Blood Circulation In Human Body Research Articles

Sleep apnea detection using electrocardiogram signal input to FAWT and optimize ensemble classifier

Sleep apnea refers to a sleep disorder consist of inconsistent breathing during sleep for extensive duration of time. During this, one faces difficulty in breathing leading to loss of oxygenated blood circulation in human body. It leads to damage in hippocampus region of the brain. Many medical problems like hypertension and inducing type two diabetes are also common in patients. The early-stage detection of apnea can save someone from these severe conditions. This work introduces the automatic apnea detection method using electrocardiogram (ECG) signals. The ECG signals are analyzed with the help of flexible analytic wavelet transform (FAWT) which allows the conversion of non-stationary ECG into predictable wavelets. Features for events of apnea and non-apnea are extracted by these wavelets. The extracted features are checked for their statistical significance and then fed into different kinds of machine learning algorithms for apnea events detection. In tested algorithms, the optimized ensemble is obtained the best classification results. The proposed approach for apnea detection has better performance as compared to other existing same dataset works.

New Viewing of Blood Circulation in Human Body

New Viewing of Blood Circulation in Human Body

Superior performances of in situ synthesized ZnO/PVDF thin film based self-poled piezoelectric nanogenerator and self-charged photo-power bank with high durability

Herein, we have synthesized multitalented β crystallite prosperous and high dielectric ZnO/PVDF thin films via in situ process for energy harvesting and energy storing applications. The impregnation of the ZnO nanoparticles (NPs) in PVDF matrix has enhanced the electroactive β crystal nucleation (F(β) ~ 84% at 0.85vol% doping) as well as piezoelectricity (d33 ~ 50 pC/N at 50Hz) and the dielectric value (~ 109 at 1.7vol% loading) effectively. The nanogenerator, named as ZPENG and the self-charged photo-power bank (PPB) developed using the optimized supercilious piezoelectric and dielectric films have exhibited superiority in power generation (power density ~ 32.5mW/cm3) and energy storage capability (~ 2000 F/) respectively that are highly impressive outputs compare to other similar prototype devices reported elsewhere till now. The realistic utilities of our fabricated ZPENG have been proved by light up 15 serially connected commercial blue light emitting diodes (LEDs) and charging capacitor (1 μF) in a small time span under repetitive finger impartation. The ZPENG is also shows continuous band like power generation under vibration occurred due to blood circulation in human body. The self-charged PPB is also able to power up blue LED that has confirmed its realistic applicability as a power source.

Disaggregation of stacked red blood cells under strong pulse magnetic field

We have investigated the dependence of magnetic field intensity and stimulation time on stacking formation of red blood cells (RBCs) to study blood circulation in human body. The pulse magnetic field with the maximum intensity of 0.27–0.07 T, pulse transition time of 0.102 ms, and pulse intervals of 1 s was applied to the distal end of palm for 5–20 min. The aggregation of RBCs was measured using microscopy. After the magnetic stimulation for 10 min, the fully stacked RBCs were almost separated from each other and moved much faster than those in unstimulated state. The disaggregation was maintained at a decreasing intensity of 0.19 T, and a few cells were stacked at the weak intensity of 0.07 T. In this work, we investigated the degree of RBCs aggregation and activity time by varying the intensity and time of magnetic stimulation to get the optimum condition of pulse magnetic field stimulus.

Extracting Heart Rate Variability from a Smartphone Camera

It is known that blood circulation in human body causes the skin tone to change concurrently with heartbeats. A number of apps have been developed to measure the heartbeat using smartphone camera; however, no any further analysis is performed. In this paper we propose an algorithm that detects heartbeats from the phone's camera and further extracts the heart rate variability (HRV). We compare the HRV extracted from the camera with the HRV extracted from the electrocardiogram. We estimated a number of commonly used HRV characteristics and compared them. Our results show that smartphone camera leads to slightly overestimated characteristics although the difference in extracted HRV signals is negligible. As a consequence we suggest that a smartphone camera can be employed in a quick heart diagnosis and diagnosis of autonomic nervous system.