本文關(guān)鍵詞：基于ECG的身份識(shí)別技術(shù)　出處：《浙江大學(xué)》2014年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 心電信號(hào) 小波變換 閾值濾波 特征提取 分類識(shí)別

【摘要】：身份識(shí)別是安全領(lǐng)域非常重要的一個(gè)組成。ECG心電信號(hào)由于其普遍性、唯一性、穩(wěn)定性和可測(cè)性,逐漸在身份識(shí)別領(lǐng)域得到了越來(lái)越廣泛的應(yīng)用。 ECG身份識(shí)別的核心在于心電信號(hào)特征的提取、分類、識(shí)別。但是,心電信號(hào)本身十分微弱,又是通過(guò)體表的導(dǎo)聯(lián)采集,常�；烊敫鞣N噪聲干擾。這些噪聲干擾影響了波形的識(shí)讀,給特征提取的準(zhǔn)確性帶來(lái)了挑戰(zhàn)。因此,對(duì)心電信號(hào)進(jìn)行預(yù)處理,濾波去除噪聲是一個(gè)很重要的課題。同時(shí),身份識(shí)別不同于疾病識(shí)別,對(duì)于特征的合理篩選也是非常關(guān)鍵的工作。 通過(guò)分析一些已有的預(yù)處理方法,本文提出了三個(gè)方面來(lái)改進(jìn)。第一,構(gòu)造心電信號(hào)模型,添加噪聲后濾波,通過(guò)濾波后的信噪比和相關(guān)系數(shù),選擇最適合的小波函數(shù)。第二,改進(jìn)傳統(tǒng)閩值濾噪法。第三,提取波形特征時(shí),選取不受心率影響的特征,避免心率變化給身份識(shí)別帶來(lái)的不利影響。 最后,本文分別用RBF神經(jīng)網(wǎng)絡(luò)、支持向量機(jī)和樸素貝葉斯三種分類方法測(cè)試身份識(shí)別的效果,都取得了滿意的識(shí)別率,用實(shí)驗(yàn)證明了該身份識(shí)別技術(shù)的可行性。
[Abstract]:Identity recognition is a very important component in the field of security. ECG ECG signal has been more and more widely used in the field of identity recognition because of its universality, uniqueness, stability and testability. The core of ECG identification is the extraction, classification and recognition of ECG signals. However, ECG signals are very weak and collected by the lead of body surface. Often mixed with a variety of noise interference, these noise interference affects the waveform reading, and brings a challenge to the accuracy of feature extraction. Therefore, the ECG signal is preprocessed. Filtering and removing noise is a very important task. At the same time, identity recognition is different from disease identification, and it is also a key task for proper feature screening. Through the analysis of some existing pretreatment methods, this paper proposes three aspects to improve. First, construct ECG signal model, filter after adding noise, filter the signal-to-noise ratio and correlation coefficient after filtering. The most suitable wavelet function is selected. Secondly, the traditional threshold method is improved. Thirdly, when extracting waveform features, the features that are not affected by heart rate are selected to avoid the adverse effect of heart rate change on identity recognition. Finally, this paper uses RBF neural network, support vector machine and naive Bayesian classification methods to test the effect of identity recognition, and achieved a satisfactory recognition rate. The feasibility of the identification technology is proved by experiments.
【學(xué)位授予單位】：浙江大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TN911.7

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本文編號(hào)：1418369