Fig. 2: Internal Components Portable ECG Monitoring Device and Smartphone App Sumarth Mehta1, Dan Puperi, Ph.D.1   1 Biomedical Engineering, The University of Texas at Austin 107 W Dean Keeton St, Austin, TX 78712, United States E-mail: sumarth.mehta@utexas.edu Abstract For a third-year engineering design project, we created a wearable, low-cost ECG monitoring solution that interfaces with the user through a smartphone app and displays medically relevant heart rhythm statistics. These features make ECG data more accessible to patients and are ideal for extending cardiac monitoring technology to populations in developing countries where smartphones are accessible. 1. Introduction An ECG measurement indicates heart electrical activity by measuring voltages on the skin; it is one of the most common tests in cardiology. While chronic conditions can be identified in a patient’s ECG at a clinic, transient symptoms such as arrhythmias can require the patient to use a Holter monitor, a wearable extended recording device that saves the ECG signal for later review by a physician. We aimed to recreate this functionality in a low cost ECG monitoring device marketed directly to patients. 2. Design The device was built from the components shown in Figures 1 and 2, as well as skin electrodes and cables, resulting in a total cost of ~$80. The Pi Zero runs a Python script to read the ECG signal at 250 Hz and transmits it to a smartphone using Bluetooth. The smartphone app shows a live reading of the signal (Fig. 3), calculates a live heart rate, and can record the signal to a file upon request or continuously for over 24 hours (Fig. 4). Additionally, the app displays and analyzes saved data using an algorithm which detects peaks (Fig. 5), calculates average intervals, and identifies and highlights instances of sinus arrhythmia and wandering pacemaker (Fig. 6). 3. Educational Significance Our team of four was tasked with defining market needs and building a portable ECG device to meet them. Due to the open ended nature of this project, we had the opportunity to select components and create a unique design. We gained electronics experience by pairing the signal conditioning chip with an ADC and Raspberry Pi, and added to our programming skills by learning how to set up Linux, code for the Pi, and communicate over Bluetooth between the Pi and an Android app. Additionally, we learned to write a peak detection and ECG interpretation algorithm. Over many rounds of prototyping and debugging, we also improved our communication skills and developed into a more efficient and productive team. Fig. 1: Exterior Design Fig. 2: Internal Components Fig. 3: Live Signal Fig. 4: Recorded Data Fig. 5: Peak Detection Fig. 6: Highlighting Anomalies Proceedings of the 2018 ASEE Gulf-Southwest Section Annual Conference The University of Texas at Austin April 4-6, 2018