فصلنامه مهندسی پزشکی زیستی
سال پانزدهم شماره 4 (زمستان 1400)

Parkinson's disease (PD) is one of the most common types of dementia associated with motor impairments and affected performance of motor skills such as writing. Brain imaging techniques are the common methods used to diagnose PD, which are expensive or invasive, and their accuracy depends on the experience and the skill of the physician. Therefore, the development of an automated, low cost, and reliable diagnostic system is desirable for researchers. In this study, a handwriting signal including cognitive and motor-perceptual components has been used as a non-invasive, cost effective and reliable characteristic in identifying PD-related cognitive and motor dysfunctions. For this purpose, the matching pursuit algorithm with high time-frequency resolution has been employed to decompose X-Y coordinates. It provides a sparse representation of the handwriting signals and quantifies the basic information about the local changes in the handwriting signals. The proposed method is evaluated on a database with 31 healthy samples and 29 Parkinson's samples using the support vector machine classifier and obtained results yields an average accuracy rate of 90%, sensitivity rate of 91.59% and specificity rate of 90%. Comparing different writing tasks has also demonstrated superior performance of writing an entire sentence for PD detection.

Early detection of fatigue helps to improve the quality and effectiveness of neurofeedback training. Diagnosis of fatigue using the EEG signal of participants during neurofeedback training in 10 training sessions is reviewed in this paper. Neurofeedback training has two different neurofeedback training protocols called protocols one and two. The first protocol is a training feature, a combination of frequency and non-frequency features, but the second protocol only includes frequency features. In the first fatigue time protocol, the slope trend of the power changes of the second low alpha sub-band in the OZ channel is decreasing and the permutation entropy in the FZ channel is increasing. The slope of the score changes is also decreasing. In the second protocol, the slope trend of power changes is the second low alpha sub-band in the OZ channel and decreases the score, in other words, the lack of feature change in line with the goal of neurofeedback training is due to fatigue and the participant cannot score. The results are based on the power slope trend of the second lower alpha sub-band and permutation entropy, which indicates that fatigue occurs for one participant in the first protocol and for three participants in the second protocol.

Geometries of leaflets play an important role in designing prosthetic valves (PVs). A valve must have a high geometric orifice area (GOA) and low regurgitation to function properly. In this paper, some polymeric tri-leaflet valves were designed based on radial and free edge curves of previous designs and together with them a new radial curve, which consists of two lines and an arc between them was proposed. Mechanical properties of polystyrene polymer are assigned to leaflets using the Mooney-Rivlin hyperelastic model. The finite element method (FEM) has been utilized to evaluate GOA, regurgitation, von Mises stress, and leaflets' coaptation in a fully closed and opened state. Results were compared, and the valve with the proposed leaflet geometry exhibited better performance among all valves. 69.1% GOA, 2.6 mm coaptation width, 0.921 MPa von Mises stress for closed-form, and 0.731 MPa von Mises stress for opened form were recorded. The significance of the radial curve in designing the geometry of leaflets has been revealed especially on the case of leaflets' coaptation. The more this curve tends to a straight line the higher the geometric orifice area and the weaker the coaptation. The proposed curve establishes an equilibrium between these two parameters.

Transcatheter aortic valves have become the standard procedure for high-risk patients with severe aortic valve stenosis. This minimally invasive procedure can expand to a wider range of patients with a lower risk of surgery. The complications after the implantation and the structural malfunction of these prostheses are the obstacles of this transition. Design optimization of the stents of these prostheses can improve their performance and reduce the post-operative complications associated with them. Since all prostheses are crimped before implantation, the designs should guarantee an acceptable structural performance after expansion, especially self-expandable stents for which the fatigue behavior strongly depends on the strain. This study applies a simple, cost-effective optimization framework to optimize the geometric parameters of these stents regarding the maximum strain during the crimping process. The design parameters include diameter profile, cell size, number of repeating components, and strut cross-section. The simplified models are evaluated and verified by the 3D simulations. The results show that the middle cells' height, number of cells, and strut width have the most prominent effect on the maximum crimping strain of the stent. This framework can be applied to a wide range of stent designs and tremendously reduce the cost of stent design and optimization.

