Kalaivani Chellappan

Surface Electromyography Signal Processing and Classification Techniques

Electromyography (EMG) signals are becoming increasingly important in many applications, including clinical/biomedical, prosthesis or rehabilitation devices, human machine interactions, and more. However, noisy EMG signals are the major hurdles to be overcome in order to achieve improved performance in the above applications. Detection, processing and classification analysis in electromyography (EMG) is very desirable because it allows a more standardized and precise evaluation of the neurophysiological, rehabitational and assistive technological findings. This paper reviews two prominent areas; first: the pre-processing method for eliminating possible artifacts via appropriate preparation at the time of recording EMG signals, and second: a brief explanation of the different methods for processing and classifying EMG signals. This study then compares the numerous methods of analyzing EMG signals, in terms of their performance. The crux of this paper is to review the most recent developments and research studies related to the issues mentioned above.

Effects of Physical Exercise on the Photoplethysmogram Waveform

Photoplethysmography is a non-invasive method which is suitable to estimate vascular compliance. Photoplethysmogram (PPG) waveform has been used to evaluate vascular characteristic changes due to exercise. Quantification of the primary and secondary peak position is of interest as a potential objective, individualized measure of the level of exercise that a subject has achieved with respect to a baseline status, prior to the beginning of the exercise session. The modified bicycle ergonomic protocol was selected for this experiment. Two different signal processing methods were employed in processing the PPG waveform to characterize its changes through a single parameter. Both, the visual inspection of the recorded PPG and the results of the analyses reinforce our opinion about the existence of continuous changes in PPG waveform through an exercise session. The experiment results confirm that PPG can be a potential approach in quantifying cardiovascular characteristics or properties of an individual.

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.

Emergency clinic multi-sensor continuous monitoring prototype using e-Health platform

Technology advancement and low cost device development may address the challenges faced in most emergency clinics in developing countries. Surveillance and detection of disease very much dependant on the medical practitioners whom has been constrained by the number of patients and limited automation in patient monitoring. In this study a low cost Arduino supported e-Health platform based temperature, pulse oximeter and air flow sensors incorporated multisensory kit integrated with the Matlab platform for continuous recording supported by graphical display and timely average by customize setting has been designed. The prototype accuracy and limitations have been tested by a data collection experiment designed for 15 candidates, both healthy male and female aged between 21 to 52 years old. The recorded SPO2 and pulse rate was clinically reliable reading compared to temperature sensor which was influenced by room temperature, which need some modification to the sensor design. Air flow was provided stable outcome in all subjects. The proposed emergency clinic, multi-sensor prototype is expected to address the continuous monitoring limitations and provide average reading at clinically required setting based on the criticality of patient condition and disease monitoring guideline.

Age-related upper limb vascular system windkessel model using photoplethysmography

Arterial stiffness is known to be affected by aging, leading to changes in peripheral pulse propagation speed and shape. In this paper, the heart-finger segment of the upper- limb vascular system of six subjects in two age-groups (below 30 and above 55 years) is modeled using a modified Windkessel model. Actual left-ventricle pressure (LVP) data has been used as the input to the model. Circuit simulation results show a significantly higher amplitude for the dicrotic notch and a lower second peak in younger subjects. Among the older subjects, there was a gradual disappearance of dicrotic notch while the second peak appeared later. Comparison of the characteristics of the model output with measured peripheral pulse shows a good degree of conformity with the actual pulse signal. The parameters in the Windkessel model provide a simple, noninvasive means for studying changes in the elastic properties of the vascular system, depending on the age and potentially state of health.

Speech enhancement using degenerate unmixing estimation technique and adaptive noise cancellation technique as a post signal processing

Speech enhancement in degenerate mixtures is a challenging task for the signal processing engineers. Degenerate Unmixing Estimation Technique (DUET) is a commonly used algorithm in speech signal separation, however there are limitations in separating the speech babble as noise and/or competing speech as noise from speech signal. These noises are important for auditory related training and testing. In this research an enhanced approach has been adopted by pairing DUET with Adaptive Noise Cancellation (ANC) which acts as the post-processor. Results from the correlation analysis and spectrogram analysis show that DUET provides separation quality correlation value of minimum 0.57 when speech signals are separated from speech babble and maximum of 0.75 when speech signal is separated from competing speech. On the contrary, both of these minimum and maximum correlation values reach to 0.77 and 0.88 when ANC is used as a postprocessor. The pairing of DUET with ANC has provided a good separation in the tested speech signals.

