Spectral delayed luminescence system for human saliva screening

Delayed luminescence (DL) is a measurement method utilizing the decay of photon counts recorded, after one second or more, after a sample is exposed to a stimulating light source. DL has been studied on various human samples including blood serum, lung cells, cancerous and tumor cells, and the skin, except saliva. Previous studies have cross-correlate the DL of respective human samples with a range of diseases comprising of diabetes, leukemia, lung cancer, and tumor. However, recent studies have shown that DL is not cell type specific due to the unknown mechanism of the photon emission. Hence, this method is not viable for the diagnosis of complex diseases but it is proposed for non-invasive disease screening. Saliva, which can be obtained non-invasively, was anticipated for the screening of diseases using the DL method. Therefore, the aim of this study is to identify the potential application for the screening of diseases using the spectral delayed luminescence (SDL) of saliva. In order to achieve this, a prototype DL system was developed. The prototype DL system was then tested with and without the cooling of the photomultiplier tube (PMT). Illumination of the sample in the DL system was then tested with the ultraviolet (UV) light emitting diode (LED) which was then compared against the white LED. DL without the PMT cooling system, shows lower photon count deviations when compared to the PMT cooled at -8 ˚C for stimulation time ranging from 50 to 950 ms. In addition, the UV LED stimulation showed higher DL photon counts compared to white LED stimulation. Optimal stimulation time was then iterated for the SDL measurements of saliva and tongue swab, and it was found that 0.5 s is the optimal stimulation time. The first set of SDL measurements of saliva and tongue swabs were model fit into eight classes of mouth conditions. The classification performances of the respective models were then tested against the second data set of SDL measurements from the same respective sample. Results shows that the most significant application of the SDL system is in detecting conditions related to mouth sores. This significance is dependent on both the SDL measurements of saliva and tongue swab with detection performance of 100 % sensitivity, 85 % specificity, and 93 % non-error rate

An effect of physical exercise-induced fatigue on the vital sign parameters: a preliminary study

Vital sign monitoring is an important body measurement to identify health condition and diagnose any disease and illness. In sports, physical exercise will contribute to the changes of the physiological systems, specifically for the vital signs. Therefore, the objective of this study was to determine the effect of physical fatigue exercise on the vital sign parameters. This is significant for the fitness identification and prediction of each individual when performing an exercise. Five male subjects with no history of injuries and random BMI were selected from students of biomedical engineering, Universiti Teknologi Malaysia. Based on the relationship between physical movement and physiology, the parameters considered were heart rate, blood pressure, and body temperature. Subjects were required to run on the treadmill at an initial speed of 4 km/h with an increase of 1 km/h at every 2 minutes interval. The effect of exercise was marked according to the fatigue protocol where the subject was induced to the maximum condition of performance. All parameters were measured twice, for pre and post exercise-induced protocol. The analysis of relationship of each parameter between pre and post fatigue was p<0.05. The results revealed that the heart rate and gap between blood pressure’s systolic and diastolic were greater for all categories except underweight, where the systolic blood pressure dropped to below 100mmHg at the end of exercise. Also, the body temperature was slightly declined to balance the thermoregulatory system with sweating. Hence, the vigorous physical movement could contribute to the active physiological system based on body metabolism. Heart rate and blood pressure presented significant effects from the fatiguing exercise whereas the body temperature did not indicate any distinguishable impact. The results presented might act as the basis of reference for physical exercise by monitoring the vital sign parameters

Development of Wearable Electromyogram for the Physical Fatigue Detection During Aerobic Activity

