Sawal Hamid Md Ali

Role of EEG as Biomarker in the Early Detection and Classification of Dementia

The early detection and classification of dementia are important clinical support tasks for medical practitioners in customizing patient treatment programs to better manage the development and progression of these diseases. Efforts are being made to diagnose these neurodegenerative disorders in the early stages. Indeed, early diagnosis helps patients to obtain the maximum treatment benefit before significant mental decline occurs. The use of electroencephalogram as a tool for the detection of changes in brain activities and clinical diagnosis is becoming increasingly popular for its capabilities in quantifying changes in brain degeneration in dementia. This paper reviews the role of electroencephalogram as a biomarker based on signal processing to detect dementia in early stages and classify its severity. The review starts with a discussion of dementia types and cognitive spectrum followed by the presentation of the effective preprocessing denoising to eliminate possible artifacts. It continues with a description of feature extraction by using linear and nonlinear techniques, and it ends with a brief explanation of vast variety of separation techniques to classify EEG signals. This paper also provides an idea from the most popular studies that may help in diagnosing dementia in early stages and classifying through electroencephalogram signal processing and analysis.

Cognitive impairment and memory dysfunction after a stroke diagnosis: a post-stroke memory assessment

Cognitive impairment and memory dysfunction following stroke diagnosis are common symptoms that significantly affect the survivors’ quality of life. Stroke patients have a high potential to develop dementia within the first year of stroke onset. Currently, efforts are being exerted to assess stroke effects on the brain, particularly in the early stages. Numerous neuropsychological assessments are being used to evaluate and differentiate cognitive impairment and dementia following stroke. This article focuses on the role of available neuropsychological assessments in detection of dementia and memory loss after stroke. This review starts with stroke types and risk factors associated with dementia development, followed by a brief description of stroke diagnosis criteria and the effects of stroke on the brain that lead to cognitive impairment and end with memory loss. This review aims to combine available neuropsychological assessments to develop a post-stroke memory assessment (PSMA) scheme based on the most recognized and available studies. The proposed PSMA is expected to assess different types of memory functionalities that are related to different parts of the brain according to stroke location. An optimal therapeutic program that would help stroke patients enjoy additional years with higher quality of life is presented.

Modelling of hybrid energy harvester with DC-DC boost converter using arbitary input sources for ultra-low-power micro-devices

This work involves the modeling of three arbitrary input sources representing Hybrid Energy Harvesters (HEH) using a DC-DC Boost converter. These sources are combined in parallel and targeted at scavenging passive human power, therefore the three suitable ambient sources are motion, thermal and indoor light. Multiple sources mitigate limitations caused by single source harvesters but suffer impedance mismatches which greatly limit the total combined power that could have been harvested. A Boost Converter with suitable parameters has been designed and integrated to the HEH and PSPICE software has been used for both the modeling of arbitrary sources as well as the integration with the Boost Converter. An input source as low as 18 mV to 907 mV was able to be boosted into a 310 mV-27.9 V output when suitable parametric values were selected for the Ultra Low Power (ULP) HEH. A duty ratio of 0.5, with 10 kΩ load, 22 μH inductor as well as a switching frequency of 25 kHz was selected to be slightly above the audio range as well as being high enough to reduce passive component sizes. While VO/ VS of the boost converter is linear, PO/PIN is a function of third order polynomial. Therefore, at the HEHs lowest combined configuration of 1 K temperature difference, 0.25 g of vibration and 100 lux of indoor lighting, a combined 14 μW can be harvested. At its maximum of 10 K heat difference, 1 g vibration and 1000 lux of indoor lighting a combined 187 μW can be harvested. At its minimum, this enables possibility of battery-less applications in powering a quartz watch at 5 μW while at its maximum capacity powering a pace maker of ~50 μW as well as micro devices of ~100 μW solely from passive human activity. Once a 33 mF input capacitor is placed between the sources and converter, an output power of between 9.61 μW-78 mW can be obtained.

Investigation into Mass Loading Sensitivity of Sezawa Wave Mode-Based Surface Acoustic Wave Sensors

In this work mass loading sensitivity of a Sezawa wave mode based surface acoustic wave (SAW) device is investigated through finite element method (FEM) simulation and the prospects of these devices to function as highly sensitive SAW sensors is reported. A ZnO/Si layered SAW resonator is considered for the simulation study. Initially the occurrence of Sezawa wave mode and displacement amplitude of the Rayleigh and Sezawa wave mode is studied for lower ZnO film thickness. Further, a thin film made of an arbitrary material is coated over the ZnO surface and the resonance frequency shift caused by mass loading of the film is estimated. It was observed that Sezawa wave mode shows significant sensitivity to change in mass loading and has higher sensitivity (eight times higher) than Rayleigh wave mode for the same device configuration. Further, the mass loading sensitivity was observed to be greater for a low ZnO film thickness to wavelength ratio. Accordingly, highly sensitive SAW sensors can be developed by coating a sensing medium over a layered SAW device and operating at Sezawa mode resonance frequency. The sensitivity can be increased by tuning the ZnO film thickness to wavelength ratio.

