基于語(yǔ)音卷積稀疏遷移學(xué)習(xí)和并行優(yōu)選的帕金森病分類算法研究
doi: 10.11999/JEIT180792
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重慶廣播電視大學(xué)? ?重慶? ?400052
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重慶大學(xué)通信工程學(xué)院? ?重慶? ?400030
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陸軍軍醫(yī)大學(xué)西南醫(yī)院神經(jīng)內(nèi)科? ?重慶? ?400038
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重慶醫(yī)科大學(xué)附一院神經(jīng)內(nèi)科? ?重慶? ?400016
基金項(xiàng)目: 國(guó)家自然科學(xué)基金(61771080, 61571069)，重慶市基礎(chǔ)與前沿研究項(xiàng)目(cstc2018jcyjAX0779, cstc2016jcyjA0043, cstc2016jcyjA0064, cstc2016jcyjA0134)，重慶市教育委員會(huì)科學(xué)技術(shù)研究項(xiàng)目(KJ1603805)，西南醫(yī)院聯(lián)合孵化項(xiàng)目(SWH2016LHYS-11)，模式識(shí)別國(guó)家重點(diǎn)實(shí)驗(yàn)室開放課題基金(201800011)
Classification Algorithm of Parkinson’s Disease Based on Convolutional Sparse Transfer Learning and Sample/Feature Parallel Selection
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Chongqing Radio & TV University, Chongqing 400052, China
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College of Communication Engineering, Chongqing University, Chongqing 400030, China
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Department of Neurology of Southwest Hospital, Army Medical University, Chongqing 400038, China
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Department of Neurology, The First Affiliated Hospital, Chongqing Medical University, Chongqing 400016, China
Funds: The National Natural Science Foundation of China (61771080, 61571069), The Chongqing Research Program of Basic Research and Frontier Technology(cstc2018jcyjAX0779, cstc2016jcyjA0043, cstc2016jcyjA0064, cstc2016jcyjA0134), The Chongqing Education Commission Science and Technology Research Program (KJ1603805), The Southwest Hospital Science and Technology Innovation Program (SWH2016LHYS-11), The Open Project Program of the National Laboratory of Pattern Recognition (201800011)
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摘要: 基于語(yǔ)音數(shù)據(jù)分析的帕金森病(PD)診斷存在樣本量小、訓(xùn)練與測(cè)試數(shù)據(jù)分布差異明顯的問題。為了解決這些問題，需要從降維和樣本擴(kuò)充兩個(gè)方面同時(shí)進(jìn)行。因此，該文提出結(jié)合加噪加權(quán)卷積稀疏遷移學(xué)習(xí)和樣本特征并行優(yōu)選的PD分類算法。該算法可從源域的公共語(yǔ)音庫(kù)中學(xué)習(xí)有利于表達(dá)PD語(yǔ)音特征的有效結(jié)構(gòu)信息，同時(shí)完成降維和樣本間接擴(kuò)充。樣本特征并行優(yōu)選考慮到了樣本和語(yǔ)音特征間的關(guān)系，從而有助于獲取高質(zhì)量的特征。首先，對(duì)公共語(yǔ)音庫(kù)進(jìn)行特征提取構(gòu)造公共特征庫(kù)；然后，以公共特征庫(kù)對(duì)PD目標(biāo)域的訓(xùn)練數(shù)據(jù)集及測(cè)試數(shù)據(jù)集進(jìn)行稀疏編碼，這里分別采用傳統(tǒng)稀疏編碼(SC)與卷積稀疏編碼(CSC)兩種稀疏編碼方法；接著，對(duì)編碼后的語(yǔ)音樣本段和特征數(shù)據(jù)進(jìn)行同時(shí)優(yōu)選；最后，采用支撐向量機(jī)(SVM)進(jìn)行分類。實(shí)驗(yàn)結(jié)果表明，該算法針對(duì)受試者的分類準(zhǔn)確率最高值達(dá)到了95.0%，均值達(dá)到了86.0%，較相關(guān)被比較算法有較大提高。此外，研究還發(fā)現(xiàn)，相較于傳統(tǒng)稀疏編碼方法，卷積稀疏編碼更有利于提取PD語(yǔ)音數(shù)據(jù)的高層特征；同樣，遷移學(xué)習(xí)也有利于提高該算法性能。
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關(guān)鍵詞:
- 遷移學(xué)習(xí) /
- 帕金森病 /
- 稀疏編碼 /
- 卷積稀疏編碼 /
- 語(yǔ)音樣本特征并行優(yōu)選
