Annu Int Conf IEEE Eng Med Biol Soc. 2022 Jul;2022:2635-2638. doi: 10.1109/EMBC48229.2022.9871453.
Distinguishing major depressive disorder (MDD) from bipolar disorder (BD) is a crucial clinical challenge due to the lack of known biomarkers. Conventional methods of diagnosis rest exclusively on symptomatic presentation, and personal and family history. As a result, BD-depressed episode (BD-DE) is often misdiagnosed as MDD, and inappropriate therapy is given. Electroencephalography (EEG) has been widely studied as a potential source of biomarkers to differentiate these disorders. Previous attempts using machine learning (ML) methods have delivered insufficient sensitivity and specificity for clinical use, likely as a consequence of the small training set size, and inadequate ML methodology. We hope to overcome these limitations by employing a training dataset of resting-state EEG from 71 MDD and 71 BD patients. We introduce a robust 3 steps ML technique: 1) a multi-step preprocessing method is used to improve the quality of the EEG signal 2) symbolic transfer entropy (STE), which is an effective connectivity measure, is applied to the resultant EEG signals 3) the ML algorithm uses the extracted STE features to distinguish MDD from BD patients. Clinical Relevance--- The accuracy of our algorithm, derived from a large sample of patients, suggests that this method may hold significant promise as a clinical tool. The proposed method delivered total accuracy, sensitivity, and specificity of 84.9%, 83.4%, and 87.1%, respectively.
区分重度抑郁症（MDD）和双相情感障碍（BD）是一项临床挑战，因为目前缺乏已知的生物标志物。传统的诊断方法完全依赖于症状表现、个人和家族病史。因此，BD 抑郁发作（BD-DE）经常被误诊为 MDD，并给予不适当的治疗。脑电图（EEG）已被广泛研究作为区分这些疾病的潜在生物标志物来源。先前使用机器学习（ML）方法的尝试结果显示，其灵敏度和特异性不足以用于临床，这可能是由于训练集规模小和 ML 方法学不足所致。我们希望通过使用 71 名 MDD 和 71 名 BD 患者的静息态 EEG 训练数据集来克服这些限制。我们提出了一种稳健的 3 步 ML 技术：1）采用多步预处理方法来提高 EEG 信号的质量；2）应用符号传递熵（STE），这是一种有效的连通性度量；3）ML 算法使用提取的 STE 特征来区分 MDD 和 BD 患者。临床相关性——我们的算法来自大量患者，其准确性表明，该方法可能作为一种有前途的临床工具。该方法的总准确率、敏感度和特异性分别为 84.9%、83.4%和 87.1%。
