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 Home > RESEARCH > Research Area > Biomedical Signal Processing & Analysis
 Synchronization of Biomedical Signals

½ÅüÀÇ °¢ ±â°üÀº Àüü¸¦ ±¸¼ºÇÏ´Â ºÎºÐÀ¸·Î¼­ ¼­·Î »óÈ£ÀÛ¿ëÇϰí ÀÖÀ¸¸ç ¿ÜºÎÀÇ Àڱؿ¡ ÀÇÇØ¼­µµ ¿µÇâÀ» ¹Þ´Â´Ù. ÀÌ ¶§ °¢ »ýü ±â°üÀÌ ½Åü ³»¿¡¼­ ¾î¶»°Ô »óÈ£ÀÛ¿ëÇϰí ÀÖ´ÂÁö, ±×¸®°í ¿ÜºÎÀÇ ÀÚ±Ø ¿äÀο¡ ÀÇÇØ¼­ ¾î¶»°Ô ¿µÇâÀ» ¹Þ´ÂÁö¿¡ ´ëÇÑ ÃÑüÀûÀÎ ÇØ¼® ¹æ¹ýÀ» µµÀÔÇÒ Çʿ䰡 ÀÖ´Ù. º» ¿¬±¸ÆÀÀº ÀÎü¸¦ ‘Holistic System’ °üÁ¡¿¡¼­ ÇØ¼®Çϱâ À§ÇØ ½Åü ³» ¼­·Î ´Ù¸¥ ±â°ü¿¡¼­ ¹ß»ýÇÏ´Â »ýü ½ÅÈ£µé »çÀÌ¿¡¼­ º¹ÇÕÀûÀ¸·Î ³ªÅ¸³ª´Â µ¿±â ¿ªÇÐÀû Ư¼º ¹× ¿ÜºÎ Àڱؿ¡ ´ëÇÑ »ýü ½ÅÈ£ µ¿±â ¿ªÇÐÀû Ư¼ºÀ» ºÐ¼®ÇÏ¿© ¸ðµ¨¸µ Çϰí, À̸¦ ÀÀ¿ëÇÏ¿© »ýü ¸®µëÀ» Á¦¾îÇϰí Àΰ£ÀÇ °¨¼º ¹× °Ç°­À» ÃßÁ¤ÇÏ´Â »õ·Î¿î ±â¼úÀ» °³¹ßÇÑ´Ù.

There are interactions in all organs of the body as a part of the whole body, which are affected by external stimuli. Therefore, it is necessary to introduce the holistic analysis in order to represent how they interact with one another and how they are influenced by other stimuli. In our research group, we develop new analyzing methods to the human body as a holistic system. Thus, we analyze complex synchronization characteristics of the physiological rhythms and external stimuli, modeling the physiological system by using them. In addition, we develop new technologies to control the physiological rhythms and to estimate physical condition and emotion.

 

´Ù¾çÇÑ »óȲ¿¡¼­ ½Åü ³»ºÎ¿¡¼­ º¹ÇÕÀûÀ¸·Î ³ªÅ¸³ª´Â Àΰ£ »ýü ½ÅÈ£ »çÀÌÀÇ µ¿±â ¿ªÇÐÀû Ư¼ºÀÇ ºÐ¼® ¹× ¸ðµ¨¸µ
We analyze the synchronization characteristics of the biomedical signals under various conditions and utilize for modeling complex interactions of physiological rhythms.
 

Figure 1. ½É¹Ú-È£Èí Áֱ⠺м® ¹× Phase Autocorrelation function [1]


Àΰ£ÀÇ »ýü¸®µë°ú ´Ù¾çÇÑ ¿ÜºÎ ÀÚ±Ø °£¿¡ ³ªÅ¸³ª´Â µ¿±âÈ­ Ư¼º ºÐ¼®
We analyze synchronization characteristics between physiological rhythms and various external stimuli.