Designing a Battery-Less Piezoelectric based Energy Harvesting Interface Circuit with 300 mV Startup Voltage

This paper presents a designing a battery-less piezoelectric based energy harvesting interface circuits with 300mV step-up voltage. A technique (i.e., DC-DC Step-Up converter) has chosen for designing the startup voltage with low voltage energy (i.e., 300mV). The proposed method consumes very little power, and is especially suitable for the ambient environmental source, where energy harvested power is very low. The energy harvesting interface circuit consists of MOSFET bridge ac-dc rectifier, voltage regulator, dc-dc step-up converter and an energy storage device with capacitor at the output terminal, replacing this by external battery. This paper will study results these important issues regarding the efficiencies of the energy harvesting power conversion interface circuits considering the storage device low voltage. The achievement of our development circuit is able to boost up minimum 1.67 V for input DC voltage of 300mV. The overall circuit efficiency is greater than 80 % following the simulation results. This research has focused on the application of Wireless Sensor Network (WSN) and bio-medical device can be operated without battery.

Selection of mother wavelets thresholding methods in denoising multi-channel EEG signals during working memory task

The aim of this pilot study was to select the most similar mother wavelet function and the most efficient threshold in order to use with wavelet basis function for the human brain electrical activity during working memory task. A 60 seconds was recorded from the scalp using the Electroencephalography (EEG). 19 electrodes were placed over different sites on the scalp where analyzed for one control subject and one post-stroke patients in the first week of his stroke onset. In this study, forty-five mother wavelet basis functions from orthogonal families with four thresholding methods were used. The selection of mother wavelet functions like Daubechies (db), symlet (sym) and coiflet (coif) and the thresholding methods these are sqtwolog, rigrsure, heursure and minimax are to check mother wavelet functions similarity with the recorded EEG signals during working memory task. The test have been done using four evaluating criteria, namely signal to noise ratio (SNR), peak signal to noise ratio (PSNR) mean square error (MSE) and crosscorelation method (xcorr). Symlet mother wavelet of order 9 (sym9) is the most compatible for all the 19 channels for both EEG datasets that selected to be examined and the best results have been obtained by using the rigrsure thresholding method.

Cognitive assessments for the early diagnosis of dementia after stroke

The early detection of poststroke dementia (PSD) is important for medical practitioners to customize patient treatment programs based on cognitive consequences and disease severity progression. The aim is to diagnose and detect brain degenerative disorders as early as possible to help stroke survivors obtain early treatment benefits before significant mental impairment occurs. Neuropsychological assessments are widely used to assess cognitive decline following a stroke diagnosis. This study reviews the function of the available neuropsychological assessments in the early detection of PSD, particularly vascular dementia (VaD). The review starts from cognitive impairment and dementia prevalence, followed by PSD types and the cognitive spectrum. Finally, the most usable neuropsychological assessments to detect VaD were identified. This study was performed through a PubMed and ScienceDirect database search spanning the last 10 years with the following keywords: “post-stroke”; “dementia”; “neuro-psychological”; and “assessments”. This study focuses on assessing VaD patients on the basis of their stroke risk factors and cognitive function within the first 3 months after stroke onset. The search strategy yielded 535 articles. After application of inclusion and exclusion criteria, only five articles were considered. A manual search was performed and yielded 14 articles. Twelve articles were included in the study design and seven articles were associated with early dementia detection. This review may provide a means to identify the role of neuropsychological assessments as early PSD detection tests.

Classification of surface electromyographic signal using fuzzy logic for prosthesis control application

This paper describes the classification stage of an electromyographic (EMG) control system for prosthetic hand application. Moving ApEn was used as main method to extract features from the two channels of surface EMG signal at the forearm of the upper limb. A fuzzy logic system is used to classify the extracted information in discriminating the final grip posture. The results demonstrate the ability of the system to classify the information related to different grip postures.

Quantitative Analysis of Spectroscopy's Study for Engine Oil Degradation Monitoring Due to Temperature Effect

This paper presents the quantitative analysis of spectroscopy measurement for engine oil degradation prediction due to the temperature effect. The main objective of this project is to study the behavior of engine oil if the continues high temperature are applied to the engine oil as compared to the effect of running condition. Two types of engine oil were used as samples in this project from grade SAE 10W40 and 20W50. The experiment was done by heating the engine oil with inductance coil at 100oC for 5 minutes and then cool it about half an hour before continue heating for other 5 minute. This process repeat continuously until 100 minutes and samples were taken at every 10 minutes after cooling process. A spectrometer is used to measure the percentage reflectance of the samples. Data was arranged and analyzed in order to predict the degradation of the engine oil. The data will be used to develop an optical sensor for signal processing in monitoring the engine oil degradation.

Post-stroke brain memory assessment framework

Post-stroke brain memory dysfunction is a precondition for the diagnosis of vascular dementia. This diagnosis in general made within months after a stroke, since post-stroke brain memory assumed to be a common cause of post-stroke. Clinical recovery and rehabilitation experience added to research recommendation in literature urges that post-stroke brain memory function perhaps reversible. The aim of the study was to systematically review the available information on the assessment of post-stroke brain memory function in stroke survivors to establish a post-stroke brain memory assessment framework. We performed systematic literature search of published research findings in various scientific publication with the following phrase: post-stroke brain memory assessment. The studies reported that different types of memory dysfunction resulted from stroke. The most commonly dysfunctional memory types are working memory, episodic memory and procedural memory. This finding was transformed into a post-stroke brain memory assessment framework. Five different brain memory assessment techniques are recommended for the proposed framework assessment. The selection of the assessment techniques based on the identified memory types. Both short term and long term memory are effected by post-stroke brain memory dysfunction. Standardized assessment of cognitive function in each patient diagnosed with post-stroke vascular dementia is crucial. Post-stroke vascular dementia may be reversible in a considerable percentage of patients with stroke, indirectly promising a healthier lifestyle.