An Optimized Analysis of Electrical Activity of a Respiratory Muscles Using Advanced Surface Electromyography (sEMG)

Surface Electromyography (sEMG) is an effective tool for measuring electrical activity of the respiratory muscles in patients receiving prolongation mechanical ventilation (MV) support and for individuals experiencing acute respiratory problems. However, the utilization of sEMG sensor in clinical applications and research is still limited due to a lack of transparency and standardization in the demonstration procedure, as well as the information regarding the data acquisition method (including the pre-processing stage of filtering and motion artifacts), important clinical parameters to take into consideration for data interpretation and processing is still a challenging task. Therefore, in this paper we present a simple cost effective data acquisition hardware and software model to analyze the electrical activity of a diaphragm muscle using sEMG sensor and Arduino UNO WiFi R3 module. The sEMG sensor is used to acquire the electrical signal from the diaphragm muscle, whereas, the Arduino module processes the data obtained from the sensor, and the WiFi module sends the data to an Internet of Things (IoT) platform for real-time visualization and interpretation, allowing the end user clinical experts to diagnose patients' conditions. Further, we considered non-invasive bioimpedance analysis to assess the patient's condition in a time-efficient manner.