Alzheimer’s disease (AD) is a highly debilitating neurodegenerative disease with no cure to date. Emerging evidence indicates aberrations of the primary inhibitory neurotransmitter GABA in the frontal, parietal and temporal cortices, and hippocampal regions of the AD brains. GABA levels have been reported to predict working memory (WM) load capacity in the healthy young population. Since working memory is impaired in AD, it opens an active area of research to investigate the influence of GABA on WM performance in AD. Advancements in neuroimaging techniques and signal processing tools can aid in neurochemical profiling of GABA in AD as well as facilitate in probing the role of GABA in AD-specific impairments of working memory.

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Current techniques of anemia classification are either invasive or inaccurate, making them ill-suited for community-based screening programs. We propose an Artificial Intelligence (AI) based anemia classification method using a multi wavelength non-invasive photometry device. A finger mounted photo-plethysmogram (PPG) device was designed to acquire PPG signals at four wavelengths (590, 660, 810, and 940 nm). A set of features extracted from the PPG signals were used to develop a three-way classification scheme using a machine-learning. In a study conducted on 1583 women of childbearing age, subjects were classified into either healthy (Hemoglobin, Hb >11 g/dL), anemic (Hb: 7-11 g/dl) or severely anemic (Hb <7g/dL). We report classification sensitivity of 92% (p<0.05) and specificity of 84% (p<0.05) in differentiating anemic and non-anemic women. We also report a sensitivity of 76% (p<0.05), and specificity of 74% (p<0.05) in identifying severe anemia. The proposed anemia classification algorithm, along with the associated sensor has the potential to be productized as a low-cost non-invasive rapid anemia screening device for community interventions.

Current techniques of anemia classification are either invasive, expensive or inaccurate, making them illsuited for community health-worker based screening programs. In this study, we propose an Artificial Intelligence (AI) based anemia classification method using a multi-wavelength non-invasive photometry device. A finger mounted photo-plethysmogram (PPG) device was designed to acquire PPG signals at four wavelengths (590, 660, 810, and 940 nm). A set of 13 attenuation and ratio-of-ratio features, derived using the peak and trough information extracted from the PPG signals, were used to develop a three-way hierarchical ensemble classification scheme using a machine-learning algorithm. PPG data from the device and true hemoglobin data from laboratory-based cell counters was collected for 1583 women of childbearing age and subjects were classified into either healthy (Hemoglobin, Hb >11 g/dL), anemic (Hb: 7-11 g/dl) or severely anemic (Hb <7g/dL) categories. We report a classification sensitivity of 92% (p<0.05) and specificity of 84% (p<0.05) in differentiating anemic and non-anemic women. We also report a sensitivity of 76% (p<0.05), and specificity of 74% (p<0.05) in identifying severe anemia. We believe that the proposed anemia classification algorithm, along with the associated sensor has the potential to be productized as a low-cost non-invasive anemia-screening device to rapidly determine next steps in clinical decision making in widespread community interventions.