AbstractsBiology & Animal Science

In monitoring depth of anesthesia, use of electroencephalogram (EEG) signal data helps to prevent intraoperative awareness and reduces the costs of anesthesia. Modern depth-of-anesthesia monitors use frontal EEG signal to derive an index value, which decreases monotonically with increasing anesthetic drug levels. In this study, electroencephalogram signal processing methods for depth-of-anesthesia monitoring were developed. The first aim was to develop a method for burst suppression detection and integrate it into the anesthetic depth monitor. Accurate detection of burst suppression improves the accuracy of depth-of-anesthesia monitoring at deep levels of anesthesia. The method developed utilizes a nonlinear energy operator and is based on adaptive segmentation. The developed monitor has been proven accurate in several scientific studies. A second aim was to develop a depth-of-anesthesia monitor that utilizes both cortical and subcortical information and is applicable with most commonly used anesthetics. The method developed is based on the spectral entropy of EEG and facial electromyogram (EMG) signals. In the method, two spectral entropy variables are derived, aiming to differentiate the cortical state of the patient and subcortical responses during surgery. The concept has been confirmed in the scientific studies conducted during surgery. Another aim was to develop a method for monitoring epileptiform activity during anesthesia. The method developed is based on a novel EEG-derived quantity, wavelet subband entropy (WSE), which followed the time evolution of epileptiform activity in anesthesia with prediction probability of 0.8 and recognized misleading readings of the depth-of-anesthesia monitor during epileptiform activity with event-sensitivity of 97%. The fourth aim was to investigate the monitoring technique developed, called Entropy, in S-ketamine anesthesia and in dexmedetomidine sedation. In S-ketamine anesthesia, high-frequency EEG oscillations turned out to be the reason for the high entropy values seen despite deep anesthesia. In dexmedetomidine sedation, Entropy proved a rapid indicator of transition phases from conscious and unconscious states. Anestesian syvyyttä monitoroitaessa aivosähkökäyrä (EEG) auttaa välttämään potilaan kirurgian aikaisen tietoisuuden tunteen sekä pienentämään anestesian kustannuksia. Anestesian syvyyden monitorit laskevat otsalta mitatusta EEG-signaalista numeroarvon, joka pienenee monotonisesti anestesialääkityksen kasvaessa. Tässä työssä kehitettiin EEG-signaalinkäsittelymenetelmiä anestesian syvyyden monitorointiin. Työn ensimmäinen tavoite oli kehittää menetelmä purskevaimentuman ilmaisemiseksi ja yhdistää tämä osaksi anestesian syvyyden monitoria, parantaen monitoroinnin tarkkuutta syvässä anestesiassa. Kehitetty menetelmä perustuu adaptiiviseen segmentointiin, jossa hyödynnetään epälineaarista energiaoperaattoria. Kehitetty monitori on osoittautunut tarkaksi menetelmäksi lukuisissa tieteellisissä tutkimuksissa. Toinen tavoite oli kehittää menetelmä anestesian…