A simple method for removing initial irregularity of an electrocardiogram during a transient state of a power supply in a defibrillator.

BACKGROUND: The defibrillator is a device that instantaneously discharges the high energy stored in the capacitor to the human body to help revitalize the heart. The circuit for charging the capacitor uses the same power source as the biosignal measurement unit. Therefore, variation in main power supply voltage, ground noise, and electromagnetic interference from the charging circuit can induce distortion into the biosignal at the initial stage of charging. OBJECTIVE: In this study, a simple method is proposed for removing the initial irregularity of an electrocardiogram due to the transient state of a power supply. METHODS: To evaluate the method, a 1-channel electrocardiogram measurement unit and peripheral units were separated from the main control module using galvanic isolation. An isolated push-pull converter was designed to power the secondary side. The method was tested under steady-state and transient conditions. RESULTS: The obtained results proved that biosignal distortion can be significantly reduced. CONCLUSION: This method could be another simple implementation approach for solving signal distortions due to the transient status of power supplies used in medical devices.

A simple segmentation and quantification method for numerical quantitative analysis of cells and tissues.

BACKGROUND: Microscopic image analysis based on image processing is required for quantitative evaluation of decellularization. Existing methods are not widely used because of expensive commercial software, and machine learning-based techniques lack generality for decellularization because many high-resolution image data has to be processed. OBJECTIVE: In this study, we developed an image processing algorithm for quantitative analysis of tissues and cells in a general microscopic image. METHODS: The proposed method extracts the color images obtained by the microscope into reference images consisting of grayscale, red (R), green (G), and blue (B) information and transforms each into a binary image. The transformed images were extracted by separating the cells and tissues through outlier noise elimination, logical multiplication and labeling. In order to verify the method, decellularization of porcine arotic valve was performed by the electrical method. Slice samples were obtained by time and the proposed method was applied. RESULTS: The experimental results show that the segmentation of cells and tissues, and quantitative analysis of the number of cells and changes in tissue area during the decellularization process was possible. CONCLUSIONS: The proposed method shows that cell and tissue extraction and quantitative numerical analysis were possible in different brightness of microscopic images.