ÇѱÛÁ¦¸ñ(Korean Title) |
½ÉÇ÷°ü°è Áúȯ Áø´ÜÀ» À§ÇÑ º¹ÇÕ Áø´Ü ÁöÇ¥¿Í ÃâÇö ÆÐÅÏ ±â¹ÝÀÇ ºÐ·ù ±â¹ý |
¿µ¹®Á¦¸ñ(English Title) |
Multi-parametric Diagnosis Indexes and Emerging Pattern based |
ÀúÀÚ(Author) |
ÀÌÇå±Ô
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·ù±ÙÈ£
Á¤µÎ¿µ
Heon Gyu Lee
Ki Yong Noh
Keun Ho Ryu
Dooyoung Jung
|
¿ø¹®¼ö·Ïó(Citation) |
VOL 16-D NO. 01 PP. 0011 ~ 0026 (2009. 02) |
Çѱ۳»¿ë (Korean Abstract) |
½ÉÇ÷°ü°è ÁúȯÀÇ Áø´Ü À§Çؼ º¹ÇÕ Áø´Ü ÁöÇ¥¸¦ ÀÌ¿ëÇÑ ÃâÇö ÆÐÅÏ ±â¹ÝÀÇ ºÐ·ù ±â¹ýÀ» Á¦¾ÈÇÏ¿´´Ù. º¹ÇÕ Áø´Ü ÁöÇ¥ Àû¿ëÀ» À§Çؼ ½É¹Úµ¿º¯À̵µÀÇ ¼±Çü/ºñ¼±ÇüÀû Ư¡µéÀ» ¼¼ °¡Áö ´©¿î ÀÚ¼¼¿¡ ´ëÇØ ºÐ¼®ÇÏ¿´°í ST-segments·ÎºÎÅÍ 4°³ÀÇ Áø´Ü ÁöÇ¥¸¦ ÃßÃâÇÏ¿´´Ù. ÀÌ ³í¹®¿¡¼´Â Áúȯ Áø´ÜÀ» À§Çؼ Çʼö ÃâÇö ÆÐÅÏÀ» ÀÌ¿ëÇÑ ºÐ·ù ¸ðµ¨À» Á¦¾ÈÇÏ¿´´Ù. ÀÌ ºÐ·ù ±â¹ýÀº ȯÀÚ ±×·ìÀÇ Áúȯ ÆÐÅϵéÀ» ¹ß°ßÇϸç, ÀÌ·¯ÇÑ ÃâÇö ÆÐÅÏÀº ½ÉÇ÷°ü°è Áúȯ ȯÀڵ鿡¼´Â ºó¹ßÇÏÁö¸¸ Á¤»óÀÎ ±×·ì¿¡¼´Â ºó¹ßÇÏÁö ¾Ê´Â ÆÐÅϵéÀÌ´Ù. Á¦¾ÈµÈ ºÐ·ù ¾Ë°í¸®ÁòÀÇ Æò°¡¸¦ À§Çؼ 120¸íÀÇ Çù½ÉÁõ(AP: angina pectrois) ȯÀÚ, 13¸íÀÇ ±Þ¼º°ü»óµ¿¸ÆÁõÈıº(ACS: acute coronary syndrome) ȯÀÚ ±×¸®°í 128¸íÀÇ Á¤»óÀÎ µ¥ÀÌÅ͸¦ »ç¿ëÇÏ¿´´Ù. ½ÇÇè °á°ú º¹ÇÕ ÁöÇ¥¸¦ »ç¿ëÇÏ¿´ ¶§, ¼¼ ±×·ìÀÇ ºÐ·ù¿¡ ´ëÇÑ Á¤È®µµ´Â ¾à 88.3%¿´´Ù. |
¿µ¹®³»¿ë (English Abstract) |
In order to diagnose cardiovascular disease, we proposed EP-based(emerging pattern-based) classification technique using multi-parametric diagnosis indexes. We analyzed linear/nonlinear features of HRV for three recumbent postures and extracted four diagnosis indexes from ST-segments to apply the multi-parametric diagnosis indexes. In this paper, classification model using essential emerging patterns for diagnosing disease was applied. This classification technique discovers disease patterns of patient group and these emerging patterns are frequent in patients with cardiovascular disease but are not frequent in the normal group. To evaluate proposed classification algorithm, 120 patients with AP (angina pectrois), 13 patients with ACS(acute coronary syndrome) and 128 normal people data were used. As a result of classification, when multi-parametric indexes were used, the percent accuracy in classifying three groups was turned out to be about 88.3. |
Å°¿öµå(Keyword) |
½ÉÇ÷°ü°è Áúȯ
ÃâÇö ÆÐÅÏ ¸¶ÀÌ´×
ºÐ·ù
½É¹Úµ¿º¯À̵µ
ST-segments
Cardiovascular Disease
Emerging Pattern Mining
Classification
Heart Rate Variability
ST-segment
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