Determination gender-based hybrid artificial intelligence of body muscle percentage by photoplethysmography signal.

BACKGROUND AND OBJECTIVE: Muscle mass is one of the critical components that ensure muscle function. Loss of muscle mass at every stage of life can cause many adverse effects. Sarcopenia, which can occur in different age groups and is characterized by a decrease in muscle mass, is a critical syndrome that affects the quality of life of individuals. Aging, a universal process, can also cause loss of muscle mass. It is essential to monitor and measure muscle mass, which should be sufficient to maintain optimal health. Having various disadvantages with the ordinary methods used to estimate muscle mass increases the need for the new high technology methods. This study aims to develop a low-cost and trustworthy Body Muscle Percentage calculation model based on artificial intelligence algorithms and biomedical signals. METHODS: For the study, 327 photoplethysmography signals of the subject were used. First, the photoplethysmography signals were filtered, and sub-frequency bands were obtained. A quantity of 125 time-domain features, 25 from each signal, have been extracted. Additionally, it has reached 130 features in demographic features added to the model. To enhance the performance, the spearman feature selection algorithm was used. Decision trees, Support Vector Machines, Ensemble Decision Trees, and Hybrid machine learning algorithms (the combination of three methods) were used as machine learning algorithms. RESULTS: The recommended Body Muscle Percentage estimation model have the perfomance values for all individuals R=0.95, for males R=0.90 and for females R=0.90 in this study. CONCLUSION: Regarding the study results, it is thought that photoplethysmography-based models can be used to predict body muscle percentage.

A new diagnostic method for chronic obstructive pulmonary disease using the photoplethysmography signal and hybrid artificial intelligence.

Background and Purpose: Chronic obstructive pulmonary disease (COPD), is a primary public health issue globally and in our country, which continues to increase due to poor awareness of the disease and lack of necessary preventive measures. COPD is the result of a blockage of the air sacs known as alveoli within the lungs; it is a persistent sickness that causes difficulty in breathing, cough, and shortness of breath. COPD is characterized by breathing signs and symptoms and airflow challenge because of anomalies in the airways and alveoli that occurs as the result of significant exposure to harmful particles and gases. The spirometry test (breath measurement test), used for diagnosing COPD, is creating difficulties in reaching hospitals, especially in patients with disabilities or advanced disease and in children. To facilitate the diagnostic treatment and prevent these problems, it is far evaluated that using photoplethysmography (PPG) signal in the diagnosis of COPD disease would be beneficial in order to simplify and speed up the diagnosis process and make it more convenient for monitoring. A PPG signal includes numerous components, including volumetric changes in arterial blood that are related to heart activity, fluctuations in venous blood volume that modify the PPG signal, a direct current (DC) component that shows the optical properties of the tissues, and modest energy changes in the body. PPG has typically received the usage of a pulse oximeter, which illuminates the pores and skin and measures adjustments in mild absorption. PPG occurring with every heart rate is an easy signal to measure. PPG signal is modeled by machine learning to predict COPD. Methods: During the studies, the PPG signal was cleaned of noise, and a brand-new PPG signal having three low-frequency bands of the PPG was obtained. Each of the four signals extracted 25 features. An aggregate of 100 features have been extracted. Additionally, weight, height, and age were also used as characteristics. In the feature selection process, we employed the Fisher method. The intention of using this method is to improve performance. Results: This improved PPG prediction models have an accuracy rate of 0.95 performance value for all individuals. Classification algorithms used in feature selection algorithm has contributed to a performance increase. Conclusion: According to the findings, PPG-based COPD prediction models are suitable for usage in practice.

An Evaluation of the Channel Effect on Detecting the Preictal Stage in Patients With Epilepsy.

Decreasing the processor load to an acceptable level challenges researchers as an important threshold in the study of real-time detection and the prediction of epileptic seizures. The main methods in overcoming this problem are feature selection, dimension reduction, and electrode selection. This study is an evaluation of the performances of EEG signals, obtained from different channels in the detection processes of epileptic stages, in epileptic individuals. In particular, it aimed to analyze the separation levels of preictal periods from other periods and to evaluate the effects of the electrode selection on seizure prediction studies. The EEG signals belong to 14 epileptic patients. A feature set was formed for each patient using 20 features widely used in epilepsy studies. The number of features was decreased to 8 using principal component analysis. The reduced feature set was divided into testing and training data, using the cross-validation method. The testing data were classified with linear discriminant analysis and the results of the classification were evaluated individually for each patient and channel. Variability of up to 29.48 % was observed in the average of classification accuracy due to the selection of channels.