Physical fatigue or muscle fatigue is a common problem that affects people who are vigorously involved in activities that require endurance movements. It becomes more complicated to measure the fatigue level when the dynamic motion of the activity is included. Therefore, this paper aims to develop a wearable device that can be used for monitoring physical fatigue condition during aerobic exercise. A 10-bit analog to digital converter (ADC) micro-controller board was used to process the data sensed by Ag/AgCl electrodes and real-time transmitted to the computer through Bluetooth's technology. The wearable was attached to the knee and connected to the biopotential electrodes for sensing the muscle movement and convert it into the electrical signal. The signal then processed by using the fourth-order Butterworth filter to filter the low-pass filter frequency and eliminate the noise signal. The results reveal that the fatigue level increased gradually based on the rating of perceived exertion (RPE), using 10-point Borg's scale, which is rated by the subject’s feeling. Both muscle's activities in lower limb rise as speed is increased, and it was also observed that the rectus femoris is functioning more than gastrocnemius due to the size of muscle fiber. Furthermore, it was established that the maximum volumetric contraction (MVC) could be used as a reference and indicator for measuring the percentage of contraction in pre-fatigue but not to fatigue induced experiment. However, this wearable device for EMG is promising to measure the muscle signal in the dynamic motion of movement. Consequently, this device is beneficial for a coach to monitor their athlete's level of exhaustion to be not over-exercise, which also can prevent severe injury

The evaluation of depth image features for awakening event detection

Falls among bedridden would increase in number if they are left unsupervised by the caregivers. The aim of this study is to evaluate the features from the Kinect-like depth image representing the bedridden in detecting the awakening event as the event that falls might occur. The images from 20 subjects performing six sleeping activities including the awakening events were obtained before image segmentation based on horizontal line profile was computed to these images in localizing the bedridden as region of interest. After that, the biggest blob selection was executed in selecting the biggest blob (blob of bedridden person body). Finally, blob analysis was formulated to the resultant image before boxplot and machine learning approach called decision tree were used to analyze the output features of blob analysis. Based on the results from the boxplot analysis, it seems that centroid-x is the most dominant feature to recognize awakening event successfully as the boxplot represent the centroid-x of awakening event were not overlap with other sleeping activities. The result from machine learning approach is also seem in good agreement with boxplot analysis whereby the modelled decision tree with solely using centroid-x achieve the accuracy of 100%. The second largest accuracy is the perimeter followed by major axis length and area

Halloysite nanotubes and halloysite-based composites for biomedical applications

Novel biomaterials for diagnostics and therapeutics of biomedical issues have been considered using biomedical science and health care. Halloysite nanotubes (HNTs) are naturally occurring aluminosilicate clay. Because of their unique hollow tubular structure, biodegradability, mechanical and surface properties, they have drawn the attention of researchers to a variety of biomedical applications. HNTs are inorganic natural aluminosilicates that are tubular-shaped and nanosized. These are well-known nanofillers and nanocontainers used to develop composites for various biomedical applications to load bioactive molecules and therapeutic agents. HNTs-polymer nanocomposites, their characterizations, properties, and applications in biomedical fields are all covered in this review paper. The current article provides an overview of HNTs and their applications in medical and biomedical settings, focusing on individualized HNTs and drug loading methods and biomedical applications, which may aid researchers in developing novel biomaterials for biomedical engineering and health care

An overview of ultrasound testing for lesion detection in human kidney

Ultrasound waves are commonly used to produce images of the human internal organs such as kidney. Most kidney cancers are found unexpectedly when patient have an ultrasound or scan for symptoms that turn out to be unrelated. Usually, the first test a medical doctor will do is an ultrasound scan, which is a real-time, moving test used to detect and differentiate between tumours, stones and cysts on the kidney. This paper presents the overview of ultrasound imaging for renal screening to detect renal lesions such as tumor, stone and cyst

Real-time implementation of twelve-lead automated electrocardiogram system measurement for QT dispersion analysis

