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Ȩ Ȩ > ¿¬±¸¹®Çå > ±¹³» ³í¹®Áö > Çѱ¹Á¤º¸Åë½ÅÇÐȸ ³í¹®Áö (Journal of the Korea Institute of Information and Communication Engineering)

Çѱ¹Á¤º¸Åë½ÅÇÐȸ ³í¹®Áö (Journal of the Korea Institute of Information and Communication Engineering)

Current Result Document :

ÇѱÛÁ¦¸ñ(Korean Title) ÃÖÀû RÆÄ °ËÃâ ±â¹ÝÀÇ RÇÇÅ© ÆÐÅÏ°ú RR°£°ÝÀ» ÅëÇÑ Á¶±â½É½Ç¼öÃà ºÐ·ù
¿µ¹®Á¦¸ñ(English Title) Premature Ventricular Contraction Classification through R Peak Pattern and RR Interval based on Optimal R Wave Detection
ÀúÀÚ(Author) Á¶Àͼº   ±ÇÇõ¼þ   Ik-sung Cho   Hyeog-soong Kwon  
¿ø¹®¼ö·Ïó(Citation) VOL 22 NO. 02 PP. 0233 ~ 0242 (2018. 02)
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(Korean Abstract)
Á¶±â½É½Ç¼öÃà(Premature Ventricular Contraction) ºÐ·ù¸¦ À§ÇÑ ±âÁ¸ ¿¬±¸µéÀº ºÐ·ùÀÇ Á¤È®¼ºÀ» ³ôÀ̱â À§ÇØ ½Å°æ¸Á, ÆÛÁö ÀÌ·Ð, Support Vector Machine µî°ú °°Àº ºñ¼±Çü ¹æ¹ýÀÌ ÁÖ·Î »ç¿ëµÇ¾î ¿Ô´Ù. ÀÌ·¯ÇÑ ´ëºÎºÐÀÇ ¹æ¹ýµéÀº µ¥ÀÌÅÍÀÇ °¡°ø ¹× ¿¬»êÀÌ º¹ÀâÇÏ´Ù. ÀÌ·¯ÇÑ ¹®Á¦Á¡À» ±Øº¹Çϱâ À§Çؼ­ ÃÖÀûÀÇ RÆĸ¦ °ËÃâÇÏ°í À̸¦ ÅëÇØ RÇÇÅ© ±â¹ÝÀÇ Æ¯Â¡Á¡¸¸À» Á¤È®ÇÏ°Ô °ËÃâÇÔÀ¸·Î½á ÃÖ¼ÒÇÑÀÇ ¿¬»ê·®À¸·Î PVC¸¦ ºÐ·ùÇÒ ¼ö ÀÖ´Â ¾Ë°í¸®ÁòÀÌ ÇÊ¿äÇÏ´Ù. µû¶ó¼­ º» ¿¬±¸¿¡¼­´Â Àü󸮸¦ ÅëÇØ ÀâÀ½ÀÌ Á¦°ÅµÈ ½ÉÀüµµ ½ÅÈ£¿¡¼­ ÃÖÀû ¹®ÅÎÄ¡¿¡ µû¸¥ RÆĸ¦ °ËÃâÇÏ°í, RR°£°Ý°ú RÇÇÅ© ÆÐÅÏÀ» ÃßÃâÇÑ´Ù. ÀÌÈÄ RR°£°Ý°ú RÇÇÅ© ÆÐÅÏ¿¡ µû¶ó PVC¸¦ ºÐ·ùÇÏ¿´´Ù. Á¦¾ÈÇÑ ¹æ¹ýÀÇ ¿ì¼ö¼ºÀ» ÀÔÁõÇϱâ À§ÇØ PVC°¡ 30°³ ÀÌ»ó Æ÷ÇÔµÈ MIT-BIH 9°³ÀÇ ·¹Äڵ带 ´ë»óÀ¸·Î ÇÑ RÆÄÀÇ Æò±Õ °ËÃâÀ²Àº 99.02%ÀÇ ¼º´ÉÀ» ³ªÅ¸³»¾úÀ¸¸ç, PVC ºÎÁ¤¸ÆÀº °¢°¢ 94.85%ÀÇ Æò±Õ ºÐ·ùÀ²À» ³ªÅ¸³»¾ú´Ù.
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(English Abstract)
Previous works for detecting arrhythmia have mostly used nonlinear method such as artificial neural network, fuzzy theory, support vector machine to increase classification accuracy. Most methods require higher computational cost and larger processing time. Therefore it is necessary to design efficient algorithm that classifies PVC(premature ventricular contraction) and decreases computational cost by accurately detecting feature point based on only R peak through optimal R wave. For this purpose, we detected R wave through optimal threshold value and extracted RR interval and R peak pattern from noise-free ECG signal through the preprocessing method. Also, we classified PVC in realtime through RR interval and R peak pattern. The performance of R wave detection and PVC classification is evaluated by using 9 record of MIT-BIH arrhythmia database that included over 30. The achieved scores indicate the average of 99.02% in R wave detection and the rate of 94.85% in PVC classification.
Å°¿öµå(Keyword) RÆÄ   »ùÇøµ ÁÖÆļö   R ÇÇÅ© ÆÐÅÏ   RR °£°Ý   Á¶±â ½É½Ç ¼öÃà   R wave   Sampling frequency   R peak pattern   RR interval   Premature ventricular contraction  
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