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Technical Brief

Automated Real-Time Processing of Single Lead Electrocardiogram for Simultaneous Heart Rate and Respiratory Rate Monitoring

[+] Author and Article Information
Disha N. Dutta

Department of Applied Physics,
University of Calcutta,
Kolkata 700 009, India
e-mail: disha.instrumentation@gmail.com

Reshmi Das

Department of Applied Physics,
University of Calcutta,
Kolkata 700 009, India
e-mail: reshmi.0989@gmail.com

Saurabh Pal

Department of Applied Physics,
University of Calcutta,
Kolkata 700 009, India
e-mail: spal76@gmail.com

Manuscript received July 21, 2016; final manuscript received January 25, 2017; published online May 3, 2017. Assoc. Editor: Marc Horner.

J. Med. Devices 11(2), 024502 (May 03, 2017) (6 pages) Paper No: MED-16-1274; doi: 10.1115/1.4035982 History: Received July 21, 2016; Revised January 25, 2017

In this article, the design and development of a real-time heart rate (HR) and respiratory rate (RR) monitoring device is reported. The proposed device is designed to impose minimum data acquisition hazards on the subject. In standard bedside monitors, HR and RR are derived from electrocardiogram (ECG) and respiration signals, respectively, and different electrodes are required for capturing the 12-lead ECG and respiration via a chest belt, which is cumbersome for patients and healthcare providers. Respiration signal has an impact on ECG due to anatomical proximity of the heart and lung, and ECG is modulated by respiration, a phenomenon known as respiratory sinus arrhythmia (RSA). In the proposed method, the ECG signal is acquired using clip electrodes at the wrists and the respiration signal is extracted from the ECG using an Arduino Uno microcontroller-based real-time processing of ECG. RR is then derived from ECG-derived respiration (EDR). The prototype is tested on healthy subjects and compared to measurements taken using a standard MP45 data acquisition device associated with a Biopac Student Lab (BSL). A mean percentage error of 5.54 ± 8.48% was observed under normal breathing conditions and an error of −3.41 ± 3.27% was observed for a single subject tested under a variety of breathing conditions, such as resting, stair-climbing, and paced breathing. The proposed algorithm can also be used in combination with standard ECG monitoring systems to measure HR and RR, without any data acquisition hazard to the subject.

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Figures

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Fig. 1

Block diagram of the proposed experimental setup

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Fig. 2

Amplitude versus time plot of online R-peak detection using Arduino Uno serial plotter (middle trace: raw ECG smoothed using exponential moving average; bottom trace: five-point derivative of the smooth ECG; top line cuts through R-peak when located)

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Fig. 3

Amplitude versus time plot of EDR using Arduino Uno serial plotter (middle trace: EDR plotted offline from the heart rates that were measured and stored online)

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Fig. 4

Flowchart for the complete algorithm (HR—heart rate, BPM—beats per minute, EDR—ECG-derived respiration, RR—respiratory rate, BrPM—breaths per minute)

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Fig. 5

Flash programmable memory used by the total sketch on Arduino Uno (maximum memory space is 32 KB on Arduino Uno)

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Fig. 6

SRAM used by the global variables of the sketch (maximum dynamic memory or SRAM space is 2 KB on Arduino Uno)

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