Combining enhanced spectral resolution of EMG and a deep learning approach for knee pathology diagnosis.

Knee osteoarthritis (OA) is a prevalent, debilitating joint condition primarily affecting the elderly. This investigation aims to develop an electromyography (EMG)-based method for diagnosing knee pathologies. EMG signals of the muscles surrounding the knee joint were examined and recorded. The principal components of the proposed method were preprocessing, high-order spectral analysis (HOSA), and diagnosis/recognition through deep learning. EMG signals from individuals with normal and OA knees while walking were extracted from a publicly available database. This examination focused on the quadriceps femoris, the medial gastrocnemius, the rectus femoris, the semitendinosus, and the vastus medialis. Filtration and rectification were utilized beforehand to eradicate noise and smooth EMG signals. Signals' higher-order spectra were analyzed with HOSA to obtain information about nonlinear interactions and phase coupling. Initially, the bicoherence representation of EMG signals was devised. The resulting images were fed into a deep-learning system for identification and analysis. A deep learning algorithm using adapted ResNet101 CNN model examined the images to determine whether the EMG signals were conventional or indicative of knee osteoarthritis. The validated test results demonstrated high accuracy and robust metrics, indicating that the proposed method is effective. The medial gastrocnemius (MG) muscle was able to distinguish Knee osteoarthritis (KOA) patients from normal with 96.3±1.7% accuracy and 0.994±0.008 AUC. MG has the highest prediction accuracy of KOA and can be used as the muscle of interest in future analysis. Despite the proposed method's superiority, some limitations still require special consideration and will be addressed in future research.

A comprehensive framework for advanced protein classification and function prediction using synergistic approaches: Integrating bispectral analysis, machine learning, and deep learning.

Proteins are fundamental components of diverse cellular systems and play crucial roles in a variety of disease processes. Consequently, it is crucial to comprehend their structure, function, and intricate interconnections. Classifying proteins into families or groups with comparable structural and functional characteristics is a crucial aspect of this comprehension. This classification is crucial for evolutionary research, predicting protein function, and identifying potential therapeutic targets. Sequence alignment and structure-based alignment are frequently ineffective techniques for identifying protein families.This study addresses the need for a more efficient and accurate technique for feature extraction and protein classification. The research proposes a novel method that integrates bispectrum characteristics, deep learning techniques, and machine learning algorithms to overcome the limitations of conventional methods. The proposed method uses numbers to represent protein sequences, utilizes bispectrum analysis, uses different topologies for convolutional neural networks to pull out features, and chooses robust features to classify protein families. The goal is to outperform existing methods for identifying protein families, thereby enhancing classification metrics. The materials consist of numerous protein datasets, whereas the methods incorporate bispectrum characteristics and deep learning strategies. The results of this study demonstrate that the proposed method for identifying protein families is superior to conventional approaches. Significantly enhanced quality metrics demonstrated the efficacy of the combined bispectrum and deep learning approaches. These findings have the potential to advance the field of protein biology and facilitate pharmaceutical innovation. In conclusion, this study presents a novel method that employs bispectrum characteristics and deep learning techniques to improve the precision and efficiency of protein family identification. The demonstrated advancements in classification metrics demonstrate this method's applicability to numerous scientific disciplines. This furthers our understanding of protein function and its implications for disease and treatment.

Islamic legal perspectives on digital currencies and how they apply to Jordanian legislation.

Background: The industrial transformation requires a speedy shift to financial digitization. One of the needs for financial digitalization in the study of Islamic contracts and Islamic business law is the use of digital platforms with digital currencies. Regarding the merits and downsides of its Sharia restrictions and its halal certification, which is currently under discussion, digital currencies and perks have generated controversy in Jordan and other Islamic countries. Methods: This study intends to analyze the legal foundations of digital currency from Jordanian and Islamic legal perspectives. The descriptive-qualitative research approach was utilized, and data collection processes included documentation and a literature review. All legal possibilities that may be drawn from Islamic law in order to investigate the legality of digital currencies are explored further and used to obtain the conclusions of this study. Results: A review of Sharia reasons and consideration for the wellbeing of the people suggests that digital currencies in their current form are incompatible with it and must adhere to the stipulations of Islamic finance. Therefore, digital currencies are unsuitable as a store of value or wealth due to their erratic swings, lack of purchasing power, and instant responsiveness to any technical problem, technical penetration, or official circumstance. Due to market instability, digital currencies can't be utilized to defer payments, settle debts, or repay loans. Conclusions: Digital currencies are speculative; not real money. Most of those who have this money are speculators seeking a quick payoff. Sharia views digital currency trading as gambling due to its high degree of volatility. Jordan's government should regulate digital currency use to meet demand. Digital currencies must be addressed under e-commerce laws.

