Applied Artificial Intelligence Institute, Deakin University, Burwood, VIC 3125, Australia.
School of Computing Technologies, RMIT University, Melbourne, VIC 3000, Australia.
Sensors (Basel). 2023 Oct 10;23(20):8375. doi: 10.3390/s23208375.
Epilepsy is a chronic neurological disorder affecting around 1% of the global population, characterized by recurrent epileptic seizures. Accurate diagnosis and treatment are crucial for reducing mortality rates. Recent advancements in machine learning (ML) algorithms have shown potential in aiding clinicians with seizure detection in electroencephalography (EEG) data. However, these algorithms face significant challenges due to the patient-specific variability in seizure patterns and the limited availability of high-quality EEG data for training, causing erratic predictions. These erratic predictions are harmful, especially for high-stake domains in healthcare, negatively affecting patients. Therefore, ensuring safety in AI is of the utmost importance. In this study, we propose a novel ensemble method for uncertainty quantification to identify patients with low-confidence predictions in ML-based seizure detection algorithms. Our approach aims to mitigate high-risk predictions in previously unseen seizure patients, thereby enhancing the robustness of existing seizure detection algorithms. Additionally, our method can be implemented with most of the deep learning (DL) models. We evaluated the proposed method against established uncertainty detection techniques, demonstrating its effectiveness in identifying patients for whom the model's predictions are less certain. Our proposed method managed to achieve 87%, 89% and 75% in accuracy, specificity and sensitivity, respectively. This study represents a novel attempt to improve the reliability and robustness of DL algorithms in the domain of seizure detection. This study underscores the value of integrating uncertainty quantification into ML algorithms for seizure detection, offering clinicians a practical tool to gauge the applicability of ML models for individual patients.
癫痫是一种影响全球约 1%人口的慢性神经系统疾病，其特征是反复发作的癫痫发作。准确的诊断和治疗对于降低死亡率至关重要。最近，机器学习 (ML) 算法的进步显示出在脑电图 (EEG) 数据中帮助临床医生检测癫痫发作的潜力。然而，由于癫痫发作模式的患者特异性变化以及用于训练的高质量 EEG 数据的有限可用性，这些算法面临着重大挑战，导致预测不稳定。这些不稳定的预测是有害的，尤其是在医疗保健的高风险领域，会对患者产生负面影响。因此，确保人工智能的安全性至关重要。在这项研究中，我们提出了一种用于不确定性量化的新集成方法，以识别基于 ML 的癫痫检测算法中低置信度预测的患者。我们的方法旨在减轻以前未见过的癫痫患者的高风险预测，从而增强现有癫痫检测算法的稳健性。此外，我们的方法可以与大多数深度学习 (DL) 模型一起实现。我们评估了该方法对既定不确定性检测技术的有效性，证明了其在识别模型预测不确定性较高的患者方面的有效性。我们提出的方法在准确性、特异性和敏感性方面分别达到了 87%、89%和 75%。这项研究代表了在癫痫检测领域提高 DL 算法可靠性和稳健性的新尝试。这项研究强调了将不确定性量化集成到 ML 算法中用于癫痫检测的价值，为临床医生提供了一种实用工具，用于评估 ML 模型在个体患者中的适用性。
