本文選題：Hadoop + 生理信號�。� 參考：《南京郵電大學》2017年碩士論文

【摘要】：隨著信息化的快速發(fā)展,來自可穿戴設(shè)備、電子病歷、便攜式監(jiān)護儀等醫(yī)療數(shù)據(jù)迅猛增長且存儲結(jié)構(gòu)多元化。傳統(tǒng)的存儲結(jié)構(gòu)與計算模型不能夠很好的解決這些數(shù)據(jù)的存儲和計算分析的問題。幸運的是大數(shù)據(jù)量的增長能夠很好的解決傳統(tǒng)機器學習的方法中數(shù)據(jù)樣本不足的問題,但是單機數(shù)據(jù)處理的計算能力達不到要求且需要專業(yè)的人員進行相關(guān)數(shù)據(jù)特征的人工提取。特征的提取過程麻煩且受到專家的主觀因素影響將會導致分析的結(jié)果不準。為了解決上面的問題,本論文主要研究內(nèi)容如下:(1)構(gòu)建了一個生理大數(shù)據(jù)的集群分析平臺,來解決數(shù)據(jù)大存儲與計算能力不足的問題。該平臺采用了Hadoop中的HDFS來解決非結(jié)構(gòu)化數(shù)據(jù)的大規(guī)模存儲問題。除此之外還采用了消息隊列、流計算框架來提升平臺的整體性能。(2)采用MapReduce計算框架解決了大數(shù)據(jù)集的計算分析問題。實現(xiàn)了基于MapReduce的BP神經(jīng)網(wǎng)絡(luò)的并行化,實驗的結(jié)果表明神經(jīng)網(wǎng)絡(luò)并行化實現(xiàn)的可行性并且能夠有效地提高分析的準確率、減少訓練時間,加快研究速度及分析效率。(3)針對傳統(tǒng)機器學習方法中需要人工的提取特征的不足,嘗試采用了深度學習技術(shù)并將其應(yīng)用到生理信號分析的方法中。目前國內(nèi)基于深度學習的生理信號研究還很少,本文使用了深度學習中的DBN、CNN、SAE網(wǎng)絡(luò)來獲取抽象特征避免了人工干擾,同時結(jié)合了傳統(tǒng)的分類器SVM及神經(jīng)網(wǎng)絡(luò)對相關(guān)生理特征進行分類。實驗的結(jié)果表明本文提出的方法可以適用于生理信號的分類,并取得了不錯的效果。
[Abstract]:With the rapid development of information technology, medical data from wearable devices, electronic medical records, portable monitors and other medical data are growing rapidly and the storage structure is diversified. The traditional storage structure and computing model can not solve the problem of data storage and analysis. Fortunately, the growth of large amounts of data can solve the problem of insufficient data samples in traditional machine learning methods. But the computing ability of single computer data processing is not up to the requirement and professional personnel are needed to carry out manual extraction of relevant data features. The feature extraction process is troublesome and influenced by the subjective factors of experts, which will lead to inaccurate analysis results. In order to solve the above problems, the main contents of this paper are as follows: 1) A cluster analysis platform of physiological big data is constructed to solve the problem of insufficient data storage and computing power. The platform uses HDFS in Hadoop to solve the problem of large-scale storage of unstructured data. In addition, the message queue and flow computing framework are used to improve the overall performance of the platform. (2) the MapReduce computing framework is used to solve the computing and analysis problem of big data set. The parallelization of BP neural network based on MapReduce is realized. The experimental results show that the parallelization of neural network is feasible and can effectively improve the accuracy of analysis and reduce the training time. To improve the research speed and analysis efficiency, aiming at the shortcomings of traditional machine learning methods, which need to extract features manually, this paper tries to adopt the deep learning technology and apply it to the analysis of physiological signals. At present, there are few researches on physiological signals based on deep learning in China. In this paper, the DBNNNSAE network is used to obtain abstract features to avoid artificial interference. At the same time, combining the traditional classifier SVM and neural network to classify the related physiological characteristics. The experimental results show that the proposed method can be applied to the classification of physiological signals, and good results have been obtained.
【學位授予單位】：南京郵電大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TN911.6;TP18

【參考文獻】

相關(guān)期刊論文 前3條

1 程學旗;靳小龍;王元卓;郭嘉豐;張鐵贏;李國杰;;大數(shù)據(jù)系統(tǒng)和分析技術(shù)綜述[J];軟件學報;2014年09期

2 孫大為;張廣艷;鄭緯民;;大數(shù)據(jù)流式計算:關(guān)鍵技術(shù)及系統(tǒng)實例[J];軟件學報;2014年04期

3 覃雄派;王會舉;杜小勇;王珊;;大數(shù)據(jù)分析——RDBMS與MapReduce的競爭與共生[J];軟件學報;2012年01期

，

本文編號：1817610