本文選題：動(dòng)態(tài)心電圖 + 遠(yuǎn)程心電監(jiān)護(hù)�。� 參考：《河北大學(xué)》2017年碩士論文

【摘要】：近年來(lái),我國(guó)人口老齡化和城鎮(zhèn)化進(jìn)程正在加速,越來(lái)越多的人受到心臟疾病的困擾。針對(duì)患者有明顯的自覺(jué)癥狀,但是靜態(tài)心電圖難以捕捉到有效的診斷依據(jù),醫(yī)生會(huì)建議進(jìn)行動(dòng)態(tài)心電監(jiān)測(cè)。在遠(yuǎn)程醫(yī)療背景下,去除動(dòng)態(tài)心電信號(hào)中的噪聲,提高遠(yuǎn)程心電監(jiān)護(hù)系統(tǒng)自動(dòng)檢測(cè)的準(zhǔn)確率,逐漸成為研究的重點(diǎn)和難點(diǎn)。曾被廣泛應(yīng)用于語(yǔ)音識(shí)別和人臉識(shí)別中的因子分析為心電信號(hào)處理提供了新的切入點(diǎn),為了去除心電信號(hào)中的復(fù)雜噪聲,本文提出了一種基于深度因子分析的降噪算法。論文主要內(nèi)容如下:(1)構(gòu)建因子分析的心電信號(hào)降噪模型。將因子分析模型引入到心電信號(hào)降噪領(lǐng)域中,基于因子分析模型與心電信號(hào)數(shù)據(jù)庫(kù),通過(guò)機(jī)器學(xué)習(xí)獲取心電信號(hào)的隱因子,并將噪聲區(qū)分出來(lái)。(2)提出一種基于深度因子分析的心電信號(hào)降噪算法。考慮到心電信號(hào)的隱因子受到噪聲的干擾,通過(guò)對(duì)隱因子逐層加深構(gòu)建因子分析模型的方法,將每層的隱因子作為輸入,獲取深層的隱因子,丟棄每層的高斯噪聲,最后由頂層隱因子自頂向下重構(gòu)出降噪后的心電信號(hào),實(shí)現(xiàn)心電信號(hào)的降噪。結(jié)果表明,基于深度因子分析的心電信號(hào)降噪算法取得了較好的去噪效果。(3)將深度因子分析降噪算法應(yīng)用到心電監(jiān)控平臺(tái)。為了驗(yàn)證本文的研究成果,將深度因子分析降噪算法應(yīng)用于團(tuán)隊(duì)自主研發(fā)的智慧心電監(jiān)測(cè)平臺(tái)中。由在平臺(tái)上抽取的用戶數(shù)據(jù)結(jié)果表明,本文提出的心電信號(hào)降噪算法可以在濾除復(fù)雜噪聲的同時(shí)保持心電信號(hào)的主要特征波形。
[Abstract]:In recent years, population aging and urbanization are accelerating, more and more people suffer from heart disease. For patients with obvious symptoms, but static electrocardiogram is difficult to capture effective diagnostic basis, doctors will recommend dynamic ECG monitoring. In the context of telemedicine, removing noise from dynamic ECG signals and improving the accuracy of automatic detection of remote ECG monitoring system have gradually become the focus and difficulty of the research. Factor analysis, which has been widely used in speech recognition and face recognition, provides a new entry point for ECG signal processing. In order to remove the complex noise in ECG signal, a denoising algorithm based on depth factor analysis is proposed in this paper. The main contents of this paper are as follows: (1) construct the ECG denoising model based on factor analysis. Factor analysis model is introduced into the field of ECG signal denoising. Based on factor analysis model and ECG database, the hidden factors of ECG signal are obtained by machine learning. This paper proposes a denoising algorithm for ECG signal based on depth factor analysis. Considering that the hidden factor of ECG signal is disturbed by noise, the hidden factor of each layer is taken as input to get the deep hidden factor, and the Gao Si noise of each layer is discarded by the method of constructing the factor analysis model layer by layer. Finally, the top hidden factor is used to reconstruct the denoised ECG signal from top to bottom to realize the de-noising of ECG signal. The results show that the de-noising algorithm of ECG signal based on depth factor analysis has achieved better denoising effect. The depth factor analysis de-noising algorithm is applied to ECG monitoring platform. In order to verify the research results of this paper, the depth factor analysis (DFA) denoising algorithm is applied to the intelligent ECG monitoring platform developed independently by the team. The results of the user data extracted on the platform show that the proposed ECG denoising algorithm can filter the complex noise while maintaining the main characteristic waveform of the ECG signal.
【學(xué)位授予單位】：河北大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：R540.4;TN911.4

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