Journal of Medical Signals and Sensors
Volume:11 Issue: 4, Oct-Dec 2021

Despite the interesting innovation proposed in the paper, “Synergy‑based functional electrical stimulation for poststroke rehabilitation of upper‑limb motor functions,” concerning the design of functional electrical stimulation (FES) profile, we are skeptical regarding the genuine effectiveness of the applied rehabilitation strategy. In this note, we argue that applying the rehabilitation method proposed in the above‑noted work cannot pave the way for eliciting a motor learning process. Consequently, the proposed method cannot be regarded as a FES‑based rehabilitation approach for poststroke rehabilitation of upper‑limb motor functions.

Nonlinear dynamics, especially the chaos characteristics, are useful in analyzing bio‑potentials with many complexities. In this study, the evaluation of arm‑tip force estimation method from the electroencephalography (EEG) signal in the vertical plane has been studied and chaos characteristics, including fractal dimension, Lyapunov exponent, entropy, and correlation dimension characteristics of EEG signals have been measured and analyzed at different levels of forces.

Electromyography signal was recorded with the help of the BIOPEC device (the Mp‑100 model) and from the forearm muscle with surface electrodes, and the EEG signals were recorded from five major motor‑related cortical areas according to 10–20 standard three times in a normal healthy 33‑year‑old male, athlete and right handed simultaneously with importing a force to 10 sinkers weighing from 10 to 100 Newton with step 10 Newton.

The findings confirm that force estimation through EEG signals is feasible, especially using fractal dimension feature. The R‑squared values for Fractal dimension, Lyapunov exponent, and entropy and correlation dimension features for linear trend line were 0.93, 0.7, 0.86, and 0.41, respectively.

The linear increase of characteristics especially fractal dimension and entropy, together with the results from other EEG and neuroimaging studies, suggests that under normal conditions, brain recruits motor neurons at a linear progress when increasing the force.

One of the common limitations in the treatment of cancer is in the early detection of this disease. The customary medical practice of cancer examination is a visual examination by the dermatologist followed by an invasive biopsy. Nonetheless, this symptomatic approach is timeconsuming and prone to human errors. An automated machine learning model is essential to capacitate fast diagnoses and early treatment.

The key objective of this study is to establish a fully automatic model that helps Dermatologists in skin cancer handling process in a way that could improve skin lesion classification accuracy.

The work is conducted following an implementation of a Deep Convolutional Generative Adversarial Network (DCGAN) using the Python-based deep learning library Keras. We incorporated effective image filtering and enhancement algorithms such as bilateral filter to enhance feature detection and extraction during training. The Deep Convolutional Generative Adversarial Network (DCGAN) needed slightly more fine-tuning to ripe a better return. Hyperparameter optimization was utilized for selecting the best-performed hyperparameter combinations and several network hyperparameters. In this work, we decreased the learning rate from the default 0.001 to 0.0002, and the momentum for Adam optimization algorithm from 0.9 to 0.5, in trying to reduce the instability issues related to GAN models and at each iteration the weights of the discriminative and generative network were updated to balance the loss between them. We endeavour to address a binary classification which predicts two classes present in our dataset, namely benign and malignant. More so, some wellknown metrics such as the receiver operating characteristic -area under the curve and confusion matrix were incorporated for evaluating the results and classification accuracy.

The model generated very conceivable lesions during the early stages of the experiment and we could easily visualise a smooth transition in resolution along the way. Thus, we have achieved an overall test accuracy of 93.5% after fine-tuning most parameters of our network.

This classification model provides spatial intelligence that could be useful in the future for cancer risk prediction. Unfortunately, it is difficult to generate high quality images that are much like the synthetic real samples and to compare different classification methods given the fact that some methods use non-public datasets for training.

Yogic breathing also called as “Pranayama” is practiced with inhalation (Pooraka), holding the breath for some time (Kumbhaka) and then exhalation (Rechaka). The effective methods of yogic breathing keep oneself healthy and also improves immunity power. The yogic breathing can be practiced irrespective of one’s age and gender and even in the office which helps to reduce the stress. To get the best results through yoga, a person has to follow certain timings and sit in a correct posture. Although many devices are existing in the market to monitor heart rate, posture and breathing during physical activity, there is a need of a device which is simple, cheap, and easy to use without an additional requirement of a smartphone. Moreover, the proposed device is able to evaluate the breathing data by transmitting it to a webpage through a Wi‑Fi hotspot of the Microcontroller.

The developed device has two subsystems: (i) A wrist subsystem to measure the heart rate, visual aid of breathing and vibration feedback for kapalabhati. (ii) A waist subsystem to monitor the posture with help of flex sensor and the results are displayed on the display of the wrist device. It also provides vibration feedback. The inertial measurement unit is used for breath detection. The subsystems are communicated through SPI communication. The breathing data are transmitted to a webpage through a Wi‑Fi hotspot of the microcontroller.

