleep EEG. Expert Systems with Applications, 39(1), 1284-1290.
Hori, T., Hayashi, M., & Morikawa, T. (1994). Topographical EEG changes and the hypnagogic experience. In Sleep onset: Normal and abnormal processes. (pp. 237-253). American Psychological Association.Jansen, B. H., & Desai, P. R. (1994). K-complex detection using multi-layer perceptrons and recurrent networks. International Journal of Bio-medical computing, 37(3), 249-257.
Jobert, M., Poiseau, E., Jähnig, P., Schulz, H., & Kubicki, S. (1992). Pattern recognition by matched filtering: an analysis of sleep spindle and K-complex density under the influence of lormetazepam and zopiclone. Neuropsychobiology, 26(1-2), 100-107.
Johnson, L., & Karpan, W. E. (1968). Autonomic correlates of the spontaneous K‐complex. Psychophysiology, 4(4), 444-452.
Klinzing, J. G., Niethard, N., & Born, J. (2019). Mechanisms of systems memory consolidation during sleep. Nature neuroscience, 22(10), 1598-1610.
Krohne, L. K., Hansen, R. B., Christensen, J. A., Sorensen, H. B., & Jennum, P. (2014). Detection of K-complexes based on the wavelet transform. 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society,
Kubicki, S., Haag‐Wüsthof, C., Röhmel, J., Herrmann, W., & Scheuler, W. (1988). The pharmacodynamic influence of three benzodiazepines on rapid eye movements, K‐complexes and sleep spindles in healthy volunteers. Human Psychopharmacology: Clinical and Experimental, 3(4), 247-255.
Liang, S.-F., Shih, Y.-H., Chen, P.-Y., & Kuo, C.-E. (2019). Development of a human-computer collaborative sleep scoring system for polysomnography recordings. PLOS ONE, 14(7), e0218948. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6619661/pdf/pone.0218948.pdf
Malinowska, U., Durka, P. J., Blinowska, K. J., Szelenberger, W., & Wakarow, A. (2006). Micro-and macrostructure of sleep EEG. IEEE engineering in medicine and biology magazine, 25(4), 26-31.
Muehlroth, B. E., & Werkle‐Bergner, M. (2020). Understanding the interplay of sleep and aging: Methodological challenges. Psychophysiology, 57(3), e13523.
Nguyen, C. D., Wellman, A., Jordan, A. S., & Eckert, D. J. (2016). Mild airflow limitation during N2 sleep increases K-complex frequency and slows electroencephalographic activity. Sleep, 39(3), 541-550.
Pan, J., Wu, J., Liu, J., Wu, J., & Wang, F. (2021). A systematic review of sleep in patients with disorders of consciousness: from diagnosis to prognosis. Brain Sciences, 11(8), 1072.
Patti, C. R., Abdullah, H., Shoji, Y., Hayley, A., Schilling, C., Schredl, M., & Cvetkovic, D. (2016). K-complex detection based on pattern matched wavelets. 2016 IEEE EMBS conference on biomedical engineering and sciences (IECBES),
Picot, A., Whitmore, H., & Chapotot, F. (2012). Detection of cortical slow waves in the sleep EEG using a modified matching pursuit method with a restricted dictionary. IEEE transactions on biomedical engineering, 59(10), 2808-2817.
Rasch, B. r., Büchel, C., Gais, S., & Born, J. (2007). Odor cues during slow-wave sleep prompt declarative memory consolidation. Science, 315(5817), 1426-1429.
Reed, C. M., Birch, K. G., Kamiński, J., Sullivan, S., Chung, J. M., Mamelak, A. N., & Rutishauser, U. (2017). Automatic detection of periods of slow wave sleep based on intracranial depth electrode recordings. Journal of neuroscience methods, 282, 1-8.
Su, B.-L., Luo, Y., Hong, C.-Y., Nagurka, M. L., & Yen, C.-W. (2015). Detecting slow wave sleep using a single EEG signal channel. Journal of neuroscience methods, 243, 47-52.
Van Cauter, E., Leproult, R., & Plat, L. (2000). Age-related changes in slow wave sleep and REM sleep and relationship with growth hormone and cortisol levels in healthy men. Jama, 284(7), 861-868.
Wauquier, A., Aloe, L., & Declerck, A. (1995). K‐complexes: are they signs of arousal or sleep protective? Journal of sleep research, 4(3), 138-143.
Younes M, Raneri J, Hanly P. (2016). Staging sleep in polysomnograms: Analysis of inter-scorer variability. J Clin Sleep Med, 12(6), 885-94.
Zhang X, Dong X, Kantelhardt JW, Li J, Zhao L, Garcia C, Glos M, Penzel T, Han F. (2015). Process and outcome for international reliability in sleep scoring. Sleep Breath, 19(1), 191