Biomedical signal and image processing

People with their busy schedules tend to ignore the importance of health. To encourage people to keep track of their health and to overcome the difficulty of making regular visits to the hospital the Medical Mirror is proposed. The Medical Mirror is a novel interactive interface that tracks and displays a user’s heart rate, height, weight, body temperature, blood pressure and sugar levels in real time. It is an approach to make ordinary people to have access and control over their own physiological data so as to play a vital role in the management of their health. However, current techniques for physiological data monitoring, require users to strap on bulky sensors, chest straps or sticky electrodes. This discourages regular use because the sensors can be uncomfortable or encumbering. This work proposes a new mirror interface for real-time, contact-free measurements of pulse rate without sensors. These techniques also involve the use of separate equipment in the measurement of each health parameters of human body. Medical Mirror integrates all the measuring facilities at one place with a compatible user interface providing a convenient means for people to track their health condition. It records the measured data and will be displayed in the form of charts. The application will understand the input given by the user and accordingly it provides feedback for the further health improvement. Users can have the experience of remote health monitoring by looking into the Medical Mirror. The Medical Mirror fits seamlessly into the ambient home environment, blending the data collection process into the course of daily lifestyle. Medical mirror will ease the user interface. Digital medical devices promise to transform the future of medicine because of their ability to produce exquisitely detailed individual physiological database. When people start accessing this technique they can easily monitor their physiological condition by referring their data.

Objective: Epilepsy is a neurological disorder affecting 50 million individuals globally. Modern research has inspected the likelihood of forecasting epileptic seizures. Algorithmic investigations are giving promising results for seizure prediction. Though mostly seizure prediction algorithm uses pre-ictal (prodromal symptoms) events for prediction. On the contrary, prodromal symptoms may not necessarily be present in every patient or subject. This paper focuses on seizure forecasting regardless of the presence of pre-ictal (prodromal symptoms) using the single robust feature with maximum accuracy. Method: We evaluated datasets having 4-aminopydine induced seizure-like events rat's hippocampa slices and cortical tissue from pharmacoresistant epilepsy patients. The proposed methodology applies the Discrete Wavelet Transform (DWT) at levels 1-5 utilizing 'Daubechies-4'. Linear Discriminant classifier (LDC), Quadratic Discriminant Classifier (QDC) and Support Vector Machine (SVM) were used to classify each signal using eight discrimi-native features. Results: Classifier performance was assessed by parameters like true detections (TD), false detection (FD), accuracy (ACC), sensitivity (SEN), specificity (SPF), and positive predicted value (PPC), negative predicted value (NPV). Highest classification feature was selected as a seizure forecasting correlation vector and decision rule was formulated for seizure forecasting. Correlation vector served as a forecaster for current EEG activity. Proposed decision rule forecasted ongoing signal activity towards possible seizure condition true or false. The suggested framework revealed forecasting of ictal events at 10 seconds before the actual seizure. Conclusion: It is worth mentioning that the proposed study utilized a single linear feature to predict seizures precisely. Moreover, utilization of single feature encouraged in subsiding system complexity, processing delays, and system latency. ARTICLE HISTORY

Tetracycline belongs to one of the extracellular produced polyketide antibiotics having hydrophilic properties. Statistical based optimization, Plackett-Burman design (PBD) and response surface methodology (RSM) were occupied to screen and optimize the medium conditions for the production of tetracycline from Streptomyces aureofaciens NCIM 2417, using solid state fermentation. pineapple peel waste was used as both the curious solid support and carbon and nitrogen sources and inorganic salts for the growth of Streptomyces aureofaciens NCIM 2417. Based on the positive influence of the half normal plot obtained from PBD on tetracycline production, three medium conditions-peanut meal, incubation period and soluble starch were screened. Central composite design (CCD) was occupied using these three medium conditions at five levels, for advance optimization, and the second order polynomial equation was derived, based on the experimental data. Response surface methodology showed that the conditions of peanut meal (0.4%), incubation period (day 2) and soluble starch (2%) were the optimal levels for maximal tetracycline production (17.98 mg/g substrate) which were validated through experiments.

Signal & Image Processing : An International Journal is an Open Access peer-reviewed journal intended for researchers from academia and industry, who are active in the multidisciplinary field of signal & image processing. The scope of the journal covers all theoretical and practical aspects of the Digital Signal Processing & Image processing, from basic research to development of application.