The various yogic breathing and normal breathing exercises are tested on different normal subjects using the developed device and analyzed. The heart rate and beats per minute are evaluated. The heart rate sensor is validated using a standard medical device and it is observed that there was a 97.4% accuracy.

The results show that the device is able to accurately monitor different kinds of breathing and additionally provide heart rate and posture information while performing the breathing exercises.

Exposure to small confined spaces evokes physiological responses such as increased heart rate in claustrophobic patients. However, little is known about electrocortical activity while these people are functionally exposed to such phobic situations. The aim of this study was to examine possible changes in electrocortical activity in this population.

Two highly affected patients with claustrophobia and two healthy controls participated in this in vivo study during which electroencephalographic (EEG) activity was continuously recorded. Relative power spectral density (rPSD) was compared between two situations of being relaxed in a well‑lit open area, and sitting in a relaxed chair in a small (90 cm × 180 cm × 155 cm) chamber with a dim light. This comparison of rPSDs in five frequency bands of EEG was intended to investigate possible patterns of change in electrical activity during fear‑related situation. This possible change was also compared between claustrophobic patients and healthy controls in all cortical areas.

Statistical models showed that there is a significant interaction between groups of participants and experimental situations in all frequency bands (P < 0.01). In other words, claustrophobic patients showed significantly different changes in electrical activity while going from rest to the test situation. Clear differences were observed in alpha and theta bands. In the theta band, while healthy controls showed an increase in rPSD, claustrophobic patients showed an opposite decrease in the power of electrical activity when entering the confined chamber. In alpha band, both groups showed an increase in rPSD, though this increase was significantly higher for claustrophobic patients.

The effect of in vivo exposure to confined environments on EEG activity is different in claustrophobic patients than in healthy controls. Most of this contrast is observed in central and parietal areas of the cortex, and in the alpha and theta bands.

Due to long-term use of computers and not maintaining the correct position and angle of the body while working with it, various skeletal and muscular problems and pain in the neck area occurs. This study aims to use a biofeedbck system to alert the computer users of an inappropriate angle of their necks, and as a result help them to establish a correct neck position.

The user’s neck angle is measured using a three dimensional accelerometer and the signal is processed, digitalized, and sent to the computer. User friendly software is designed to process the received data and warn the users when their neck angle is inappropriate.

The results show that the application of the biofeedback system reduces the users’ total time with inappropriate neck angle to <50%.

results demonstrated that training with the biofeedback system has been sufficient to make the habit of maintaining the neck in the correct angle.

The latest World Health Organization statistics show that the number of people living with COVID‑19 disease is now more than 42 million worldwide. Some diagnosis methods include detecting and observing clinical symptoms associated with the disease (fever, dry cough, shortness of breath, sore throat, and muscle fatigue). Some other methods, such as computed tomography (CT)‑scan imaging from the lungs, are the more accurate diagnostic methods. In this study, we examine the types of abnormal COVID‑19 can cause in the lungs of infected subjects and detect and classify this disease. In this paper, we used data from the lung’s CT‑scan images from the 79 participants. To do this, in this article, for processing CT‑scan images of the lungs to diagnose and classification of the COVID‑19 disease in men and women of different ages, for rapid diagnosis and high accuracy of this disease by the automatic classification algorithm is used. The final results showed that the proposed method could base on different categories (gender, age categories, and type of damage caused by COVID‑19) with high detection and classification accuracy. The algorithm presented in this article has accurately identified the data of healthy subjects and patients with coronavirus.

It is important to have an accurate and reliable brain tumor segmentation for cancer diagnosis and treatment planning. There are few unsupervised approaches for brain tumor segmentation. In this paper, a new unsupervised approach based on graph coloring for brain tumor segmentation is introduced. In this study, a graph coloring approach is used for brain tumor segmentation. For this aim, each pixel of brain image assumed as a node of graph and difference between brightness of a couple of pixels considered as edge. This method was applied on T1‑enhanced magnetic resonance images of low‑grade and high‑grade patients. Since a rigid graph was needed for graph coloring, edges must be divided into existing or nonexisting edge using a threshold. The value of this threshold has affected the accuracy of image segmentation, so the choice of the optimal threshold was important. The optimal value for this threshold was 0.42 of maximum value of difference of brightness between pixels that caused the 83.62% of correlation accuracy. The results showed that graph coloring approach can be a reliable unsupervised approach for brain tumor segmentation. This approach, as an unsupervised approach, shows better accuracy in comparison with neural networks and neuro‑fuzzy networks. However, as a limitation, the accuracy of this approach is dependent on the threshold of edges.