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Development of a Wearable 3-Risk Factor Accumulated Epileptic Seizure Detection System with IoT Based Warning Alarm

机译:具有基于物联网的警告警报的可穿戴式三险种累积性癫痫发作检测系统的开发

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In this work, a fully-functional prototype of a wristworn device has been developed for detecting convulsive epileptic seizure. A 3-risk factor based detection scheme employing accelerometer derived muscle spasm, PPG acquired heart rate variability and body temperature variation has been proposed by utilizing the distinctive change in the patterns of these extracerebral modalities during seizure. Since the reproduction of these specific combination of patterns is less likely to occur in case of nonseizure events during patients’ regular activities, the developed device identifies the seizure occurrence quite efficiently. An advanced warning alarm system is also incorporated in the design via IoT intervention. A master Bluetooth module is used for transmitting data and another Bluetooth module is used as a slave to notify the nearby people regarding the threat of the patient as well as drive a loud alarm. The user-friendliness is also ensured based on the feedback from volunteers. With a sensitivity of 85%, a missed alarm rate of 15% and a false alarm rate of 26.09%, the device has definitely demonstrated promising performance for the detection of convulsive epileptic seizure. Still, there are scopes of further improvement in terms of efficacy by algorithm optimization and deep learning involvement.
机译:在这项工作中,已经开发出用于检测惊厥性癫痫发作的腕戴设备的全功能原型。通过利用癫痫发作期间这些脑外形态的模式的独特变化,提出了一种基于加速度计衍生的肌肉痉挛,PPG获得的心率变异性和体温变异性的基于3风险因子的检测方案。由于在患者正常活动期间未发生癫痫发作的情况下,不太可能发生这些特定模式组合的复制,因此开发的设备可以非常有效地识别癫痫发作的发生。通过物联网干预,先进的警告警报系统也被纳入设计中。主蓝牙模块用于传输数据,另一个蓝牙模块用作从模块,以通知附近的人员有关患者的威胁以及发出响亮的警报。根据志愿者的反馈,还可以确保用户友好性。该设备具有85%的灵敏度,15%的误报警率和26.09%的误报警率,绝对证明了其在抽搐性癫痫发作检测中的良好性能。尽管如此,通过算法优化和深度学习参与,在功效方面还有进一步改进的范围。

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