Background: Understanding the long-term behavior of intracortically-recorded signals is essential for improving the performance of Brain Computer Interfaces. However, few studies have systematically investigated chronic neural recordings from an implanted microelectrode array in the human brain.
Methods: In this study, we show the applicability of wavelet decomposition method to extract and demonstrate
the utility of long-term stable features in neural signals obtained from a microelectrode array implanted in the motor
cortex of a human with tetraplegia. Wavelet decomposition was applied to the raw voltage data to generate mean
wavelet power (MWP) features, which were further divided into three sub-frequency bands, low-frequency MWP
(lf-MWP, 0–234 Hz), mid-frequency MWP (mf-MWP, 234 Hz–3.75 kHz) and high-frequency MWP (hf-MWP, >3.75 kHz).
We analyzed these features using data collected from two experiments that were repeated over the course of about
3 years and compared their signal stability and decoding performance with the more standard threshold crossings,
local field potentials (LFP), multi-unit activity (MUA) features obtained from the raw voltage recordings.
Results: All neural features could stably track neural information for over 3 years post-implantation and were less
prone to signal degradation compared to threshold crossings. Furthermore, when used as an input to support vector
machine based decoding algorithms, the mf-MWP and MUA demonstrated significantly better performance, respectively,
in classifying imagined motor tasks than using the lf-MWP, hf-MWP, LFP, or threshold crossings.
Conclusions: Our results suggest that usingMWP features in the appropriate frequency bands can provide an effective
neural feature for brain computer interface intended for chronic applications.

Signal & Image Processing : An International Journal is an Open Access peer-reviewed journal intended for researchers from academia and industry, who are active in the multidisciplinary field of signal & image processing. The scope of the journal covers all theoretical and practical aspects of the Digital Signal Processing & Image processing, from basic research to development of application.

the fields of information, control and system theory, understands the design and function of any system and the relationship among these applications. The conference will bring together leading researchers, engineers and scientists in the domain of interest from around the world. This conference aims to provide a platform for exchanging ideas in new emerging trends that needs more focus and exposure and will attempt to publish proposals that strengthen our goals. Authors are solicited to contribute to the conference by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the following areas, but are not limited to: Topics of interest include, but are not limited to, the following:

Competitive rehabilitation games can enhance motivation and exercise intensity compared to solo exercise; however, since such games may be played by two people with different abilities, game difficulty must be dynamically adapted to suit both players. State-of-the-art adaptation algorithms are based on players' performance (e.g., score), which may not be representative of the patient's physical and psychological state. Instead, we propose a method that estimates players' states in a competitive game based on the covariation of players' physiological responses. The method was evaluated in 10 unimpaired pairs, who played a competitive game in 6 conditions while 5 physiological responses were measured: respiration, skin conductance, heart rate, and 2 facial electromyograms. Two physiological linkage methods were used to assess the similarity of the players' physiological measurements: coherence of raw measurements and correlation of heart and respiration rates. These linkage features were compared to traditional individual physiological features in classification of players' affects (enjoyment, valence, arousal, perceived difficulty) into 'low' and 'high' classes. Classifiers based on physiological linkage resulted in higher accuracies than those based on individual physiological features, and combining both feature types yielded the highest classification accuracies (75% to 91%). These classifiers will next be used to dynamically adapt game difficulty during rehabilitation.

Scope 6 th International Conference on Signal, Image Processing and Multimedia (SPM 2019) is a forum for presenting new advances and research results in the fields of Signal, Image Processing and Multimedia. The conference will bring together leading researchers, engineers and scientists in the domain of interest from around the world. Authors are solicited to contribute to the conference by submitting articles that illustrate research results, projects, surveying works and industrial experiences.

International Journal of Embedded Systems and Applications (IJESA) is a quarterly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Embedded Systems and applications. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on understanding Embedded Systems and establishing new collaborations in these areas.

Measuring complexity of dynamical systems is a mighty tool for electrophysiological signal processing. There are plenty of entropies for estimating complexity measure. Approximate entropy (ApEn), sample entropy (SampEn), fuzzy entropy (FuzzyEn), wavelet entropy (WE) and wavelet packet entropy (WPE) was used for surface EMG feature extraction for face movements classification. Linear discriminant analysis (LDA) selected for classification. Classification performance was determined by mean square error (MSE) for different window sizes. Fuzzy entropy is the most robust and succeeding method of them. Principal component analysis used to improve classification performance however just results of approximate entropy feature were refined. MSE of wavelet entropy and wavelet packet entropy are also decent methods for this classification problem.

