Smart computer algorithms and signal processing techniques have led to rapid development in the field of patient monitoring. Accelerated growth in the field of medical science has made data analysis more demanding and thus the complexity of decision-making procedures. Anaesthetists working in the operating theatre are responsible for carrying out a multitude of tasks which requires constant vigilance and thus a need for a smart decision support system has arisen. It is anticipated that such an automated decision support tool, capable of detecting pathological events can enhance the anaesthetist’s performance by providing the diagnostic information to the anaesthetist in an interactive and ergonomic display format. The main goal of this research was to develop a clinically useful diagnostic alarm system prototype for monitoring pathological events during anaesthesia. Several intelligent techniques, fuzzy logic, artificial neural networks, a probabilistic alarms and logistic regression were explored for developing the optimum diagnostic modules in detecting these events. New real-time diagnostic algorithms were developed and implemented in the form of a prototype system called real time – smart alarms for anaesthesia monitoring (RT-SAAM). Three diagnostic modules based on, fuzzy logic (Fuzzy Module), probabilistic alarms (Probabilistic Module) and respiration induced systolic pressure variations (SPV Module) were developed using MATLABTM and LabVIEWTM. In addition, a new data collection protocol was developed for acquiring data from the existing S/5 Datex-Ohmeda anaesthesia monitor in the operating theatre without disturbing the original setup. The raw physiological patient data acquired from the S/5 monitor were filtered, pre-processed and analysed for detecting anaesthesia related events like absolute hypovolemia (AHV) and fall in cardiac output (FCO) using SAAM. The accuracy of diagnoses generated by SAAM was validated by comparing its diagnostic information with the one provided by the anaesthetist for each patient. Kappa-analysis was used for measuring the level of agreement between the anaesthetist’s and RT-SAAM’s diagnoses. In retrospective (offline) analysis, RT-SAAM that was tested with data from 18 patients gave an overall agreement level of 81% (which implies substantial agreement between SAAM and anaesthetist). RT-SAAM was further tested in real-time with 6-patients giving an agreement level of 71% (which implies fair level of agreement). More real-time tests are required to complete the real-time validation and development of RT-SAAM. This diagnostic alarm system prototype (RT-SAAM) has shown that evidence based expert diagnostic systems can accurately diagnose AHV and FCO events in anaesthetized patients and can be useful in providing decision support to the anaesthetists.

Abstract Brain disorders such as epilepsy, dementia and other mental illnesses induce increasing costs on health care systems with aging populations. The most effective treatment of these disorders would be either prevention or intervention of the disorder before irreversible damage develops. However, despite the increased interest in different brain diseases, many of them are still detected too late. One reason for this is the lack of appropriate functional imaging modality that can critically sample the targeted physiological phenomenon. Furthermore, it has been shown that one imaging modality is not enough to cover brain functionality properly; a multimodal approach is required. The main goal of this thesis was to validate near-infrared spectroscopy (NIRS) for brain measurement and to integrate it into a multimodal neuroimaging setup that can critically sample basic human physiological phenomena. A novel key element was the combined use of NIRS with ultra-fast magnetic resonance encephalography (MREG), electroencephalography (EEG), continuous non-invasive blood pressure and anesthesia monitoring as a synchronous system. This unique multimodal neuroimaging set-up with a new functional magnetic resonance imaging sequence, MREG, can sample human brain physiology at 10 Hz sampling rate without cardiorespiratory aliasing. The implemented setup was successfully used in scanning multiple patient and control populations. With the help of critical sampling rate, non-stationarity between the measured signals reflecting brain pulsations could be detected. Combined NIRS and EEG showed the capability to monitor therapeutic opening of the blood-brain barrier during treatment of central nervous system lymphoma for the first time in humans. Furthermore, our multimodal neuroimaging setup enabled the mapping of the recently described brain avalanches and glymphatic pulsation mechanisms of the brain. In conclusion, the ultra-fast multimodal laboratory with integrated NIRS offers novel and more comprehensive views on basic brain physiology. The measures from this thesis also have the potential to offer new, quantitative biomarkers for the detection of different brain disorders prior to irreversible damage. / Tiivistelmä Aivosairaudet kuten epilepsia, dementia ja muut mielenterveyden häiriöt aiheuttavat kasvavissa määrin kuluja ikääntyvien ihmisten terveydenhuollossa. Näiden tautien tehokkain hoitokeino olisi joko ennaltaehkäisy tai varhainen havaitseminen ennen peruuttamattomien kudosvaurioiden kehittymistä. Lisääntyneestä kiinnostuksesta huolimatta monet aivosairaudet havaitaan edelleen liian myöhään. Osasyy tähän on sopivan toiminnallisen kuvausmenetelmän puuttuminen, jolla voitaisiin kuvata haluttu fysiologinen ilmiö riittävän nopeasti. Onkin osoitettu, ettei yksittäinen kuvausmenetelmä riitä aivojen toiminnan riittävän tarkkaan ymmärtämiseen, vaan siihen tarvitaan eri menetelmien yhdistämistä. Tämän väitöskirjatutkimuksen päätarkoituksena oli arvioida lähi-infrapunaspektroskopian (NIRS) soveltuvuutta aivojen toiminnan mittaamisessa sekä integroida se osaksi multimodaalista neurokuvantamisjärjestelmää. Uutena elementtinä NIRS:iä käytettiin yhdessä ultranopean magneettiresonanssienkefalogrammin (MREG), aivosähkökäyrän (EEG), jatkuva-aikaisen kajoamattoman verenpaineen mittauksen ja anestesiamonitoroinnin kanssa samanaikaisesti, ajallisesti synkronoituna. Yhdessä uuden toiminnallisen magneettikuvaussekvenssin, MREG:n, kanssa tällä ainutlaatuisella multimodaalisella neurokuvantamisjärjestelmällä voidaan kuvata ihmisen aivojen perusfysiologiaa 10 Hz näytteistysnopeudella ilman sydämen sykkeen ja hengityksen laskostumista. Toteutetulla multimodaalisella mittausjärjestelmällä tehtiin useita onnistuneita kuvauksia eri potilasryhmillä ja terveillä koehenkilöillä. Kriittisen näytteistämisen ansiosta voitiin havaita epästationaarisuutta aivojen pulsaatioita heijastelevien signaalien välillä. NIRS:n ja EEG:n samanaikainen mittaaminen mahdollisti ensimmäistä kertaa ihmisen veriaivoesteen aukeamisen monitoroinnin keskushermostolymfoomapotilaiden hoidossa. Lisäksi multimodaalinen neurokuvantamisjärjestelmä mahdollisti hiljattain havaittujen aivojen vyöryjen (engl. avalanches) ja glymfaattisten pulsaatioiden kartoittamisen. Yhteenvetona voidaan todeta, että väitöskirjatyön aikana toteutettu multimodaalinen laboratorio yhdessä NIRS:n kanssa mahdollistaa aivojen perusfysiologian edistyksellisen ja tarkan tutkimisen. Nyt kehitetyt mittarit saattavat myös tarjota uusia, kvantitatiivisia biomarkkereita eri aivosairauksiin ennen vakavien vaurioiden syntymistä.