Wireless Point-of-Care Diagnosis for Sleep Disorder With Dry Nanowire Electrodes

Abstract

Currently, available sleep monitoring systems use electrical recording where the electrodes make contact with the patient’s skin using a conducting gel. The electrode wires are connected to a processing recording system. The subject has to be in close proximity of these machines due to the direct electrical connections with the body and the machine. The conductive gel along with many wires connected to the biopotential electrodes makes them uncomfortable for the subject, with the result that recording and monitoring of the patient’s sleep patterns can become very difficult. The patient has to be in a sleep lab and/or a hospital at all times and at least one technician needs to watch the patient’s sleep behavior via video. The patient may not experience normal sleep patterns under such environments and as such, the diagnostic results are not really very conclusive. The commonly monitored biopotential electrodes are electrocardiogram, electroencephalogram, electromyogram, and electrooculogram. The electrodes used for monitoring these signals are Ag/AgCl and gold, which require skin preparation by means of scrubbing to remove the dead cells and application of electrolytic gel to reduce the skin contact resistance. The gel takes a role of reducing skin contact impedance in the conventional Ag/AgCl electrode and its usage is directly related to the sensitivity. However, the wet conventional Ag/AgCl electrode has some drawbacks such as difficulty in long time monitoring because the gel dries out after few hours and skin irritations. Usually, physiological parameters are monitored over an extended period of time during the patient’s normal daily life to diagnose a disease. In this case, the wet conventional Ag/AgCl cannot be used because of the dry-out of gel. The dry-out of gel increases the impedance between skin and electrode and it is reflected in the poor signal sensitivity. Also noises, such as motion artifact and baseline wander, are added to the biopotential signals as the electrode floats over the electrolytic gel during monitoring. To overcome these drawbacks, dry nanoelectrodes are proposed in this paper where the electrodes are held against the skin surface to establish contact with the skin without the need for electrolytic fluids or gels. The results are presented along with a wireless communication such that the proposed system is ideal for point-of-care diagnosis of the patient at home.

Wireless remote monitoring system for sleep apnea

“…Sleep disorders and sleep deprivation affect more than 30 million people while another 6 million have moderate to severe sleep apnea in which breathing briefly stops. That is nearly one in five Americans and it signifies that sleep apnea is as prevalent as asthma or diabetes [2]. Sleep apnea is breathing disorder which is characterized by brief stop breathing during sleep and the number of breathing pause may be as high as 20 to 30 or more per hour.…”

“…Apart from the physical health risk, sleep disorders can lead to social problems when sleep apnea is not diagnosed and treated. Nevertheless, it was reported that the prevalence of undiagnosed sleep apnea is high among population and it accounts for over $22 billion in unnecessary health care cost [2,5]. PSG (Polysomnography), also called an overnight sleep study, is generally used to diagnose sleep apnea by monitoring electrical activity of the brain, eye movement, muscle activity, heart, breathing patterns, air flow and blood oxygen levels.…”

“…Sensors to diagnose sleep apnea: (a) Ag/AgCl skin electrodes for ECG, (b) vertically aligned gold nanowires electrode for ECG[2], (c) Gyroscope for body position, (d) Pressure sensor for nasal airflow, (e) piezoelectric sensor for chest effort, (f) oximeter sensor for oxygen saturation.…”

Wireless brain-machine interface using EEG and EOG: brain wave classification and robot control

“…Different rhythmic brain activities are shown depending on the level of consciousness. The brain waves are classified according to the frequency band: Delta (0.1-3 Hz), Theta (4-7 Hz), Alpha (8)(9)(10)(11)(12), Beta (12-30Hz) and Gamma (30-100Hz). These rhythms are affected by different actions and thoughts, for example the thinking of movement attenuates or changes a typical brain rhythm.…”

“…The BMI controls the movement of robot by extracting the features of the EEG and EOG signals. The EEG and EOG signals are captured with gold nanowire electrodes attached on a headband [9]. The classified rhythmic brain waves are used to control the acceleration and deceleration or stopping of the robot by setting the threshold.…”