Identification of Coronary Artery Diseases Using Photoplethysmography Signals and Practical Feature Selection Process.

A low-cost, fast, dependable, repeatable, non-invasive, portable, and simple-to-use vascular screening tool for coronary artery diseases (CADs) is preferred. Photoplethysmography (PPG), a low-cost optical pulse wave technology, is one method with this potential. PPG signals come from changes in the amount of blood in the microvascular bed of tissue. Therefore, these signals can be used to figure out anomalies within the cardiovascular system. This work shows how to use PPG signals and feature selection-based classifiers to identify cardiorespiratory disorders based on the extraction of time-domain features. Data were collected from 360 healthy and cardiovascular disease patients. For analysis and identification, five types of cardiovascular disorders were considered. The categories of cardiovascular diseases were identified using a two-stage classification process. The first stage was utilized to differentiate between healthy and unhealthy subjects. Subjects who were found to be abnormal were then entered into the second stage classifier, which was used to determine the type of the disease. Seven different classifiers were employed to classify the dataset. Based on the subset of features found by the classifier, the Naïve Bayes classifier obtained the best test accuracy, with 94.44% for the first stage and 89.37% for the second stage. The results of this study show how vital the PPG signal is. Many time-domain parts of the PPG signal can be easily extracted and analyzed to find out if there are problems with the heart. The results were accurate and precise enough that they did not need to be looked at or analyzed further. The PPG classifier built on a simple microcontroller will work better than more expensive ones and will not make the patient nervous.

Detection of cardiac ischaemia using bispectral analysis approach.

This paper highlights a new detection method based on higher spectral analysis techniques to distinguish the Electrocardiogram (ECG) of normal healthy subjects from that with a cardiac ischaemia (CI) patient. Higher spectral analysis techniques provide in-depth information other than available conventional spectral analysis techniques usually used with ECG analysis. They provide information within frequency parts and information regarding phase associations. Bispectral analysis- Bispectrum and Quadratic Phase Coupling techniques are utilized to detect as well as to characterize phase combined harmonics in ECG. The work is developed, tested and validated using Normal Sinus Rhythm Data from the MIT-BIH Database and CI data from the ST Petersburg European ST-T Database. The results validate the efficacy of the introduced method by maintaining 100% sensitivity and achieving 93.33% positive predictive accuracy. The simplicity and robustness of the proposed method makes it feasible to be used within available ECG systems.

Methods of EEG signal features extraction using linear analysis in frequency and time-frequency domains.

Technically, a feature represents a distinguishing property, a recognizable measurement, and a functional component obtained from a section of a pattern. Extracted features are meant to minimize the loss of important information embedded in the signal. In addition, they also simplify the amount of resources needed to describe a huge set of data accurately. This is necessary to minimize the complexity of implementation, to reduce the cost of information processing, and to cancel the potential need to compress the information. More recently, a variety of methods have been widely used to extract the features from EEG signals, among these methods are time frequency distributions (TFD), fast fourier transform (FFT), eigenvector methods (EM), wavelet transform (WT), and auto regressive method (ARM), and so on. In general, the analysis of EEG signal has been the subject of several studies, because of its ability to yield an objective mode of recording brain stimulation which is widely used in brain-computer interface researches with application in medical diagnosis and rehabilitation engineering. The purposes of this paper, therefore, shall be discussing some conventional methods of EEG feature extraction methods, comparing their performances for specific task, and finally, recommending the most suitable method for feature extraction based on performance.

A practical reconstructed phase space approach for ECG arrhythmias classification.

Atrial and ventricular arrhythmias are symptoms of the main common causes of rapid death. The severity of these arrhythmias depends on their occurrence either within the atria or ventricles. These abnormalities of the heart activity may cause an immediate death or cause damage of the heart. In this paper, a new algorithm is proposed for the classification of life threatening cardiac arrhythmias including atrial fibrillation (AF), ventricular tachycardia (VT) and ventricular fibrillation (VF). The proposed technique uses a simple signal processing technique for analysing the non-linear dynamics of the ECG signals in the time domain. The classification algorithm is based upon the distribution of the attractor in the reconstructed phase space (RPS). The behaviour of the ECG signal in the reconstructed phase space is used to determine the classification features of the whole classifier. It is found that different arrhythmias occupy different regions in the reconstructed phase space. Three regions in the RPS are found to be more representative of the considered arrhythmias. Therefore, only three simple features are extracted to be used as classification parameters. To evaluate the performance of the presented classification algorithm, real datasets are obtained from the MIT database. A learning dataset is used to design the classification algorithm and a testing dataset is used to verify the algorithm. The algorithm is designed to guarantee achieving both 100% sensitivity and 100% specificity. The classification algorithm is validated by using 45 ECG signals spanning the considered life threatening arrhythmias. The obtained results show that the classification algorithm attains a sensitivity ranging from 85.7-100%, a specificity ranging from 86.7-100% and an overall accuracy of 95.55%.