Cardamom grading is a vital agricultural product processing industry which always seeks for better methods of mechanization. Sorting is the process of segregating cardamom based on its size using sieve of required sieve size. Grading is the process of assigning grades and thus categorizing cardamom to different shipment qualities. Machine learning techniques like k-NN, Decision tree and combined with image processing can be used as a platform to the purpose of grading. The applications of machine learning can be divided into two major areas classification and clustering. In classification there is a list of labels as the target set available, and the goal is to map to any of these labels in the set. The aim is to distribute data into different groups.

Scope International Journal of Artificial Intelligence and Soft Computing (IJAISC) is an open access peer-reviewed journal that provides an excellent international forum for sharing knowledge and results in theory, methodology and applications of Artificial Intelligence, Soft Computing. The Journal looks for significant contributions to all major fields of the Artificial Intelligence, Soft Computing in theoretical and practical aspects. The aim of the Journal is to provide a platform to the researchers and practitioners from both academia as well as industry to meet and share cutting-edge development in the field. Authors are solicited to contribute to the journal by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the areas of Soft Computing, Artificial Intelligence and Applications.

We invite you to join us in Copenhagen, Denmark on June 29-30, 2019 for 6th International Conference on Signal and Image Processing (Signal 2019)

The Conference is a forum for presenting new advances and research results in the fields of Signal, Image Processing & Pattern Recognition. The Workshop will bring together leading researchers, engineers and scientists in the domain of interest from around the world.

Background and aim: Surgical telepresence has been implemented using Mixed reality (MR) but, MR is theory based and only used for investigating research. The Aim of this paper is to propose and implement a new solution by merging augmented video (generating in local site) and virtual expertise surgeon hand (remote site). This system is to improve the visualization of surgical area, overlay accuracy in the merged video without having any discoloured patterns on hand, smudging artefacts on surgeon hand boundary and occluded areas of surgical area. Methodology: The Proposed system consists of an Enhanced Multi-Layer Mean Value Cloning (EMLMV) algorithm that improves the overlay accuracy, visualization accuracy and the processing time. This proposed algorithm includes trimap and alpha matting as a pre-processing stage of merging process, which helps to remove the smudging and discoloured artefacts surrounded by remote surgeon hand. Results: Results showing that the proposed system improved the accuracy by reducing the overlay error of merging image from 1.3 mm (Millimeter) to 0.9 mm. Furthermore, it improves the visibility of surgeon hand in the final merged image from 98.4% (visibility of pixels) to 99.1% (visibility of pixels). Similarly, the processing time in our proposed solution is reduced, which is computed as 10 s to produce 50 frames, whilst, the state of art solution computes 11 s for the same number of frames. Conclusion: The proposed system focuses on the merging of augmented reality video (local site), and the virtual reality video (remote site) with the accurate visualization. we consider discoloured areas, smudging artefacts and occlusion as the main aspects to improve the accuracy of merged video in terms of overlay error and visualization error. So, the proposed system would produce the merged video with the removal of artefacts around the expert surgeon hand.

6th International Conference on Signal and Image Processing (Signal 2019) is a forum for presenting new advances and research results in the fields of Digital Image Processing. The conference will bring together leading researchers, engineers and scientists in the domain of interest from around the world. The scope of the conference covers all theoretical and practical aspects of the Signal, Image Processing & Pattern Recognition.

Auscultation is still the main procedure by the physician in determining the health condition of a person's lungs. Auscultation heavily depends on the physician's skill and experience. Electronics auscultation using computer assistance is used to identify abnormalities in lung sounds for reducing the subjectivity. One of the signal processing methods that is often used to determine the lung sounds is wavelet decomposition method. This study aimed to compare several methods of lung sound classification using wavelet analysis. Some methods combined wavelet decomposition techniques and features extraction to obtain a method that produces the highest accuracy with the fewest number of features. The results showed that the DWT order 7 with DB2 mother wavelet and 46 features produce the highest accuracy of 97.98%. This method was tested on five classes of lung sound data.

Lung sounds provide important information about the health of the lungs and airways. Lung sounds have a special and distinguishable pattern related to abnormalities that might occur in the lungs or respiration tract. Automatic lung sound recognition is directed to reduce subjectivity in assessing lung sounds. Hjorth descriptor is one method used for observing natural biological signals. Hjorth descriptor was measured to reveal biological signal’s complexity. In this paper, lung sound characteristics were measured using Hjorth descriptors. Hjorth descriptor will be used to look at complexity of lung sounds. Hjorth descriptors of lung sounds are measured in the time domain and the frequency domain, and clustered using the Kmeans clustering method. These parameters were tested as to whether they could function as features in automatic lung sound recognition. The experimental results show that the Hjorth descriptor in the time domain is promising in order to be used as a feature for lung sound classification.