Research and study of the electrocardiogram evolves with the advancement of digital signal processing and artificial intelligence. Unfortunately, readily available electrocardiogram machines in the market do not provide automated measurement of the QT dispersion. Therefore, a twelve-lead electrocardiogram system is developed in order to assist the cardiologists in carrying out their research on the cardiac diseases. The development of the system consists of several phases. The first phase includes the construction of a real-time twelve channels data acquisition unit with the universal serial bus (USB) interface. The following phase includes the study and development of the electrocardiogram signal conditioning circuits. The third phase is the study of the designed anti-aliasing filters and its effect to the electrocardiogram distortion. The Butterworth and the Bessel filters, each with orders of two four and eight are compared and the 8th order Bessel low pass filter appears to be the best candidate. The subsequent phase is the implementation of the time-domain subtraction technique to remove the power line noise in the electrocardiogram signal with minimal distortion. The filter is compared to a notch Twin-T filter, and results showed that not only the time-domain subtraction technique suppresses noise, it also preserves the original signal with minimal distortion. The automated QT interval measurement algorithm is validated upon an annotated standard database, the Physikalisch-Technische Bundesanstalt (PTB) Diagnostic Electrocardiogram database which is being the focused for the International QT Interval Challenge 2006. Result shows that 28.53% of the database is correctly identified for the QRS onset and T offset locations due to the dissimilar morphologies of the electrocardiogram signal

Physical fatigue prediction based on heart rate variability (HRV) features in time and frequency domains using artificial neural networks model during exercise

Awareness on fatigue level is important for people in order to understand their physiology in daily activities. This situation become more critical when involving physical exercise and reach the maximum threshold fatigue which can lead to injury. Additionally, sedentary people become the most group who is difficult to understand and know their fatigue condition based on feeling compared to the recreational exercise people and sports athlete. Therefore, this study is aims to help sedentary to predict the level of fatigue based on HRV features using artificial neural network (ANN). Eighteen sedentary peoples who are volunteer to participated in this study required to perform fatigue-induced protocol to achieve the heart rate maximum (HRmax). Those participants were run on the treadmill with speed intensities from 4km/h to 12km/h depends on their ability. During running, single-lead ECG was attached on the chest by using Ag/AgCl wet electrodes. The raw signals which accumulate together with noise and motion artefacts were then filtered in 4th order Butterworth filter. A new signal of HRV was used to analyze by extracting the features in each level of fatigue based on Edward’s Method zones. Eight features of time and frequency domains were selected in the neural network as input and predicts the fatigue zones as an output. HRV and HRmax were found as significant parameters to detect fatigue by differentiate its pattern in pre and post exercise. The results reveal that the prediction model with accuracy as high as 80.6% in the output of five fatigue classes. The results presented here may facilitate improvements in identifying the level of fatigue based on prediction algorithm compared to the RPE method during physical exercise

An effect of physical exercise-induced fatigue on the vital sign parameters: A preliminary study

Vital sign monitoring is an important body measurement to identify health condition and diagnose any disease and illness. In sports, physical exercise will contribute to the changes of the physiological systems, specifically for the vital signs. Therefore, the objective of this study was to determine the effect of physical fatigue exercise on the vital sign parameters. This is significant for the fitness identification and prediction of each individual when performing an exercise. Five male subjects with no history of injuries and random BMI were selected from students of biomedical engineering, Universiti Teknologi Malaysia. Based on the relationship between physical movement and physiology, the parameters considered were heart rate, blood pressure, and body temperature. Subjects were required to run on the treadmill at an initial speed of 4 km/h with an increase of 1 km/h at every 2 minutes interval. The effect of exercise was marked according to the fatigue protocol where the subject was induced to the maximum condition of performance. All parameters were measured twice, for pre and post exercise-induced protocol. The analysis of relationship of each parameter between pre and post fatigue was p<0.05. The results revealed that the heart rate and gap between blood pressure’s systolic and diastolic were greater for all categories except underweight, where the systolic blood pressure dropped to below 100mmHg at the end of exercise. Also, the body temperature was slightly declined to balance the thermoregulatory system with sweating. Hence, the vigorous physical movement could contribute to the active physiological system based on body metabolism. Heart rate and blood pressure presented significant effects from the fatiguing exercise whereas the body temperature did not indicate any distinguishable impact. The results presented might act as the basis of reference for physical exercise by monitoring the vital sign parameters