Alzheimer's disease is estimated to affect around 50 million people worldwide and is rising rapidly, with a global economic burden of nearly a trillion dollars. This calls for scalable, cost-effective, and robust methods for detection of Alzheimer's dementia (AD). We present a novel architecture that leverages acoustic, cognitive, and linguistic features to form a multimodal ensemble system. It uses specialized artificial neural networks with temporal characteristics to detect AD and its severity, which is reflected through Mini-Mental State Exam (MMSE) scores. We first evaluate it on the ADReSS challenge dataset, which is a subject-independent and balanced dataset matched for age and gender to mitigate biases, and is available through DementiaBank. Our system achieves state-of-the-art test accuracy, precision, recall, and F1-score of 83.3% each for AD classification, and state-of-the-art test root mean squared error (RMSE) of 4.60 for MMSE score regression. To the best of our knowledge, the system further achieves state-of-the-art AD classification accuracy of 88.0% when evaluated on the full benchmark DementiaBank Pitt database. Our work highlights the applicability and transferability of spontaneous speech to produce a robust inductive transfer learning model, and demonstrates generalizability through a task-agnostic feature-space. The source code is available at https://github.com/wazeerzulfikar/alzheimers-dementia
翻译:阿尔茨海默氏病估计会影响到全世界大约5 000万人,并且正在迅速上升,全球经济负担将近一万亿美元。这要求用可扩缩、具有成本效益和稳健的方法来检测阿尔茨海默氏痴呆症(AD)。我们展示了一个新的结构,利用声学、认知和语言特征来形成一个多式混合系统。它使用具有时间特点的专门人工神经网络来检测AD及其严重程度,这通过Mini-Mental State Exam(MMSE)分数反映出来。我们首先评估了ADRESS挑战数据集,这是一个与年龄和性别相匹配的主题和平衡的数据集,可以用来减轻偏见。我们系统实现了最先进的测试精度、精确度、回顾和F1级的测试精度,以形成一个多式组合组合组合组合组合组合系统。 最先进的ADADADADS-AS-A级数据集将进一步达到AD级的精确度和精确度。在评估PIADADA级的可调级数据库中,一个可更新性、可更新性、可更新性、可调试性数据库。