基于可穿戴设备的支持向量机分类器高精度癫痫发作检测系统

2021 International Conference on Microelectronics (ICM) Pub Date : 2021-12-19 DOI:10.1109/ICM52667.2021.9664898

Mohamed Fawzy, H. Mostafa

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引用次数: 2

摘要

本文旨在利用支持向量机(support vector machine, SVM)分类，开发一种基于不同可穿戴设备的高效可靠的癫痫发作检测系统。实验结果表明，针对可穿戴设备的不同信号组合方式，本文算法的平均灵敏度为100%，平均准确率为97%。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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High Accuracy Epileptic Seizure Detection System Based on Wearable Devices Using Support Vector Machine Classifier

This paper aims to develop an efficient and reliable epileptic seizure detection system based on different wearable devices using support vector machine (SVM) classification. The proposed seizure detection system achieves Seizure detection results show that our algorithm achieving an average sensitivity of 100% and an average accuracy 97% with proposed different combining methods for the signals of wearable devices.

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来源期刊

2021 International Conference on Microelectronics (ICM)

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