 

Figure 2. ½Ã°¢ ¹× û°¢ Àڱذú »ýü ¸®µë »çÀÌÀÇ µ¿±âÈ­ Ư¼º [2]
 

Àΰ£ »ýü ½ÅÈ£ÀÇ µ¿±â ¿ªÇÐÀû Ư¼º¿¡ ´ëÇÑ ºÐ¼®À» ÅëÇÑ Àΰ£ÀÇ °¨¼º, °Ç°­ ÃßÁ¤ ¹× »ýü ½ÅÈ£ Á¦¾î¸¦ À§ÇÑ È¿°úÀûÀÎ ¿ÜºÎ ÀÚ±Ø ±â¼ú °³¹ß
We develop effective technologies to control the biomedical signals and physiological condition and to estimate the health condition and emotion. 

 

Figure 3. Áúº´°ú »ýü ¸®µë »çÀÌÀÇ µ¿±âÈ­ Ư¼º [3-4]
 

Reference
[1] C. Schafer et al., “Heartbeat synchronized with ventilation”, Nature, vol.392, 1998

[2] V. S. Anishchenko, et al., “Synchronization of Cardiorhythm by Weak External Forcing,” Discrete Dynamics in Nature and Society, vol. 4, pp. 201-206, 2000

[3] CS. Yoo, et al., “A comparative study of phase synchronization and pattern synchrony between heartbeat and respiration for the wellness monitoring,” 6th International Conference on Multimedia Information Technology and Application, pp. 176-179. 2010

[4] J. Peupelmann, et al, “Cardio-respiratory coupling indicates suppression of vegal activity in acute schizophrenia,” Schizophrenia Research, vol. 112, pp. 153-157, 2009

 


 
 Parkinson's Disease

¼¾¼­¸¦ ÀÌ¿ëÇÏ¿© ȯÀÚÀÇ Áõ»óÀ» °´°üÀûÀ¸·Î Áø´ÜÇÏ°í ¸ð´ÏÅ͸µÇÏ´Ù

ÀÇ»çµéÀº º´¿ø¿¡¼­ ȯÀÚ¸¦ Áø·áÇÒ ¶§ ¹®Áø, ½ÃÁø, ÃËÁø, ŸÁø, ûÁø µîÀ» ÇÏ°Ô µÈ´Ù. ÀÌ·¸°Ô ¸ð¾ÆÁø Á¤º¸µéÀ» ÅëÇØ ȯÀÚÀÇ Áõ»ó¿¡ ´ëÇØ ÀÌÇØÇϰí, ¾î¶² Áúº´ÀÎÁö ÆÇ´ÜÀ» ³»¸°´Ù. ÇÏÁö¸¸, ÀÌ·± Áø´Ü¸¸À¸·Î´Â ȯÀÚÀÇ ¸ðµç °ÍÀ» ¾Ë ¼ö ¾ø±â¿¡ ÈξÀ °´°üÀûÀÌ°í ¸í·áÇÏ°Ô È¯ÀÚÀÇ Áõ»óÀ» »ìÆìº¸±â À§ÇØ ÀÇ·á±â±â°¡ ÇÊ¿äÇÏ´Ù. ¿¹¸¦ µé¾î, x-ray°¡ ¾ø´Ù¸é ȯÀÚÀÇ »À¿¡ ´ëÇÑ Á¤º¸¸¦ ¾Ë ¼ö°¡ ¾ø´Ù. ±×·¡¼­, º´¿ø¿¡´Â MRI, PET, X-ray, ÃÊÀ½ÆÄ µî ÀÌ·ç ¼¿ ¼ö ¾øÀÌ ¸¹Àº ÀÇ·á±â±â°¡ ÀÖ´Ù. ¿ì¸®°¡ Àß ¾Ë°í ÀÖ´Â ÀÌ·¸°Ô À¯¸íÇϰí Àß ¾Ë·ÁÁø ÀÇ·á±â±âµéÀº ÀÇ»çµéÀÌ º¸°í, µè°í, ¸¸Áö°í, ¹°¾îº¸´Â ¹æ¹ýÀ¸·Î´Â ¾Ë ¼ö ¾ø´Â ¸¹Àº Á¤º¸µéÀ» ÁÖ´Â ¿ªÇÒÀ» ÇÑ´Ù.

