Key result
A comparative study of low-cost, low-power, and low-compute-based ECG systems was performed to analyze better approaches for future design, alongside a proposed prototype.
Why the study?
Cardiovascular diseases are difficult to diagnose without expensive equipment such as conventional ECG systems that cost between $2000 and $10,000.
Low-cost, low-compute ECG monitoring systems using microcontrollers or FPGAs and machine learning offer a potential affordable alternative to conventional expensive ECG systems.
Answers are grounded in this paper and cite their sources.
Low-cost ECG prototypes merit engineering refinement; hypothesis-generating for clinical use pending validation trials.
According to WHO, cardiovascular disease (CVD) is the leading cause of death globally. Unfortunately, these diseases are difficult to diagnose without proper equipment which is not cheap. One of the reasons for such a high cost of treatment is the use of expensive technologies like ECG or electrocardiograph monitoring systems. These monitoring systems are usually implemented using expensive high-compute hardware and proprietary algorithms. Conventional ECG systems cost between $2000 and $10,000. But in theory, these systems can also be developed through low-compute hardware (such as microcontrollers or FPGA) and machine learning. This paper performs a comparative study on the implementation of low-cost, low-power, and low-compute-based ECG systems and analyzes better approaches for future design. Additionally, it also proposes a designed prototype developed based on that approach.
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Zishan et al. (2022) studied Cardiovascular disease. Low-cost, low-power, and low-compute-based ECG systems vs. Conventional ECG systems was evaluated. A comparative study of low-cost, low-power, and low-compute-based ECG systems was performed to analyze better approaches for future design, alongside a proposed prototype.
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