Abstract: To solve the problems that there are few labeled data in speech data for diagnosis of Parkinson’s Disease (PD), and the distributed condition of the training and the test data is different, the two aspects of dimension reduction and sample augment are considered. A novel transfer learning algorithm is proposed based on noise weighting sparse coding combined with speech sample / feature parallel selection. The algorithm can learn the structural information from the source domain and express the effective PD features, and achieves dimension reduction and sample augment simultaneously. Considering the relationship between the samples and features, the higher quality features can be extracted. Firstly, the features are extracted from the public data set and the feature data set is constructed as source domain. Then the training data and test data of the target domain are sparsely represented based on source domain. Spares representing includs traditional Sparse Coding(SC) and Convolutional Sparse Coding(CSC); Next, the sparse representing data are screened according to sample feature selection simultaneously, so as to improve the accuracy of the PD classification; Finally, the Support Vector Machine(SVM) classifier is adopted. Experiments show that it achieves the highest classification accuracy of 95.0% and the average classification accuracy of 86.0%, and obtains obvious improvement according to the subjects, compared with the relevant algorithms. Besides, compared with sparse coding, convolutional sparse coding can be beneficial to extracting high level features from PD data set; moreover, it is proved that transfer learning is effective. -
表 1 基于語(yǔ)音卷積稀疏遷移學(xué)習(xí)和并行優(yōu)選的PD分類算法
輸入：公共數(shù)據(jù)集${\text{S}}$，目標(biāo)領(lǐng)域數(shù)據(jù)集${\text{A}}$，樣本總數(shù)H，每個(gè)樣
本的特征數(shù)N，受試者數(shù)M輸出：分類準(zhǔn)確率，靈敏度，特異度 步驟： (1)對(duì)公共數(shù)據(jù)集${\text{S}}$疊加不同類型不同信噪比的噪聲擴(kuò)展其為
集合${\text{S}}'$；(2)根據(jù)式(1)，對(duì)集合${\text{S}}'$的語(yǔ)音樣本提取特征構(gòu)造特征庫(kù)即
源領(lǐng)域數(shù)據(jù)集${\text{Y}}$；(3)根據(jù)2.2.2節(jié)及2.2.3節(jié)，提取特征庫(kù)${\text{Y}}$的字典(稀疏編
碼)，卷積核(卷積稀疏編碼)；(4)根據(jù)1.2.4節(jié)，計(jì)算目標(biāo)領(lǐng)域數(shù)據(jù)集${\text{A}}$的稀疏表達(dá)系數(shù)矩
陣${\text{E}}$(稀疏編碼)或特征圖矩陣${\text{E}}$(卷積稀疏編碼)；(5)根據(jù)語(yǔ)音樣本特征并行優(yōu)選算法，將特征矩陣${\text{E}}$進(jìn)行特征
擴(kuò)展為${\text{G}}$并歸一化為${\text{G}}'$，并進(jìn)行樣本特征同時(shí)優(yōu)選得矩陣${\text{P}}$；(6)基于SVM分類器進(jìn)行受試者留一法(LOSO)分類計(jì)算。 
下載: 導(dǎo)出CSV
表 2 各種算法分類結(jié)果對(duì)比(%)
分類算法 基于受試者的留一法 準(zhǔn)確率 靈敏度 特異度 SVM(線性核函數(shù)) 平均 65.0 65.0 65.0 最好 65.0 65.0 65.0 SVM(徑向基核函數(shù)) 平均 67.5 80.0 55.0 最好 67.5 80.0 55.0 文獻(xiàn)[6]算法 平均 52.0 55.0 49.0 最好 85.0 85.0 90.0 DBN算法 平均 54.6 52.4 56.8 最好 57.0 56.0 58.0 CNN算法 平均 60.0 63.0 57.0 最好 65.0 61.0 69.0 autoencoder+SVM(TL) 平均 72.5 75.0 70.0 最好 72.5 75.0 70.0 autoencoder+SVM 平均 67.5 65.0 70.0 最好 67.5 65.0 70.0 DBN+SVM(TL) 平均 55.5 60.0 51.0 最好 60.0 65.0 55.0 DBN+SVM 平均 50.5 53.0 48.0 最好 57.5 65.0 50.0 PD_SC&S2 平均 68.5 69.5 67.5 最好 90.0 85.0 95.0 PD_SC&S2_TL 平均 81.0 79.5 82.5 平均 92.5 95.0 90.0 PD_CSC&S2 平均 70.0 73.0 67.0 最好 75.0 74.0 76.0 PD_CSC&S2_TL 平均 86.0 91.0 81.0 最好 95.0 100.0 90.0
下載: 導(dǎo)出CSV
