• Progress in Medical Physics
• /
• v.14 no.4
• /
• pp.211-217
• /
• 2003

The Principal component analysis (PCA) is a well-known data analysis method that is useful in linear feature extraction and data compression. The PCA is a linear transformation that applies an orthogonal rotation to the original data, so as to maximize the retained variance. PCA is a classical technique for obtaining an optimal overall mapping of linearly dependent patterns of correlation between variables (e.g. neurons). PCA provides, in the mean-squared error sense, an optimal linear mapping of the signals which are spread across a group of variables. These signals are concentrated into the first few components, while the noise, i.e. variance which is uncorrelated across variables, is sequestered in the remaining components. PCA has been used extensively to resolve temporal patterns in neurophysiological recordings. Because the retinal signal is stochastic process, PCA can be used to identify the retinal spikes. With excised rabbit eye, retina was isolated. A piece of retina was attached with the ganglion cell side to the surface of the microelectrode array (MEA). The MEA consisted of glass plate with 60 substrate integrated and insulated golden connection lanes terminating in an 8${\times}$8 array (spacing 200 $\mu$m, electrode diameter 30 $\mu$m) in the center of the plate. The MEA 60 system was used for the recording of retinal ganglion cell activity. The action potentials of each channel were sorted by off­line analysis tool. Spikes were detected with a threshold criterion and sorted according to their principal component composition. The first (PC1) and second principal component values (PC2) were calculated using all the waveforms of the each channel and all n time points in the waveform, where several clusters could be separated clearly in two dimension. We verified that PCA-based waveform detection was effective as an initial approach for spike sorting method.

• Journal of the Institute of Electronics and Information Engineers
• /
• v.52 no.12
• /
• pp.124-133
• /
• 2015

Electro interstitial scan shows potential as a non-invasive screening method. It can discriminate some diseases based on electric current response to induce low intensity direct current to limbs or local area of body. DDFAO was invented in France and it is claimed that multi-channel EIS(Electro Interstitial Scan) is useful for various diseases, especially, diagnoses of endocrine system such as diabetics are very effective. In this study, we verified the repeatability and sensitivity of DDFAO by using a RC phantom model and its clinical usefulness using data obtained from normal and diabetes subject groups. As a result, it showed the repeatability and the output change according to change of phantom characteristic, but it was hard to distinguish normal and patient groups non-invasively with just six surface electrodes of DDFAO. The repeatability and the clinical accuracy was not sufficient for screening or diagnostic purposes, as well. In spite of the results with low repeatability and accuracy conducted in this study, we still need further investigations to improve the EIS-based measurement method; EIS is very convenient and simple and it shows potential as a screening tool of the whole body health conditions rather than localized disease diagnosis.