睡眠分期為睡眠醫學與神經科學研究中的重要課題，傳統睡眠分期通常依賴人工判讀多導睡眠檢查（Polysomnography， PSG）資料，不僅耗時且具有主觀性。隨著深度學習技術的發展，自動化睡眠分類方法逐漸成為重要研究方向。本研究利用連續小波轉換（Continuous Wavelet Transform， CWT）將腦波訊號（Electroencephalography， EEG）轉換為時頻圖表示，並結合卷積神經網路（Convolutional Neural Network， CNN）進行睡眠分類分析。 本研究分別針對小鼠 EEG 與人類 EEG 睡眠資料建立分類模型。在小鼠 EEG 實驗中，以清醒（Awake）與睡眠（Sleep）兩種狀態進行二分類，並採用以個體為分群之交叉驗證方式評估模型表現。實驗結果顯示，小鼠 EEG 二分類模型在交叉驗證與外部測試資料上皆可達到約 99% 的分類準確率，顯示模型能有效辨識小鼠清醒與睡眠狀態之腦波特徵。 在人類 EEG 睡眠分期實驗中，本研究使用 Sleep-EDF Database Expanded 資料集進行六分類睡眠階段辨識，並採用五折交叉驗證評估模型效能。實驗結果顯示，人類 EEG 六分類模型平均分類準確率約為 76%。雖然多分類睡眠分期任務較二分類具有更高複雜度，但結果仍顯示以 CNN 結合 EEG 時頻特徵進行睡眠階段辨識具有良好可行性。 此外，進一步導入跨物種轉移學習（transfer learning）策略，將以人類 EEG 訓練之預訓練模型應用於小鼠 EEG 分類任務中。實驗結果顯示，跨物種轉移學習模型於小鼠 Awake/Sleep 二分類任務中之平均分類準確率約為 98.4%，相較於由頭訓練之小鼠 EEG 基準模型（約 99.8%）略低。然而，轉移學習模型仍維持接近 98% 以上之分類準確率，顯示人類 EEG 所學習之時頻特徵並未因物種差異而完全失效，在一定程度上仍具有跨物種之可遷移性。此結果亦支持利用人類 EEG 預訓練模型作為小鼠 EEG 分類之特徵提取基礎具有可行性。 綜合而言，本研究驗證了深度學習結合 EEG 時頻表示於睡眠分類任務中的有效性，並初步探討跨物種特徵遷移之可行性。研究成果顯示，卷積神經網路不僅能有效擷取腦波特徵，亦具備跨物種應用之潛在能力，為未來 EEG 睡眠分析與神經科學研究提供重要參考。;Sleep staging is an important topic in sleep medicine and neuroscience research. Traditional sleep staging generally relies on manual scoring of polysomnography (PSG) recordings， which is both time-consuming and subjective. With the rapid development of deep learning techniques， automated sleep classification methods have become an increasingly important research direction. In this study， electroencephalography (EEG) signals were transformed into time-frequency representations using Continuous Wavelet Transform (CWT)， and Convolutional Neural Networks (CNNs) were employed for sleep classification analysis. Classification models were developed for both mouse EEG data and human EEG sleep datasets. In the mouse EEG experiment， a binary classification task was performed to distinguish between Awake and Sleep states. A subject-based cross-validation strategy was adopted to evaluate model performance. The experimental results demonstrated that the mouse EEG classification model achieved approximately 99% classification accuracy in both cross-validation and external testing datasets， indicating that the proposed model could effectively identify EEG characteristics associated with wakefulness and sleep in mice. For human EEG sleep staging， the Sleep-EDF Database Expanded dataset was utilized to perform six-class sleep stage classification. Five-fold cross-validation was employed to evaluate model performance. The experimental results showed that the human EEG classification model achieved an average accuracy of approximately 76%. Although multi-class sleep staging is considerably more complex than binary classification， the results indicate that CNNs combined with EEG time-frequency features provide a feasible approach for automatic sleep stage recognition. Furthermore， a cross-species transfer learning strategy was investigated by applying a pre-trained model trained on human EEG data to a mouse EEG classification task. The experimental results demonstrated that the transfer learning model achieved an average classification accuracy of approximately 98.4% in the mouse Awake/Sleep classification task. Although this performance was slightly lower than that of the baseline mouse EEG model trained from scratch (approximately 99.8%)， the transfer learning model still maintained an accuracy exceeding 98%. These findings suggest that the time-frequency features learned from human EEG data do not become entirely ineffective due to species differences and retain a certain degree of cross-species transferability. The results further support the feasibility of utilizing human EEG pre-trained models as feature extractors for mouse EEG classification tasks. In summary， this study verified the effectiveness of combining deep learning techniques with EEG time-frequency representations for sleep classification tasks and preliminarily explored the feasibility of cross-species feature transfer. The findings demonstrate that convolutional neural networks can effectively extract EEG characteristics and possess potential applicability across different species， providing valuable insights for future EEG-based sleep analysis and neuroscience research. Overall， this study verifies the effectiveness of combining deep learning with EEG time-frequency representations for sleep classification tasks and preliminarily explores the feasibility of cross-species feature transfer. The findings suggest that convolutional neural networks can not only effectively extract discriminative EEG features but also possess potential for cross-species applications， providing valuable insights for future EEG sleep analysis and neuroscience research.