 

Fig.1. º¸Çà ºÐ¼® °úÁ¤

ÇöÀç ¿¬±¸Çϰí ÀÖ´Â º¸Çà ºÐ¼® ¹× Æ®·¹¸Ó(tremor) Áø´Ü ºÐ¼®±â±âÀÌ´Ù. Æ®·¹¸Ó Áõ»óÀº ȯÀÚÀÇ ÀÇÁö¿Í´Â »ó°ü¾øÀÌ ¸öÀÇ Æ¯Á¤ºÎÀ§°¡ ¶³¸®´Â Áõ»óÀε¥ º¸Åë ÆÄŲ½¼ º´À̳ª ¼öÀüÁõ °°Àº ÀÌ»ó¿îµ¿ÁúȯÀ» °¡Áø ȯÀÚ¿¡°Ô¼­ ³ªÅ¸³­´Ù. ÇöÀçÀÇ Æ®·¹¸Ó Áõ»ó Áø´Ü ¹æ¹ýÀº ÀÇ»çµéÀÌ ´«À¸·Î Á÷Á¢ º¸°í, Áú¹®ÇÑ ÈÄ Áõ»óÀÇ Áߵ¿¡ µû¶ó Á¡¼ö¸¦ ¸Å±ä´Ù. ¶³¸®´Â Áõ»ó¿¡ ´ëÇØ °´°üÀûÀÎ ÃøÁ¤ ¾øÀÌ ´«À¸·Î ¸¹ÀÌ ¶°´ÂÁö Àû°Ô ¶°´ÂÁö Áö¼ÓÀûÀ¸·Î ¶°´ÂÁö °£ÇæÀûÀ¸·Î ¶°´ÂÁö¿¡ ´ëÇÑ Á¤º¸¸¦ 0~4 ¹üÀ§ÀÇ ¼ýÀÚ·Î ¸Å±â±â ¶§¹®¿¡ ÁÖ°üÀûÀÎ °æ¿ì°¡ ¸¹¾Æ °´°üÀûÀ̰í, Á¤·®ÀûÀÎ Áø´ÜÀ» ³»¸®±â¿¡´Â ÇѰ谡 ÀÖ´Ù. ¶ÇÇÑ, ȯÀÚµé Áß¿¡´Â ³»¿ø½Ã¿Í ±×·¸Áö ¾ÊÀº »óȲ¿¡¼­ÀÇ Áõ»ó º¯È­°¡ ½ÉÇÏ¿© ³»¿ø ½Ã Áõ»ó¸¸À¸·Î´Â ȯÀÚÀÇ Á¤È®ÇÑ Áø´ÜÀÌ ¾î·Á¿î ½ÇÁ¤ÀÌ´Ù.

 Fig.2. °³¹ßµÈ º¸Çà ºÐ¼® ½Ã½ºÅÛ

¿¬±¸½Ç¿¡¼­´Â ȯÀÚ º¸Çà ±¸ºÐ ¹× µ¿°áº¸Çà ÀνÄÀ» À§ÇÑ °¡¼Óµµ°è¿Í ¹ß ¾Ð·Â ¼¾¼­¸¦ ÀÌ¿ëÇÏ¿© ÆÄŲ½¼ ȯÀÚÀÇ º¸Çà ºÐ¼® ¸ð´ÏÅ͸µ ½Ã½ºÅÛÀ» ÀÌ¹Ì °³¹ßÇÏ¿´À¸¸ç, °¡¼Óµµ°è¿Í ÀÚÀ̷νºÄÚÇÁ °°Àº ¿òÁ÷ÀÓ ÃßÁ¤ÀÌ °¡´ÉÇÑ ¼¾¼­¸¦ ÀÌ¿ëÇÏ¿© Æ®·¹¸Ó Áø´Ü ºÐ¼®±â±â¸¦ °³¹ßÇϰí ÀÖ´Ù. ¶ÇÇÑ, Áõ»ó ÃßÁ¤ÀÌ °¡´ÉÇÑ ¾Ë°í¸®ÁòÀ» °³¹ßÇÏ¿© clinical decision support system (CDSS)À» ±¸ÃàÇϰí ÀÖ´Ù. À̸¦ À§ÇØ machine learning, non-linear time series analysis µîÀ» ¿¬±¸Çϰí ÀÖÀ¸¸ç, À̸¦ ȯÀÚÀÇ ÀÌ»ó¿îµ¿ Áúȯ Áõ»ó¿¡ Àû¿ëÇϰí ÀÖ´Ù. ÀÌ·± ³»¿ëµéÀº ¼­¿ï´ë º´¿ø ½Å°æ°ú¿Í Çù¾÷ÇÏ¿© ÀÓ»ó½ÃÇèÀ» ÁøÇà ÁßÀÌ´Ù. °³¹ß ÁßÀÎ Æ®·¹¸Ó Áø´Ü ºÐ¼®±â±â´Â ³»¿øÇÏÁö ¾Ê¾ÒÀ» ¶§¿¡µµ Áõ»óºÐ¼®ÀÌ °¡´ÉÇÒ ¼ö ÀÖµµ·Ï ubiquitous healthcare¿¡µµ ÀÀ¿ëÇÏ¿© Áø´ÜÀÇ ¹üÀ§¸¦ ³ÐÈú ¼öµµ ÀÖÀ» °ÍÀÌ´Ù.