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BENGE J F, ROBERTS R L, KEKECS Z, et al. Brief report: knowledge of, interest in, and willingness to try behavioral interventions in individuals with Parkinson's disease[J]. Advances in Mind-Body Medicine, 2018, 32(1): 8–12. PLOUVIER A O A, OLDE HARTMAN T C, VAN LITSENBURG A, et al. Being in control of Parkinson's disease: a qualitative study of community-dwelling patients' coping with changes in care[J]. European Journal of General Practice, 2018, 24(1): 138–145. doi: 10.1080/13814788.2018.1447561 CHIU Y F and FORREST K. The impact of lexical characteristics and noise on intelligibility of Parkinsonian speech[J]. Journal of Speech, Language, and Hearing Research, 2018, 61(4): 837–846. doi: 10.1044/2017_JSLHR-S-17-0205 LITTLE M A, MCSHARRY P E, HUNTER E J, et al. Suitability of dysphonia measurements for telemonitoring of Parkinson's disease[J]. IEEE Transactions on Biomedical Engineering, 2009, 56(4): 1015–1022. doi: 10.1109/TBME.2008.2005954 TSANAS A, LITTLE M A, MCSHARRY P E, et al. Novel speech signal processing algorithms for high-accuracy classification of Parkinson's disease[J]. IEEE Transactions on Biomedical Engineering, 2012, 59(5): 1264–1271. doi: 10.1109/TBME.2012.2183367 SAKAR B E, ISENKUL M E, SAKAR C O, et al. Collection and analysis of a Parkinson speech dataset with multiple types of sound recordings[J]. IEEE Journal of Biomedical and Health Informatics, 2013, 17(4): 828–834. doi: 10.1109/JBHI.2013.2245674 GODINO-LLORENTE J I, SHATTUCK-HUFNAGEL S, CHOI J Y, et al. Towards the identification of idiopathic Parkinson's disease from the speech. New articulatory kinetic biomarkers[J]. PLOS One, 2017, 12(12): e0189583. doi: 10.1371/journal.pone.0189583 KAYA M and BILGE H ?. Classification of Parkinson speech data by metric learning[C]. Proceedings of the 2017 International Artificial Intelligence and Data Processing Symposium, Malatya, Turkey, 2017: 1–5. JI Wei and LI Yun. Stable dysphonia measures selection for Parkinson speech rehabilitation via diversity regularized ensemble[C]. International Conference on Acoustics, Speech and Signal Processing, Shanghai, China, 2016: 2264–2268. doi: 10.1109/ICASSP.2016.7472080. KIM J, NASIR M, GUPTA R, et al. Automatic estimation of Parkinson's disease severity from diverse speech tasks[C]. Proceedings of the 16th Annual Conference of the International Speech Communication Association, Dresden, Germany, 2015: 914–918. SHAHBAKHTI M, TAHERIFAR D, and SOROURI A. Linear and non-linear speech features for detection of Parkinson's disease[C]. Proceedings of the 6th 2013 Biomedical Engineering International Conference, Amphur Muang, Thailand, 2013: 1–3. doi: 10.1109/BMEiCon.2013.6687667. DUBEY H, GOLDBERG J C, ABTAHI M, et al. EchoWear: Smartwatch technology for voice and speech treatments of patients with Parkinson's disease[C]. Proceedings of the Conference on Wireless Health, Bethesda, USA, 2015: Article No.15. doi: 10.1145/2811780.2811957. ARIAS-VERGARA T, VASQUEZ-CORREA J C, OROZCO-ARROYAVE J R, et al. Parkinson's disease progression assessment from speech using GMM-UBM[C]. Conference of the International Speech Communication Association, San Francisco, USA, 2016: 1933–1937. doi: 10.21437/Interspeech.2016-1122. GEMAN O. Data processing for Parkinson's disease: Tremor, speech and gait signal analysis[C]. Proceedings of the 2011 E-Health and Bioengineering Conference, Iasi, Romania, 2011: 1–4. 