Heart rate variability (HRV) extracted from human physiological turn to be important data as indicators of human health. Originally, HRV calculated and monitored from Electrocardiography (ECG) signal. This research proved that photoplethysmography (PPG) as a simple non-invasive method can measure HRV in motion condition. PPG used Light Photo Sensor (LPS) to capture light reflectance intensity on the finger. The signal is sampled, filtered, processed and sent wirelessly through ZigBee protocol. The system applied on glove to make it wearable and easy to wear. Experimental results show that the system functioning properly with HRV received, displayed and recorded by the base station.
Lack of effective tools to diagnose lung cancer at an early stage has caused high mortality in cancer patients especially in lung cancer patients. Electronic nose (E-Nose) technology is believed to offer non-invasive, rapid and reliable analytic approach by measuring the odour released from cancer to assist medical diagnosis. In this work, using a commercial E-nose (Cyranose-320), we aimed to detect the volatile organic compounds (VOCs) emitted by different types of cancerous cells. The lung cancer cell (A549) and breast cancer cell (MCF-7) were used for this study. Both cells were cultured using Dulbecco's Modified Eagle's Medium (DMEM) with 10% of Fetal Bovine Serum (FBS) and incubated for three days. The static headspace of cell cultures and blank medium were directly sniffed by Cyranose-320. The preliminary results from this study showed that, the E-nose is able to detect and distinguish the presence of VOCs in cancerous cells with accuracy of 100% using LDA. To this end, the VOCs emitted from cancerous cells can potentially used as biomarker.
The existing clinical diagnostics for lung cancer are mostly based on physics, biochemical and imaging techniques. The use of electronic nose (E-nose) system to detect volatile organic compounds (VOCs) in lung cancer cells or exhaled air breath of a patient is expected to be able to classify different volatile components leading to the diagnosis of lung cancer at an early stage. In this preliminary study, a commercialized E-nose consists of an array of 32 conducting polymer sensors (Cyranose 320) was used to detect and discriminate the VOCs emitted from cancer cells which is A549 (lung cancer cell line) between MCF7 (breast cancer cell line). Blank medium was used to obtain controlled value. The VOC profiles of each sample were characterized using a classification algorithm called k-Nearest Neighbors (KNN) to test and benchmark the performance of Enose in identifying VOCs of lung cancer from different cancer cell lines. The E-nose with KNN classifier was able to classify the VOCs of lung cancer cell with over 90% successful accuracy in 30 seconds. This study can conclude that e-nose is capable to rapidly discriminate volatile organic compounds of cancerous cells which generated during cell growth.
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