Sheeraz Muhammad, Aslam Abdul Rehman, Drakakis Emmanuel Mic, Heidari Hadi, Altaf Muhammad Awais Bin, Saadeh Wala
Department of Bioengineering, Imperial College London, London SW7 2AZ, UK.
Department of Computer Engineering, University of Engineering and Technology Taxila, Taxila 47050, Pakistan.
Sensors (Basel). 2024 Nov 24;24(23):7489. doi: 10.3390/s24237489.
Autism spectrum disorder (ASD) is a chronic neurological disorder with the severity directly linked to the diagnosis age. The severity can be reduced if diagnosis and intervention are early (age < 2 years). This work presents a novel ear-worn wearable EEG system designed to aid in the early detection of ASD. Conventional EEG systems often suffer from bulky, wired electrodes, high power consumption, and a lack of real-time electrode-skin interface (ESI) impedance monitoring. To address these limitations, our system incorporates continuous, long-term EEG recording, on-chip machine learning for real-time ASD prediction, and a passive ESI evaluation system. The passive ESI methodology evaluates impedance using the root mean square voltage of the output signal, considering factors like pressure, electrode surface area, material, gel thickness, and duration. The on-chip machine learning processor, implemented in 180 nm CMOS, occupies a minimal 2.52 mm² of active area while consuming only 0.87 µJ of energy per classification. The performance of this ML processor is validated using the Old Dominion University ASD dataset.
自闭症谱系障碍（ASD）是一种慢性神经疾病，其严重程度与诊断年龄直接相关。如果能在早期（年龄<2岁）进行诊断和干预，严重程度可以降低。这项工作展示了一种新型的可穿戴式耳部脑电图系统，旨在帮助早期检测自闭症谱系障碍。传统的脑电图系统常常存在电极笨重、有线连接、功耗高以及缺乏实时电极-皮肤界面（ESI）阻抗监测等问题。为了解决这些局限性，我们的系统集成了连续、长期的脑电图记录、用于实时自闭症谱系障碍预测的片上机器学习以及一个被动ESI评估系统。被动ESI方法利用输出信号的均方根电压来评估阻抗，同时考虑压力、电极表面积、材料、凝胶厚度和持续时间等因素。在180纳米互补金属氧化物半导体（CMOS）工艺中实现的片上机器学习处理器，仅占用最小2.52平方毫米的有源面积，每次分类仅消耗0.87微焦耳的能量。该机器学习处理器的性能使用老自治领大学自闭症谱系障碍数据集进行了验证。
