The authors have developed a brain retractor with optical probes for monitoring the oxygenation state of living tissue for the purpose of preventing tissue damage during brain retraction. In neurosurgical operations, brain retraction is an essential technique. If the mechanical stress induced on the brain tissue during retraction is too high, blood vessels can become occluded and the resultant blocking of blood flow could cause low oxygen saturation (SO₂), which may damage the brain tissue. Neurosurgeons know the safety limit for retraction based on past experience, however a technique to monitor the oxygenation state of retracted tissue is required. We have developed a retractor with optical probes that enables the quantitative measurement of SO₂ in the retracted tissue. One optical fiber that emits near infrared light and three optical fibers for detecting the light reemitted from the brain surface are built into the retractor. The emitted light propagates in the brain tissue through absorption and scattering. The absorption of light occurs in the endogenous chromophores of the tissue, such as oxyhemoglobin, deoxyhemoglobin or cytochrome oxidase. The intensities of the reemitted light are measured (λ=780, 805, 830nm) and processed to calculate the SO₂ in the brain tissue by means of spatially resolved spectroscopy. The relationship between mechanical stress and SO₂ in a piglet brain has been obtained experimentally, and the feasibility of SO₂ measurement using the retractor with optical probes during brain retraction has been confirmed by experiments using phantoms and piglet brains.

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The sinoatrial node, the physiological pacemaker of the mammalian heart, is a heterogeneous structure, and activation patterns in the sinoatrial node vary dynamically as a consequence of various physiological and patho-physiological interventions. In the present study, we developed a new extracellular potential mapping system that makes it possible to analyze complex beat-to-beat changes of activation sequences in and around the sinoatrial node. The system is composed of an array of modified bipolar electrodes (MBEs), custom-made amplifiers with a low-frequency filter setting and a personal computer equipped with an A/D converter for data processing. MBEs are superior to conventional unipolar and bipolar electrodes for isolating localized extracellular signals because of their exceptional common mode rejection of far-field signals and the 60Hz AC noise. Low-frequency band-pass filtering (0.5-3.0Hz) was the most suitable for amplifying the extracellular potentials recorded from the sinoatrial node, which were characterized by low amplitude and slow deflection. In addition, digital data processing using a morphological operation was employed for reliable automatic determination of local activation time. Using this system, it was possible to analyze detailed beat-to-beat changes of epicardial activation sequences in rabbit superfused right atrial preparations including the whole sinoatrial node during interaction between normal sinoatrial node automaticity and ectopic atrial activities (e. g. triggered activity induced by digitalis). This system was useful for investigating precise activation patterns in and around the sinoatrial node under various conditions.

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As an electroencephalogram (EEG) is capable of reflecting at least part of the functional status of a patient's brain, EEGs interpreted by electroencephalographers (EEGers) are used as a diagnostic aid for diseases that affect the brain. Over the past 10 years, the authors have worked to develop an automatic interpretation technique for awake background EEGs. Automatic integrative interpretation of awake background EEGs in clinical use requires the appropriate selection of EEG segments from a lengthy EEG record, which may contain various artifacts such as eye blinks and change in characteristic according to the patient's vigilance level or open-and-closed eye movement during the recording period. In this study, a method for automatically selecting the EEG segments was newly developed by combining the automatic recognition of dominant rhythm organization, artifacts, vigilance level and open-eye state of the patients. The method was applied to the lengthy EEG records of 30 patients. The EEG segments were appropriately selected by the proposed method, and then subjected to the automatic background EEG interpretation method. The proposed method avoided serious mistakes in interpretation and improved the accuracy of automatic EEG interpretation.

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