دانلود مقاله ISI انگلیسی شماره 46857
عنوان فارسی مقاله

خوشه بندی سری زمانی زیست پزشکی چند کاناله از طریق تجزیه و تحلیل معنایی نهفته سلسله مراتبی احتمالی

کد مقاله سال انتشار مقاله انگلیسی ترجمه فارسی تعداد کلمات
46857 2014 9 صفحه PDF سفارش دهید محاسبه نشده
خرید مقاله
پس از پرداخت، فوراً می توانید مقاله را دانلود فرمایید.
عنوان انگلیسی
Multichannel biomedical time series clustering via hierarchical probabilistic latent semantic analysis
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Computer Methods and Programs in Biomedicine, Volume 117, Issue 2, November 2014, Pages 238–246

کلمات کلیدی
بسته ای از کلمات - مدل موضوعی - آموزش بدون نظارت
پیش نمایش مقاله
پیش نمایش مقاله خوشه بندی سری زمانی زیست پزشکی چند کاناله از طریق تجزیه و تحلیل معنایی نهفته سلسله مراتبی احتمالی

چکیده انگلیسی

Biomedical time series clustering that automatically groups a collection of time series according to their internal similarity is of importance for medical record management and inspection such as bio-signals archiving and retrieval. In this paper, a novel framework that automatically groups a set of unlabelled multichannel biomedical time series according to their internal structural similarity is proposed. Specifically, we treat a multichannel biomedical time series as a document and extract local segments from the time series as words. We extend a topic model, i.e., the Hierarchical probabilistic Latent Semantic Analysis (H-pLSA), which was originally developed for visual motion analysis to cluster a set of unlabelled multichannel time series. The H-pLSA models each channel of the multichannel time series using a local pLSA in the first layer. The topics learned in the local pLSA are then fed to a global pLSA in the second layer to discover the categories of multichannel time series. Experiments on a dataset extracted from multichannel Electrocardiography (ECG) signals demonstrate that the proposed method performs better than previous state-of-the-art approaches and is relatively robust to the variations of parameters including length of local segments and dictionary size. Although the experimental evaluation used the multichannel ECG signals in a biometric scenario, the proposed algorithm is a universal framework for multichannel biomedical time series clustering according to their structural similarity, which has many applications in biomedical time series management.

خرید مقاله
پس از پرداخت، فوراً می توانید مقاله را دانلود فرمایید.