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.

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.

Ageing is one of the main contributing factors towards increasing arterial stiffness, leading to changes in peripheral pulses propagation. Therefore the characteristics of the photoplethysmogram (PPG) pulse, especially the rising edge and peak position, are greatly affected. In this study, the PPG pulse rising edge and corresponding peak position have been investigated non-invasively in human subjects as a function of age. Fifteen healthy subjects were selected and grouped in five age intervals, from 20 to 59 years, based on their comparable systolic-diastolic blood pressure and PPG amplitude. As expected, the peripheral pulse shows a steep rise and early peak in younger subjects. With age, the slope becomes blunted and in older subjects, the rise is very gradual and the pulse peak appears much later. Qualitative results were further verified by a modified 10-element Windkessel model to quantify the lumped parameter changes with ageing. This verification highlighted some specific changes in vascular parameters with aging. The rising edge could be considered as one parameter in determining the age-dependent vascular state.

Adolescence is an evolution era formed in between childhood and adulthood. At this period of time, adolescents experience enormous changes in the development of biological, emotional, social, cognitive and intellectual (Vera, Shin, Montgomery, Mildner & Speight, 2004). The dominant task involved during the adolescence period is the readiness to enter adulthood stage, which is believed to influence the prospects of any culture attachment in the future (Larson, Wilson, Brown, Fursternberg & Verma, 2002). Adolescence is the stage where the individual is expected to confront and adapt to the rigorous modification in terms of school, social and family life. Thus, at this stage of life, adolescents usually endure a fusillade of challenges. Adolescents who are also known as young adults face changes in various aspects of their lives (Schulenberg, Bryant & O'Malley, 2004). Besides, at this period of life, they are involved in the transition period to adulthood due to the comparison between the values held in childhood and the values learned throughout their growing phase (Özbay, 1997). Hence, adolescence is seen as the best time that illustrates higher level of exploration about own self (Jessor, Donovan & Costa, 1991) and at the same time initiates them in committing with more matured roles (Erikson, 1968). Therefore, it is essential for an individual to hold confidence in the ability to control one's environment which is known as self-efficacy. At this stage, the task of upholding one's confidence is believed to produce a fruitful outcome in terms of subjective well-being.

ABSTRAK Dua petanda kesihatan salur darah baru yang dihasilkan daripada gelombang fotopletismografi jari (PPG) telah diperkenalkan berdasarkan populasi rakyat Malaysia iaitu indeks kecergasan PPG (PPGF) dan indeks jangkaan risiko salur darah (VRPI). Antara objektif kajian ini adalah untuk mengkaji hubungan antara PPGF dengan petanda penyakit jantung (CVD (Beta = 0.35, p < 0.01), p < 0.01), p = 0.04) dan ketinggian (Beta = 0.24, p < 0.01 ABSTRACTTwo new vascular health markers which are derived from finger photoplethysmography (PPG) waveform have been introduced based on Malaysian population, namely PPG fitness index (PPGF) and vascular risk prediction index (VRPI). The objectives of this study were to investigate the associations between PPGF and other cardiovascular disease (CVD) markers such as carotid femoral pulse wave velocity (PWV CF ), to compare PPGF between those with and without CVD risk factors and to determine the sensitivity of VRPI in identifying young subjects with CVD risk factors. A total of 114 men age 20 to 40 yrs with and without CVD risk factors were recruited. Risk factors included hypertension, smoking, dyslipidemia, abdominal obesity and family history of premature CVD. Subjects were divided into healthy, those with one risk factor and those with at least two risk factors. Their weight, height, peripheral and central blood pressure (BP), PWV CF and PPGF were measured and the sensitivity of VRPI in predicting subjects with CVD risk factor was calculated. Data was analyzed via SPSS version 15 and p < 0.05 was considered significant. The mean age of the subjects was 28.94 ± 4.86 yrs. No differences in PPGF was observed between groups (p > 0.05). The independent variables for PPGF were forward pressure (Beta = 0.35, p < 0.01), PWV CF (Beta = -0.26, p < 0.01), systolic BP (Beta = -0.26, p = 0.04) and height (Beta = 0.24, p < 0.01). The sensitivity of VRPI was 82.02%. In conclusion, PPGF was correlated to PWV CF and may be a potential marker of arterial stiffness. In addition, VRPI is sensitive to be used as an early screening of CVD risk factors.