Experimental Feasibility Study of Estimation of the Normalized Central Blood Pressure Waveform from Radial Photoplethysmogram

The feasibility of a novel system to reliably estimate the normalized central blood pressure (CBPN) from the radial photoplethysmogram (PPG) is investigated. Right-wrist radial blood pressure and left-wrist PPG were simultaneously recorded in five different days. An industry-standard applanation tonometer was employed for recording radial blood pressure. The CBP waveform was amplitude-normalized to determine CBPN. A total of fifteen second-order autoregressive models with exogenous input were investigated using system identification techniques. Among these 15 models, the model producing the lowest coefficient of variation (CV) of the fitness during the five days was selected as the reference model. Results show that the proposed model is able to faithfully reproduce CBPN (mean fitness = 85.2% ± 2.5%) from the radial PPG for all 15 segments during the five recording days. The low CV value of 3.35% suggests a stable model valid for different recording days.

A study on EEG signals during eye-closed and eye-open using discrete wavelet transform

Analysis of the electroencephalogram (EEG) signal is a popular method for brain activity tracing. However, a precise experiment design is required in ensuring accuracy and reliability of the recorded signals. This includes the eye state whether to be in a closed or open position based on the experiment requirement. In this study, we have demonstrated significant difference between EC and EO in terms of sub-band frequencies using discrete wavelet transform (DWT). The average percentage energy for DWT coefficients related to delta and theta waves are significantly decreased while interestingly, the percentage energy for DWT coefficients related to alpha waves increased significantly from EO to EC. Meanwhile, there is no significant change in the DWT coefficients related to beta and gamma waves. The significant change is determined when p-value is less than 0.01 for an independent t-test. We conclude that this study has proven that it is important to maintain specific eye states during EEG recording to avoid misinterpretation on the analysis of the brain activities.

Upper limb rehabilitation in post stroke patients: Clinical observation

A pilot study was conducted in the stroke physiotherapy unit, Department of Rehabilitative Services, Pusat Perubatan Universiti Kebangsaan Malaysia (PPUKM) to identify the types of post stroke upper limb impairment commonly found in the Malaysian population, to improve services and physical outcome. This was an observational study focused on post stroke patients aged 50 years and above, who are actively participating and receiving physical rehabilitation in stroke physiotherapy unit. We also collected baseline data on stroke type, cardiovascular disease (CVD) risk factors, activity of daily living (ADL) and frequency of rehabilitation practice. Our results show a male preponderance (21 of 32 in total). High blood pressure (hypertension) and smoking are the most common CVD risk factors among our stroke patients suffered. Newly enrolled patients undergo training twice a week for the first 12 weeks, followed by weekly training sessions to recovery. An increase in rehabilitation practice frequency has been identified as a potential means to expedite the recovery period. This would require more resources, infrastructure and time allocation for rehabilitative services. Further studies are necessary to validate the hypothesis that increased exercise frequency will result in shorter periods of intensive rehabilitation and lead to better outcomes.

Carotid intima media thickness among young males with cardiovascular risk factors

Introduction: Atherosclerosis is a condition where there are fat, cells and connective tissue deposition in the arterial wall. This may lead to reduced blood flow to the vital organs such as heart (ischemic heart disease) and brain (stroke). There is increasing prevalence of ischemic heart disease (IHD) among the young population, especially males. Carotid intima media thickness (CIMT) of the common carotid artery is a novel marker of atherosclerosis. The aims of the current study were to compare these markers among young males with cardiovascular disease (CVD) risk factors with healthy subjects and to determine the associations between these markers with other CVD risk factors. Methods: One hundred and eighty one men aged 20 to 40 years old were recruited from areas around Klang Valley. These comprised those having one or more risk factors of CVD [hypertension, abdominal obesity, dyslipidemia, first degree family history (FH) of CVD and smoker, n=128] and without risk factors (n=53). Measurements included peripheral blood pressure (BP), heart rate (HR), body mass index (BMI), waist circumference (WC), total cholesterol (TC), triglyceride (TG), high density lipoprotein (HDL), low density lipoprotein (LDL) and fasting blood sugar (FBS). Right and left CIMT were also measured via B mode ultrasound. Data were analyzed via SPSS version 15 and significance level was set at P<; 0.05. Results: The mean age of all the subjects was 26.47 ± 5.09 years old. Those with risk factors were older, had significantly higher BP, BMI, WC and lipid profiles. Nevertheless, most of these variables were within normal range. Other variables such as HR and FBS were comparable between the two groups. After adjustment for age, those with CV risk factors had significantly higher right CIMT (0.5600 ± 1417 vs 0.5267 ± 0733 mm, P<;0.05) but not left CIMT (0.5450 ± 0.1500 vs 0.5167 ± 1033 mm, P > 0.05) compared to healthy group. Both CIMTs correlate with other major CV risk factors. The predictors of CIMT were age, SBP and WC which contribute about 12% of the variance in CIMT. Conclusion: Young males with CV risk factors have increased RCIMT and this may be the early markers of CVD.