A4 Refereed article in a conference publication
An 8.7 μJ/class. FFT accelerator and DNN-based configurable SoC for Multi-Class Chronic Neurological Disorder Detection
Authors: Taufique Zain, Zhu Bingzhao, Coppola Gianluca, Shoaran Mahsa, Saadeh Wala, Muhammad Awais Bin Altaf
Conference name: 2021 IEEE Asian Solid-State Circuits Conference (A-SSCC)
Publishing place: Busan, Korea, Republic of
Publication year: 2021
Chronic Neurological Disorders (CNDs) such as epilepsy [1], [2], migraine [3], and autism [4] can be persistent for extensive periods. Untreated CNDs may lead to perpetual debilities. Therefore, it is crucial to diagnose them at an early stage to perform a timely, meaningful intervention. A routine medical checkup often cannot provide the timely mediation required for CNDs. A chronic attack consists of pre-ictal, ictal, and post-ictal stages, while an effective intervention necessitates CND detection and remedial response during the pre-ictal stage. Therefore, monitoring CNDs 24/7 is crucial, irrespective of patient’s location and clinical state. The electroencephalogram (EEG) is utilized for monitoring and detection of most CNDs in a wearable environment [1]–[6].
