Cost-Efficient Classification for Neurological Disease Detection

Abstract

Cost-efficient machine learning is essential for on-chip processing of data in resource-limited applications such as brain implants, wearable sensors, and IoT devices. In this paper, we propose a hardware-friendly machine learning model based on gradient boosted decision trees for neurological disease detection. Our model combines fixed point quantization and cost-efficient inference to enable low-power embedded learning. Testing this model on the intracranial EEG data from 14 epilepsy patients, we can reduce the feature extraction cost by 53.1% and quantize the leaf weights with 4 bits, while maintaining the seizure detection performance. In a second experiment on Parkinsonian tremor detection from local field potentials of 12 patients, we achieve a 55.4% cost reduction and 12-bit leaf quantization. The proposed model offers a hardware-friendly solution for on-chip and real-time detection of neurological disorders.