 

 Fig.3. Clinical decision support system - º¯À§ ºÐ¼® °á°ú

 

 

 Fig.4. Clinician decision support system - À§»ó ºÐ¼® °á°ú

 

Fig.5. À¯ÇコÄÉ¾î ¿¹ - ¾ÆÀÌÆùÀ» ÀÌ¿ëÇÑ Æ®·¹¸Ó Áø´Ü ¾ÆÀ̵ð¾î (Ãâó: www.histalkmobile.com)

 

To examine and monitor patients using a sensor

Asking questions, observing thoroughly a problematic spot, touching or tapping on a certain body part or paying attention to certain sound, this is usually what doctors do when they examine their patient. This is how they understand patients' symptoms and conclude what causes their pain. However, there's a limitation to this to discover every detailed information about their patients, and this is where a necessity of the certain medical equipment arises for a precise examination. For example, it's impossible to know what has happened to a patient's bones without an x-ray. This is as to why there are so many medical equipment at a hospital such as MRI, PET and an X-RAY. These popular medical equipment solve the problem of very primitive examination and provide doctors with a lot of valuable information about their patients.

This machine is for gait analysis and tremor analysis, which is still being developed. Tremor symptom refers to "trembling" that occurs regardless of a patient's will, and it is common for those patients who suffers from parkinson's disease and Essential tremor. Currently, doctors examine tremor symptom by observing a patient, asking questions and deciding how severe it is based on the information gathered by observing and asking. It is difficult to examine precisely without any data when doctors only check whether a patient tremble severely or not, or whether tremble continuously or not and rank it from 0 to 4. Also, some patients show a totally different symptom when they are being examined at a hospital compared to when they are not. With this inconsistency, a precise and objective examination for a patient is almost impossible.

In the BMSIL, the accelerometer that detects a patient's gait and freezing, and, an equipment using a sensor such as an accelerometer and gyroscope to detect a certain movement is still being developed to analyze tremor. We have also developed an algorithm that tracks the symptom and have being building CDSS. Machine learning, non...analysis have been developed for this, and they have been applied to analyze movement disorder’s symptoms. We have collaborated with a neurology department at SNU hospital and are doing clinical trial. This equipment for tremor analysis could be used for ubiquitous healthcare to broaden a scope of the examination as patients' could be examined with it when they don't go to a hospital.

 Sleep Medicine

¼ö¸é ´Ü°è°¡ º¯ÇÔ¿¡ µû¶ó¼­ ÀÚÀ²½Å°æ°è ÁöÇ¥µéÀÌ º¯ÇÑ´Ù´Â »ç½ÇÀº ÀÌ¹Ì ¸¹Àº ¿¬±¸µé¿¡¼­ °ËÁõÀÌ µÇ¾ú´Ù. ÀÌ·¯ÇÑ ÀÚÀ²½Å°æ°è º¯È­¸¦ ¹Ý¿µÇÏ´Â ¿©·¯ ÁöÇ¥µé Áß Çϳª´Â ½É¹ÚÀÇ º¯È­ÀÌ°í ±× º¯È­¸¦ ÀÌ¿ëÇÏ´Â ¹æ¹ýÀÌ ½É¹Úº¯ÀÌÀ² ºÐ¼® ÀÌ´Ù. ÀÌ·± Çö»ó¿¡ ±Ù°ÅÇØ ½É¹Úº¯ÀÌÀ² ¹æ¹ýÀ» ÀÌ¿ëÇÏ¿© ¼ö¸é´Ü°è¸¦ ÃßÁ¤ÇϰíÀÚ ÇÏ¿´´Ù.

