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The ultimate aim of the project is to develop a low-cost spectrometer that analyses the samples in the Nanoscale range and to minimise the usage of samples for diagnostic application. The reflection, absorption or transmission phenomenon alters the incident light during the interaction with the sample. A spectrometer measures this change over a range of incident wavelengths of electromagnetic radiation that has interacted with a sample. The light that acts as a source is passed through the sample. The prepared test sample is analysed in order to validate the developed system. Following the test samples, urine is analysed. 10 volunteers were involved as subjects. The light from the sample passes through a slit and then reflected by a collimating mirror. The wavelength of the light from the sample is analysed by Thermino software in the UV and visible range. The input to this software is provided by the webcam. A 1000 lines/mm diffraction grating is utilized to split the light into its constituent wavelength. This is a low-cost system than the available commercial spectrometers used in the laboratory.

This article presents an automatic diagnostic system to classify intramuscular electromyography (iEMG) signals, thereby detecting neuromuscular disorders. To this end, we tailored the center symmetric local binary pattern (CSLBP) to analyze one‐dimensional (1‐D) signals. In this approach, the 1‐D CSLBP feature extracted from a decimated iEMG signal is fed to a combination of classifiers, which in turn assigns a set of labels to the signal, and ultimately the signal category is determined by the Boyer‐Moore majority voting (BMMV) algorithm. The proposed framework was investigated with a benchmark iEMG dataset that contains signals recorded from three different muscles: biceps brachii (BB), deltoideus (DE), and vastus medialis (VM). In a repeated 10‐fold cross‐validation, CSLBP‐Combined‐Classifiers‐BMMV (CSLBP‐CC‐BMMV) achieved an average classification accuracy of 92.80%, 94.25%, and 93.71% for the iEMG signals recorded from BB, DE, and VM muscle, respectively. Interestingly, the performance of CSLBP‐CC‐BMMV surpassed the other published approaches and ensemble learning methods that are akin to our scheme in terms of classification accuracy and computational time.

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Electrogastrogram (EGG) is the non-invasive graphical representation of stomach’s electrical activity for diagnosing stomach Disorders. EGG signal compression has an important role in Tele-diagnosis, Tele-prognosis and survival analysis of all stomach dysrhythmias, when the patient is geographically isolated. There are plenty of signal compression techniques available and proposed over years. Due to some drawbacks like high cost, signal loss and poor compression ratio leads the signal into inefficient at receiver’s end. The compression of digital EGG in telemedicine holds three major advantages like efficient & economic usage of storage data, reduction of the data transmission rate and good transmission bandwidth conversation. In this study EGG signals are tested with different wavelet transforms such as Biorthogonal, coiflet, Daubechies, Haar, reverse biorthogonal and symlet wavelet transforms using MATLAB software, in order to find best performance wavelet for telemedicine. The performance is mathematically analyzed using the values of Percent Root Mean Square Difference (PRD), Compression ratio (CR) and recovery ratio. The result of better compression performance in signal compression could definitely be a great asset in telemedicine field for transferring more quantities of Biological signals.

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