李勇明, 楊劉洋, 劉玉川, 等. 基于語(yǔ)音樣本重復(fù)剪輯和隨機(jī)森林的帕金森病診斷算法研究[J]. 生物醫(yī)學(xué)工程學(xué)雜志, 2016, 33(6): 1053–1059.LI Yongming, YANG Liuyang, LIU Yuchuan, et al. Research on diagnosis algorithm of Parkinson's disease based on speech sample multi-edit and random forest[J]. Journal of Biomedical Engineering, 2016, 33(6): 1053–1059. 張小恒, 王力銳, 曹垚, 等. 混合語(yǔ)音段特征雙邊式優(yōu)選算法用于帕金森病分類研究[J]. 生物醫(yī)學(xué)工程學(xué)雜志, 2017, 34(6): 942–948. doi: 10.7507/1001-5515.201704061ZHANG Xiaoheng, WANG Lirui, CAO Yao, et al. Combining speech sample and feature bilateral selection algorithm for classification of Parkinson's disease[J]. Journal of Biomedical Engineering, 2017, 34(6): 942–948. doi: 10.7507/1001-5515.201704061 SHARMA P, ABROL V, DILEEP A D, et al. Sparse coding based features for speech units classification[J]. Computer Speech & Language, 2018, 47: 333–350. doi: 10.1016/j.csl.2017.08.004 ZHOU Haotian, ZHUANG Yin, CHEN Liang, et al. Ship Detection in Optical Satellite Images Based on Sparse Representation[M]. Singapore: Springer, 2018: 164–171. doi: 10.1007/978-981-10-7521-6_20. LI Jinming. Sparse representation based single image super-resolution with low-rank constraint and nonlocal self-similarity[J]. Multimedia Tools and Applications, 2018, 77(2): 1693–1714. doi: 10.1007/s11042-017-4399-1 LEE H, BATTLE A, RAINA R, et al. Efficient sparse coding algorithms[C]. Proceedings of the 19th International Conference on Neural Information Processing Systems, Canada, 2006: 801–808. CHANG Hang, HAN Ju, ZHONG Cheng, et al. Unsupervised transfer learning via multi-scale convolutional sparse coding for biomedical applications[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018, 40(5): 1182–1194. doi: 10.1109/TPAMI.2017.2656884 ZHANG He and PATEL V M. Convolutional sparse and low-rank coding-based image decomposition[J]. IEEE Transactions on Image Processing, 2018, 27(5): 2121–2133. doi: 10.1109/TIP.2017.2786469 HU Xuemei, HEIDE F, DAI Qionghai, et al. Convolutional sparse coding for RGB+NIR imaging[J]. IEEE Transactions on Image Processing, 2018, 27(4): 1611–1625. doi: 10.1109/TIP.2017.2781303 WOHLBERG B. Efficient algorithms for convolutional sparse representations[J]. IEEE Transactions on Image Processing, 2016, 25(1): 301–315. doi: 10.1109/TIP.2015.2495260 PERKINS S and THEILER J. Online feature selection using grafting[C]. Proceedings of the Twentieth International Conference on Machine Learning, Washington DC, USA, 2003: 592–599. BOYD S, PARIKH N, CHU E, et al. Distributed optimization and statistical learning via the alternating direction method of multipliers[J]. Foundations and Trends ? in Machine Learning, 2011, 3(1): 1–122. doi: 10.1561/2200000016 EMADI M, MIANDJI E, and UNGER J. OMP-based DOA estimation performance analysis[J]. Digital Signal Processing, 2018, 79: 57–65. doi: 10.1016/j.dsp.2018.04.006 ?OREL M and ?ROUBEK F. Fast convolutional sparse coding using matrix inversion lemma[J]. Digital Signal Processing, 2016, 55: 44–51. doi: 10.1016/j.dsp.2016.04.012 -
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