¼ö¸é ´Ü°è¸¦ ÃßÁ¤Çϱâ À§ÇØ, »ç¿ëÀÚÀÇ ¼ö¸éÀ» ¹æÇØÇÏÁö ¾Ê°í ½ÉÀå ¹Úµ¿À̳ª, È£Èí, ¿òÁ÷ÀÓ ½ÅÈ£¸¦ ÃøÁ¤ÇÒ ¼ö ÀÖ´Â ¼¾¼­µéÀ» »ç¿ëÇÏ¿´´Ù. ½ÉÀå ¹Úµ¿¿¡ ÀÇÇØ ¹ß»ýÇÏ´Â ½Éźµµ ½ÅÈ£¸¦ ¹«±¸¼Ó ¼¾¼­¸¦ ÅëÇÏ¿© ÃøÁ¤ ÇÏ¿´°í À̸¦ ÀÌ¿ëÇÏ¿´´Ù.

½É¹Ú ½ÅÈ£¸¦ ÀÌ¿ëÇÏ¿© ¼ö¸é/°¢¼º »óŸ¦ ¸ÕÀú ±¸ºÐÇÏ¿´´Ù. ±× ÈÄ ¹«±¸¼Ó ¼¾¼­·ÎºÎÅÍ ³ª¿Â ½ÅÈ£¸¦ ó¸® ÇÏ¿© È£Èí ½ÅÈ£¸¦ ÃßÃâ ÇÏ¿´´Ù. È£Èí ½ÅÈ£´Â ·¥ ¼ö¸é¿¡¼­ Áõ°¡ÇÏ°í °í¸£Áö ¸øÇÑ Æ¯¼ºÀÌ Àֱ⿡ À̸¦ ÀÌ¿ëÇÏ¿© ·¥/ºñ(Þª)·¥ ¼ö¸éÀ» ±¸ºÐ ÇÏ¿´´Ù. Ãß°¡ÀûÀ¸·Î, ÃßÃâ µÈ È£Èí ½ÅÈ£ Å©±âÀÇ º¯È­¸¦ ÀÌ¿ëÇÏ¿© ¼ö¸é Áß ¹«È£ÈíÁõ ¹ß»ý ¿©ºÎ¸¦ ÃßÁ¤ÇÏ¿´´Ù. ÃÖÁ¾ÀûÀ¸·Î´Â ½Éźµµ ½ÅÈ£¿¡ ±Ù°ÅÇÑ ½É¹Úº¯ÀÌÀ² ºÐ¼®À» ÅëÇÏ¿© ±íÀº ¼ö¸é/¿¶Àº ¼ö¸éÀ» ±¸ºÐÇÏ¿´´Ù. °£·«È÷ ¸»ÇØ, ¼ö¸é ´Ü°è¿Í ¼ö¸é ¹«È£Èí À̺¥Æ®¸¦ À§¿¡¼­ ³ª¿­ÇÑ ½ÅÈ£µéÀ» ÀÌ¿ëÇÏ¿© ±Ù»çÇÏ°Ô ÃßÁ¤ÇÏ¿´´Ù.  

Fig.1.

Fig.2.

Fig.3.

 

Fig.4.

In many previous studies, it has been reported that autonomic nervous system (ANS) is varying with sleep stage transition. Heart rate variability (HRV) is one of the indices which reflect the changes of autonomic nervous system. In this point of view, sleep stage can be estimated by observing the HRV variation.

To assess the sleep stages, unconstrained sensors (load cell, PVDF film) which can unconsciously detect the heart beats, respiration and activity of subjects during sleep were used. The pressure to the sensor changes with the pulsation of the heart and it is known as ballistocardiogram (BCG), the physical heart beat signal.

By using heart beat signal, sleep or wake status of subjects were estimated. Respiration signal was derived from load-cell or PVDF sensor data and it used to estimate REM sleep or Non-REM sleep because the signal has irregular and increasing pattern during REM sleep. In addition, apnea events during sleep were estimated using amplitude changes of derived respiration signals. Deep or light sleep can be classified by HRV signal from the BCG. In summary, sleep stages and sleep apnea events were estimated roughly but unconstrainedly by using these signals.