Zero-valent iron nanoparticles containing nanofiber scaffolds for nerve tissue engineering.

Regeneration of nerve tissue is a challenging issue in regenerative medicine. Especially, the peripheral nerve defects related to the accidents are one of the leading health problems. For large degeneration of peripheral nerve, nerve grafts are used in order to obtain a connection. These grafts should be biodegradable to prevent second surgical intervention. In order to make more effective nerve tissue engineering materials, nanotechnological improvements were used. Especially, the addition of electrically conductive and biocompatible metallic particles and carbon structures has essential roles in the stimulation of nerves. However, the metabolizing of these structures remains to wonder because of their nondegradable nature. In this study, biodegradable and conductive nerve tissue engineering materials containing zero-valent iron (Fe) nanoparticles were developed and investigated under in vitro conditions. By using electrospinning technique, fibrous mats composed of electrospun poly(ε-caprolactone) (PCL) nanofibers and Fe nanoparticles were obtained. Both electrical conductivity and mechanical properties increased compared with control group that does not contain nanoparticles. Conductivity of PCL/Fe5 and PCL/Fe10 increased to 0.0041 and 0.0152 from 0.0013 Scm-1 , respectively. Cytotoxicity results indicated toxicity for composite mat containing 20% Fe nanoparticles (PCL/Fe20). SH-SY5Y cells were grown on PCL/Fe10 best, which contains 10% Fe nanoparticles. Beta III tubulin staining of dorsal root ganglion neurons seeded on mats revealed higher cell number on PCL/Fe10. This study demonstrated the impact of zero-valent Fe nanoparticles on nerve regeneration. The results showed the efficacy of the conductive nanoparticles, and the amount in the composition has essential roles in the promotion of the neurites.

In obstructive sleep apnea patients, automatic determination of respiratory arrests by photoplethysmography signal and heart rate variability.

Obstructive sleep apnea is a disease that occurs in connection to pauses in respiration during sleep. Detection of the disease is achieved using a polysomnography device, which is the gold standard in diagnosis. Diagnosis is made by the steps of sleep staging and respiration scoring. Respiration scoring is performed with at least four signals. Technical knowledge is required for attaching the electrodes. Additionally, the electrodes are disturbing to an extent that will delay the patient's sleep. It is needed to have systems as alternatives for polysomnography devices that will bring a solution to these issues. This study proposes a new approach for the process of respiration scoring which is one of the diagnostic steps for the disease. A machine-learning-based apnea detection algorithm was developed for the process of respiration scoring. The study used Photoplethysmography (PPG) signal and Heart Rate Variability (HRV) that is derived from this signal. The PPG records obtained from the patient and control groups were cleaned out using a digital filter. Then, the HRV parameter was derived from the PPG signal. Later, 46 features were derived from the PPG signal and 40 features were derived from the HRV. The derived features were classified with reduced machine-learning techniques using the F-score feature-selection algorithm. The evaluation was made in a multifaceted manner. Besides, Principal Component Analysis was performed to reduce system input (features). According to the results, if a real-time embedded system is designed, the system can operate with 16 PPG feature 95%, four PPG feature 93.81% accuracy rate. These success rates are highly sufficient for the system to work. Considering all these values, it is possible to realize a practical respiration scoring system. With this study, it was agreed upon that PPG signal may be used in the diagnosis of this disease by processing it with machine learning and signal processing techniques.

Zero valent zinc nanoparticles promote neuroglial cell proliferation: A biodegradable and conductive filler candidate for nerve regeneration.

Regeneration of nerve, which has limited ability to undergo self-healing, is one of the most challenging areas in the field of tissue engineering. Regarding materials used in neuroregeneration, there is a recent trend toward electrically conductive materials. It has been emphasized that the capacity of conductive materials to regenerate such tissue having limited self-healing ability improves their clinical utility. However, there have been concerns about the safety of materials or fillers used for conductance due to their lack of degradability. Here, we attempt to use poly(Ɛ-caprolactone) (PCL) matrix consisting of varying proportions of zero valent zinc nanoparticles (Zn NPs) via electrospinning. These conductive, biodegradable, and bioactive materials efficiently promoted neuroglial cell proliferation depending on the amount of Zn NPs present in the PCL matrix. Chemical characterizations indicated that the incorporated Zn NPs do not interact with the PCL matrix chemically and that the Zn NPs improved the tensile properties of the PCL matrix. All composites exhibited linear conductivity under in vitro conditions. In vitro cell culture studies were performed to determine the cytotoxicity and proliferative efficiency of materials containing different proportions of Zn NPs. The results were obtained to explore new conductive fillers that can promote tissue regeneration.