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首页> 外文期刊>Biomedical and Health Informatics, IEEE Journal of >Characterization of Depressive States in Bipolar Patients Using Wearable Textile Technology and Instantaneous Heart Rate Variability Assessment
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Characterization of Depressive States in Bipolar Patients Using Wearable Textile Technology and Instantaneous Heart Rate Variability Assessment

机译：使用可穿戴纺织技术和瞬时心率变异性评估双相情感障碍患者的抑郁状态

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摘要

The analysis of cognitive and autonomic responses to emotionally relevant stimuli could provide a viable solution for the automatic recognition of different mood states, both in normal and pathological conditions. In this study, we present a methodological application describing a novel system based on wearable textile technology and instantaneous nonlinear heart rate variability assessment, able to characterize the autonomic status of bipolar patients by considering only electrocardiogram recordings. As a proof of this concept, our study presents results obtained from eight bipolar patients during their normal daily activities and being elicited according to a specific emotional protocol through the presentation of emotionally relevant pictures. Linear and nonlinear features were computed using a novel point-process-based nonlinear autoregressive integrative model and compared with traditional algorithmic methods. The estimated indices were used as the input of a multilayer perceptron to discriminate the depressive from the euthymic status. Results show that our system achieves much higher accuracy than the traditional techniques. Moreover, the inclusion of instantaneous higher order spectra features significantly improves the accuracy in successfully recognizing depression from euthymia.
机译：对情绪相关刺激的认知和自主反应的分析可以为在正常和病理状态下自动识别不同的情绪状态提供可行的解决方案。在这项研究中，我们介绍了一种方法学应用，该方法描述了基于可穿戴纺织技术和瞬时非线性心率变异性评估的新型系统，能够仅考虑心电图记录来表征双相型患者的自主神经状态。作为此概念的证明，我们的研究显示了八名躁郁症患者在正常日常活动中获得的结果，这些结果是根据特定的情绪协议通过呈现与情感相关的图片而得出的。使用基于点过程的新型非线性自回归积分模型计算线性和非线性特征，并将其与传统算法进行比较。估计的指标用作多层感知器的输入，以区分抑郁症与正常状态。结果表明，我们的系统比传统技术具有更高的精度。此外，包含瞬时高阶光谱特征可显着提高成功识别出来自淫秽症的抑郁症的准确性。

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