 

 Anesthesiology

ÀÓ»óȯ°æ¿¡¼­ »ýü½ÅÈ£¸¦ ÀÌ¿ëÇÑ ½ÉÇ÷°ü°è ¸ð´ÏÅ͸µ
Bio-signal assessment for cardiovascular system in clinical situations


±¤Ã¼ÀûÈí±¤µµ (PPG) ½ÅÈ£´Â °£ÆíÇϰí Àú°¡ÀÎ ±¤ÇÐ Àåºñ·Î »ýüÁ¶Á÷³»¿¡ÀÇ Ç÷¾×ÀÇ ºÎÇǺ¯È­¸¦ ¸ð´ÏÅ͸µ ÇÒ ¼ö ÀÖ´Ù. PPG ½ÅÈ£´Â ½É¹Ú¿¡ µ¿±âÈ­µÇ¾î º¯ÇÏ´Â Ç÷¾×ÀÇ º¯È­·®À» ¸ð´ÏÅ͸µÇÏ´Â AC¼ººÐ°ú È£Èí, ÀÚÀ²½Å°æ°è ¹× ü¿ÂÁ¶Àý¿¡ °ü·ÃµÈ ÀúÁÖÆÄ ´ë¿ªÀÇ DC¼ººÐÀ¸·Î ±¸¼ºµÈ´Ù. ¶§¹®¿¡ ¿ì¸®´Â »ê¼ÒÆ÷È­µµ, Ç÷¾Ð, ½É¹ÚÃâ·®, ÀÚÀ²½Å°æ°è ¹× ¸»Æ÷½Å°æÀÇ ¸ð´ÏÅ͸µÀ» À§ÇÑ ÀÇ·áÀåºñ·Î¼­ PPGÀÇ »ç¿ë °¡´É¼ºÀÌ ³ô´Ù°í ±â´ëÇϰí ÀÖ´Ù. ÇöÀç ¿ì¸®´Â ¼­¿ï´ëÇб³º´¿ø ¸¶ÃëÅëÁõÀÇÇаúÀÇ ÀÓ»óÀǵé°ú ÇÔ²² PPG½ÅÈ£¸¦ ÀÌ¿ëÇÏ¿© ÀÚÀ²½Å°æ°è¸¦ Æò°¡Çϱâ À§ÇÑ ¿¬±¸¸¦ ¼öÇàÇϰí ÀÖÀ¸¸ç, ¸¶ÃëÁß ¿©·¯ Á¾·ùÀÇ ¼ö¼ú°úÁ¤À» °ÅÄ¡´Â ȯÀڵ鿡 ´ëÇÏ¿© PPG½ÅÈ£¸¦ ¼öÁýÇÏ¿© ºÐ¼®Çϰí ÀÖ´Ù.


Figure 1. Example of 4Ch PPG AC and DC measuring from patients in the operating room


Figure 2. Spectral analysis of ECG and PPG signals during sympathetic blockade

 

Photoplethysmography (PPG) is a simple and low-cost optical technique that can be used to detect blood volume change in the microvascular bed of tissue. The PPG waveform comprises a pulsatile (‘AC’) physiological waveform attributed to cardiac synchronous changes in the blood volume with each heart beat, and is superimposed on a slowly varying (‘DC’) baseline with various lower frequency components attributed to respiration, sympathetic nervous system activity and thermoregulation. Therefore, we expect that the PPG technology can be used in wide range of medical devices for measuring oxygen saturation, blood pressure and cardiac output, assessing autonomic function and also detecting peripheral vascular disease. Currently, we collaborate with clinicians from Department of Anesthesiology and Pain Medicine of Seoul National University Hospital for exploring autonomic nervous system using PPG signal. Now, we have collected PPG signals from patients under general anesthesia and various kinds of surgical procedures.

À̵¿:  
 


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TEL : +82-2-2072-3135, FAX : +82-2-3676-1175, E-mail : pks@bmsil.snu.ac.kr
Department of Biomedical Engineering, College of Medicine, Seoul National University,
103, Daehak-ro, Jongno-gu, Seoul, 110-799, Republic of Korea
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