Quality assessment of ECG compression techniques using a wavelet-based diagnostic measure.

Electrocardiograph (ECG) compression techniques are gaining momentum due to the huge database requirements and wide band communication channels needed to maintain high quality ECG transmission. Advances in computer software and hardware enable the birth of new techniques in ECG compression, aiming at high compression rates. In general, most of the introduced ECG compression techniques depend on their evaluation performance on either inaccurate measures or measures targeting random behavior of error. In this paper, a new wavelet-based quality measure is proposed. A new wavelet-based quality measure is proposed. The new approach is based on decomposing the segment of interest into frequency bands where a weighted score is given to the band depending on its dynamic range and its diagnostic significance. A performance evaluation of the measure is conducted quantitatively and qualitatively. Comparative results with existing quality measures show that the new measure is insensitive to error variation, is accurate, and correlates very well with subjective tests.

A quantitative analysis approach for cardiac arrhythmia classification using higher order spectral techniques.

Ventricular tachyarrhythmias, in particular ventricular fibrillation (VF), are the primary arrhythmic events in the majority of patients suffering from sudden cardiac death. Attention has focused upon these articular rhythms as it is recognized that prompt therapy can lead to a successful outcome. There has been considerable interest in analysis of the surface electrocardiogram (ECG) in VF centred on attempts to understand the pathophysiological processes occurring in sudden cardiac death, predicting the efficacy of therapy, and guiding the use of alternative or adjunct therapies to improve resuscitation success rates. Atrial fibrillation (AF) and ventricular tachycardia (VT) are other types of tachyarrhythmias that constitute a medical challenge. In this paper, a high order spectral analysis technique is suggested for quantitative analysis and classification of cardiac arrhythmias. The algorithm is based upon bispectral analysis techniques. The bispectrum is estimated using an autoregressive model, and the frequency support of the bispectrum is extracted as a quantitative measure to classify atrial and ventricular tachyarrhythmias. Results show a significant difference in the parameter values for different arrhythmias. Moreover, the bicoherency spectrum shows different bicoherency values for normal and tachycardia patients. In particular, the bicoherency indicates that phase coupling decreases as arrhythmia kicks in. The simplicity of the classification parameter and the obtained specificity and sensitivity of the classification scheme reveal the importance of higher order spectral analysis in the classification of life threatening arrhythmias. Further investigations and modification of the classification scheme could inherently improve the results of this technique and predict the instant of arrhythmia change.

Perceptually tuned JPEG coder for echocardiac image compression.

In this work, we propose an efficient framework for compressing and displaying medical images. Image compression for medical applications, due to available Digital Imaging and Communications in Medicine requirements, is limited to the standard discrete cosine transform-based joint picture expert group. The objective of this work is to develop a set of quantization tables (Q tables) for compression of a specific class of medical image sequences, namely echocardiac. The main issue of concern is to achieve a Q table that matches the specific application and can linearly change the compression rate by adjusting the gain factor. This goal is achieved by considering the region of interest, optimum bit allocation, human visual system constraint, and optimum coding technique. These parameters are jointly optimized to design a Q table that works robustly for a category of medical images. Application of this approach to echocardiac images shows high subjective and quantitative performance. The proposed approach exhibits objectively a 2.16-dB improvement in the peak signal-to-noise ratio and subjectively 25% improvement over the most useable compression techniques.

Comments on "an efficient coding algorithm for the compression of ECG signals using the wavelet transform".

The author proposed an effective wavelet-based ECG compression algorithm (Rajoub, 2002). The reported extraordinary performance motivated us to explore the findings and to use it in our research activity. During the implementation of the proposed algorithm several important points regarding accuracy, methodology, and coding were found to be improperly substantiated. This paper discusses these findings and provides specific subjective and objective measures that could improve the interpretation of compression results in these research-type problems.