A high-level framework using biomedical signal processing was presented to tackle the diagnosis of noncommunicable diseases in low- and middle-income countries.
Low- and middle-income countries (LMICs) continue to face major challenges in providing high-quality and universally accessible health care. Researchers, policy makers, donors, and program implementers consistently strive to develop and provide innovative approaches to eliminate geographical and financial barriers to health care access. Recently, interest has increased in using mobile health (mHealth) as a potential solution to overcome barriers to improving health care in LMICs. Moreover, with use increasing and cost decreasing for mobile phones and Internet, mHealth solutions are becoming considerably more promising and efficient. As part of mHealth solutions, biomedical signals collection and processing may play a major role in improving global health care. Information extracted from biomedical signals might increase diagnostic precision while augmenting the robustness of health care workers’ clinical decision making. This paper presents a high-level framework using biomedical signal processing (BSP) for tackling diagnosis of noncommunicable diseases, especially in LMICs. Researchers can consider each of these elements during the research and design of BSP-based devices, enabling them to elevate their work to a level that extends beyond the scope of a particular application and use. This paper includes technical examples to emphasize the applicability of the proposed framework, which is relevant to a wide variety of stakeholders, including researchers, policy makers, clinicians, computer scientists, and engineers.
Elgendi et al. (Mon,) conducted a review in Noncommunicable diseases. Biomedical signal processing framework was evaluated. A high-level framework using biomedical signal processing was presented to tackle the diagnosis of noncommunicable diseases in low- and middle-income countries.