ABSTRACT
Photoplethysmography (PPG) based healthcare devices have gained enormous interest in the detection of cardiac abnormalities. Limited research has been implemented for myocardial infarction (MI) detection. Moreover, PPG-based detection of angina is still a research gap. PPG signals are not always informative. Therefore, this research work presents the use of PPG signals and their second derivative to evaluate myocardial infarction and angina using a novel set of morphological features. The obtained morphological features are fed onto the feed-forward artificial neural network for the identification of the type of MI and unstable angina (UA). The initial experiments have been carried out on non-ambulatory (public) subjects for feature extraction and later evaluated on ambulatory (self-generated) databases. The intended method attains accuracy, sensitivity, and specificity of 98%, 97%, 98% on the public database and 94%, 94%, 94% on the self-generated database. The result shows that the proposed set of features can detect MI and UA with significant accuracy.
Subject(s)
Myocardial Infarction , Photoplethysmography , Humans , Photoplethysmography/methods , Myocardial Infarction/diagnosis , Angina Pectoris , Angina, Unstable , Neural Networks, ComputerABSTRACT
Optical properties (µa , µs and g) of certain human tissue types such as skin and blood have been very well investigated. However until today, for internal body organs such as the esophagus they are not well characterized. For ex-vivo measurements "Inverse Adding Doubling" (IAD) and Inverse Monte-Carlo-Simulation (IMCS) are state of the art. Both methods need the measurement of the collimated transmission. Current methods lack a proper way of measuring the collimated transmission. Hence, this measurement of the g-factor has a systematic error. Therefore, for the measurement of the collimated transmission, a new approach has been developed and evaluated with intralipid. Finally, the optical properties of mucosa, sub mucosa, muscularis and adventitia of pig esophagus tissue are calculated with IAD. The results are promising and in agreement with published literature.
Subject(s)
Esophagus , Optical Phenomena , Spectrum Analysis/instrumentation , Animals , Anisotropy , Equipment Design , SwineABSTRACT
For in-vivo diagnostics of cancer and pre-cancer in the stomach, there is no endoscopic procedure offering both high sensitivity and high specificity. Our data suggest that multispectral or hyperspectral imaging may be helpful to solve this problem. It is successfully applied to the detection and analysis of easily reachable carcinomas, ex-vivo samples of hollow organ mucosal carcinomas and also histological samples. An endoscopy system which allows flexible multispectral videoendoscopy for in-vivo diagnostics has so far been unavailable. To overcome this problem, we modified a standard Olympus endoscopy system to conduct in-vivo multispectral imaging of the upper GI tract. The pilot study is performed on 14 patients with adeno carcinomas in the stomach. For analysis, Support Vector Machine with linear and Gaussian Kernel, AdaBoost, RobustBoost and Random-Forest-walk are used and compared for the data classification with a leave-one-out strategy. The margin of the carcinoma for the training of the classifier is drawn by expert-labeling. The cancer findings are cross-checked by biopsies. We expect that the present study will help to improve the further development of hyperspectral endoscopy and to overcome some of the problems to be faced in this process.
