Results indicate that the proposed hardware and algorithm could replace the manual counting method, uncomfortable nasal airflow sensor, chest band, and impedance pneumotachography often used in hospitals. The system also takes advantage of the prevalence of smartphone usage and increase the monitoring frequency of the current ECG of patients with critical illnesses.

Abnormal vital signs often predict a serious condition of acutely ill hospital patients in 24 hours. The notable fluctuations of respiratory rate (RR) are highly predictive of deteriorations among the vital signs measured. Traditional methods of detecting RR are performed by directly measuring the air flow in or out of the lungs or indirectly measuring the changes of the chest volume. These methods require the use of cumbersome devices, which may interfere with natural breathing, are uncomfortable, have frequently moving artifacts, and are extremely expensive. This study aims to estimate the RR from electrocardiogram (ECG) and photoplethysmogram (PPG) signals, which consist of passive and non-invasive acquisition modules. Algorithms have been validated by using PhysioNet’s Multiparameter Intelligent Monitoring in Intensive Care II (MIMIC-II)’s patient datasets. RR estimation provides the value of mean absolute error (MAE) for ECG as 1.25 bpm (MIMIC-II) and 1.05 bpm for the acquired data. MAE for PPG is 1.15 bpm (MIMIC-II) and 0.90 bpm for the acquired data. By using 1-minute windows, this method reveals that the filtering method efficiently extracted respiratory information from the ECG and PPG signals. Smaller MAE for PPG signals results from fewer artifacts due to easy sensor attachment for the PPG because PPG recording requires only one-finger pulse oximeter sensor placement. However, ECG recording requires at least three electrode placements at three positions on the subject’s body surface for a single lead (lead II), thereby increasing the artifacts. A reliable technique has been proposed for RR estimation.

This paper presents a FPGA design and implementation of Electrocardiogram (ECG) biomedical embedded system (ECG-SoC). It performs ECG pre-processing and heart rate variability (HRV) feature extraction which suitable for remote homecare monitoring and rural health care application. The ECG-SoC is designed using hardware/software co-design technique based on offline dataset from MIT-BIH database. Altera Cyclone II DE2-115 FPGA platform was used for system prototyping and functionality verification. The computation results are displayed on Nios II-Linux terminal and produce output files for post analysis executed on the host personal computer (PC).

Ultrasound devices provide either diagnostic or therapeutic purpose in biomedical application. To avoid unwanted power exposure to the patient for safety concern but at the same time maintaining optimum diagnostic and therapeutic effect, ultrasound power meter is used to measure and calibrate the output power and intensity of the ultrasound machine. Most of the current ultrasound power meters are limited for either high power therapeutic or low power diagnostic purposes but not both and it is expensive. To enable Polyvinylidene fluoride (PVDF) for low cost ultrasound power meter, a robust low cost casing has been designed for optimum ultrasound power capturing from both therapeutic and diagnostic ultrasound machine. The system has been designed to minimize interference effect and noise, as well as to stabilize mechanical construction of the sensor. This paper presents a PVDF sensor design of an ultrasound power measurement system that is compact and simple in construction, easy and user friendly, but at the same time provides a reliable power measurement result. The power meter is designed using PVDF sensor and Altera Cyclone II Field Programmable Gate Array (FPGA) technology. Results show that this in-house power measurement system is able to measure 0.5 MHz -10 MHz of the frequency range and 1 mW/cm 2 to 10 W/cm 2 of the intensity range.