Improving Endoscopic Image Quality through the Use of High Dynamic Range Imaging-Like Method with Real-Time Performance.

High Dynamic Range (HDR) imaging is a digital image processing technique used to produce a wider range of brightness and color by using multiple captures of a scene taken with different exposures times. It enables capturing more details and producing a more natural-looking image with less washed-out highlights and deeper, more saturated colors. In medical endoscopy, HDR imaging enhances the visibility and clarity of images captured during endoscopy procedures. It provides enhanced visualization of subtler details in both dark cavities and bright areas, resulting in a uniformly exposed view and improved contrast among various tissue types. Standard HDR imaging methods are often complex and computationally demanding, making them unsuitable for performance-critical applications like endoscopy, where real-time performance is crucial. This paper introduces a more efficient and less complex method for achieving HDR-like image quality in real time. The method takes a high-pixel-bit-depth frame and generates multiple low-pixel-bit-depth frames and uses them to generate the high quality image. The focus of the paper is to enhance endoscopic image quality using HDR imaging, and the proposed method is demonstrated to be effective in achieving this goal with real-time performance. The method is implemented in the FPGA System-on-a-Chip (SoC) of a bronchoscope video processor system, and its effectiveness is verified through a simulated study using a phantom, which confirms the improved image quality and real-time performance.

Pre-surgery Stress Monitoring Using Heart Rate Variability Measures.

Pre-surgery stress is common in patients hospitalized for undergoing surgeries. High levels of stress could prolong post-operative recovery time, increasing the duration of hospitalization. Abnormally high stress levels could sometimes have irreversible impacts, leading to post-operative physiological and psychological disorders. Continuous monitoring of patients during the pre-operative period could help in taking necessary measures to control the stress levels. Electrocardiogram (ECG) is one of the signals which is usually monitored continuously for patients in clinical settings. The usability of ECG for Heart Rate Variability (HRV) based stress detection has been explored in this study. HRV features derived from ECG data acquired from 51 patients admitted in the surgical ward during their pre-operative phase were studied. The trend of the features showed similarity in pre-surgery stress experienced by the patients. Using chest leads connected by wires to a wrist wearable for collecting ECG was obtrusive to patients and resulted in loss of more than 50% of the data. Unobtrusive data collection using chest patches can make HRV based stress detection feasible for clinical use. However, an additional monitoring system would require additional responsibility on the part of the healthcare staff involved in patient care. Integrating the HRV based stress detection into the patient monitors already being used in these clinical settings could therefore make the monitoring of stress feasible.

Machine Learning based SpO2 Computation Using Reflectance Pulse Oximetry.

Continuous monitoring of blood oxygen saturation level (SpO2) is crucial for patients with cardiac and pulmonary disorders and those undergoing surgeries. SpO2 monitoring is widely used in a clinical setting to evaluate the effectiveness of lung medication and ventilator support. Owing to its high levels of accuracy and stability, transmittance pulse oximeters are widely used in the clinical community to compute SpO2. Transmittance pulse oximeters are limited to measure SpO2 only from peripheral sites. Reflectance pulse oximeters, however, can be used at various measurement sites like finger, wrist, chest, forehead, and are immune to faulty measurements due to vasoconstriction and perfusion changes. Reflectance pulse oximeters are not widely adopted in clinical environments due to faulty measurements and inaccurate R-value based calibration methods. In this paper, we present the analysis and observations made using a machine learning model for SpO2 computation using reflectance Photoplethysmogram (PPG) signals acquired from the finger using the custom data acquisition platform. The proposed model overcomes the limitations imposed by the traditional R-value based calibration method through the use of a machine learning model using various time and frequency domain features. The model was trained and tested using the clinical data collected from 95 subjects with SpO2 levels varying from 81-100% using the custom SpO2 data acquisition platform along with reference measures. The proposed model has an absolute mean error of 0.5% with an accuracy of 96 ± 2% error band for SpO2 values ranging from 81-100%.

An ocular compression device for reduction of elevated post anesthetic intraocular pressure.

Rise in Intra Ocular Pressure (IOP), after administration of regional ophthalmic anesthesia for surgery, is a commonly observed clinical phenomenon. Rise in IOP increases risk of retinal ischemia and leads to surgical complications. The current clinical practice for reduction of IOP, after delivery of local anesthesia, is manually administered digital compression. The highly subjective nature of manual compression, results in unknown duration and magnitude of the pressure applied, thus limiting the clinical effectiveness of the procedure. The work presented here addresses the need for a device that delivers all the benefits of digital compression, while eliminating the uncertainty and risks involved. Design, development and clinical validation of an air pressure based compression device have been presented in this paper. This device makes the compression procedure safe and reliable by quantifying all compression parameters applied and considering safety limits for individual subjects.