Keratoconus (KC) is a non-inflammatory and degenerative disease of the cornea. It is manifested by the formation of cone-shaped regions accompanying severe eyesight issues. Implantation of intrastromal corneal ring segments (ICRS)‎ is a popular treatment to improve visual acuity. Controversies exist over restoring functionality of different ICRSs. In this study, numerical models were used to quantify the mechanical and optical effects of different ICRSs on a reference cornea with central cone. Finite element (FE) simulations were used to simulate the implantation of two classes of ICRS sets common in clinical settings: a) single segment arcs of 360º (1 × 360), 350º (1 × 350), 320º (1 × 320), and b) symmetric double-segment arcs of 160º each (2 × 160), 150º each (2 × 150), 120º each (2 × 120), and 90º each (2 × 90). Results showed that implantation of symmetric double-segment arcs caused the symmetric displacement and stress distribution contours on both anterior and posterior corneal surfaces. This study shows the potential impact of a detailed mechanical analysis of ICRS placement and represents a first step toward the development of an evidence-based nomogram for the different implantation techniques and the optimization of the surgical intervention based on patient-specific modeling.

Obsessive-Compulsive Disorder (OCD) is the fourth most common mental disorder and the tenth cause of disability worldwide. This disorder can lead to cognitive impariments in attention, memory, thinking, auditory processing of words and visual cognition.Previous studies have demonstrated that OCD is associated with changes in connectivity between different lobes of the brain. Hence, the quantification of symmetry and connectivity between different brain regions has attracted great attention. This study has provided a new efficient approach based on analytic representation of EEG signals and statistical features to quantify the difference of intrinsic components of brain activity between brain lobes. For this purpose, phase spectra and amplitude envelopes of the analytic EEG signals have been extracted and analyzed. Furthermore, Non-Negative Least Square sparse classification method has been used for discriminating between healthy control group and OCD patients. The detection capability of the proposed method has been studied in 19 healthy subjects and 11 patients, performing simple flanker tasks. The obtained results have demonstrated the effectiveness of the combined amplitude and phase information in OCD detection with high average accuracy rate of 93.78 %. In comparison between different regions, the inter-hemispheric features and those extracted from the frontal lobe and frontal-parietal network have shown more efficiency in diagnosing the OCD. This study has also highlighted more importance of amplitude information in the OCD detection.

To interact with such an ever-changing environment in which we live, our brain requires to continuously generate and update expectations about relevant upcoming events and their estimation for the corresponding sensory and motor responses. The goal of this study is to investigate the connectivity in time perception in the two predictable and unpredictable conditions. The data needed for the study from EEG signals recorded from the existing database that included an experiment was conducted on 29 healthy subjects in the two predictable and unpredictable conditions and in 4 delays of 83, 150, 400, 800 ms for each person was done. To estimate the functional connectivity between brain regions, we used the phase lag index method. This method is used to detect time perception in two conditions, predictable and unpredictable events. Initially, by comparing the two conditions in 4 delays was shown that more of the differences were in the gamma, beta, and theta bands. Also, the significant difference between the delays in the predictable condition was greater than the unpredictable condition. Then, the difference between the two conditions in each delay was discussed. The results showed a significant difference in all delays. The alpha band in the unpredictable condition in 400-ms delay, the number of connectivity between occipital and temporal regions was increased and stronger, and also the mean of the unpredictable connectivity was higher than predictable condition. In the delta band for 150, 400 and 800-ms delays, there was connectivity between the central and frontal regions, whereas in 83-ms-delay there was stronger connectivity between the central and prefrontal regions. The right hemisphere of the prefrontal is important in time perception. At the longest delay (800 ms), in three bands, delta, theta, and beta, connectivity decreased in both conditions compared to the other delays.