In this study, a fully integrated electroencephalogram/functional near-infrared spectroscopy (EEG/fNIRS) brain monitoring system was designed to fulfill the demand for a miniaturized, light-weight, low-power-consumption, and low-cost brain monitoring system as a potential tool with which to screen for brain diseases. The system is based on the ADS1298IPAG Analog Front-End (AFE) and can simultaneously acquire two-channel EEG signals with a sampling rate of 250 SPS and six-channel fNIRS signals with a sampling rate of 8 SPS. AFE is controlled by Teensy 3.2 and powered by a lithium polymer battery connected to two protection circuits and regulators. The acquired EEG and fNIRS signals are monitored and stored using a Graphical User Interface (GUI). The system was evaluated by implementing several tests to verify its ability to simultaneously acquire EEG and fNIRS signals. The implemented system can acquire EEG and fNIRS signals with a CMRR of −115 dB, power consumption of 0.75 mW/ch, system weight of 70.5 g, probe weight of 3.1 g, and a total cost of USD 130. The results proved that this system can be qualified as a low-cost, light-weight, low-power-consumption, and fully integrated EEG/fNIRS brain monitoring system.

In surgeries where general anesthesia is required, the auscultation of heart and lung sounds is essential to provide information on the patient’s cardiorespiratory system. Heart and lung sounds can be recorded using an esophageal stethoscope; however, there is huge background noise when this device is used in an operating room. In this study, a digital esophageal stethoscope system was designed. A 3D-printed case filled with Polydimethylsiloxane material was designed to hold two electret-type microphones. One of the microphones was placed inside the printed case to collect the heart and lung sound signals coming out from the patient through the esophageal catheter, the other was mounted on the surface of the case to collect the operating room sounds. A developed adaptive noise canceling algorithm was implemented to remove the operating room noise corrupted with the main heart and lung sound signals and the output signal was displayed on software application developed especially for this study. Using the designed case, the noise level of the signal was reduced to some extent, and by adding the adaptive filter, further noise reduction was achieved. The designed system is lightweight and can provide noise-free heart and lung sound signals.